**3. WD and SC Algorithms**

Walk detection (WD) and step counting (SC) algorithms were generally designed following a similar routine: (1) feature extraction; (2) feature detection; and (3) state recognition. In an implementation, the design methodologies can be roughly categorized into: (1) heuristic-based; (2) signal processing based; and (3) machine learning-based. The related algorithms are briefly summarized and introduced.

Since we aim to give a comprehensive evaluation of various contexts, a proper set of algorithms that could exactly reflect the contexts' changes should be selected. Some features and algorithms that are too sensitive or not widely applied could not provide fair comparisons of different contexts. Too complicated features and some 'best' algorithms should be avoided because they may generate biased results. Therefore, we surveyed many papers on both feature extractions and classification algorithms and selected the most stable features and algorithms to conduct the experiments and evaluations.


**Abstract**

This chapter delves into the relatively new DNA technique of probabilistic genotyping, which aims to a more precise determination of complex DNA profiles of multiple contributors. It explains the forensic value of this methodology compared to traditional DNA techniques such as Combined Probability of Inclusion (CPI). In particular, this forensic value is demonstrated in light of the reversal of several wrongful convictions in the USA and Europe. Apart from having a potential exculpatory effect, the advance of probabilistic genotyping can also contribute to discerning the real perpetrator of a crime. As a result, this chapter emphasizes the relevance of probabilistic genotyping for both defense lawyers and prosecutors in criminal cases.

**Keywords:** Mark Perlin, Greg Hampikian, DNA evidence, forensic evidence, probabilistic genotyping, DNA mixtures, wrongful convictions, defense lawyers, prosecutors, Lydell Grant, Dutch Proveniers case


**5. Discussions**

Table 2 proves that Hypotheses H1 and H3 (H3a, H3b, H3c and H3d) are valid, while H2 and H4 (H4a, H4b, H4c and H4d) are not. This suggests that (1) innovation agglomeration contributes to the reduction of industrial emissions in the region but does not have a significant effect on its surrounding regions; (2) it is feasible to enhance the effect of innovation agglomeration on industrial pollution reduction by land use transition on the region, but this effect does not have a significant influence on the surrounding regions. Specifically: hypothesis H3a holds, which suggests that an increase in the human capital dimension will, on the one hand, help to drive land use transition and thus strengthen the reduction of industrial pollution emissions from innovation agglomeration; on the other hand, the government will be more active in implementing strategies to reduce emissions and retain elite talent. Based on the weight of the secondary indicators of human capital dimension, it can be further argued that full-time equivalent of R & D personnel plays the greatest role, followed by population density and finally average educational level. This enlightens us that increasing the workload of R & D personnel and attracting the inflow of foreign population can bring greater benefits. Hypothesis H3b holds, which suggests that an increase in the material capital dimension will help to remove financial barriers to research, ease the "difficulty of financing" the use of environmental equipment by firms, accelerate land use transition and reduce industrial pollution emissions. In addition, the demand of capitalists for adequate returns on capital will also increase the level of land use in the region, thus promoting land use change and reducing industrial pollution emissions. Based on the weight of the secondary indicator of material capital dimension, it can be further argued that although the role of internal expenditure of R & D funds in reducing emissions is stronger than that of financial institutions density, both are very important. This reveals to us that althoughR&D expenses are important, the role of financial institutions in financing should not be neglected, which reduces the difficulty of innovative enterprises in obtaining funds. Hypothesis H3c holds, indicating that the upgrading of the urban function dimension is conducive to the transition and upgrading of industries in the region, achieving land use transition while also reducing industrial pollution emissions and, on the other hand, encouraging the government to build more humane and innovative infrastructure, thus accelerating land use transition. Based on the weight of the secondary indicators of the urban function dimension, it can be further argued that industrial structure evolution is the main force in promoting innovation and achieving industrial pollution reduction, which reveals to us that promoting industrial structure upgrading and accelerating the service-oriented economy is a triple-win path of accelerating land use transition, promoting innovation and achieving environmental protection. Hypothesis H3d holds that the government's efforts help to accelerate land use transition and reduce industrial pollution emissions. Based on the weights of the secondary indicators of the government dimension, it can be further argued that, on the one hand, the government, by supporting innovation development, has the guidance to promote land use transition and strengthen innovation agglomeration, thus reducing industrial pollution; on the other hand, the government's greening efforts help retain elites and provide an environmental basis for innovation agglomeration, which also indirectly curbs industrial pollution emissions.

For other variables, the direct effect coefficient of Per\_GDP is negative and passes the 10% significance level test. The indirect effect coefficient was also negative and passed the 10% significance level test. This indicates that an increase in per capita GDP within a region not only helps to reduce industrial pollution emissions in the region but also helps to reduce emissions in neighbouring regions. This is consistent with the findings of Buryn et al. [51]. The coefficient of the direct effect of FDI is negative and passes the 10% significance level test. However, its indirect effect is not significant. This suggests that as the Chinese and the government have become more environmentally conscious, the role of foreign investors in increasing emissions has been reversed and has instead helped to reduce industrial pollution, which is consistent with Jorgenson's findings [52]. This is because China is focusing on bringing in high quality FDI (advanced production technologies) and gradually abandoning its role as a "processing plant". The direct effect coefficient of energy structure is negative and passes the 10% significance level test. The indirect effect coefficient is also negative and passes the 10% significance level test. This indicates, when the proportion of traditional coal energy drops, that the proportion of other relatively clean energy (wind power, hydroelectric power, etc.) in China has risen, thereby affecting industrial pollution emissions. This is consistent with the results of Wang et al.'s study [53], implying the importance of energy structure transformation in reducing industrial pollution.


**5.5. Paraoxonases**

Changes in antioxidant enzymes, such as the family of paraoxonases (PONs), may partially explain some of the mechanisms involved in HAART-associated dyslipidemia and conse‐ quently characterize a higher risk for cardiovascular diseases and atherosclerosis [63]. The hypothesis that the PIs can promote reductions in the activity of PONs and an increased risk for atherosclerotic disease in HIV-1 patients has been shown through previous evidence. PON1 is an antioxidant enzyme present in serum is strongly associated with apolipoprotein-A1 (apoAl) from HDL and protects LDL against oxidative modifications [69, 70]. The action of serum PON1 most likely occurs through the involvement of the enzyme in reverse cholesterol transport, a well-established anti-atherogenic propriety of HDL [71]. PON1 has the ability to inhibit LDL oxidation (oxLDL) and significantly reduce the lipid peroxidase enzyme, which decreases the accumulation of cholesterol in peripheral tissues [72]. The oxidative modification of LDL in the arterial wall plays a central role in the pathogenesis of atherosclerosis, which is characterized by the deposition of lipids and the formation of atherosclerotic plaques that cause narrowing of the blood vessels [73]. The inhibition of oxLDL by HDL is attributed to the high antioxidant content of the lipoprotein possibly due to the antioxidant properties of apoA1 and by the presence of other different antioxidant enzymes, such as glutathione peroxidase and PON, which prevent the formation of or degrade bioactive products of oxLDL [68]. Some studies have shown that the activity of PON1 may be affected and/or inactivated by oxidative stress, which could explain its reduced activity during HIV-1 infection [69-71]. In HIV-1 patients and those who undergo HAART, there is a significant increase in oxidative stress. In asymptomatic HIV-1 patients, there is an increased oxidative stress characterized by elevated lipid peroxidation products and/or a quantitative decrease in antioxidants compared to seronegative controls that are considered to be in a healthy condition. Therefore, possible reductions in the activity of PON1 and HDL concentrations may characterize an increased cardiovascular risk in individuals infected with HIV-1 [70, 71, 75]. The PON1 activity that was reduced in ART-naïve patients, and restored in patients treated with HAART suggested that the activity of PON1 is associated with the immune status in HIV-1 patients. However, in individuals treated with lopinavir/ritonavir, even with low plasma viremia, PON1 activity was reduced and a higher atherogenic risk was shown by the high TC:HDL ratio, suggesting that a PI-based regimen affects the mechanisms involved in the oxidation of LDL, which promotes greater atherogenic risk [69-74].

## **5.6. LDL oxidation**

are essential for mitochondrial biogenesis and metabolic function. Furthermore, functional changes of mitochondria in skeletal tissue promote insulin resistance and consequent dysli‐

136 Trends in Basic and Therapeutic Options in HIV Infection - Towards a Functional Cure

HAART-associated lipodystrophy and dyslipidemia may be related to genetic predisposition. Studies on HIV-1 patients with hypertriglyceridemia and low HDL were shown to be associ‐ ated with different polymorphisms in the *APOCIII* gene. Promoter polymorphisms -455T>C and -482C>T in the *APOCIII* gene are both associated with increased levels of TG containing lipoproteins (VLDL) and low HDL values. Carriers of the -455T>C genetic variant had 30% lower levels of HDL compared to those without this polymorphism and plasma lipid concen‐ trations increase according to the number of these variant alleles. Another variant nucleoside, the -1131T>C promoter polymorphism in the *APOA5* gene, was associated with hypertrigly‐

Changes in antioxidant enzymes, such as the family of paraoxonases (PONs), may partially explain some of the mechanisms involved in HAART-associated dyslipidemia and conse‐ quently characterize a higher risk for cardiovascular diseases and atherosclerosis [63]. The hypothesis that the PIs can promote reductions in the activity of PONs and an increased risk for atherosclerotic disease in HIV-1 patients has been shown through previous evidence. PON1 is an antioxidant enzyme present in serum is strongly associated with apolipoprotein-A1 (apoAl) from HDL and protects LDL against oxidative modifications [69, 70]. The action of serum PON1 most likely occurs through the involvement of the enzyme in reverse cholesterol transport, a well-established anti-atherogenic propriety of HDL [71]. PON1 has the ability to inhibit LDL oxidation (oxLDL) and significantly reduce the lipid peroxidase enzyme, which decreases the accumulation of cholesterol in peripheral tissues [72]. The oxidative modification of LDL in the arterial wall plays a central role in the pathogenesis of atherosclerosis, which is characterized by the deposition of lipids and the formation of atherosclerotic plaques that cause narrowing of the blood vessels [73]. The inhibition of oxLDL by HDL is attributed to the high antioxidant content of the lipoprotein possibly due to the antioxidant properties of apoA1 and by the presence of other different antioxidant enzymes, such as glutathione peroxidase and PON, which prevent the formation of or degrade bioactive products of oxLDL [68]. Some studies have shown that the activity of PON1 may be affected and/or inactivated by oxidative stress, which could explain its reduced activity during HIV-1 infection [69-71]. In HIV-1 patients and those who undergo HAART, there is a significant increase in oxidative stress. In asymptomatic HIV-1 patients, there is an increased oxidative stress characterized by elevated lipid peroxidation products and/or a quantitative decrease in antioxidants compared to seronegative controls that are considered to be in a healthy condition. Therefore, possible reductions in the activity of PON1 and HDL concentrations may characterize an increased cardiovascular risk in individuals infected with HIV-1 [70, 71, 75]. The PON1 activity that was reduced in ART-naïve patients, and restored in patients treated with HAART suggested that

pidemia [62-64].

**5.4. Genetic factors**

**5.5. Paraoxonases**

ceridemia in PI-based patients [65-68].

Oxidative modifications to LDL, which are considered the initial event in the pathogenesis of atherosclerosis, are attributed to oxidative stress mechanisms initiated by agents such as superoxide, nitric oxide and hydrogen peroxide (H2O2) that transform LDL into oxLDL [77, 77]. The deposition of oxLDL in the arterial intimal layer promotes a cytotoxic effect on the vascular endothelium, followed by inflammation and modification of monocytes into macro‐ phages that phagocytose oxLDL particles to form the foam cells which accumulate in the intima and lead to the development of atheromatous plaques [78]. The oxLDL particles are immuno‐ genic, and serum levels of anti-oxLDL antibodies (Abs) can be used as indicators of oxidative stress [76-78]. The immunoglobulin G (IgG) anti-oxLDL Abs are pro-atherogenic and can predict the progression of coronary and carotid atherosclerosis, whereas IgM anti-oxLDL Abs appear to be associated with a possible protective role against the development of atheroma‐ tous plaques [79]. During the process of infection by HIV-1, the increase in atherogenic risk results from changes in lipid metabolism associated with the severity, duration and stages of infection. Different degrees of lipodystrophy occur in patients along with a decrease in LDL receptor expression, which could lead to increased oxidation of LDL particles and the conse‐ quent development of atherosclerosis [80]. HIV-1 patients treated with lopinavir/ritonavir have shown higher levels of IgG anti-oxLDL Abs compared to patients treated with efavirenz or nevirapine regimens, and these levels were associated with an increase in the atherogenic indices [78-80].


*Article* **Cardiopulmonary Exercise Testing Is an Accurate Tool for the Diagnosis of Pulmonary Arterial Hypertension in Scleroderma Related Diseases**

**Mattia Bellan 1,2,3,\*, Ailia Giubertoni 1,2, Cristina Piccinino 2, Mariachiara Buffa 1, Debora Cromi 1, Daniele Sola 2, Roberta Pedrazzoli 2, Ileana Gagliardi 1, Elisa Calzaducca 1, Erika Zecca 1, Filippo Patrucco 1,2, Giuseppe Patti 1,2, Pier Paolo Sainaghi 1,2,3 and Mario Pirisi 1,2,3**

**Abstract:** The early diagnosis of pulmonary arterial hypertension (PAH) is a major determinant of prognosis in patients affected by connective tissue diseases (CTDs) complicated by PAH. In the present paper we investigated the diagnostic accuracy of cardiopulmonary exercise testing (CPET) in this specific setting. We recorded clinical and laboratory data of 131 patients who underwent a CPET at a pulmonary hypertension clinic. Out of them, 112 (85.5%) had a diagnosis of CTDs; 8 (6.1%) received a diagnosis of CTDs-PAH and 11 (8.4%) were affected PH of different etiology. Among CPET parameters the following parameters showed the best diagnostic performance for PAH: peak volume of oxygen uptake (VO2; AUC: 0.845, CI95% 0.767–0.904), ratio between ventilation and volume of exhaled carbon dioxide (VE/VCO2 slope; AUC: 0.888, CI95%: 0.817–0.938) and end-tidal partial pressures (PetCO2; AUC: 0.792, CI95%: 0.709–0.861). These parameters were comparable among CTDs-PAH and PH of different etiology. The diagnostic performance was even improved by creating a composite score which included all the three parameters identified. In conclusion, CPET is a very promising tool for the stratification of risk of PAH among CTDs patients; the use of composite measures may improve diagnostic performance.

**Keywords:** pulmonary arterial hypertension; systemic sclerosis; scleroderma; cardiopulmonary exercise testing

#### **1. Introduction**

Pulmonary arterial hypertension (PAH) is a progressive disease affecting the precapillary pulmonary vascular bed, leading to an increase in pulmonary vascular resistance and right ventricular failure, burdened by a high mortality rate [1]. PAH is a severe complication of different connective tissue disease (CTDs), particularly: systemic sclerosis (SSc), mixed connective tissue diseases (MCTD) and SSc overlapping with other CTDs [2]. The early diagnosis of SSc-PAH is difficult, since PAH is initially minimally symptomatic or asymptomatic, but absolutely pivotal: indeed, the early initiation of an effective treatment is the most relevant prognostic factor in patients affected by PAH [3]. This is why patients diagnosed with CTDs are commonly followed-up and screened for the development of PAH; the two-step algorithm DETECT is the most commonly used screening tool [4]. The DETECT algorithm includes a first step in which patients are indicated to echocardiography according to a composite score derived from the following variables: forced vital

**Citation:** Bellan, M.; Giubertoni, A.; Piccinino, C.; Buffa, M.; Cromi, D.; Sola, D.; Pedrazzoli, R.; Gagliardi, I.; Calzaducca, E.; Zecca, E.; et al. Cardiopulmonary Exercise Testing Is an Accurate Tool for the Diagnosis of Pulmonary Arterial Hypertension in Scleroderma Related Diseases. *Pharmaceuticals* **2021**, *14*, 342. https://doi.org/10.3390/ph14040342

Academic Editors: Barbara Ruaro, Francesco Salton and Paola Confalonieri

Received: 26 February 2021 Accepted: 30 March 2021 Published: 8 April 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

capacity (FVC) and diffusing capacity of the lung for carbon monoxide (DLCO); presence of teleangectasias and anti-centromere antibody; serum urate; N-terminal probrain natriuretic peptide and presence of right axis deviation on electrocardiogram. On the basis of the result of echocardiography, at risk patients will be further tested with right heart catheterization (RHC). The detect score well performs in this setting, showing a very high sensitivity (96%), as required by any screening tool; however, the specificity is low (48%) having as a direct consequence the need for a high number of unnecessary invasive measurement of pulmonary pressure, by RHC [5]. This is why there is an unmet need of novel biomarkers able to refine the PAH risk stratification among CTDs patients [6,7]. In the last years, cardiopulmonary exercise testing (CPET) has been proposed as a novel tool to better select those patients at higher risk of PAH, thus requiring RHC [8]. CPET provides an important insight into exercise physiology and, according to recent data, may contribute to the identification of PAH among SSc patients [9]. In the present study we aimed to confirm this observation and to evaluate whether CPET findings among PAH-CTDs patients differ from patients affected by PH of a different etiology, in a pilot study.

#### **2. Results**

We recruited 131 patients, 115 (87.8%) females; the median age was 61.5 (52.0–69.5) years. Out of them, 112 (85.5%) had a diagnosis of CTDs alone: 84 were affected by SSc, 15 by overlap syndrome, 5 by MCTD and 8 by Undifferentiated connective tissue disease (UCTD). The median disease duration of CTDs was 5 (2–11) years; antirheumatic treatment mainly included hydroxychloroquine (*N* = 57, 50.9%) and methotrexate (*N* = 15, 13.4%). 37 patients (33.0%) were receiving steroids when CPT was performed.

8 (6.1%) received a diagnosis of CTDs-PAH: 6 were affected by SSc, 1 by overlap syndrome and 1 by MCTD; 6 patients were on endothelin receptor antagonists, 5 patients were receiving phosphodiesterare 5 inhibitors. Moreover 1 patient was receiving riociguat and 1 patient selexipag. The median time to pulmonary hypertension diagnosis was 5 (3–6) years.

Finally, 11 (8.4%) patients received a diagnosis of PH of different etiology (5 PAH, 4 chronic thromboembolism, 1 unknown).

In Table 1 we report the main clinical and laboratory features of the three study groups:

**Table 1.** Main general features of the study population and comparison between groups. For abbreviation: CTDs, connective tissue diseases; PAH, pulmonary arterial hypertension; PH, pulmonary hypertension; M, males; F, females; FVC, forced vital capacity; FEV1, forced expiratory volume in the 1st second; DLCO, diffusing capacity of the lung for carbon monoxide; LVEF, left ventricular ejection fraction; sPAP, systolic pulmonary arterial pressure. \*A vs. B; § A vs. B and C.

As shown in the Table 1, patients affected by PH show higher sPAP and lower DLCO, as expected. Among CTD patients, those with PAH are significantly older. Looking at the CPET parameters, we compared the results of the test among groups. The results are shown in Table 2:

**Table 2.** Comparison of the main CPET parameters among groups between groups. For abbreviation: VO2, volume of oxygen uptake; VE, ventilation; VCO2, volume of exhaled carbon dioxide; PetCO2, end-tidal partial pressures for CO2. \*A vs. B; § A vs. B and C; ◦ C vs. A and B.

> Patients with PH have a significantly lower peak VO2 and basal PetCO2, and a significantly higher VE/VCO2 slope. These parameters were comparable among CTDs-PAH patients and PH patients with different etiologies. We finally evaluated the diagnostic accuracy of the different CPET parameters considered. In Figure 1 we reported the ROC curve for peak VO2 for the diagnosis of PAH in patients affected by CTDs. As shown, this parameter has very good diagnostic performance (AUC: 0.845; CI 0.767–0.904); a value ≤ 14.1 is 87.5% sensitive (LR− 0.15, CI95% 0.02–0.90) and 83.05% specific (LR+ 5.16, CI95%: 3.2–8.4) for PAH. The NPV is 98.4% (CI 95% 90.5–99.7%), while the PPV is 36.4% (CI 95% 26.1–48.2%).

**Figure 1.** ROC curve for peak VO2. We diagnosed 8 CTD-PAH.

In Figure 2 we reported the ROC curve for VE/VCO2 slope, which again demonstrates a very good diagnostic accuracy (AUC: 0.888; CI95%: 0.817–0.938); a threshold > 33.96 is 87.5% sensitive (LR− 0.15, CI95% 0.02–1.0) and 82.14% specific (LR+ 4.9, CI95%: 3.0–7.9) specific for PAH. The NPV is 98.3% (CI95% 90.4–99.7) and the PPV is 35.3 (CI95%: 25.3–46.7).

**Figure 2.** ROC curve for peak VE/VCO2 slope. We diagnosed 8 CTD-PAH. For abbreviation: AUC, Area under the curve.

Moreover, in Figure 3 we reported the ROC curve for basal PetCO2; a threshold ≤ 27.2 is 87.5% sensitive (LR− 0.18; CI95% 0.03–1.1) and 71.43% specific (LR+ 3.1; CI95%: 2.1–4.5) specific for PAH, while the AUC is: 0.792 (CI95%: 0.709–0.861). The PPV is 25.4% (CI 95% 18.7–33.5) and the NPV is 98.1% (89.1–99.7).

**Figure 3.** ROC curve for basal PetCO2. We diagnosed 8 CTD-PAH.

We finally tried to evaluate the diagnostic accuracy of a composite predictive model including all these three parameters. We scored 1 point for each parameter considered: a peak VO2 ≤ 14.1; a VE/VCO2 slope > 33.96; a basal PetCO2 ≤ 27.2. In Table 3 we report the different scores according to the presence/absence of PAH among CTDs patients. The distribution was significantly different (χ2 for trend; 34.3 *p* < 0.0001).

**Table 3.** Application of the CPET scoring system among CTDs patients.

As shown in the ROC curve (Figure 4), a score of 3 is 87.5% (CI95% 47.3–99.7) sensitive and 99.1% (CI95%:95.1–100.0) specific for PAH. The LR− is 0.13 (CI95%: 0.02–0.8) and the LR+ 98 (CI 95%: 13.7–701.8). The PPV is 91.6% (CI 95%: 60.3–98.7) and the NPV is 98.6% (CI 95%: 91.9–99.8).

**Figure 4.** ROC curve for the composite model. We diagnosed 8 CTD-PAH.

#### **3. Discussion**

In the present paper we aimed at preliminary evaluating the diagnostic accuracy of CPET in the diagnosis of PAH among CTDs patients. According to our data, CPET is a potentially sensitive and specific tool for the diagnosis of pulmonary hypertension, independently from the underlying etiology. The use of composite, CPET-based scores might improve the diagnostic accuracy and should be evaluated on a larger scale, since our pilot study provides only preliminary data. These findings will be herein discussed on the basis of the current literature.

SSc is a potentially severe condition; according to a meta-analysis published in 2012, which pooled data from different cohort studies covering over 50 years of observation, patients affected by SSc have a standardized mortality ratio 3.5 times higher than the general population [10]. PAH is a major determinant of this excess of deaths; in fact, according to the EUSTAR cohort study, more than half SSc patients dies because of a condition related to their underlying CTD; more specifically, around 15% of deaths are related to the development of PAH [11]. Patients affected by PAH have a severe prognosis; the annual mortality is around 10% in idiopathic PAH [3] and even worse in CTDs-related PAH, particularly when SSc is the underlying rheumatic condition [12]. Indeed, according to Mukerjee et al., the survival rate at 1-, 2- and 3-years was respectively 81%, 63%, and 56% [13] and a comparable prognosis has been reported more recently in different registry based studies [14,15].

The degree of hemodynamic impairment is universally considered a main prognostic predictor in PAH [3,14–16]; being available different therapeutic strategies effective in the management of PAH, it is reasonable to consider early diagnosis fundamental to impact on patients' prognosis. This is why SSc patients should undergo a regular screening strategy to early identify cardiopulmonary involvement and to start the treatment as soon as possible. According to the lastly updated Eular guidelines for the management of SSc-PAH, the treatment of this condition should include the same classes of drugs used in the other forms of PAH; this recommendation belongs to the results of different high quality clinical trials including heterogeneous population of PAH patients among whom CTDs-PAH was included [17].

The most commonly used screening protocol is the DETECT algorithm, firstly described by Coghlan et al. in 2014; it is a very well performing diagnostic tool, particularly in the context of a screening strategy, because of its optimal sensitivity. However, it should

be acknowledged that its specificity is quite low. This is not negligible; in fact, the relatively low positive predictive value accounts for an excess of patients needing to be tested with the gold standard for PAH diagnosis, the RHC. This test is invasive and burdened by potential complications which, although rare, may be severe and even fatal [18]; thus, a better tailoring of screening strategy and individual's risk stratification may allow to reduce the costs and limit the risk for patients, by reducing the number of patients unnecessary tested.

In the last few years an increasing number of papers investigated the diagnostic role of CPET in the identification of those CTDs patients with PAH [19]. Although this test is not currently included in the guidelines for the management of SSc, the current literature is highly consistent in confirming its potential in the identification of PAH in this specific group of individuals.

In our study, we selected the best performing parameters and finally we built a composite score to improve their diagnostic accuracy. We reported that peak VO2 is significantly reduced in CTDs-PAH, with respect to CTDs alone; this reduction is comparable to what observed in patients affected by PH with different etiologies. Similar results were previously reported by Dumitrescu et al. and may be explained by the fact that peak VO2 is closely related to cardiac output during exercise; therefore, a high peak VO2 reflects a good hemodynamic adaptation to exercise and is able to rule out PAH with high accuracy. The authors also evaluated the best performing threshold for this parameter, identifying a cut-off of 13.8, very close to our one [8]. Another common finding is the good clinical performance of VE/VCO2 slope. In a recent paper of an Italian group, VE/VCO2 slope was the best parameter able to identify PAH at RHC, on top of a positive DETECT screening. Interestingly, once more, the cut-off used was quite close to the one that we identified (35.5 vs. 33.9) and showed an optimal sensitivity and a good specificity yielding a PPV of 0.636 (0.556–0.750) [9]. We also tested the role of PetCO2; as already reported by Dumitrescu et al. [8], this marker of ventilator efficiency is predictive of PAH, although its diagnostic power is lower than peakVO2. Interestingly, these alterations are not specific for CTDs-PAH; we, indeed, included a subgroup of subjects suffering for PH of different etiologies, demonstrating that the CPET is more generally able to detect cardiopulmonary involvement. A major novel finding of our work is the observation that combining different CPET parameters may improve the diagnostic accuracy of the test; in particular, we propose a 3-points score based on the three previously discussed parameters which very well fits to our population. Despite having a sensitivity which is similar to the one of any single parameter tested, the use of a composite score significantly enhances the specificity and the PPV. This may, finally, significantly increase the diagnostic accuracy of the present tool. On this basis, we can postulate that CPET may contribute to a better stratification of those patients really requiring a RHC; it can be argued that, in the context of a screening procedure, the use of less sensitive tools than DETECT algorithm can cause the loss of some PAH cases with relevant clinical implications; however, RHC is an invasive procedure. Thus, we might hypothesize that RHC may be postponed in those patients with an indication according to DETECT but with a normal CPET. These subjects may be addressed to a stricter follow-up to early identify clinical deterioration. Moreover, we can also postulate that, giving the very high PPV of CPET, it could even represent an alternative to RHC in those patients at higher procedural risk. However, we acknowledge that the low number of cases and the cross-sectional design of the study limit the possibility to truly test our score as a diagnostic tool. Prospective studies are indeed required to evaluate its potential clinical application.

A further element of discussion belongs to the observation that SSc patients may have less marked alteration of CPET even in absence of PAH; according to previous findings, in fact, patients with SSc may show increased VE/VCO2 slope and decreased peak VO2 with respect to the general population [20]. It will be interesting to evaluate, in the next future, whether those patients with an altered CPET may represent a subset of individuals at higher risk for PAH development. Obviously, our study because of its cross-sectional design is not able to give an answer to this relevant research question.

Our paper has some limitations: first of all, the number of patients affected by PAH included in the final analysis is low; our study should be, in fact, considered for what it actually is: a pilot study with preliminary results that, although promising, require a confirmation on a larger scale. Furthermore, some of the patients received a diagnosis of pulmonary hypertension on the basis of the echocardiographic findings, being RHC contraindicated. Despite this is in line with the current international guidelines, we should acknowledge that RHC is the gold standard for the diagnosis of pulmonary hypertension. Moreover, the diagnostic efficacy of this CPET-based score should be tested in a more comprehensive strategy, as integration of the standard DETECT algorithm. A further relevant limitation is that we considered prevalent PAH, rather than incident PAH; patients were not naïve to treatment, which might have affected our findings.

#### **4. Materials and Methods**

We performed a cross-sectional, observational study on patients evaluated at the Pulmonary Hypertension Clinic of the Cardiology Division, University Hospital of Novara from 3 October 2016 to 12 December 2019. The clinic was the main referral for Rheumatology Units of the geographic area, representing a major facility for PAH screening of CTDs patients. The study protocol was approved by the local ethical committee and conducted in strict accordance with the principles of the Declaration of Helsinki. Written informed consent was obtained from all individual participants included in the study.

We included all the patients older than 18 years who underwent, under clinical indication a CPET. We excluded from the study those who refused to sign the informed consent. We included in the study both CTDs patients and patients with a diagnosis of PH with a different etiology. The following criteria were applied to classify the different rheumatic conditions:

All the included patients also underwent a comprehensive clinical evaluation and a biochemistry panel; moreover, respiratory function and echocardiography were performed as described in previous papers belonging to the same project [6,7].

We recorded the following standardized measurements: forced vital capacity (FVC), forced expiratory volume in one second (FEV1), and FEV1/FVC%), diffusing capacity of the lung for carbon monoxide adjusted for alveolar volume (DLCO VA), measured with the single-breath Jones-Meade protocol, corrected for alveolar ventilation, systolic pulmonary pressure (sPAP), right atrium area (RAA), right ventricle diameter (RVD), and ejection fraction (EF).

According to the application of international guidelines, those patients with a suspected PAH underwent right heart catherization. PAH was defined by mean pulmonary artery pressure (mPAP) ≥ 25 mmHg, pulmonary capillary wedge pressure ≤ 15 mmHg, and pulmonary vascular resistance >3 wood units. Whenever contraindications to RHC occurred, pulmonary hypertension was diagnosed based on echocardiography-estimated sPAP ≥ 35 mmHg and additional high probability criteria (1 patient in the group B and 5 patients in the group C), in agreement with the 2015 ESC/ESR guidelines [1].

CPET was performed on a stationary bicycle ergometer, within two weeks from echocardiographic assessment and PFTs. The exercise protocol consists of 3 min of rest followed by the incremental work rate to the patients' maximum tolerance, then 5 min of recovery. The incremental work rate was selected according to the patient's exercise capacity to aim for 8–12 min in length. Gas exchange was measured breath-by-breath during the test using a Schiller Cardiovit CS-200 Ergo-Spiro System (Baar, Switzerland); we used the Ganshorn Medizine Eletronic software for pulmonary function testing (v. LF8.5M SR3, Niederlauer, Germany). Equipment was calibrated before each exam. ECG and pulse oximetry were continuously monitored, and blood pressure was measured every three minutes. Minute ventilation (VE), heart rate (HR), oxygen uptake (VO2), carbon dioxide production (VCO2), CO2 ventilatory equivalent (VE/VCO2 or EQCO2), O2 ventilatory equivalent (VE/VO2 or EQO2), end tidal O2 (PetO2), end tidal CO2 (PetCO2), tidal volume and PulseO2 were averaged every 10 s. Predicted value for peak VO2 were calculated according to the standard formula. The first ventilatory threshold was determined from gas exchange by the V-slope method, derived from the plot with VO2 and VCO2 recognizing the point where VCO2 started increasing faster than VO2, in all patients. The relationship between VE and VCO2 (VE/VCO2 slope) was calculated as the slope of the linear relationship between VE and VCO2 from one minute after the beginning of loaded exercise to the end of the isocapnic buffering period. We considered maximal effort ad achieved if the respiratory exchange ratio (RER) calculated as the ratio between VO2 and VCO2 was above 1,10. All CPET were executed and analyzed by one physician's blinded to patients' clinical features.

#### *Statistical Analysis*

All the data were recorded in a database and analyzed by the statistical software package MedCalc v.19.6.4 (MedCalc Software, Broekstraat 52, 9030, Mariakerke, Belgium). Continuous variables are presented as medians and interquartile range [IQR]. We compared continuous variables among groups by Kruskal-Wallis test, while categorical distribution was tested by Pearson's χ2.

To test the diagnostic performance of different CPET parameters among CTDs patients, receiver operating characteristics curves were built, with calculation of the areas under the curve (AUC). Moreover, we calculated the sensitivity, specificity, positive and negative likelihood ratio (LR+ and LR−), negative predictive value (NPV) and positive predictive value (PPV) for the different thresholds. NPV and PPV were calculated on the basis of an estimated rate of CTDs-PAH of 10%. The best diagnostic thresholds were identified according to the Youden index J and used to build a composite score which was tested for its diagnostic performance.

The level of significance chosen for all statistical analysis was 0.05 (two-tailed).

#### **5. Conclusions**

In conclusion, our paper supports the idea that CPET should be considered for the extensive use in the follow-up of CTDs patients at risk for PAH; a multiparametric diagnostic strategy might be more effective to improve the diagnostic performance of this examination.

**Author Contributions:** Conceptualization, M.B. (Mattia Bellan), A.G., C.P., P.P.S. and M.P.; data curation, M.B. (Mariachiara Buffa), A.G., C.P., M.B. (Mattia Bellan), D.C., D.S., R.P., I.G., E.C., E.Z. and F.P.; formal analysis, M.B. (Mattia Bellan), G.P. and M.P.; investigation, M.B. (Mattia Bellan), A.G., C.P., D.S., R.P., I.G., E.C., E.Z., F.P. and G.P.; methodology, M.B. (Mattia Bellan), A.G., C.P. and P.P.S.; project administration, P.P.S.; writing—original draft, M.B. (Mattia Bellan); writing—review and editing, M.B. (Mattia Bellan), G.P., P.P.S. and M.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of Novara (protocol code 108/16, approved on 09/09/2016).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** Data are available upon reasonable request to the corresponding author.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


*Quantum Chemical Methods as Efficient Tools to Study Corrosion Inhibitors*

Quantum chemical methods are seen to be very effective in determining the molecular structures as well as explaining the electronic structures and reactivity's of molecules. Computational chemistry is considerably used to assess the efficiency of corrosion inhibitors, this method helps to search compounds of desired property employing computational modelling. Quantum chemical method and molecular modelling techniques help in defining a large number of molecular parameters illustrating

the reactivity, shape, and binding properties of complete molecules as well as of individual molecular fragments and substituents. The prominent quantum chemical parameters are atomic charges, molecular orbital energies (*E*HOMO, *E*LUMO and Δ*E*gap), dipole moment, charge distribution. Density functional theory has successfully been applied to explain the importance of structure of corrosion inhibitors and their adsorption efficiency on the metal surfaces, however the properties of corrosion inhibitors like *E*HOMO, *E*LOMO, Δ*E*gap, dipole moment (μ), electronegativity ( Щ ), and atomic charge have by far achieved the appropriate correlation with corrosion inhibition efficiency.

#### **5. Emerging Embedment Methods of Corrosion Inhibitors**

Corrosion inhibitors can be incorporated into the coating formulation through different ways. One of the most commonly used methods is the direct addition of inhibitors in the primer or topcoat. However, a too high concentration or low solubility of the inhibitors often results in a deterioration of the integrity and physical barrier properties of the matrix of the protection system [101]. In addition, the existing interaction of the inhibiting agents with the protective matrix often leads to significantly reduced stability of the protective layer and the deactivation of the inhibitors. Recently, different new methods for inhibitor incorporation have emerged to prevent the direct interaction of inhibitor with the matrix. One of the most common methods is application of inhibitor loaded coatings. Coatings based on inhibitor loaded containers protect the metal by releasing corrosion inhibitor in response to changes in the coating integrity (cracks) or local environment (pH shift) caused by corrosion attack. These systems have been extensively investigated, because they are potential replacements for the banned chromate-based coatings [102]. Out of these, self-healing coating is an emerging and broad field to replace the chromate for corrosion control and autonomic repair of coatings (self-healing), which is discussed in this section.


**2. Applications**

This section presents a number of applications of non-invasive monitoring methods for individuals.

#### **2.1 Monitoring health status of patients in the intensive care unit on the basis of realtime measured physiological variables and advanced modelling technology**

In cardiac surgery it would be very helpful to have a system that provides an early alert if there is a high probability that a patient will be disconnected from ventilation during the

Non-Invasive Methods for Monitoring Individual Bioresponses in Relation to Health Management 145

parameters of which were used as the features of the examples. One of the two possible class labels was assigned to each example. Training examples for each classifier were labelled positive (+1) if the moment when the patient became stable started within the first nine hours after admission and were labelled negative (-1) otherwise. The classification performance can be calculated by the aROC (area under the receiver operating characteristic

Table 1 gives the obtained aROCs for each experiment with the GP. The middle column contains the results obtained when using a logistic regression (LOGREG) model, included here as a baseline for performance. The increase in performance for all GP models versus the LOGREG models was found to be significant, except for the model based on admission. So, although logistic regression techniques are commonly used in medical applications, other classifiers might lead to better results. This was, among others, also concluded by Sakai et al. (2007) and Erol et al. (2005). It is also shown that the approach including dynamic information (MAR) performs better than the model purely based on admission information

> aROC LOGREG GP Avgstd (20) 0.628 0.713 MAR 0.591 0.708

This application shows that taking into account dynamic information in analysing time series of patients can be of added value when monitoring the health status of individual

Disease management is becoming increasingly important in our current society, especially considering the growing population of elderly and immune compromised people. There is a general agreement that sepsis and the systemic inflammatory response syndrome (SIRS) are characterized by an inability to regulate the inflammatory response. The cause of this perturbation is still unknown. So far research did not result in a dramatic reduction of the high mortality rates which for critically ill patients in intensive care units where sepsis and

As strategies for the early treatment of sepsis mostly failed, this fully justifies the development of a novel biosensor array at the heart of an on line early warning monitoring system for prediction of disease evolution and subsequent adaptation of life saving therapy. Complex biological processes are involved in these phenomena and therefore it is a challenge to quantify infection and inflammation processes in real-time. It is expected however that the use of biosensors in combination with real-time signal analysis allows

Developing such new sensing techniques requires an interdisciplinary approach between engineers, immunologists, medical experts and sensor developers. In a first step, we aimed at demonstrating the proof of principle in animal experiments (pigs). In a next step, the

**2.2 Dynamic algorithms of biomarkers for monitoring infection/inflammation**

curve) for each classifier.

(in terms of the aROC).

Table 1. Classification results

SIRS remain major causes of death.

monitoring infection/inflammation processes in real-time.

developed methodologies might be transferred to human patients.

patients.

**processes**

next day since this would lead to a more optimal planning in the Intensive Care Unit (ICU). It was shown that alterations in vital signals are relevant to patient management (Rivera-Fernandez et al., 2007), so we wanted to use the trends of some of those vital signals during the first hours of ICU stay to predict a short or prolonged length of stay from early on. All living organisms are characterised by the fact that they are complex, individually different time-variant and dynamic (so called CITD systems) (Quanten et al., 2006). Consequently, it is expected that taking these characteristics into account will lead to better models of the physiological signals of intensive care patients. So far, univariate and multivariate autoregressive analyses as well as the calculation of the cepstrum of physiological variables have been applied in several medical studies (Wada et al., 1988; Curcie and Craelius, 1997) to analyze individual patients. For making classifications using many variables at the same time, several data mining techniques are available. However, in most cases no dynamic information about the patients is taken into account when applying the data mining approach. Several attempts on temporal feature extraction for time series classification have been made (Verduijn et al., 2007). We describe here a study in which information of patients' dynamics was used to predict the timeframe when the conditions to start weaning of mechanical ventilation are reached.

#### **2.1.1 Analysis of time series of patient data**

Physiological variables, such as heart rate (bpm), systolic arterial blood pressure (mmHg), systolic pulmonary pressure (mmHg), blood temperature (°C) and oxygen saturation are routinely monitored in these patients and can be sampled frequently when Patient Data Management System are used. In this example, we show results of a total of 203 patients that were followed in the Intensive Care unit of the University Hospital of Leuven. More information can be found in the work of Van Loon et al. (2010).

Besides the mean and standard deviations of the signals (Avgstd), more advanced time series models can be applied that allow quantifying the dynamics of these physiological variables such as univariate and multivariate autoregressive (AR) models.

Dynamic features that are extracted from time series of patient data, can be used in a next step to determine the status of individual patients when applying them to machine learning techniques such as Support Vector Machines or Gaussian Processes.

Gaussian processes (GP), a type of kernel method, are a machine learning technique that has been successfully used to model and forecast real dynamic systems. In probabilistic binary classification the task is to determine for an unlabeled test input vector the probability of belonging to a given class when a training set is given. The training set is comprised of training input vectors and their corresponding binary class labels (+1 if the input vector belongs to the class, -1 otherwise).

The considered task in the presented example can be restated as follows: Predict the probability that the patient will begin to satisfy the stability criteria within each of the following time frames (classes): class 1: earlier than nine hours after admission; class 2: later than nine hours after admission. This nine hour threshold was chosen such that the resulting classes contained roughly the same amount of patients. This division also conforms to an intuitive classification used by intensivists into patients that recover quickly and those that require prolonged ICU stays. Data from each patient, collected during the first four hours ICU stay, were used to generate the different time-series models, the parameters of which were used as the features of the examples. One of the two possible class labels was assigned to each example. Training examples for each classifier were labelled positive (+1) if the moment when the patient became stable started within the first nine hours after admission and were labelled negative (-1) otherwise. The classification performance can be calculated by the aROC (area under the receiver operating characteristic curve) for each classifier.

Table 1 gives the obtained aROCs for each experiment with the GP. The middle column contains the results obtained when using a logistic regression (LOGREG) model, included here as a baseline for performance. The increase in performance for all GP models versus the LOGREG models was found to be significant, except for the model based on admission. So, although logistic regression techniques are commonly used in medical applications, other classifiers might lead to better results. This was, among others, also concluded by Sakai et al. (2007) and Erol et al. (2005). It is also shown that the approach including dynamic information (MAR) performs better than the model purely based on admission information (in terms of the aROC).

Table 1. Classification results

144 Health Management – Different Approaches and Solutions

next day since this would lead to a more optimal planning in the Intensive Care Unit (ICU). It was shown that alterations in vital signals are relevant to patient management (Rivera-Fernandez et al., 2007), so we wanted to use the trends of some of those vital signals during the first hours of ICU stay to predict a short or prolonged length of stay from early on. All living organisms are characterised by the fact that they are complex, individually different time-variant and dynamic (so called CITD systems) (Quanten et al., 2006). Consequently, it is expected that taking these characteristics into account will lead to better models of the physiological signals of intensive care patients. So far, univariate and multivariate autoregressive analyses as well as the calculation of the cepstrum of physiological variables have been applied in several medical studies (Wada et al., 1988; Curcie and Craelius, 1997) to analyze individual patients. For making classifications using many variables at the same time, several data mining techniques are available. However, in most cases no dynamic information about the patients is taken into account when applying the data mining approach. Several attempts on temporal feature extraction for time series classification have been made (Verduijn et al., 2007). We describe here a study in which information of patients' dynamics was used to predict the timeframe when the conditions to start weaning of

Physiological variables, such as heart rate (bpm), systolic arterial blood pressure (mmHg), systolic pulmonary pressure (mmHg), blood temperature (°C) and oxygen saturation are routinely monitored in these patients and can be sampled frequently when Patient Data Management System are used. In this example, we show results of a total of 203 patients that were followed in the Intensive Care unit of the University Hospital of Leuven. More

Besides the mean and standard deviations of the signals (Avgstd), more advanced time series models can be applied that allow quantifying the dynamics of these physiological

Dynamic features that are extracted from time series of patient data, can be used in a next step to determine the status of individual patients when applying them to machine learning

Gaussian processes (GP), a type of kernel method, are a machine learning technique that has been successfully used to model and forecast real dynamic systems. In probabilistic binary classification the task is to determine for an unlabeled test input vector the probability of belonging to a given class when a training set is given. The training set is comprised of training input vectors and their corresponding binary class labels (+1 if the input vector

The considered task in the presented example can be restated as follows: Predict the probability that the patient will begin to satisfy the stability criteria within each of the following time frames (classes): class 1: earlier than nine hours after admission; class 2: later than nine hours after admission. This nine hour threshold was chosen such that the resulting classes contained roughly the same amount of patients. This division also conforms to an intuitive classification used by intensivists into patients that recover quickly and those that require prolonged ICU stays. Data from each patient, collected during the first four hours ICU stay, were used to generate the different time-series models, the

mechanical ventilation are reached.

belongs to the class, -1 otherwise).

**2.1.1 Analysis of time series of patient data**

information can be found in the work of Van Loon et al. (2010).

variables such as univariate and multivariate autoregressive (AR) models.

techniques such as Support Vector Machines or Gaussian Processes.

This application shows that taking into account dynamic information in analysing time series of patients can be of added value when monitoring the health status of individual patients.

#### **2.2 Dynamic algorithms of biomarkers for monitoring infection/inflammation processes**

Disease management is becoming increasingly important in our current society, especially considering the growing population of elderly and immune compromised people. There is a general agreement that sepsis and the systemic inflammatory response syndrome (SIRS) are characterized by an inability to regulate the inflammatory response. The cause of this perturbation is still unknown. So far research did not result in a dramatic reduction of the high mortality rates which for critically ill patients in intensive care units where sepsis and SIRS remain major causes of death.

As strategies for the early treatment of sepsis mostly failed, this fully justifies the development of a novel biosensor array at the heart of an on line early warning monitoring system for prediction of disease evolution and subsequent adaptation of life saving therapy.

Complex biological processes are involved in these phenomena and therefore it is a challenge to quantify infection and inflammation processes in real-time. It is expected however that the use of biosensors in combination with real-time signal analysis allows monitoring infection/inflammation processes in real-time.

Developing such new sensing techniques requires an interdisciplinary approach between engineers, immunologists, medical experts and sensor developers. In a first step, we aimed at demonstrating the proof of principle in animal experiments (pigs). In a next step, the developed methodologies might be transferred to human patients.

Non-Invasive Methods for Monitoring Individual Bioresponses in Relation to Health Management 147

Fig. 1. Criterion for early detection of survival/non-survival based on SAA: stability of model. Overview of a-parameters of AR-models for all surviving (blue) and non-surviving

Also for the biomarker IL-6, there was a significant difference in a-parameters between survivors and non-survivors (mean S = -0.1272, mean NS = -0.5751, p=0.0085). Fig. 2 shows a

Fig. 2. Criterion for early detection of survival/non-survival based on IL-6: trend vs noise. Overview of a-parameters of AR-models for all surviving (blue) and non-surviving (red)

(red) pigs

pigs

scatter plot of the a-parameters for all pigs.

#### **2.2.1 Data generation**

The aim of the experiments was to quantify the dynamics of 3 cytokines and 4 acute-phase proteins (APP) before and after infection by *Actinobacillus pleuropneumoniae* in pigs*.* More specific, the cytokines, tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6) and interleukin-10 (IL-10), and the acute-phase proteins, C-reactive protein (CRP), haptoglobin (Hp), major acute phase protein (MAP) and serum-amyloid A (SAA), were analysed. In total, 22 pigs were infected. The blood sampling frequency that was used for the experiments was as follows: 1 sample/day: before infection, 1 sample/2 hours: starting from 2 hours before infection to end. The higher sampling frequency after infection was necessary to determine the dynamics of the response on the infection and to measure the entire course of the biomarker response. Due to the measuring frequencies, the focus of the modelling analysis was on the period starting with the infection of the pigs until the end of the experiment.

#### **2.2.2 Modelling of biomarker responses to infection**

In this example, the dynamics of all biomarkers were modelled using univariate autoregressive (AR) models. These time series models were defined as follows (Taylor et al., 2007):

$$\mathbf{y}(\mathbf{k})\mathbf{A}(\mathbf{z}^{-1}) = \mathbf{e}(\mathbf{k})\tag{1}$$

Where y(k) is the considered biomarker, 1 A(z ) is the polynomial of the model parameters and e(k) is additive noise, a serially uncorrelated sequence of random variables with variance σ2 that accounts for measurement noise, modelling errors and effects of unmeasured inputs to the process (assumed to be a zero mean). For the modelling the order of the AR models were ranged from 1 to 2, resulting in 2 possible AR models per biomarker for every pig. Since no significant results were found for second order AR models, all AR models described in this report are simple first order models which can be written in the time series form:

$$\mathbf{y(k)} = -\mathbf{a}\_1 \mathbf{y(k-1)} + \mathbf{e(k)} \tag{2}$$

As the results of SAA and IL-6 were most significant, we will focus in this example on these two biomarkers. In a first step, models were developed for each individual pig. In a second step, the individual models of the pigs were compared to develop model-based criteria for the early detection of survival/non-survival.

#### **2.2.3 Monitoring criteria**

All the modelling results described below will focus on the prediction of disease outcome (survival vs. non-survival). Since many pigs died shortly after the infection, only the data of a short period after infection were used for the development of the TF- and AR-models. To obtain comparable models, samples of the same time interval were used for the calculation of these models. More specifically, for all pigs data were used starting from the moment of infection until 16 hours after infection.

For the biomarker SAA a significant difference between survivors (S) and non-survivors (NS) was found for the a-parameters of the AR models (mean S = -0.7915, mean NS = -1.3204, p = 0.04). Fig. 1 shows a scatter plot of the a-parameters of the different pigs.

The aim of the experiments was to quantify the dynamics of 3 cytokines and 4 acute-phase proteins (APP) before and after infection by *Actinobacillus pleuropneumoniae* in pigs*.* More specific, the cytokines, tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6) and interleukin-10 (IL-10), and the acute-phase proteins, C-reactive protein (CRP), haptoglobin (Hp), major acute phase protein (MAP) and serum-amyloid A (SAA), were analysed. In total, 22 pigs were infected. The blood sampling frequency that was used for the experiments was as follows: 1 sample/day: before infection, 1 sample/2 hours: starting from 2 hours before infection to end. The higher sampling frequency after infection was necessary to determine the dynamics of the response on the infection and to measure the entire course of the biomarker response. Due to the measuring frequencies, the focus of the modelling analysis was on the period starting with the infection of the pigs until the end of the

In this example, the dynamics of all biomarkers were modelled using univariate autoregressive (AR) models. These time series models were defined as follows (Taylor et al.,

Where y(k) is the considered biomarker, 1 A(z ) is the polynomial of the model parameters and e(k) is additive noise, a serially uncorrelated sequence of random variables with variance σ2 that accounts for measurement noise, modelling errors and effects of unmeasured inputs to the process (assumed to be a zero mean). For the modelling the order of the AR models were ranged from 1 to 2, resulting in 2 possible AR models per biomarker for every pig. Since no significant results were found for second order AR models, all AR models described in this report are simple first order models which can be written in the

As the results of SAA and IL-6 were most significant, we will focus in this example on these two biomarkers. In a first step, models were developed for each individual pig. In a second step, the individual models of the pigs were compared to develop model-based criteria for

All the modelling results described below will focus on the prediction of disease outcome (survival vs. non-survival). Since many pigs died shortly after the infection, only the data of a short period after infection were used for the development of the TF- and AR-models. To obtain comparable models, samples of the same time interval were used for the calculation of these models. More specifically, for all pigs data were used starting from the moment of

For the biomarker SAA a significant difference between survivors (S) and non-survivors (NS) was found for the a-parameters of the AR models (mean S = -0.7915, mean NS = -1.3204,

p = 0.04). Fig. 1 shows a scatter plot of the a-parameters of the different pigs.

1 y(k)A(z ) e(k) (1)

y(k) a y(k 1) e(k) 1 (2)

**2.2.1 Data generation**

experiment.

time series form:

**2.2.3 Monitoring criteria**

2007):

**2.2.2 Modelling of biomarker responses to infection**

the early detection of survival/non-survival.

infection until 16 hours after infection.

Fig. 1. Criterion for early detection of survival/non-survival based on SAA: stability of model. Overview of a-parameters of AR-models for all surviving (blue) and non-surviving (red) pigs

Also for the biomarker IL-6, there was a significant difference in a-parameters between survivors and non-survivors (mean S = -0.1272, mean NS = -0.5751, p=0.0085). Fig. 2 shows a scatter plot of the a-parameters for all pigs.

Fig. 2. Criterion for early detection of survival/non-survival based on IL-6: trend vs noise. Overview of a-parameters of AR-models for all surviving (blue) and non-surviving (red) pigs

Non-Invasive Methods for Monitoring Individual Bioresponses in Relation to Health Management 149

diagnostic tests (Bates et al., 1999). The nurses have stated their expectations of Personal Digital Assistant (PDA) devices in the nursing practice (Nilsson et al., 2007). The computerized versions of the pain scales and questionnaires have lately been introduced successfully in pain assessment practice (Wincent et al. 2003). It has been reported in the literature that the computerized version of the self-report pain questionnaires is not altering the response of the patients (Caro et al., 2001). It is also decreasing the number of the missing responses by obligating the patients to answer before proceeding to the next question (Caro et al., 2001). Compared to the paper versions, the electronic pain self-report questionnaires and electronic diaries are offering considerable advantages: 1) completeness of data (Hanscom et al., 2002); 2) entered data are date and time stamped (Burton et al., 2007); 3) saving of time and reduction of errors from entering written data manually to the

During the Painvision project (www.painvision.be), a digital scale was developed for data collection. This project took place in a specific geriatric centre, and was approved by a medical ethical committee. In this pilot study facial images of a bedside two-camera system were linked to the pain scores of the digital device (a tablet PC with a touch screen, Fig. 3, a commercial version has been introduced to the market by BioRICS nv. more details can be found on www.assessmentscales.com) carried out at the bedside by a nurse.

Fig. 3. Two implementations of the assessment scales (pictures courtesy of BioRICS nv

As input for the digital device, 3 valid and reliable scales were chosen: the Pain Assessment Checklist for Seniors with Limited Ability to Communicate (PACSLAC), the Discomfort Scale - Dementia of Alzheimer Type, and the Faces Pain Scale Revised. After an informed consent was signed by a relative, two nurses tracked nineteen bedridden patients, with limited ability to communicate, for 6 random days, in which 6 assessment sessions were performed at clinically interesting moments (before – during – after care, before - after manipulation, and at rest). The usability was more concretely evaluated by ten other professional caregivers of this geriatric centre, via the 'think aloud method' and a

database for the analysis Ryan et al., 2002.

(www.biorics.com)

These results show that there tend to be differences in dynamics of biomaekers between surviving and non-surviving pigs after infection. Although many more experiments are needed in order to confirm the actual findings, it is expected that the modelling results could be the first steps towards the development of an objective individualised method for a sensor-based early detection of sepsis and inflammation in animals and later on in humans. Combining the modelling approach with novel biosensors should allow monitoring the health status of animals and human patients in real-time and could form the basis of health management systems.

#### **2.3 Pain management in elderly suffering from dementia**

Pain in elderly at the latest stages of dementia is an underestimated factor for discomfort and quality of living. Being able to objectively measure pain allows caregivers to adjust the treatment of these patients with limited communication skils. In this regard, a big number of pen-paper observational pain scales have been developed by various researchers. However, data handling from these scales is not very easy and their use is limited by the amount of time that a caregiver can spend on a patient. To account for this, the Painvision (www.painvision.be) consortium has developed an electronic version of three popular observational pain scales (see section 2.3.1 below) that is currently sold by BioRICS nv (a K.U.Leuven spin-off, www.biorics.com).

The overall objective of the Painvision consortium was to develop an automatic pain detection system based on cameras. Since facial expression is a well-known and reliable indicator of pain, the algorithms of the system exploit this relationship and estimate the pain level of the observed patient. Continuous information about the pain level of a patient can subsequently be used to evaluate the medical as well as the physical treatment that the patient is receiving, their comfort level throughout the day and eventually their quality of life.

#### **2.3.1 Electronic pain observational scales (assessment scales)**

Pen-paper observational scales have proven to be useful to assess pain in severely demented elderly, but also have a lot of disadvantages. Their usability is often limited, they are timeconsuming because of their length, difficulties in calculating scores, and post processing required to evaluate the pain evolution. Also the timing and timing patterns of the indicators could be very valuable to pain assessment, but cannot be grasped using pen-paper assessment instruments. The American Pain Society has indicated the importance of the pain assessment and suggested the guidelines for improving its quality (Max et al., 1995). In case a patient is not able to report his/her pain experience verbally, it is recommended to measure the painrelated behaviours (e.g., grimacing, restlessness, vocalisation, etc.). Demented elderly have limited ability to communicate verbally, as a result of which self report of pain is difficult. Therefore, different observational pain assessment scales have been developed and validated for this group of patients (Herr et al., 2006). Despite the introduction of new technologies in healthcare, to the authors' knowledge, the scores for all pain scales for severely demented elderly are still obtained manually on the paper.

Computerized technologies are already introduced in home care (Koch, 2006) and gaining high satisfaction response among patients (Chae et al., 2001; Lind et al., 2008) . The computerized-assisted decision systems are utilized in clinical practice (Mikulich, et al. 2001). The information systems are reducing costs and improving quality in managing

These results show that there tend to be differences in dynamics of biomaekers between surviving and non-surviving pigs after infection. Although many more experiments are needed in order to confirm the actual findings, it is expected that the modelling results could be the first steps towards the development of an objective individualised method for a sensor-based early detection of sepsis and inflammation in animals and later on in humans. Combining the modelling approach with novel biosensors should allow monitoring the health status of animals and human patients in real-time and could form the basis of health

Pain in elderly at the latest stages of dementia is an underestimated factor for discomfort and quality of living. Being able to objectively measure pain allows caregivers to adjust the treatment of these patients with limited communication skils. In this regard, a big number of pen-paper observational pain scales have been developed by various researchers. However, data handling from these scales is not very easy and their use is limited by the amount of time that a caregiver can spend on a patient. To account for this, the Painvision (www.painvision.be) consortium has developed an electronic version of three popular observational pain scales (see section 2.3.1 below) that is currently sold by BioRICS nv

The overall objective of the Painvision consortium was to develop an automatic pain detection system based on cameras. Since facial expression is a well-known and reliable indicator of pain, the algorithms of the system exploit this relationship and estimate the pain level of the observed patient. Continuous information about the pain level of a patient can subsequently be used to evaluate the medical as well as the physical treatment that the patient is receiving, their comfort level throughout the day and eventually their quality of

Pen-paper observational scales have proven to be useful to assess pain in severely demented elderly, but also have a lot of disadvantages. Their usability is often limited, they are timeconsuming because of their length, difficulties in calculating scores, and post processing required to evaluate the pain evolution. Also the timing and timing patterns of the indicators could be very valuable to pain assessment, but cannot be grasped using pen-paper assessment instruments. The American Pain Society has indicated the importance of the pain assessment and suggested the guidelines for improving its quality (Max et al., 1995). In case a patient is not able to report his/her pain experience verbally, it is recommended to measure the painrelated behaviours (e.g., grimacing, restlessness, vocalisation, etc.). Demented elderly have limited ability to communicate verbally, as a result of which self report of pain is difficult. Therefore, different observational pain assessment scales have been developed and validated for this group of patients (Herr et al., 2006). Despite the introduction of new technologies in healthcare, to the authors' knowledge, the scores for all pain scales for severely demented

Computerized technologies are already introduced in home care (Koch, 2006) and gaining high satisfaction response among patients (Chae et al., 2001; Lind et al., 2008) . The computerized-assisted decision systems are utilized in clinical practice (Mikulich, et al. 2001). The information systems are reducing costs and improving quality in managing

management systems.

life.

**2.3 Pain management in elderly suffering from dementia**

**2.3.1 Electronic pain observational scales (assessment scales)**

(a K.U.Leuven spin-off, www.biorics.com).

elderly are still obtained manually on the paper.

diagnostic tests (Bates et al., 1999). The nurses have stated their expectations of Personal Digital Assistant (PDA) devices in the nursing practice (Nilsson et al., 2007). The computerized versions of the pain scales and questionnaires have lately been introduced successfully in pain assessment practice (Wincent et al. 2003). It has been reported in the literature that the computerized version of the self-report pain questionnaires is not altering the response of the patients (Caro et al., 2001). It is also decreasing the number of the missing responses by obligating the patients to answer before proceeding to the next question (Caro et al., 2001). Compared to the paper versions, the electronic pain self-report questionnaires and electronic diaries are offering considerable advantages: 1) completeness of data (Hanscom et al., 2002); 2) entered data are date and time stamped (Burton et al., 2007); 3) saving of time and reduction of errors from entering written data manually to the database for the analysis Ryan et al., 2002.

During the Painvision project (www.painvision.be), a digital scale was developed for data collection. This project took place in a specific geriatric centre, and was approved by a medical ethical committee. In this pilot study facial images of a bedside two-camera system were linked to the pain scores of the digital device (a tablet PC with a touch screen, Fig. 3, a commercial version has been introduced to the market by BioRICS nv. more details can be found on www.assessmentscales.com) carried out at the bedside by a nurse.

Fig. 3. Two implementations of the assessment scales (pictures courtesy of BioRICS nv (www.biorics.com)

As input for the digital device, 3 valid and reliable scales were chosen: the Pain Assessment Checklist for Seniors with Limited Ability to Communicate (PACSLAC), the Discomfort Scale - Dementia of Alzheimer Type, and the Faces Pain Scale Revised. After an informed consent was signed by a relative, two nurses tracked nineteen bedridden patients, with limited ability to communicate, for 6 random days, in which 6 assessment sessions were performed at clinically interesting moments (before – during – after care, before - after manipulation, and at rest). The usability was more concretely evaluated by ten other professional caregivers of this geriatric centre, via the 'think aloud method' and a

Non-Invasive Methods for Monitoring Individual Bioresponses in Relation to Health Management 151

Initially the incoming frame is passed to the face detector if the previous image in the sequence didn't contain a face. On the other hand when the previous frame did contain a face, a tracker will be used to relocate faces. At this stage it is known if the frame contains face(s) and their location. After detecting/tracking the face a rough 3D position of the head is known. However the normalization step requires an accurate estimation of the pose. Therefore the normalization step is headed by the pose estimation step. This step will iteratively estimate a more accurate pose. Once the pose in the incoming frame is known a normalization step can be preformed to bring the face to a frontal and fixed scale face. Next a 2D active appearance model (AAM) will be fitted. The parameters of this adaptive model describe both texture and shape information of the face. Now AAM parameters, the normalized face image, landmarks in the original and normalized image can be used in

It should be noted that the normalized texture is a forward warp of the original image pixels rather than a synthetic AAM instance. Although the warp could be deformed and incomplete, detailed texture features are preserved since the original texture is used. For instance wrinkles and person specific spots remain visible in the normalized image. These clues could be crucial in classification problems. Next, Fig. 5 is presenting the algorithm result for the detection of an 'open mouth' that is a pain indicator for demented

Fig. 5. Example of the different algorithm steps (i.e. face detection, 3D rigid fit, pose normalisation, AAM mask fit). From the different AAM parameters (top plot), an index is extracted (middle plot) that results in the detection of an open mouth

extracting pain related information.

elderly.

(bottom plot)

questionnaire. They performed a digital pain assessment twice, with an interval of four weeks, on two patients with severe dementia. Usability criteria were learnability, efficiency of use, number of manipulation errors and satisfaction.

The digital device allows the nurse to record facial indicator events, such as frequency and duration, as they occur in real time. Subsequently, the scores are calculated automatically. The digital information is stored in a database, improving administration and allowing database applications. The ten professional caregivers stated that the tool was easy to learn. After the second measurement their assessment time was reduced with approximately 50%, the number of detected manipulation errors was up to four times lower, and the general satisfaction has significantly increased (p =0.04).

The Digital Pain Labelling Tool provides data completeness, reduces errors from the manual pen paper pain scores and offers easier and faster analysis of the patient's current condition. These findings illuminate the potential of implementing the computerized observational pain scale in nursing practice.

#### **2.3.2 Automatic pain identification using a two-camera system**

The Painvision project focused on the development of an algorithm for automatic estimation of the pain levels of elderly suffering from dementia by use of a two-camera system. The cameras are directed to the face of the patient that is bedridden. Initially, the face of the person is detected in both videos and, subsequently, pain indicators are automatically extracted. This procedure is presented in Fig. 4 and the different steps are explained in more detail in the following.

Fig. 4. Block diagram of the video processing algorithm for automatic estimation of pain in demented elderly based on visual information

questionnaire. They performed a digital pain assessment twice, with an interval of four weeks, on two patients with severe dementia. Usability criteria were learnability, efficiency

The digital device allows the nurse to record facial indicator events, such as frequency and duration, as they occur in real time. Subsequently, the scores are calculated automatically. The digital information is stored in a database, improving administration and allowing database applications. The ten professional caregivers stated that the tool was easy to learn. After the second measurement their assessment time was reduced with approximately 50%, the number of detected manipulation errors was up to four times lower, and the general

The Digital Pain Labelling Tool provides data completeness, reduces errors from the manual pen paper pain scores and offers easier and faster analysis of the patient's current condition. These findings illuminate the potential of implementing the computerized observational

The Painvision project focused on the development of an algorithm for automatic estimation of the pain levels of elderly suffering from dementia by use of a two-camera system. The cameras are directed to the face of the patient that is bedridden. Initially, the face of the person is detected in both videos and, subsequently, pain indicators are automatically extracted. This procedure is presented in Fig. 4 and the different steps are explained in more

of use, number of manipulation errors and satisfaction.

satisfaction has significantly increased (p =0.04).

**2.3.2 Automatic pain identification using a two-camera system**

Video sequence

Use tracking? Detect face Track face

> Normalisation (pose, scale)

Pose detection (3D masking)

Apply mask (2D AAM)

> Good quality?

demented elderly based on visual information

Extract parameters

Fig. 4. Block diagram of the video processing algorithm for automatic estimation of pain in

Classifiers Pain

Indicators

pain scale in nursing practice.

detail in the following.

Discard image

Initially the incoming frame is passed to the face detector if the previous image in the sequence didn't contain a face. On the other hand when the previous frame did contain a face, a tracker will be used to relocate faces. At this stage it is known if the frame contains face(s) and their location. After detecting/tracking the face a rough 3D position of the head is known. However the normalization step requires an accurate estimation of the pose. Therefore the normalization step is headed by the pose estimation step. This step will iteratively estimate a more accurate pose. Once the pose in the incoming frame is known a normalization step can be preformed to bring the face to a frontal and fixed scale face. Next a 2D active appearance model (AAM) will be fitted. The parameters of this adaptive model describe both texture and shape information of the face. Now AAM parameters, the normalized face image, landmarks in the original and normalized image can be used in extracting pain related information.

It should be noted that the normalized texture is a forward warp of the original image pixels rather than a synthetic AAM instance. Although the warp could be deformed and incomplete, detailed texture features are preserved since the original texture is used. For instance wrinkles and person specific spots remain visible in the normalized image. These clues could be crucial in classification problems. Next, Fig. 5 is presenting the algorithm result for the detection of an 'open mouth' that is a pain indicator for demented elderly.

Fig. 5. Example of the different algorithm steps (i.e. face detection, 3D rigid fit, pose normalisation, AAM mask fit). From the different AAM parameters (top plot), an index is extracted (middle plot) that results in the detection of an open mouth (bottom plot)

Non-Invasive Methods for Monitoring Individual Bioresponses in Relation to Health Management 153

focuses on REM sleep because this state resembles most to wakefulness and is being linked to dreaming and memory consolidation (Karni et al., 1994; Tilley & Empson, 1978; Takahara et al., 2008). In the same direction, during the course of the EASI project, an algorithm was developed that is automatically detecting periods of REM sleep. Additionally, the algorithm is contributing to the discussion of whether dreams occur only during REM sleep or not, by exploiting the concept of Additional Heart Rate and its link to emotions (Myrtek, 2004) during sleep. An example of the algorithm output is

1 2 3 4 5 6 7

Sleep stage Detected awakening

Time (h)

Fig. 6. Manual scoring of the sleep stages and the output of the developed algorithm for

Fig. 7. Example of the algorithm output for the REM detection algorithm

presented in Fig. 7.

Short Awakening

SWS

awakening detection

S2

S1 REM Awakening


# **Energy Metabolism and Diet**

Edited by Arie Nieuwenhuizen and Evert van Schothorst Printed Edition of the Special Issue Published in *Nutrients*

www.mdpi.com/journal/nutrients

**Energy Metabolism and Diet**


Current Topics and Emerging Issues in Malaria Elimination

*Edited by Alfonso J. Rodriguez-Morales*

Published in London, United Kingdom

## *Supporting open minds since 2005*

Current Topics and Emerging Issues in Malaria Elimination http://dx.doi.org/10.5772/intechopen.87323 Edited by Alfonso J. Rodriguez-Morales

Part of IntechOpen Book Series: Infectious Diseases, Volume 8 Book Series Editor: Shailendra K. Saxena

#### Contributors

Kingsley Badu, Kwadwo Boampong, Amma Aboagyewa Larbi, Arantxa Roca-Feltrer, Ann-Sophie Stratil, James K. Tibenderana, Ilunga-Ilunga Félicien, Leveque Alain, Dramaix Michèle, Mbuyi Kanyinda Veve, Mbikayi Muya Jean Paul, Yahaya Mohammed Mohammed Katagum, Faisal Bin Ibrahim, Hayati Binti Kadir Shahar, Anisah Baharom, Rafee Baharudin, Chukwudi Michael Michael Egbuche, Alexandre Costa, Anna Caroline Campos Aguiar, Dhelio Batista Pereira, Angelina Moraes Silva, Isaac K. Quaye, Larysa Aleksenko, Liwang Cui, Awtum Brashear, Lynette Menezes, John Adams, Anupam Nath Jha, Upasana Pathak, Sanchaita Rajkhowa, Abhichandan Das, Francis S.O Ugwu, Cheikh Cambel Dieng, Eugenia Lo, Jennifer Huynh, Yaw A. Afrane, Linda E. Amoah, Colby T. T. Ford, Daniel A Janies, Susanta Ghosh, Chaitali Ghosh, Mary Oboh, Mamadou Ndiath, Olumide Ajibola, Kolapo Oyebola, Alfred Amambua-Ngwa, Sarat K. Dalai, Nikunj U Tandel, Alfonso J. Rodriguez-Morales, Jaime A. Cardona-Ospina, D. Katterine-Bonilla-Aldana, Luis Andrés Salas-Matta, Wilmer E. Villamil-Gómez, Juan Pablo Escalera-Antezana, Lucia E. Alvarado-Arnez, Carlos Franco-Paredes, Juan-Carlos Navarro, Tomas Orduna, José A. Suárez

#### © The Editor(s) and the Author(s) 2021

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Current Topics and Emerging Issues in Malaria Elimination Edited by Alfonso J. Rodriguez-Morales p. cm. Print ISBN 978-1-83968-483-8 Online ISBN 978-1-83968-484-5 eBook (PDF) ISBN 978-1-83968-485-2 ISSN 2631-6188