*3.4. Genotoxicity Assessment*

To estimate the genotoxicity of substances released from the surface of studied implants during the 28-day incubation in PBS, the Ames assay was carried out. It was especially important to assess if silver release from TNT/AgNPs coatings could be mutagenic. This issue is associated with an increasing number of reports warning about the genotoxicity of silver nanoparticles [39,40]. The assay was performed in five genetically modified bacteria strains according to OECD guidelines TG471 (http://www.oecd.org), allowing detection of deletion, base substitution, or frameshift mutations, depending on the tester strain's engineered genotype. The number of revertant bacterial colonies corresponds to the mutagenic potential of the analyzed agents (Figure 6). None of the implant coatings demonstrated genotoxic potential in any of the bacteria strains in this assay.

**Figure 6.** Assessment of implants' genotoxicity by the Ames assay performed in five genetically modified bacteria strains: (**a**) TA98, (**b**) TA100, (**c**) TA1535, (**d**) TA1537, and (**e**) *E.coli*; to improve the readability of Ames assay results of TA98 (a), TA1535 (c), and TA1537 (d), their enlarged versions are added.


*5.3.2. Sensitivity to transaction costs* 25% % of maximal energy

52.57 \$/MWh

48.68 \$/MWh

\$/MWh

42.25 \$/MWh

50%

75%

100%

buying replacement power in the spot market.

The third case of study is identical to the base case but considering a transaction cost of 7% of the sold amount instead of 3%. The optimal trading policy for the case of increased transaction costs are shown in Figure 11. In this case, the generator sells only 1.32% of its capacity in an annual future contract due to the irreversibility introduced by the higher transactions costs and the larger contracting volume. Under these circumstances, the risk premium offered in the annual future price render insufficient for attracting the generator to enter in such a longlasting commitment. On the other hand, the sell volume in quarterly futures is higher than in the base case, staying between 62% and 76% in a rather static trading policy. Under higher transaction fees, it is desirable to be able to rebalance the portfolio in future stages with smaller changes and hence smaller transaction costs. Expectedly, the expected profit is lower due to higher costs. The delivery risk for the first month is negligible, due to the fact that the most likely failures can still be covered by the remaining operative units without buying replace‐ ment power in the spot market. Figure 10. Optimal trading strategy for a 5x2MW generation portfolio with reduced availability **5.3.2. Sensitivity to transaction costs**  The third case of study is identical to the base case but considering a transaction cost of 7% of the sold amount instead of 3%. The optimal trading policy for the case of increased transaction costs are shown in Figure 11. In this case, the generator sells only 1.32% of its capacity in an annual future contract due to the irreversibility introduced by the higher transactions costs and the larger contracting volume. Under these circumstances, the risk premium offered in the annual future price render insufficient for attracting the generator to enter in such a long-lasting commitment. On the other hand, the sell volume in quarterly futures is higher than in the base case, staying between 62% and 76% in a rather static trading policy. Under higher transaction fees, it is desirable to be able to rebalance the portfolio in future stages with smaller changes and hence smaller transaction costs. Expectedly, the expected profit is lower due to higher costs. The delivery risk for the first month is negligible, due to 0% 1st Quarter 2nd Quarter 3rd Quarter 4th Quarter

48.15 \$/MWh

the fact that the most likely failures can still be covered by the remaining operative units without

Figure 11. Optimal trading strategy for a 5x2MW generation portfolio with higher transaction costs

**Figure 11.** Optimal trading strategy for a 5x2MW generation portfolio with higher transaction costs


#### Barbara Bartolini1 , Serena Bertoldi2 , Laura Benedan3 , Carlotta Galeone3 , Paolo Mariani3 , Francesca Sofia2 , Mariangela Zenga4 1 HPS-AboutPharma srl, Milano, Italy 2 Science Compass, Milano, Italy **Development of an innovative methodology to define patient-designed quality of life: a new version of a wellknown concept in healthcare**

Development of an innovative methodology to define patient-designed quality of life: a new version of a wellknown concept in healthcare

3 Bicocca Applied Statistics Center, University of Milano-Bicocca, Milano, Italy 4 Dipartimento di Statistica e Metodi Quantitativi, University of Milano-Bicocca, Milano, Italy Barbara Bartolini, Serena Bertoldi, Laura Benedan, Carlotta Galeone, Paolo Mariani, Francesca Sofia, Mariangela Zenga

# 1 Introduction

Quality of life (QoL) is a concept embracing several aspects and functionalities of people's lives. Some of the areas affected are health, relationships, socializing, leisure (The International Society for Quality of Life Research). The achievement of a good QoL is recognized as an essential aim of health assistance, regardless of the pathology and the administered therapy (M. Asadi-Lari et al., 2004). QoL is therefore a pivotal parameter used by clinicians to evaluate how treatments and therapies influence patients' functionality and emotional state, aiming to ameliorate interventions and their outcomes.

QoL is determined by indices assessed by administering questionnaires that can be either generic or disease-specific (D. L. Patrick & Deyo, R. A., 1989; R. Rabin & de Charro, F., 2001; J. E. Ware, Jr. et al., 2016). Currently, the majority of the QoL questionnaires are designed with the main contribution of clinicians and, therefore, include items that are centered on the disease rather than on its multifaceted impact on people's life. These tools are useful for clinicians in determining the best clinical approach, but may fail to truly grasp the patients' perspective, needs, aspirations, perceptions and emotional state, resulting in a major drawback that sets medical care on clinical parameters alone. A proper tool defining the patient's perception of the pathology is missing.

To bridge this existing gap, the definition of a bottom-up patient-designed QoL index could provide a new, patient-centric, unbiased tool to evaluate the patients' perception of their own well-being. Here we describe the development of an innovative methodology to define patientdesigned QoL, based predominantly on patients' contribution.

## 2 Working group and methodology

To define a patient-centric QoL tool, we used a consensus technique aiming to favor the expression of the major players involved in dealing with the pathology.

 The Delphi method is currently widely used in academic research, industry, social sciences and healthcare to reach consensus (R. Boulkedid et al., 2011; I. R. Diamond et al., 2014; M. K. Murphy et al., 1998; Robinson N Trevelyan EG, 2015). The main goal is to collect different opinions to be evaluated by the panel, with the aim of reaching pluralistic evaluations of an issue. In the Delphi panel, the participants are either technical or non-technical experts (i.e., patient representatives) reporting their own point of view (G. Mazziotta Marbach, C. & Rizzi, A., 1991).

In our model, patients and healthcare professionals constitute the working group to build the settings and assertions of the questionnaire.

1

Paolo Mariani, University of Milano-Bicocca, Italy<EMAIL_ADDRESS>0000-0002-8848-8893 Francesca Sofia, Science Compass, Italy<EMAIL_ADDRESS>
Mariangela Zenga, University of Milano-Bicocca, Italy<EMAIL_ADDRESS>0000-0002-8112-5627

FUP Best Practice in Scholarly Publishing (DOI 10.36253/fup\_best\_practice)

Barbara Bartolini, Serena Bertoldi, Laura Benedan, Carlotta Galeone, Paolo Mariani, Francesca Sofia, Mariangela Zenga, *Development of an innovative methodology to define patient-designed quality of life: a new version of a wellknown concept in healthcare*, pp. 155-159, © 2021 Author(s), CC BY 4.0 International, DOI 10.36253/978-88-5518-461-8.30, in Bruno Bertaccini, Luigi Fabbris, Alessandra Petrucci (edited by), *ASA 2021 Statistics and Information Systems for Policy Evaluation. Book of short papers of the on-site conference*, © 2021 Author(s), content CC BY 4.0 International, metadata CC0 1.0 Universal, published by Firenze University Press (www.fupress.com), ISSN 2704-5846 (online), ISBN 978-88-5518-461-8 (PDF), DOI 10.36253/978-88-5518-461-8

141 Barbara Bartolini, HPS, Health Publishing and Services, Italy<EMAIL_ADDRESS>0000-0001-9646-9310 Serena Bertoldi, Science Compass, Italy<EMAIL_ADDRESS>
The items of the QoL questionnaire were defined during focus groups involving a panel of patients, two clinicians, one statistician and one facilitator.

Patients are the active players that identify the settings and the main items. According to the European Patients' Academy on Therapeutic Innovation (EUPATI) (K. Warner et al., 2018) definition, patients involved may be:

● Individual Patients, i.e., "persons with experience of living with a disease. Their main role is to contribute with their subjective experience";

● Patient Organization Representatives, i.e., "persons who are mandated to represent and express the collective views of a patient organization on a specific issue or disease area".

Clinicians offer the technical knowledge of the pathology, supporting the precise description of the items. Facilitators act as a guide for discussion among the parties, with the important role of harmonizing the contribution of all the participants to avoid a decisional bias due to the influential opinion of the clinicians on patients' decision.

 The Pseudo-Delphi we propose is an iterative process with subsequent steps aiming to identify a shared solution. It is a flexible method, useful when the identification of the statistical model to be applied is uncertain and when the gist of the discussed problem is not completely known.

The Pseudo-Delphi steps are as follows:

● Second round to discuss questionnaire's answers and definition of the Likert scale and the closed questions

● Definition of the second questionnaire, open questions and closed questions with the respective agreement scale

● Administration of the second questionnaire to each expert

● Third round, with discussion on the questionnaire's answers, identification of the scale of importance with the evaluation of the possible consequences of each decision and the feasibility of each defined option

● Moderated feedback, result aggregation and sharing with the experts. In this round, the new questionnaires resulting from the previous opinions are also shared

● Repetition of the questionnaire, if necessary, to reach the agreement.

 Working anonymously allows to avoid the prevalence of a charismatic individual over the others, which can freely express their opinions without any social pressure.

Moreover, the feedback control allows the experts to be provided with all the information needed to reach the final agreement. With the Pseudo-Delphi method all the participants can analyze and re-consider a variety of aspects included in the questionnaire. This method entails a great effort for the methodologists in estimating and summarizing facts, quantitative data and subjective variables. This approach is of value in the analysis of real-world data, especially in QoL evaluation (S. Pietersma et al., 2014).

The workflow starts from the patients' evaluation of a list of settings identified by a literature search. Patients independently provide social, economic, and organizational information related to their pathology and their relationship with the healthcare system. This allows the identification of the settings of the questionnaire.

The setting evaluation is carried out by every single patient anonymously, to avoid any kind of psychological subjection from the healthcare professional's opinion. At the end of this process, the group meets in a roundtable session to openly discuss about the settings emerged and to rank them according to the perceived order of importance. This step is crucial to skim the settings and find those to be included in the questionnaire, to make it usable in daily practice.

Within each setting, a series of assertions are generated individually and anonymously by each patient. Following similar steps, a set of assertions is identified and included in the questionnaire. Every assertion is then associated to a four-point Likert scale. On the basis of the score, a synthetic patient-centric QoL index is then defined (Figure 1).

Figure 1. Flow chart showing the generation of the QoL questionnaire.

The final version of the questionnaire contains the scales for agreement and importance measures. They aim to link the agreement to one item with the importance for the patient in her/his life and they are built on the basis of the Customer Satisfaction Techniques. After the identification of the different settings (e.g., physical, emotional, social, functional and economic) the level of agreement and the level of importance of the statements within each setting are rated on a 4-points Likert scale (response categories: not at all; a little; quite a bit; very much) by the participants. The methodology allows the production of a composite index for "uneasiness", which will be then compared to the internal control ‒provided by the evaluation of each own QoL on a one to ten scale. The composite index is defined as follows.

Let ��� (i=1,…, n; j=1,…, �; s=1,...S) be the agreement of the i-th respondent on the j-th statement for the s-th setting. The categories on the agreement part for a statement are treated as numeric variable where "not at all" =0.001; "a little" =0.33; "quite a bit" =0.67; "very much" =1. In this case we transform the variable at 4 categories in 3 categories where the distance between each successive item category is equivalent and equal to 0.33. The agreement on not at all is treated as the lack respect to the statement. Moreover let ��� (i=1,…, n; j=1,…, �; s=1,...S) be the importance given by the i-th respondent to the j-th statement for the s-th setting. In this case the categories for the importance for a statement are "not at all" =0.25; "a little" =0.5; "quite a bit" =0.75; "very much" =1.

An indicator on the j-th statement for the s-th setting given by the i-th respondent is given by

$$
\mu\_{ljs} = \mathfrak{x}\_{ljs} \cdot \mathfrak{w}\_{ljs} \tag{l}
$$

The ��� takes values in [0.00025; 1]. For each value of ���, it is possible to find the correct combination of ��� and ���.

The questionnaire includes a section with structural questions exploring the current state of the disease, personal evaluation about the psychological state and the type of assistance received, geographical and demographic characteristics. This information completes the patient profile and can be used for further analysis and stratification.

For the i-th respondent, it is possible to create an uneasiness score for the s-th setting as

$$U\_{\rm is} = \sum\_{j=1}^{k\_s} \boldsymbol{\pi}\_{\rm ijs} \cdot \boldsymbol{w}\_{\rm ijs} \tag{\rm II}$$

In (II) the statements running in the opposite direction for the s-th setting are reversed for the score. The Uis could take values in [ks · 0.00025; ks].

For the i-th respondent, the total composite index is given by:

$$T\mathcal{U}\_i = \sum\_{s=1}^{S} \mathcal{U}\_{is} \tag{\text{III}}$$

that takes values in [0.00025 ∑ � � ��� ; ∑ � � ��� ]. The linear transformation of (III) in

$$TU\_i^{10} = (10 - 1) \cdot \frac{T U\_i - \sum\_{s=1}^{S} k\_5 \cdot 0.00025}{\sum\_{s=1}^{S} k\_5 (1 - 0.00025)} + 1\tag{1V}$$

allows that � �� ∈ [1; 10]. The � �� represents the synthetic patient-centric QoL index. It is possible to compare � �� respect to the i-th respondent to the score of the quality of life of the i-th respondent QoLi.

## 3 Conclusions

With this pilot study we suggest a methodology to set up a questionnaire for the identification of a synthetic index that allows the evaluation of the overall QoL of patients, regardless of the clinical data. The index enhances the patients' awareness of their subjective experience with the disease and enables them to better present their situation to the clinicians. This methodology can be considered in light of the idea of improving patient engagement as highlighted by the EUPATI PARADIGM project (P. Spindler & Lima, B. S., 2018). This methodology needs to be further validated through administration to patients suffering from different pathologies, and compared to the methodologies already available from international sources. An index directly generated by the patients can provide a descriptive model helpful not only to patients, but also to clinicians and third parties, that can be further integrated with clinical details to obtain an overall view of the course of treatment for each patient.

## References


**Tao Bai 1,\*, Xia Liu 1, Yan-ping HA 2, Jian-xia Chang 1, Lian-zhou Wu 1, Jian Wei 1 and Jin Liu 3**

Received: 23 February 2020; Accepted: 17 March 2020; Published: 24 March 2020

**Abstract:** Given the increasingly worsening ecology issues in the lower Yellow River, the Xiaolangdi reservoir is chosen as the regulation and control target, and the single and multi-objective operation by ecology and power generation in the lower Yellow River is studied in this paper. This paper first proposes the following three indicators: the ecological elasticity coefficient (f1), the power generation elasticity coefficient (f2), and the ecological power generation profit and loss ratio (k). This paper then conducts a multi-target single dispatching study on ecology and power generation in the lower Yellow River. A genetic algorithm (GA) and an improved non-dominated genetic algorithm (NSGA-II) combining constraint processing and feasible space search techniques were used to solve the single-objective model with the largest power generation and the multi-objective optimal scheduling model considering both ecology and power generation. The calculation results show that: (1) the effectiveness of the NSGA-IIcombined with constraint processing and feasible spatial search technology in reservoir dispatching is verified by an example; (2) compared with the operation model of maximizing power generation, the power generation of the target model was reduced by 0.87%, the ecological guarantee rate was increased by 18.75%, and the degree of the impact of ecological targets on the operating results was quantified; (3) in each typical year, the solution spatial distribution and dimensions of the single-target and multi-target models of change are represented by the Pareto-front curve, and a multi-objective operation plan is generated for decision makers to choose; (4) the f1, f2, and k indicators are selected to analyze the sensitivity of the five multi-objective plans and to quantify the interaction between ecological targets and power generation targets. Ultimately, this paper discusses the conversion relationship and finally recommends the best equilibrium solution in the multi-objective global equilibrium solution set. The results provide a decision-making basis for the multi-objective dispatching of the Xiaolangdi reservoir and have important practical significance for further improving the ecological health of the lower Yellow River.

**Keywords:** multi-objective optimal operation model; feasible search space; Pareto-front optimal solution set; loss–benefit ratio of ecology and power generation; elasticity coefficient


**1. Introduction**

Integration and miniaturization are the two main factors in the device engineering research, and these two factors are also the main features of the lab‐on‐a‐chip technology. The main contributor for this major trend is the fast advances of integrated circuit (IC) technology. As a result of miniaturization, portable and cheaper equipment replaced the traditional bulky and expensive equipment.

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

In the field of cell biology, the use of micro‐ and nano‐fabricated lab‐on‐a‐chip using active substrates and integrated sensors [1] is still at experimental stage. In order to replace traditional optical inspection techniques, these techniques have been tested for the identification of objects at microscopic level to envision several applications [2]**.**

Many research groups believe that lab‐on‐a‐chip technology will be the major contributor to novel diagnostic tools. The target of these research groups is to have lab‐on‐a‐chip systems that will allow healthcare suppliers and workers in poorly equipped clinics to do different tests which need more sophisticated equipment in a simpler way, for example, nucleic acid assays and immunoassays tests [3]. Thus, lab‐on‐a‐chip innovation is a vital part of the endeavors to upgrade and enhance worldwide healthcare system, especially through the advancement of clinics and laboratories' testing techniques [4].

One dynamic area of lab‐on‐a‐chip research includes approaches to analyze and oversee HIV infections. In view of UNAIDS 2014 report [5], more than 40 million individuals are tainted with HIV around the world; 13 million of these individuals get hostile to retroviral treatment. Around 55% of individuals with HIV have never been tested for the infection [5].

Measuring the quantity of CD4+ T lymphocytes in a man's blood is an exact and right approach to figure out whether a man has HIV and to track the advancement of a HIV contamination. Right now, flow cytometry is the brilliant standard for acquiring CD4 checks; yet, flow cytometry is a confounded method that is not accessible in most developing regions since it requires prepared professionals and costly instruments [6, 7].

The vicinity of the complementary metal oxide semiconductor (CMOS) technology allows the integration of sensors, amplifiers, filters and other circuitry on a single chip. Also, CMOS technology leads to a totally electronic integrated lab‐on‐a‐chip utilizing a homogenous technology [8, 9]. CMOS is an innovation used in building integrated circuits. It is used in microcontrollers, static RAM, registers, microchips and other digital circuits. CMOS technol‐ ogy is utilized also for a wide assortment of analog circuits, for example, image sensors, amplifiers, analog to digital converters, and transceivers for communication modules. Low static power consumption and high noise immunity are two advantages of CMOS devices [10].

Numerous specialists and research teams have begun to utilize the CMOS technology in lab‐ on‐a‐chip applications [11, 12]. These applications incorporate, yet not restricted to, identifi‐ cation of neurons' activities, microorganism discovery and portrayal, electric field imaging, flow cytometry and polymerase chain reaction (PCR) applications [13–17].

This chapter covers and presents the state of the art in CMOS circuits and systems for lab‐on‐ a‐chip applications. It summarizes and reviews different circuits and systems of CMOS‐based lab‐on‐a‐chip technology at the cell level. These circuits and systems include: polymerase chain reaction (PCR), microorganism detection and characterization, neuronal activity detection, flow cytometry and electric field imaging applications.