*2.8. Expression Analysis of HSF Genes in M. sativa in Response to Abiotic Stresses*

To investigate the expression levels of *MsHSF* genes under abiotic stress, we analyzed the expression patterns of *MsHSF* genes under treatment with cold stress, abscisic acid (ABA), drought, and salt using published transcriptome data (Figure 10). The results of the analysis showed that the genes that functioned in *M. sativa* under different stress treatments were slightly different compared to the control. However, the genes that appeared to be the first to function and to be highly expressed in the immediate period of abiotic stress were *MsHSF09* and *MsHSF15*, while *MsHSF07* and *MsHSF08* gradually became more highly expressed as the time of stress increased to counteract the external stimuli. By the later stages of stress, it is *MsHSF04*, *MsHSF05* and *MsHSF13* that play a role. Interestingly, most of the *MsHSF* genes were induced to increase in response to abiotic stress, except for *MsHSF02* and *MsHSF16*, which were suppressed in response to cold stress, which may be related to cellular trauma in *M. sativa* during cold stress.

**Figure 9.** A heat map representation of *MsHSF* expression between different tissues. The values in the rectangle represent the magnitude of the gene expression.

#### *2.9. GO Annotation and Enrichment Analysis of M. sativa HSF Protein*

Plants have evolved complex mechanisms to sense and respond to biotic and abiotic stresses, and HSF is an important component of these defense systems. We carried out GO annotation and enrichment analysis on 16 MsHSF proteins in order to learn more about the biological functions of this protein (Figure 11). MsHSF was enriched for 55 biological processes, 1 cellular component, and 2 molecular functions in comparison to the entire GO database. According to the GO enrichment data, MsHSF transcription factors are primarily involved in biological processes including responding to abiotic stimuli, responding to temperature stimuli, responding to heat, and responding to xenobiotic stimuli. The findings again suggest that HSF genes play an extremely important role in resisting abiotic stresses.


**1. Introduction**

Type 2 diabetes mellitus is a widespread disease (~8% of adults), often resulting from a metabolic disorder caused by over-feeding, an unhealthy diet, and physical inactivity [1,2]. It covers all age groups of the population, and recently it has spread epidemiologically among children and adolescents [3,4]. The blood glucose levels of diabetic patients reaches abnormally high values, which leads to serious damage to many body systems, especially nerves and blood vessels, causing heart and kidney diseases, blindness, and even the amputation of limbs [5]. The preferred way to maintain good health for people with type 2 diabetes or prediabetes is a control of the input of glucose from the digestive tract into the blood stream [6–8]. For this purpose, the medicines based on inhibitors of pancreatic α-amylase are used. GlucobayTM, the active ingredient of which, acarbose, inhibits porcine pancreatic α-amylase (PPA) and human saliva α-amylases (HSA) with Ki's of 0.797 and 1.265 μM, respectively, is one of the most common of that type of drug [9]. In some countries, medicinal plants are traditionally used to

treat diabetes; the study of their composition revealed the presence of low molecular weight substances inhibiting mammalian α-amylases [10,11]. Since the effectiveness of currently existing drugs is limited and they have some side effects, the search for new highly effective inhibitors of pancreatic α-amylase is an attractive goal in the field of drug discovery.

The medicines based on proteins and peptides are poorly represented in the pharmacological market, but attract the interest of specialists due to their high selectivity and effectiveness, combined with relative safety and good tolerability. A large number of proteinaceous α-amylase inhibitors have been isolated from plants, but they are highly specific and interacted with plant α-amylases to control the breakdown of stored starch or with insect α-amylases for defense [12]. Several very effective proteinaceous inhibitors of mammalian, but not plant or microbial α-amylases were found in bacteria belonging to the genus *Streptomyces* [13–16]. However, it was shown that α-amylase inhibitors isolated from bacteria, for example, tendamistat (Ki 9−200 pM), have a high immunogenicity due to their β-sandwich fold and cannot be used in clinical practice [17].

Among animals, amylase inhibitors were found only in sea anemones, ancient sessile predators inhabiting marine environment. Helianthamide (PPA, Ki = 100 pM; human pancreatic α-amylase (HPA), Ki = 10 pM), the first representative of a new group of α-amylase inhibitors belonging to the β-defensins family, was isolated from *Stichodactyla helianthus* in 2016 [18]. This inhibitor is very active, and in contrast to tendamistat, has a more compact structure, which significantly decreases the likelihood of an immune response. Recently, as a result of the proteomic analysis of the sea anemone *H. magnifica* mucus, we have revealed that α-amylase inhibitors are major components, numbering dozens isoforms [19]. Major α-amylase inhibitor, magnificamide, was identified and sequenced (44 aa, 4770 Da) [19]. It shares 84% of sequence identity to helianthamide (44 aa, 4716 Da). The biological relevance of the presence of inhibitors of α-amylases in the mucus of Cnidaria, such as the sea anemone *H. magnifica*, remains largely unexplained. It is hypothesized that inhibition of α-amylase activity intervenes with the metabolism of starch, which forms a major source of nutrition for many organisms. Organisms exposed to α-amylase inhibitors, therefore, suffer from a reduced availability of carbohydrates that serve as an energy resource.

The results presented here are a continuation of an in depth study of magnificamide, more precisely, of a recombinant analog of the peptide with a detailed investigation of its biological activity.


**A Multi-Disciplinary Perspective on the Diagnosis and Treatment of Urinary Incontinence in Young Women**

Mariola Bidzan1, Jerzy Smutek2,3, Krystyna Garstka-Namysł4, Jan Namysł5 and Leszek Bidzan6 *1Department of Clinical Psychology and Neuropsychology, Institute of Psychology, University of Gdansk 2Department of Obstetrics, Medical University of Gdansk 3Pro-Vita Private Medical Center for Urinary Incontinence, Gdansk 4*Department of Tourism and Recreation*, University of Physical Education, Poznan 5INNOMED - Poznan, Private Neurorehabilitation Centre 6Department of Developmental Psychiatry, Psychotic Disorders and Old Age Psychiatry, Medical University of Gdansk Poland*


**6. Postoperative hip instability**

### **6.1 Hip precautions and does anterior hip need precautions**

Hip precautions have often been utilized to help aid in prevention of dislocation in the acute postoperative period. This is often done in patients who undergo a posterior approach where the short external rotators and the posterior capsule is compromised. Many physicians use these precautions. A recent prospective randomized trial from

Journal of Arthroplasty 2022 examined 346 consecutive patients all via posterior approach to the hip with a mean follow up of 2.3 years. This study demonstrated that if intraoperative hip stability was obtained at 90 degrees of flexion 45 degrees internal rotation and 0 degrees of abduction, postoperative hip precautions are no longer necessary. This study, however powerful, excluded patients with previous lumbar fusion, scoliosis or abductor insufficiency [149]. Mounts 2022 study is in accordance with another recently published a systematic review that included 6900 patients. This study demonstrated no statistically significant decrease in dislocations with the use of posterior hip precautions [150].

Since anteriorly based approachs are often regarded as a more stable approach post operatively, surgeons have questioned the need for precautions post operatively. Talbot et al. studied 499 cases of primary total hip arthroplasty done through an anterolateral approach and documented the dislocation rate when restrictions were not imposed. There were 3 early dislocations (within 6 weeks of surgery) all of which were close reduced, and every patient subsequently achieved a stable hip without further intervention [151]. Restrepo et al., also demonstrated a 0.16% dislocation rate which is significantly lower than the 2% overall that was found to occur within the 1st year by Maratt et al. in anterior and posterior approaches [34, 152]. The evidence for hip precautions after an anterior-based hip approach seems to be in favor of not requiring restrictions.

#### **6.2 Recognition of postop instability from infection, poly wear, ALTR**

To conclude, the surgeon must correctly identify the etiology of the instability to direct treatment. Early postoperative instability is likely due to component malpositioning or acute infection [116]. In cases of late stage instability the surgeon should consider component subsidence, aseptic loosening, osteolysis, indolent infection or the development of an adverse local tissue reaction (ALTR). Acute infection may be challenging to diagnose if obvious wound complications are not present [153, 154]. Both acute and chronic infection can present with loosening of one or both the acetabular and femoral components which may require staged revision. Another important cause of instability is aseptic loosening from polyethylene debris leading to macrophage induced osteolysis. This can ultimately lead to movement or dislodging of the implants which should be closely evaluated and may require revision surgery. Osteolysis can destroy available bone stock requiring the surgeon to become facile with bone grafting, cages, or even custom triflange implants for the acetabulum [155]. In the case of femoral bone loss there may be a need for diaphyseal engaging implants, bone grafting or even proximal femoral replacement [156]. Another potential cause of instability is the development of an ALTR from metal on metal (MOM) bearing surfaces. Diagnosis of ALTR is made from clinical history, radiography and serum metal ion levels. If surgery is deemed necessary, additional information may be obtained from ultrasound and metal artifact reduction sequence magnetic resonance imaging (MARS-MRI) to evaluate soft tissue destruction and possible need for augments or constraint due to abductor deficiency. Ultimately if serum ion levels continue to rise or patient functionality declines the patient will require revision surgery [157]. Although post-operative hip instability frequently requires revision surgery it is important to identify the root cause. This will allow the surgeon better surgical preparation, more readily available implants and the ability to manage infection with possible staged surgery or prolonged IV antibiotics.

*Instability in Total Hip Arthroplasty DOI: http://dx.doi.org/10.5772/intechopen.105801*


**1. Introduction**

In a global database of landslide disasters given by Froude and Petley (2018) [1], three–quarters of all landslide events between 2004 to 2016 occurred in Asian countries, with substantial events in the Himalayas. Indian Himalayas are highly susceptible to landslides which are triggered primarily by rainfall [2] and Sikkim and Darjeeling Himalayas are among the most highly vulnerable landslide

zones. Due to rapid urbanization and increase in population in such areas, landslides and associated loss are an increasing concern [3] and early warning systems are regarded as a promising tool for landslide forecasting and risk management [4].

Rainfall being the most common triggering factor for landslides, early warning systems are typically based on empirical rainfall thresholds that describe the interaction between the primary cause (rainfall) and the final effect (landslide). In a few words, a triggering threshold is represented by a mathematical equation describing the critical rainfall condition above which landslides are triggered. The only input data used for the threshold definition are a dataset of rainfall recordings and a catalogue of landslides for which the time and location of occurrence are known with sufficient approximation. This approach completely bypasses the physical mechanism of triggering, thus simplifying the modeling effort, the computational resources required, and the amount of data needed for the analysis [5].

During the last few decades, many attempts were made across the world to define critical rainfall thresholds based on a number of different rainfall parameter, but the most common are intensity and duration (ID thresholds) [6–13], total event rainfall and duration (ED thresholds) [14–18] and antecedent rainfall [19–22]. The selection of the optimal rainfall parameters which are used for defining the threshold depends mainly on the landslide typology and physical characteristics of the region. It is well accepted that shallow landslides and debris flows are triggered by high intensity-short rainfalls and deep-seated landslides occur as a result of less intense rain over a long time [16,23,24]

When an area is prone to both shallow and rapid and deep-seated and slow moving landslides, a threshold model which can accommodate the effect of both the cases should be defined. A model that holds this characteristic is SIGMA (sistema integrato gestione monitoraggio allerta—integrated system for management, monitoring and alerting), which was developed for managing the risk associated with landslides triggered by rainfall in the Emilia-Romagna Region, Italy [23,25,26]. The model takes cumulative rainfall as input, and it considers the long-term and short-term behavior in order to account for shallow and deep landslides, respectively. Another important advantage of this method is the indication of warning level. The model can be calibrated with respect to the severity of landslides and can be used for developing regional site specific thresholds.

An ongoing research continuously produced new upgrades and optimization of the model SIGMA [19,26,27], making it possible to be used across the world for landslide hazard warning. However, to our knowledge, the model has never been applied outside Italy, thus leaving the claimed flexibility of application only theoretical. Hence, this study applies the SIGMA model to Kalimpong town in the Darjeeling Himalayas and thus tests the exportability of the SIGMA model in different climatic and geomorphological settings.

## **2. Study Area and Input Data**

Kalimpong town is a part of the Kalimpong district of West Bengal state, India, as shown in Figure 1. This hilly town belongs to Darjeeling Himalayas, hemmed between rivers Tista in the west and Relli in the east, with an elevation ranging from 355 m to 1646 m above mean sea level. The slopes in the western face of the town are steep, while the eastern slopes are gentle.

The geological setting of the region is associated with the evolution of Darjeeling Himalayan ranges. Precambrian high-grade gneiss and quartzite, calc–silicate and quartzite, high-grade schist phyletic etc. are the dominant rock types found in the region [28]. Upper sedimentary layers of the young folded mountains get eroded during heavy rainfalls. The area consists of several joints and cracks that accelerates the weathering of the rock and the formation of unconsolidated matter [29]. The bedrock throughout the study area is composed of Daling series quartz mica schist of golden to silver colors [30]. The inclination of bed towards the east and northeast varies from 20◦ near river Tista to about 40◦ towards town. These slopes in can be morphometrically classified into escarpment category A (>45◦), steep slope category B (30◦–45◦), moderate steep slope category C (20◦–30◦) and gentle slope category D (10◦–20◦). Silt to medium grained sand and loam constitutes a major portion of the topsoil of the area. According to GSI, more than 60% of the region comprises colluvium followed

by older debris (24%) and young debris (2.5%). This geomorphological setting makes the region very prone to landslides, and rainfall is the main triggering factor.

**Figure 1.** Location details (**a**) India; (**b**) West Bengal; (**c**) digital elevation model of Kalimpong (modified after [31]).

The geology of the area allows rainwater to percolate, increasing the pore pressure, therefore the shear strength of the soil decreases. The change in water content due to intense rainfall leads to the saturation of material and a sudden increase in the unit weight. This mechanism reduces the stability and resistance of parent rocks. The average annual precipitation in this area was observed to be 1872 mm during the study period, and the drainage density of the region is also very high. The area is drained by numerous mountainous natural streams (kholas) and their tributaries (jhoras). The precipitation with daily accuracy was collected for this study from the rain gauge maintained in Tirpai, Kalimpong (Save The Hills). The months from June to September are considered a monsoon period and the monthly rainfall from 2010 to 2017 is given in Table 1.

**Table 1.** The monthly rainfall (mm) during monsoon seasons in the study area town (2010–2017).

A landslide catalogue was prepared from the reports of the Geological Survey of India, newspapers and field surveys. The database contains the spatial and temporal distribution of rainfall-induced landslide events during 2010–2017. The dataset from 2010 to 2015 was used for model calibration and the dataset from 2016 to 2017 was used for model validation. The major fatal landslides happened in the region were shallow and rapid in nature, but there are some areas which experience continuous sinking because of slow, deep-seated movements, especially near major jhoras [32]. The movements are occurring gradually and are observed as cracks in buildings and roads after each monsoon. Since 2017, these slow movements are monitored using micro-electro-mechanical tilt sensors installed at Chibo [33]. During the validation period (years 2016–2017), ground displacements were observed on 7 days at two locations [2].

The annual cumulative rainfall for these years is plotted in Figure 2a and the temporal distribution of landslides along with the average rainfall is shown in Figure 2b. It is observed that the number of landslide events is maximum in the month of July where the rainfall peak is recorded. From Figure 2b, it is clear that the number of landslides is directly related to the rainfall amount. Landslides are becoming an increasing menace in the region during monsoon season. The havocs related to landslides have multilevel impacts on the livelihood of population. Loss of farm lands and disruption of roads are affecting the income sources of the people. The socioeconomic development of the region is throttled by the disasters and associated setbacks. Hence, it is critical to adopt measures to minimize the impact of landslides in the region. An effective approach is to develop an early warning system using rainfall thresholds to forecast the occurrence of landslides. Since the area is affected by landslides of mixed typology (rapid shallow slides and slow deep seated movements), we took into account the SIGMA model [23], specifically conceived for similar settings, and we customized it for an application in the study area.

**Figure 2.** (**a**) Yearly cumulative rainfall; (**b**) Monthly distribution of landslide occurrence and average rainfall, (2010–2017) in mm.

#### **3. The SIGMA Model**

The SIGMA model was developed for the Emilia-Romagna region in Italy [23]. This model uses the standard deviation of a statistical distribution as the key parameter for the analysis and defines thresholds as a function of standard deviation, predicting the potential of rainfall to initiate landslide events in the study area. Since the model is based on statistical distribution, it is easily exportable to other regions [23]. Adopting the methodology from Martelloni et al. (2012) [23], a customized SIGMA model for Kalimpong town is derived in this study. The modifications are in accordance with the historical database collected for the study area, thus making SIGMA compatible for a different hydro-meteo-geological setting than the area for which it is originally developed. The daily precipitation data were added at '*n*' days, with an '*n*' day wide shifting window which moves at everyday time steps throughout rainfall data. The values of '*n*' will vary from 1 to 365. To calculate the cumulative probability distribution for each data set, a standard distribution, which is the target function is chosen as a model [34].This transformation relates the cumulative rainfall (z) with the target distribution (y = a,σ) ('σ' is the standard deviation of the series and 'a' is a multiplication constant). For each '*n*' day cumulative rainfall series, the values are sorted in ascending order such that

$$z\_1 \prec z\_2 \prec z\_3 \prec \cdots \prec z\_k \prec \cdots \prec z\_n \tag{1}$$

and a cumulative sample frequency is defined as

$$P\_k = \frac{k}{n} - \frac{0.5}{n} = G(y) \tag{2}$$

where, 1 ≤ *k* ≤ *n*.

The conversion is carried out using the cumulative distribution function of z, termed as F(z). For each value of zk, F(zk) defines the probability that the variable z takes a value less than zk, where *k* varies between 1 to *n*.

The original data z transformed to y is obtained as:

$$\mathbf{G}^{-1}(\mathbf{F}(\mathbf{z})) \to \mathbf{G}^{-1}(P\_k) = \mathbf{y} \tag{3}$$

After applying the transformation function, from a particular value of standard deviation or its multiple, cumulative sample frequency and precipitation can be calculated. The same procedure is repeated for all values of *n* from 1 to 365 and precipitation curves (σ curves) are plotted. The probability curves derived are used as the input values in the algorithm. A level of warning is predicted for everyday based on the rainfall thresholds. Rainfall recordings were cumulated with one day time steps for a particular time interval. These values are compared with the precipitation curves, from shorter to longer time frames [23]. In the case of shallow landslides, the analysis should focus on the immediate effect of rainfall: the cumulative rainfall values up to two days before the day of analysis is considered. The decisional algorithm used is given in Equation 4:

$$C\_{1-3} = \left[\sum\_{i=1}^{n} P(t+1-i)\right]\_{n=1,2,3} \ge \left[S\_n(\Delta)\right]\_{n=1,2,3} \tag{4}$$

where, Δ = a,σ, *C*1–3 is the vector indicating the cumulated rainfall at time t and *Sn*(Δ) are the thresholds relative to number of days *n* and Δ [23]. In the case of slow movements, the algorithm ponders the effect of cumulative rainfall from 4 days up to 63 days [23]. The condition for crossing the threshold is given by:

$$\mathbb{C}\_{4-63} = \left[\sum\_{i=1}^{n+3} P(t-2-i)\right]\_{n=1,2,\ldots,60} \ge [\mathbb{S}\_{n+3}(\Delta)]\_{n=1,2,\ldots,60} \tag{5}$$

The definitions of vector *C* are kept the same and have been used in the study for the analysis. The analysis was carried out in the same method proposed by the developers of the SIGMA model, to define the thresholds for Kalimpong town.


*2.1. Determination of Proteolytic Activity of Lactobacillus Strains*

Both the initial and the overall proteolytic activity of all cultures were determined using the method of Church et al. [15]. The proteolytic activity of each *Lactobacillus* strain was determined in reconstituted skim milk (RSM) after 6 and 24 h of fermentation at a temperature of 37 ◦C for determination of the initial and the overall proteolytic activity, respectively. All the organisms were activated from their frozen forms by one transfer into MRS broth (*Lactobacillus* Broth acc. to DE MAN, ROGOSA and SHARPE, Merck, Poland). The obtained cultures were passaged twice by a transfer 1 mL of inoculum into 100 mL of sterile supplemented (with glucose and yeast extract) reconstituted skim milk (SRSM). Then, 1 mL of the inoculum from the SRSM was transferred into 100 mL of RSM and after 24 h of incubation at 37 ◦C, and 1 mL of culture was transferred to 100 mL of RSM and incubated for either 6 or 24 h.

For determinations of the proteolytic activity of the analyzed strains, trichloroacetic acid (TCA) filtrates of the samples were prepared by mixing 5 mL of the sample with 1 mL of distilled water and 10 mL of 0.75 N TCA (Avantor Performance Materials, Poland), followed by centrifugation (MPW-352R centrifuge, Poland) at 4000 g and 4 ◦C for 30 min. The supernatants were filtered through a 0.45 μm syringe membrane filter (MILLEX HV, Milipore, Poland). The proteolytic activity of all cultures was determined by the reaction of 150 μL of the TCA filtrate with 3 mL of o-phthaldialdehyde reagent (OPA, Sigma-Aldrich, Poland). Absorbance was measured after vortexing and 2 min incubation at room temperature at 340 nm (Genesis UV-VIS Spectrophotometer, Thermo Scientific).