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Background: Having a person with mental illness in the family invariably leads to stigmatic experiences to the family members. Perceived stigma in caregivers leads to social isolation, psychological distress, and suboptimal functioning. Additionally, perceived stigma among such caregivers is one of the barriers that prevent them from taking appropriate care of their dependents with mental illness. There has been little research in this field in the Indian armed forces population. Objective: The objective of this study was to assess the perceived stigma among caregivers of armed forces personnel suffering from mental illnesses. Methods: The calculated sample size was 139 but in our study period of time (December 1, 2019–February 28, 2020) we received the enough number of patients and we continued our study with 150 study subjects which is enough more than the 139. Hence, the total number of 150 caregivers visiting the psychiatry outpatient department of a tertiary care military hospital in North India along with their mentally ill dependents were included in the study after obtaining valid informed consent. The subjects were interviewed, their sociodemographic data were collected, and stigma scores were assessed on a 15-item Explanatory Model Interview Catalogue (EMIC) Stigma Scale. Results: Total stigma score ranged from 20 to 40 with a median of 31. Caregivers with lower level of education, lower income, rural residency, elder age, and diagnosis of psychosis/substance abuse in dependent were found to have higher scores of perceived stigma. Conclusions: Caregivers of patients with mental illnesses experience significant stigma which needs to be addressed so that they can provide optimum care to their mentally ill dependents. Limitation: As EMIC does not provide any standard cutoff for low or high stigma or to categorize persons as having stigma or not, it was difficult to compare results with other studies that used different tools to assess stigma.

Researchers have devoted decades to striving to create a plethora of distinctive distributions in order to meet specific objectives. The argument is that traditional distributions have typically been found to lack fit in real-world situations, which include pharmaceutical studies, the field of engineering, hydrology, environmental science, and a number of others. The Weibull-inverse Nadarajah Haghighi (WINH) distribution is developed by combining the Weibull and inverse Nadarajah Haghighi distributions. The proposed distribution's fundamental characteristics have been established and analyzed. Several plots of the distributional properties, notably probability density function (PDF) with corresponding cumulative distribution function (CDF) are displayed. The estimation of model parameter is performed via the MLE procedure. Simulation-based research is conducted to demonstrate the performance of proposed estimator’s using some measure, like the average bias, variance, and associated mean square error (MSE). Two real datasets represent the morality due to COVID 19 in France and Canada are illustrated to see the practicality of the recommended model.

IntroductionInfluenza-associated pulmonary aspergillosis (IAPA) is a severe complication of influenza infection that occurs in critically ill patients and results in higher mortality compared to influenza infection alone. Interleukin-17 (IL-17) and the Type 17 immune signaling pathway cytokine family are recognized for their pivotal role in fostering protective immunity against various pathogens. In this study, we investigate the role of IL-17 and Type 17 immune signaling components during IAPA.MethodsWild-type mice were challenged with influenza A H1N1 (flu) and then exposed to Aspergillus fumigatus ATCC42202 resting conidia on day 6 post-influenza infection, followed by the quantification of cytokines and chemokines at 48 h post-fungal infection.Results and discussionThe gene and protein expression levels revealed that IL-17 and Type 17 immune cytokines and antimicrobial peptides are downregulated during IAPA compared to mice singularly infected solely with A. fumigatus. Restoration of Type 17 immunity was not sufficient to provide protection against the increased fungal burden observed during IAPA. These findings contrast those observed during post-influenza bacterial super-infection, in which restoration of Type 17 immune signaling protects against exacerbation seen during super-infection. Our study highlights the need for future studies to understand the immune mechanisms that increase susceptibility to fungal infection.

Acute respiratory infection (ARI) represents a pervasive global health concern, contributing to significant morbidity and mortality. Our findings reveal a notably high prevalence of ARI in Ladakh. Employing a cross-sectional design, the total number of households surveyed was 401, among them, the total population was 2107; males, 1153, and females, 954. The highest incidence rate of 160.26/1000 persons was observed in the age group below 14 years, followed by those above 59 years (47.76), while the age group of 14–59 exhibited the lowest incidence rate at 21.23. To explore the factors contributing to ARI in Ladakh, we employed binary logistic regression modeling. Our analysis highlights significant associations between ARI incidence and various socioeconomic and environmental determinants. Notably, primary occupation, family type, kitchen ventilation, domestic fuel consumption, crowding, and smoking behaviors within families were found to be statistically significant determinants of ARI among the sample population of Ladakh.

Since standard distributions do not fundamentally have an acceptable fit to all types of data sets, it is necessary to construct extensions of standard distributions to increase their capability in data modeling. As a result of this shortage in old ones, we proposed a novel generator based on the trigonometric function (Arctan). We selected the Frechet distribution as the baseline for the generator’s applicability. This generator produces the “new Arctan Frechet distribution” (NATFD). The fundamental properties of the proposed distribution have been taken into consideration. Estimating the given distribution’s parameters is accomplished using the maximum-likelihood method. A simulation study is carried out to evaluate the superiority of the proposed distribution, and two actual data sets are used.

The main aim of the investigation is to explore how the interaction between hosts and tourists influences tourists' intention to revisit a particular destination. Furthermore, the study seeks to examine the role of place attachment in mediating the relationship between host-tourist interactions and revisiting intention. In this regard, a questionnaire was developed based on existing literature and subjected to a pilot study to ensure its validity and reliability. The research employed a descriptive research design and adopted a positivist approach. A total of 360 valid questionnaires were analysed for the study, which were collected from tourists visiting Kashmir Valley using convenience sampling. The collected data were processed using SPSS and Smart-PLS software. The results of the analysis indicated a significant and positive correlation between host-tourist interaction and tourists' intention to revisit the destination. Additionally, the findings demonstrated that place attachment played a mediating role in the connection between host tourist interaction and revisit intention. These findings emphasize the importance of promoting interactions between hosts and tourists, as they have the potential to positively influence tourists' likelihood of revisiting the destination. By fostering meaningful connections and attachment to the place, the destination can enhance the overall experience for tourists, increasing the probability of their return visits.

Objective: The recommended course of treatment for early-stage breast cancer is breast-conserving surgery followed by adjuvant whole-breast radiation. It is challenging to obtain homogeneous dose distribution using standard procedures because of breast irregularities. Currently, it is possible to use different breast irradiation techniques such as a field in field (FIF) which produces a more homogenous distribution of doses within the target volumes while sparing the organs at risk, leading to a better treatment outcome. Materials and Methods: The present study aimed to compare the conformal and the FIF techniques dosimetrically. In this retrospective dosimetric study, we evaluate 20 patients with early-stage breast cancer who underwent computed tomography. For each patient, two different treatment plans were created, three-dimensional conformal radiotherapy and a FIF plan. A dose of 50 Gy in 25 fractions was prescribed to the planning target volume (PTV). Results: The plans were compared with each other on volume coverage (conformity and homogeneity) and OAR sparing. The dosimetric parameters and monitor components were compared with paired sample t-test. The FIF technique obtained a significantly lower dose homogeneity index, lower maximum doses, and higher median doses in PTV (P < 0.05). In ipsilateral lungs, FIF significantly reduced the maximum and mean doses (P < 0.05). In patients with left-sided breast cancer, minimum and maximum doses and V40 of the heart were significantly decreased in FIF plans (P < 0.05). Doses to the contralateral lung differ to a little degree as well. Conclusion: The use of forward intensity-modulated radiation therapy (RT) technique for RT of high-risk node-positive breast cancer patients provides an efficient and reliable method for achieving superior dose uniformity, conformity, and homogeneity in the breast chest wall volume with minimal doses to the OAR. Monitor units were less in FIF, with less time on the machine and less machine consumption.

Background/Aim: Acute Diarrhoeal disease (ADD) has a significant impact on public health and is the most common cause of mortality and morbidity. The present study attempts to study the spatiotemporal distribution of incidences of ADD and also tries to identify the geographical risk factors by associating socioeconomic and environmental factors with the prevalence of ADD. Methods: This cross-sectional study conducted micro-level analysis in 24 randomly chosen villages, representing 5 % of Ladakh's medical blocks, to gather household data on diarrhoeal diseases, encompassing demographic, socio-economic, and residential environmental factors. Our dataset combines primary and secondary data. We utilized binary logistic regression to evaluate associations between these factors and diarrhoeal disease prevalence. Results: Spatio-temporal analysis in Ladakh's medical blocks revealed significant variations in diarrhoeal disease incidence, with Leh district showing higher rates compared to Kargil. The under-14 age group exhibited the highest ADD incidence (163.46 per 1000 persons), followed by the over-59 age group (43.73) and the 14–59 age group (8.90), with an overall prevalence rate of 38.91 per 1000 population. Factors such as education levels, household income, sewage drainage facilities, residential environment, and drinking water sources showed statistically significant associations with diarrhoea prevalence among surveyed households of Ladakh. Conclusions: This research highlights the importance of addressing environmental and socio-economic determinants to mitigate the burden of diarrhoeal diseases in Ladakh. Effective interventions targeting improved sanitation, access to clean water, and socio-economic development are crucial for reducing diarrhoeal disease prevalence in the region.

Background: Irritability, defined as proneness to anger, can reach a pathological extent. It is a defining symptom of disruptive mood dysregulation disorder and one of the most common reasons youths present for psychiatric evaluation and care. Aberrant responses to frustrative nonreward (FNR), the response to omission of expected reward, are central to the pathophysiology of irritability. FNR is a translational construct to study irritability across species. The development of preclinical FNR models would advance mechanistic studies of the important and relatively understudied clinical phenomenon of irritability. Methods: We used FNR as a conceptual framework to develop a novel mouse behavioral paradigm named alternate poking reward omission. Juvenile mice were exposed to alternate poking reward omission and then examined with a battery of behavioral tests to determine the behavioral effect of FNR. Results: FNR increased locomotion and aggression regardless of sex. These behavioral changes elicited by FNR resemble the symptoms observed in youth with severe irritability. FNR had no effect on anxiety-like, depression-like, or nonaggressive social behaviors. Conclusions: Our alternate poking reward omission paradigm effectively elevated aggression and locomotion in juvenile mice. These frustration effects are directly related to behavioral symptoms of youth with severe irritability. Our novel behavioral paradigm lays the groundwork for further mechanistic studies of frustration and irritability in rodents.

Aim and Objectives: The study was conducted to to compare the dosimetric superiority between the Field in Field and Box techniques. Meterials and Methods: We have taken 22 Patients of Ca esophagus for our study from our Institute database. Patients were already treated Box (4 fields) plan. Retrospectively additional Field in Field (FIF) plan was created with Planning target volume (PTV) prescription dose of 59.4Gy in 33 fractions. Prescription dose of 95% of PTV was chosen for the comparison between two treatment planning strategies. The Dose volume histogram (DVH) was used to analyze and compare various plan quality index and doses to the organs at risk (OARs). For each patient, two treatment plans were created: Box 4FB and FIF. The parameters compared included the conformity index (CI), homogeneity index (HI), Conformation number (CN), DMean, DMax, V20Gy and V30Gy of the heart and lungs, and monitor units per fraction (MU/fr). Results: A paired t-test analysis significant differences (P < 0.05) between the two techniques in terms of the CI, HI, and CN when the FIF was applied. The Dmax of the PTV, lung, and spinal cord were also significantly better with the FIF. Moreover, the V20Gy of the significantly reduced when the FIF was used and the MU/fr was significantly decreased. The FIF technique enables better dose distribution in the PTV and reduces dose to OARs in esophageal radiotherapy. Conclusion: Although all plans were within acceptable limits, dosimetric comparison of different planning techniques revealed that FIF had statistically significant differences from Box planning technique in terms of planned target volume (PTV) coverage, OAR protection, HI, CI, CN, MU and treatment duration. From the aspect of treatment duration and MU parameters, FIF technique can be considered as an alternative planning option.

This work proposes the techno-economic analysis of a three-phase, three wire grid integrated third-generation perovskite solar cells-wind energy conversion system (PSC-WECS) controlled by a two-level voltage source converter (VSC) which is driven by the application of polynomial zero-attracting least mean square (PZA-LMS) algorithm. The proposed algorithm enhances the dynamic performance of the system by introducing sinusoidal grid currents with low total harmonic distortion (THD) which enhances system performance by providing acceptable outcomes under dynamic loading, fluctuating wind velocity, and solar insolation by compensating for load reactive power and balancing load and power demands at the point of coupling. This work replaces the conventional silicon PVs with PSCs to improve the economics of power generation thanks to their higher efficiency. For relative comparisons, the performance of the proposed 36 kW PSC-WECS is compared with the conventional silicon PV-WECS. The results reveal that the proposed system has a low net present cost (NPC) of ${\$}$ 68843 compared to the conventional system which costs ${\$}$ 160917, translating to a reduction of 58% in total cost. Sensitivity Analysis (SA) is performed to analyse the dependence of optimal solutions on the uncertainty of key variables namely NPC, cost of energy (COE), windspeed & insolation. The results show that the total NPC is more sensitive to the COE and the average wind speed as compared to the other variables. Moreover, the area covered by the proposed system is reduced by 50% while as the cost of power generation is reduced by 72% due to the high efficiency of PSCs. The results obtained in MATLAB/Simulink and HOMER to determine the optimum quantity of the renewable energy sources (RES) model demonstrate the effectiveness of the recommended system.

Background/Objectives: Biofilm-associated infections, particularly those involving Candida auris and Staphylococcus aureus, pose significant challenges in clinical settings due to their resilience and resistance to conventional treatments. This study aimed to synthesize novel triazole derivatives containing a piperazine ring via click chemistry and evaluate their efficacy in disrupting biofilms formed by these pathogens. Methods: Triazole derivatives were synthesized using click chemistry techniques. The antimicrobial activity of the compounds was tested against planktonic cells of C. auris and S. aureus in single and dual-species culture conditions. Biofilm disruption efficacy was assessed, alongside the evaluation of physicochemical properties, oral bioavailability potential, and toxicity profiles. Results: The compound T3 demonstrated potent antimicrobial activity against planktonic cells of C. auris and S. aureus in both single and dual-species cultures. T3 exhibited significant efficacy in reducing microbial viability within biofilms formed by these pathogens. Physicochemical analyses revealed favorable solubility and permeability profiles, supporting its potential for oral bioavailability. Toxicity assessments showed a non-toxic profile, highlighting a promising safety margin for further development. Conclusions: This study underscores the anti-biofilm properties of novel triazole-piperazine derivatives, particularly T3, against single and dual-species biofilms of C. auris and S. aureus. These findings position T3 as a promising candidate for developing therapies targeting polymicrobial infections and provide a foundation for future research into alternative strategies for combating biofilm-associated infections.

The Resource-Based View (RBV) philosophy examines the relationship between Corporate Social Responsibility (CSR) and Environmental Performance (EP). This paper addresses the lack of scholarly attention to Corporate Social Responsibility (CSR) in managing Environmental Performance (EP) in Indian industrial enterprises. A cross-sectional study using structured questionnaires was conducted on 500 participants from 10 prominent Delhi industrial organisations. This research found significant results among the variables using Partial Least Squares (PLS) software for the analysis of quantitative data collected from the respondents. The findings show that corporate social responsibility (CSR) has a significant impact on environmental performance (Beta, T value, P values (0.174, 3.638, 0.000 for CSR and EP) and that green innovation (GI), green capacity (GC), environmental strategy (ES), and green transformational leadership (GTL) mediates the relationship. This study helps industry leaders and stakeholders enhance Environmental Performance and decision-makers to establish internal plans for long-term profitability and informed industrial governance.

In the energy sector, it is important to meticulously choose an accurate forecasting model because making informed decisions is crucial for optimal grid operation. This article proposes a hybrid graph neural network (GNN) that successfully captures complex patterns by combining interactions based on features and time. The proposed architecture uses diverse decomposition methods, such as statistical, dynamic, and spectral, to uncover hidden patterns. The integration of attention and graph convolution layers improves the flow of information, and the cross-modal fusion layer competently combines nodes and edges. This configuration can efficaciously assimilate disparate features, providing an advantage over other approaches. The hybrid GNN with an analytic hierarchy process (AHP) performs better than the existing models when tested exhaustively on diverse regional energy demand and pricing time series. It captures complex patterns more effectively than K-nearest neighbour (KNN), random forest regressor (RFR), and GNN-based model suggestion methods. For the nonstationary price data from New South Wales and the Punjab energy demand time series, the Kendall’s Tau coefficient is 0.73 and 0.81 and the Spearman’s Rank coefficient is 0.74 and 0.91, respectively. This paper advances the field of time series forecasting by offering a novel strategy for improving model proposals by efficiently merging hybrid GNN with multiple feature modalities.

The COVID-19 pandemic has become a significant global issue in terms of public health. While it is largely associated with respiratory complications, recent reports indicate that patients also experience neurological symptoms and other health issues. The objective of this study is to examine the network of protein-protein interactions (PPI) between SARS-CoV-2 proteins and human host proteins, pinpoint the central genes within this network implicated in disease pathology, and assess their viability as targets for drug development.The study adopts a network-based approach to construct a network of 29 SARS-CoV-2 proteins interacting with 2896 host proteins, with 176 host genes being identified as interacting genes with all the viral proteins. Gene ontology and pathway analysis of these host proteins revealed their role in biological processes such as translation, mRNA splicing, and ribosomal pathways. We further identified EEF2, RPS3, RPL9, RPS16, and RPL11 as the top 5 most connected hub genes in the disease-causing network, with significant interactions among each other. These hub genes were found to be involved in ribosomal pathways and cytoplasmic translation. Further a disease-gene interaction was also prepared to investigate the role of hub genes in other disorders and to understand the condition of comorbidity in COVID-19 patients. We also identified 13 drug molecules having interactions with all the hub genes, and estradiol emerged as the top potential drug target for the COVID-19 patients. Our study provides valuable insights using the protein-protein interaction network of SARS-CoV-2 proteins with host proteins and highlights the molecular basis of manifestation of COVID-19 and proposes drug for repurposing.As the pandemic continues to evolve, it is anticipated that investigating SARS-CoV-2 proteins will remain a critical area of focus for researchers globally, particularly in addressing potential challenges posed by specific SARS-CoV-2 variants in the future.

In this paper, a Two-Level, Three-Phase, Reduced-Switch (2L3PRS) Voltage Source Converter (VSC) based Distribution Static Synchronous Compensator (DSTATCOM) integrated with a grid-tied solar photo-voltaic (PV) array has been discussed, and its performance investigation has been carried out. A versatile control strategy is proposed, which gives the system maximum power point tracking capability with a stepped perturbation and observation (P&O) algorithm and the grid current is maintained at unity power factor. The layout, in addition to the control technique, helps in supplying active power to the power grid and feeding active and reactive power demand of the 3-phase reactive unbalanced load. Modified Synchronous Reference Frame (MSRF) theory-based current control methodology is implemented to sustain DC-link voltage and to maintain the voltage balancing across split capacitors under all loading conditions. This modified algorithm generates switching pulses for three-phase VSI with only four IGBT switches for fulfilling the desired objectives like maintaining a constant DC link voltage, balancing split-capacitor voltages, balancing the power-sharing between the power grid and the solar-PV energy system, balancing the grid currents and voltages in case of unbalanced loading conditions and maintaining unity power factor operation of the grid by making the D-STATCOM compensate for load reactive power. The proposed system is replicated in MATLAB/Simulink using SimPowerSystems software-based environment. The experimental outcomes have been obtained with a digital real-time simulator to vindicate the simulation results. The experimental, as well as simulation results of the proposed real-time-based model, are demonstrated for three unbalanced single-phase inductive linear loads and with a load on one Phase kept open in the Power Factor Correction (PFC)mode of operation of D-STATCOM.

This study focuses on a novel family of distributions inspired by the hyperbolic sine function. The Rayleigh distribution is the base model for the newly formed family of distributions known as the new hyperbolic Sine-Rayleigh distribution. The recommended distribution’s distinct structural traits have been examined. The behaviors of the distributional functions of the proposed model are depicted in several figures. The maximum likelihood estimation procedure is employed to estimate the specified distribution parameters. A simulation study was carried out to examine and evaluate the behavior of the estimators. Moreover, the efficacy of the specified distribution is supported by realistic data sets pertaining to engineering science.

Osteoporosis, a prevalent ailment worldwide, compromises bone strength and resilience, particularly afflicting the elderly population. This condition significantly heightens susceptibility to fractures even from trivial incidents, such as minor falls or impacts. A major challenge in diagnosing osteoporosis is the absence of discernible symptoms, allowing osteoporosis to remain undetected until the occurrence of a fracture event. Early symptom detection and swift diagnosis are critical for preventing severe issues related to bone diseases. Assessing bone turnover markers aids in identifying, diagnosing, and monitoring these conditions, guiding treatment decisions. However, conventional techniques for measuring bone mineral density are costly, time-consuming, and require specialized expertise. The integration of sensor technologies into medical practices has transformed how we monitor, diagnose, and treat various health conditions, including bone health and orthopedics. This review aims to provide a comprehensive overview of the current state of sensor technologies used in bone, covering their integration with bone tissue, various applications, recent advancements, challenges, and future directions.

Down Syndrome (DS), characterized by trisomy of chromosome 21, leads to the overexpression of several genes contributing to various pathologies, including cognitive deficits and early-onset Alzheimer’s disease. This study aimed to identify the intersection genes of two polyphenolic compounds, apigenin and naringenin, and their potential therapeutic targets in DS using network pharmacology. Key proteins implicated in DS, comprising DYRK1A, APP, CBS, and ETS2, were selected for molecular docking and dynamics simulations to assess the binding affinities and stability of the protein–ligand interactions. Molecular docking revealed that naringenin exhibited the highest binding affinity to DYRK1A with a score of −9.3 kcal/mol, followed by CBS, APP, and ETS2. Moreover, molecular docking studies included positive control drugs, such as lamellarin D, valiltramiprosate, benserazide, and TK216, which exhibited binding affinities ranging from −5.5 to −8.9 kcal/mol. Apigenin showed strong binding to APP with a score of −8.8 kcal/mol, suggesting its potential in modulating amyloid-beta levels. These interactions were further validated through molecular dynamics simulations, demonstrating stable binding throughout the 100 ns simulation period. Root mean square deviation (RMSD) and root mean square fluctuation (RMSF) analyses indicated minimal fluctuations, confirming the stability of the complexes. The findings suggest that apigenin and naringenin could serve as effective therapeutic agents for DS by targeting key proteins involved in its pathology. Future studies should focus on in vivo validation, clinical trials, and exploring combination therapies to fully harness the therapeutic potential of these compounds for managing DS. This study underscores the promising role of network pharmacology in identifying novel therapeutic targets and agents for complex disorders like DS.

Background: Bovine babesiosis, a global disease, has not been studied so far in Kashmir valley, which is having temperate type of climate as compared to rest of India having tropical to sub-tropical climate, so we felt the need to investigate it. Methods: To diagnose the babesiosis in clinically suspected cattle (n=450), peripheral blood film examination and PCR tests using generic and species-specific primers targeting Babesia/Theleria genera and B. bigemina, B. bovis as well as B. divergens, respectively were conducted. Four PCR products were sequenced and subjected to BLASTn analysis. Ticks were collected from the clinically suspected animals and identified as per the standard morphological keys. Results: The prevalence of babesiosis among suspected cattle in central Kashmir by peripheral blood film examination and PCR technique was 11.11% and 33.62%, respectively. The 18S rRNA gene of Isolate B1 of Babesia spp. showed 99.0 to 100% nucleotide sequence homology with 18S rRNA gene of different isolates of B. bigemina registered in the GenBank, while as 18S rRNA gene of Isolate Z showed 98.5 to 99.2% and 93.1 to 93.9% nucleotide sequence homology with 18S rRNA gene of different isolates of Babesia spp. and B. bigemina, respectively, registered in the GenBank. Rhipicephalus spp. and Haemaphysalis spp. were the two major tick genera identified in the present study. Conclusion: Bovine Babesiosis in Kashmir is attributed to B. bovis, B. bigemina and some other Babesia spp. or strains which needs further investigation. To our knowledge, this is the first report of Babesia bovis from northern India in cattle.

In this research, we establish the novel sin-G class, an innovative and comprehensive class of trigonometric distributions centered on the sine function. A sin-G class analytical approach comprises calculations for the probability density function and reliability indicators. Various estimation methods were presented to estimate the specified model parameters. Applying randomly created datasets and the provided estimating techniques, the behavior of these variables was investigated. Then, the focus is directed toward a specific member of this class, the novel sin-Lomax distribution, which is established with the Lomax distribution serving as the baseline. Exploring the innovative sin-Lomax distribution’s inferential characteristics, the constructed distribution is then fitted to real datasets, demonstrating the actual application of the Sin-G class.

This research offers a thorough examination of thermal transmission within the context of the squeezing flow of a Casson fluid that is confined within two circular plates aligned in parallel. The development of a feasible mathematical framework involves combining the principles of conservation with appropriate similarity transformations. The resulting framework generates a couple of strongly non-linear ordinary differential equations. To tackle these equations, established analytical methods like the (HPM) and its variation, the Least Square Homotopy Perturbation Method (LSHPM), are employed.Additionally, for the validation of the analytical findings, a numerical approach using the BVP5C technique is utilized. A comparative evaluation reveals that the LSHPM consistently provides results of exceptional accuracy compared to the traditional HPM. The investigation delves into observing how the flow performs when experiencing diverse variations in physical attributes, elucidating the complexities through comprehensive visual representations. It is worth noting that the presented problem is subject to specific parameter limitations, which are extensively discussed and taken into account throughout the study. The investigation encompasses a thorough analysis across a range of parameters, including the squeeze number, the Casson fluid parameter, the Prandtl number, the Eckert number and. Notably, an acceleration in the rate of motion is observed concerning the squeeze number and the Casson fluid parameter. In terms of the temperature profile, it is revealed that this profile demonstrates a decreasing trend in relation to both the squeeze number and the Casson fluid parameter. Conversely, it displays an increasing trend with respect to the Prandtl number and the Eckert number.

The study’s major goal is to design a superior creative distribution by using the Kumaraswamy-G family of distributions to power Frechet distribution. The Kumaraswamy power Frechet distribution (KPFD) with four parameters is the full name of the revolutionary model. The distribution’s probability density function may take numerous forms and graphs and can be used to describe complicated data sets efficiently. Several features of the new distribution are obtained, including dependability, hazard rate, quantile, and moments. The estimation of the unknown parameters of KPFD are provided using the KPFD maximum likelihood estimation technique. Furthermore, a study was performed using the Monte Carlo simulation approach to test estimator accuracy regarding average bias (AB) and mean square error (MSE). Last but not least, two genuine data sets are supplied to compare the proposed model to existing models.

Background: Digital mammography (DM) is the only screening modality that has been proven to reduce mortality from breast cancer through early detection. However, to reduce false positive results and improve the sensitivity of DM, we undertook a study to compare the impact of digital breast tomosynthesis (DBT) to DM in the evaluation of symptomatic women using the breast imaging reporting and data system (BIRADS) score. Aims and Objectives: The aim of the study was to compare the impact of DBT to DM in the evaluation of symptomatic women using BIRADS score. Materials and Methods: This cross-sectional study of 100 symptomatic patients in the age group of 35–85 years who underwent DM and DBT was included in the study and was analyzed using the American College of Radiology-BIRADS score. Patients with a palpable breast lump, pain, nipple discharge, skin dimpling, or skin changes over breast and nipple inversion or other nipple abnormalities were included in the study. Results: Comparison based on the diagnostic accuracy of mammography alone and mammography plus DBT based on BIRADS score revealed DM had a sensitivity of 96%, specificity of 40.6%, a positive predictive value (PPV) of 72.9% and negative predictive value (NPV) of 86.7% with an accuracy of 75.3% while DM plus DBT had a sensitivity of 98.1%, specificity of 46.9%, the PPV of 75.4%, and NPV of 78.8%. There was no significant increase in sensitivity (P=1.000), specificity (P=0.614), PPV (0.734), and accuracy (0.584). Conclusion: DM plus DBT does not change the BIRADS scoring but its addition increases the diagnostic confidence while there is no significant increase in sensitivity/specificity/PPV/NPV or diagnostic accuracy using DM plus DBT.

Over the last few years, the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) unleashed a global public health catastrophe that had a substantial influence on human physical and mental health, the global economy, and socio-political dynamics. SARS-CoV-2 is a respiratory pathogen and the cause of ongoing COVID-19 pandemic, which testified how unprepared humans are for pandemics. Scientists and policymakers continue to face challenges in developing ideal therapeutic agents and vaccines, while at the same time deciphering the pathology and immunology of SARS-CoV-2. Challenges in the early part of the pandemic included the rapid development of diagnostic assays, vaccines, and therapeutic agents. The ongoing transmission of COVID-19 is coupled with the emergence of viral variants that differ in their transmission efficiency, virulence, and vaccine susceptibility, thus complicating the spread of the pandemic. Our understanding of how the human immune system responds to these viruses as well as the patient groups (such as the elderly and immunocompromised individuals) who are often more susceptible to serious illness have both been aided by this epidemic.COVID-19 causes different symptoms to occur at different stages of infection, making it difficult to determine distinct treatment regimens employed for the various clinical phases of the disease. Unsurprisingly, determining the efficacy of currently available medications and developing novel therapeutic strategies have been a process of trial and error. The global scientific community collaborated to research and develop vaccines at a neck-breaking speed. This review summarises the overall picture of the COVID-19 pandemic, different mutations in SARS-CoV-2, immune response, and the treatment modalities against SARS-CoV-2.

Purpose: The purpose of the current study is to determine whether patients diagnosed with glioblastoma multiforme (GBM) who underwent radiotherapy (RT) using intensity-modulated RT (IMRT) technique were benefitting from this highly conformal treatment in terms of doses received by planning target volume (PTV) and organs at risk (OARs) in comparison to three-dimensional conventional RT (3DCRT). Materials and Methods: Twelve patients treated with IMRT for GBM were selected for dosimetric comparison with 3DCRT. The prescribed dose was 60 Gy in 30 fractions and seven non-coplanner beams were used in IMRT to cover 95% of target volume. The irradiated patients of GBM were retrieved and replanned with 3DCRT techniques. Dosimetric comparison was done by performing two plans for the same patient; prescription dose and normal tissue constraints were identical for both plans. The dose–volume histograms of target volumes and OAR, dose conformity, and dose homogeneity with 3DCRT and IMRT planning were compared. Statistical analysis was performed to determine the differences. Results: The mean conformity index was 0.99 ± 0.001 for IMRT and 0.97 ± 0.002 for 3DCRT, P = 0.001. The mean homogeneity index was 1.03 ± 0.02 for IMRT and 1.06 ± 0.009 for 3DCRT, P = 0.003, which is statistically significant. The IMRT technique enables dose reduction of normal tissues including brainstem (Dmean by 33.78 ± 5.34 and Dmax 51.84 ± 4.43), optic chiasm (Dmean by 36.92 ± 1.99 and Dmax 44.61 ± 3.72), left optic nerve (Dmean by 28.97 ± 6.51 and Dmax 46.08 ± 10.58), right optic nerve (Dmean by 31.93 ± 11.68 and Dmax 44.63 ± 13.54), left eye (Dmean by 18.66 ± 8.92 and Dmax 37.43 ± 13.47), right eye (Dmean by 14.40 ± 4.87 and Dmax 40.37 ± 11.37), left lens (by Dmax 5.45 ± 1.85), and right lens (Dmax 5.07 ± 0.63). Conclusion: The IMRT provides a real dosimetric advantage, especially for normal brain tissue, and in terms of target coverage. It allows treatment of tumors while respecting OARs' dose constraints. The IMRT technique shows significant advantage in PTV coverage, dose homogeneity, and conformity. In IMRT, the coverage is better where PTV was overlapping with critical OARs.

Cancer is the leading cause of death worldwide, resulting in the mortality of more than 10 million people in 2020, according to Global Cancer Statistics 2020. A potential cancer therapy involves targeting the DNA repair process by inhibiting PARP-1. In this study, classification models were constructed using a non-redundant set of 2018 PARP-1 inhibitors. Briefly, compounds were described by 12 fingerprint types and built using the random forest algorithm concomitant with various sampling approaches. Results indicated that PubChem with an oversampling approach yielded the best performance, with a Matthews correlation coefficient > 0.7 while also affording interpretable molecular features. Moreover, feature importance, as determined from the Gini index, revealed that the aromatic/cyclic/heterocyclic moiety, nitrogen-containing fingerprints, and the ether/aldehyde/alcohol moiety were important for PARP-1 inhibition. Finally, our predictive model was deployed as a web application called PARP1pred and is publicly available at https://parp1pred.streamlitapp.com, allowing users to predict the biological activity of query compounds using their SMILES notation as the input. It is anticipated that the model described herein will aid in the discovery of effective PARP-1 inhibitors.

Introduction: The purpose of the study was to determine forward-planned intensity-modulated radiotherapy which improves the sparing of organs at risk (OAR), heart, lungs, and contralateral breast, when compared with multi-field inverse-planned intensity-modulated radiation therapy (RT). The anatomy of the chest wall is curved, which makes it difficult to achieve a homogeneous dose; therefore, it is essential to know the technique by which we can render a better treatment. Methods: The three-dimensional field-in-field (FIF) technique for radiotherapy is an advanced state-of-the-art method that uses multileaf collimators to generate a homogeneous and conformal dose distribution through segmental subfields. The planning computer tomography (CT) scans of 30 women with left-sided breast cancer previously treated with whole breast RT on an inclined breast board with both arms supported above the head were retrieved. The whole-breast planning target volume (PTV) was defined by clinical markup and contoured on all relevant CT slices as were the OARs. For each patient, two plans were generated using FIF and five-field inverse-planned IMRT, with a prescription dose of 50 Gy in 25 fractions to the whole breast. The mean and maximum doses to the OARs, conformity index, and homogeneity index (HI) of the whole-breast PTV were compared. Results: The FIF technique significantly reduced the maximum dose of the PTV as well as the mean doses of the heart, ipsilateral lung, contralateral lung, esophagus, and contralateral breast (P < 0.001 for each). When the organ at risk volumes irradiated with 10, 20, 30, and 40 Gy were compared, the results were in favor of the FIF technique. The volume receiving

The ant genus Vollenhovia Mayr, 1865 (Myrmicinae, Crematogastrini) mostly occurs in the Australasian and Oriental regions. We revised its diversity in India and Sri Lanka based on qualitative and quantitative morphology, recognizing eleven taxa, including a new species which is described herewith: V. escherichi Forel, 1911, V. gastropunctata Bharti & Kumar, 2013, V. karimalaensis Dhadwal et al., 2023, V. keralensis Kripakaran & Sadasivan, 2022, V. mawrapensis Dhadwal et al., 2023, V. oblonga laevithorax Emery, 1889, V. penetrans (Smith, 1857), V. pfeifferi Bharti et al., 2023, V. taylori Rilta et al., 2023, V. terayamai Rilta et al., 2023, and V. yasmeenae sp. nov. The subspecies status of V. oblonga laevithorax and its relationship with V. penetrans, whose type series does not contain workers, still requires to be assessed in the context of a broader revision including the whole Oriental region. The known distribution of the genus in the Indian subcontinent appears to be fragmentary, still requiring extensive sampling efforts. Four species are from the Western Ghats biodiversity hotspot in the southern Indian state of Kerala, one is endemic to the biogeographically related Sri Lanka, three are known from Eastern India near the border with Bangladesh, two are reported from the Andaman and Nicobar Islands, and one is restricted to the Himachal Pradesh in northern India. A comprehensive key of the known Vollenhovia species from India and Sri Lanka is provided.

A review of the genus Stratiomys from India is presented. The new species Stratiomys brunettii sp. nov. is described based on male and female specimens collected from the Kashmir Himalayas. The only other congener previously recorded in India, Stratiomys approximata, is redescribed. A key to the species is presented.

The emergence of New Delhi metallo-beta-lactamase-1 (NDM-1) has conferred enteric bacteria resistance to almost all beta-lactam antibiotics. Its capability of horizontal transfer through plasmids, amongst humans, animal reservoirs and the environment, has added up to the totality of antimicrobial resistance control, animal husbandry and food safety. Thus far, there have been no effective drugs for neutralizing NDM-1. This study explores the structure-activity relationship of NDM-1 inhibitors. IC50 values of NDM-1 inhibitors were compiled from both the ChEMBL database and literature. After curation, a final set of 686 inhibitors were used for machine learning model building using the random forest algorithm against 12 sets of molecular fingerprints. Benchmark results indicated that the KlekotaRothCount fingerprint provided the best overall performance with an accuracy of 0.978 and 0.778 for the training and testing set, respectively. Model interpretation revealed that nitrogen-containing features (KRFPC 4080, KRFPC 3882, KRFPC 677, KRFPC 3608, KRFPC 3750, KRFPC 4287 and KRFPC 3943), sulfur-containing substructures (KRFPC 2855 and KRFPC 4843), aromatic features (KRFPC 1566, KRFPC 1564, KRFPC 1642, KRFPC 3608, KRFPC 4287 and KRFPC 3943), carbonyl features (KRFPC 1193 and KRFPC 3025), aliphatic features (KRFPC 2975, KRFPC 297, KRFPC 3224 and KRFPC 669) are features contributing to NDM-1 inhibitory activity. It is anticipated that findings from this study would help facilitate the drug discovery of NDM-1 inhibitors by providing guidelines for further lead optimization.

Purpose: To compare the dose distribution of three-dimensional conformal radiation therapy (3DCRT) with intensity-modulated radiation therapy (IMRT) for post-mastectomy radiotherapy (PMRT) to left chest wall. Materials and Methods: 30 post-MRM female breast cancer patients with histologically confirmed infiltrating ductal carcinoma of unilateral left breast without evidence of distant metastasis or second malignancy were found eligible during January 2017 to December 2021. All patients received 45 Gy in 20 fractions. Planning target volume (PTV) parameters -D2, D98, Dmean, V95, and V107—homogeneity index (HI), and conformity index (CI) were compared. The mean doses of lung and heart, percentage volume of ipsilateral lung receiving 5 Gy (V5), 20 Gy (V20), and 49 Gy (V49) and that of heart receiving 5 Gy (V5), 25 Gy (V25), and 42 Gy (V42) were extracted from dose-volume histograms and compared. Results: PTV parameters were comparable between the two groups. CI was significantly improved with IMRT (1.118 vs. 1.224, p < 0.04) but HI was similar (0.0951 vs. 0.0962, p = 0.125) compared to 3DCRT. IMRT in comparison to 3DCRT significantly reduced the high-dose volumes of lung (V20, 24.52% vs. 29.62%; V49, 3.56% vs. 6.42%; p < 0.001) and heart (V25, 5.89% vs. 8.24%; V42, 1.64% vs. 6.12%; p < 0.001); mean dose of lung and heart (10.21 vs. 11.96 Gy and 3.86 vs. 7.42 Gy, respectively; p < 0.001). Conclusions: For left sided breast cancer, IMRT significantly improves the conformity of plan and reduce the mean dose and high-dose volumes of ipsilateral lung and heart compared to 3DCRT, but 3DCRT is superior in terms of low-dose volume.

ABSTRACT Most investigations into the distribution of methicillin resistant Staphylococcus aureus (MRSA) have focused exclusively on bloodborne infections within individual health care institutions for shorter time periods. This has limited the analysis of a community-spread pathogen to snapshots within the hospital domain. Therefore, in this study we determined the demographic and geographical patterns of MRSA infections and their fluctuation in 10 years within all public hospitals in Gauteng, South Africa. A retrospective analysis of S. aureus samples was done by deduplicating samples in two groups. The sample groups were placed into subsets with respect to demographic and geographical fields and compared across the studied period. Logistic regression was utilized to determine odds ratios for resistant infections in univariate and multivariable configurations. A total of 66,071 unique infectious events were identified from the 148,065 samples received over a 10-year period, out of which 14,356 were identified as bacteremia. MRSA bacteremia rates in Gauteng peaked in 2015 and have since decreased. Within Gauteng, metropolitan areas have the greatest burden of MRSA with children under 5 years of age and males being most affected. Medical wards have the highest S. aureus bacteremia rates, while intensive care units have the highest MRSA bacteremia rates. Patient age, admitting ward, and geographical district are the most important associated factors of resistance. MRSA acquisition rates have shown tremendous growth since 2009 but have since spiked and subsequently decreased. This may be due to the initiation of the National Guidelines on Antimicrobial Stewardship and Infectious Disease Surveillance. Further studies to determine the trajectory of infections are required to support these claims. IMPORTANCE S. aureus is the leading cause of a variety of devastating clinical conditions, including infective endocarditis, bacteremia, and pleuropulmonary infections. It is an important pathogen responsible for substantial morbidity and mortality. MRSA is a variant of interest originally responsible for difficult to treat hospital-acquired infections that has since achieved community spread throughout the world. Most investigations into the distribution of MRSA have focused exclusively on bloodborne infections within individual health care institutions for shorter periods. This has limited the analysis of a community-spread pathogen to snapshots within the hospital domain. This study sought to determine the demographic and geographical patterns of MRSA infections as well as how these have fluctuated over time within all public hospitals. This will also help in understanding the epidemiology and resistance trends of S. aureus, which will help clinicians to understand the clinical prospective and policy makers to design guidelines and strategies for treating such infections.

Osteoporosis occurs due to a dysregulation in bone remodeling, a process requiring both bone-forming osteoblasts and bone-resorbing osteoclasts. Current leading osteoporosis therapies suppress osteoclast-mediated bone resorption but show limited therapeutic effects because osteoblast-mediated bone formation decreases concurrently. We developed a gene therapy strategy for osteoporosis that simultaneously promotes bone formation and suppresses bone resorption by targeting two microRNAs (miRNAs)—miR-214-3p and miR-34a-5p. We modulated the expression of these miRNAs using systemically delivered recombinant adeno-associated viral (rAAV) vectors targeting the bone. rAAV-mediated overexpression of miR-214-3p or inhibition of miR-34a-5p in the skeleton resulted in bone loss in adult mice, resembling osteoporotic bones. Conversely, rAAV-mediated inhibition of miR-214-3p or overexpression of miR-34a-5p reversed bone loss in mouse models for postmenopausal and senile osteoporosis by increasing osteoblast-mediated bone formation and decreasing osteoclast-mediated bone resorption. Notably, these mice did not show any apparent pathological phenotypes in non-skeletal tissues. Mechanistically, inhibiting miR-214-3p upregulated activating transcription factor 4 in osteoblasts and phatase and tensin homolog in osteoclasts, while overexpressing miR-34a-5p downregulated Notch1 in osteoblasts and TGF-β-induced factor homeobox 2 in osteoclasts. In summary, bone-targeting rAAV-mediated regulation of miR-214-3p or miR-34a-5p is a promising new approach to treat osteoporosis, while limiting adverse effects in non-skeletal tissues.

Viruses are minuscule infectious agents that reproduce exclusively within the living cells of an organism and are present in almost every ecosystem. Their continuous interaction with humans poses a significant threat to the survival and well-being of everyone. Apart from the common cold or seasonal influenza, viruses are also responsible for several important diseases such as polio, rabies, smallpox, and most recently COVID-19. Besides the loss of life and long-term health-related issues, clinical viral infections have significant economic and social impacts. Viral enzymes, especially proteases which are essential for viral multiplication, represent attractive drug targets. As a result, screening of viral protease inhibitors has gained a lot of interest in the development of anti-viral drugs. Despite the availability of anti-viral therapeutics, there is a clear need to develop novel curative agents that can be used against a given virus or group of related viruses. This review highlights the importance of yeasts as an in vivo model for screening viral enzyme inhibitors. We also discuss the advantages of yeast-based screening platforms over traditional assays. Therefore, in the present article, we discuss why yeast is emerging as a model of choice for in vivo screening of anti-viral molecules and why yeast-based screening will become more relevant in the future for screening anti-viral and other molecules of clinical importance.

High Strength Self-Compacting Concrete (HSSCC) is known for its various advantages and becoming very common among construction industries. Because of the various advantages of HSSCC, many researchers are working on improving the overall performance of HSSCC. But, since it is quite a relatively new material, shear design guidelines for high strength are not available in major design codes. These guidelines may not even be safe and adequate to use in designing HSSCC beams. The shear behaviour of HSSCC beams differs much from normal SCC beams. Thus, a systematic analysis of the shear behaviour of HSSCC beams is very important. In this experimental program, the shear behaviour of slender HSSCC beams without web reinforcement was studied by casting 27 beams for three mixes having compressive strength 70 MPa, 80 MPa and 90 MPa and without transverse reinforcement. In the present article, the various parameters discussed includes- failure loads of the beams, shear strength and failure angles, cracking patterns and failure modes, the effect of longitudinal steel ratio and shear span to depth (a/d) ratio on the shear strength of beams and load-deflection curves.

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is yet to be controlled worldwide, especially in India. The second wave of coronavirus disease 2019 (COVID-19) led to panic and confusion in India, owing to the overwhelming number of the population that fell prey to this highly infectious virus of recent times. In the second wave of COVID-19, the patients had to fight both the virus and opportunistic infections triggered by fungi and bacteria. Repeated use of steroids, antibiotics, and oxygen masks during the management of severely and critically ill COVID-19 patients nurtured opportunistic infections such as mucormycosis. Despite mucormycosis being a decades-old disease, it has gained notice of its widespread occurrence in COVID-19 patients throughout India. Instances of mucormycosis are usually unearthed in immunocompromised individuals and are caused by the inhalation of filamentous fungi, either from the natural environment or through supportive care units. In the recent outbreak during the second wave of COVID-19 in India, it has been seen to cause secondary infection as it grows along with the treatment of COVID-19. Furthermore, COVID-19 patients with comorbidities such as diabetes were more likely to have the mucormycosis co-infection because of their challenged immune systems’ inability to fight it.Despite the hype, mucormycosis still remains neglected and least studied, which is predominantly due to all focus on diagnostics, vaccine, and therapeutic research. In this review, we emphasize mainly on the association of mucormycosis in COVID-19 patients. We also present the molecular mechanism of mucormycosis for a better understanding of the fungal infections in patients who have recently been infected with SARS-CoV-2. Better understanding of fungal pathogens, immediate diagnosis, and management of the infections are crucial in COVID-19 patients, as high mortalities have been recorded in co-infected patients despite recovery from COVID-19.

Current study was conducted to evaluate the effect of important land uses and soil depth on soil organic carbon pools viz. total organic carbon, Walkley and black carbon, labile organic carbon, particulate organic carbon, microbial biomass carbon and carbon management index (CMI) in the north Western Himalayas, India. Soil samples from five different land uses viz. forest, pasture, apple, saffron and paddy-oilseed were collected up to a depth of 1 m (0–30, 30–60, 60–90 cm). The results revealed that regardless of soil depth, all the carbon pools differed significantly (p

Self-Compacting Concrete (SCC) generally has lesser coarse aggregate contents and also the maximum size of aggregates is limited as compared to Normal Vibrating Concrete (NVC) for the same class of strength. This results in reduced aggregate interlock in Self-Compacting concrete as against NVC, which affects the shear strength of slender beams and thus, SCC might have lower shear strength. In this article, an experimental program that includes six slender beams of High-Strength Self Compacting Concrete (HSSCC) with compressive strength more of than 90 MPa and with different stirrup spacing is presented. Experimental test results of shear strength of HSSCC beams are compared with high strength NVC beams for different stirrups spacing. The results showed the ultimate shear stress of HSSCC beams is lower than NVC beams and an increase in transverse reinforcement index, ρw fy, in HSSCC beams decreases this difference. The results are also compared with different code provisions. Not much work has been done on beams with lower transverse reinforcement index (ρw fy) and in the present work, ρw fy ranges between 0.276 to 0.80.

Introduction: Novel coronavirus disease-2019 pandemic continues to rage the world with enormous economic loss, sickness, and deaths. In management of infectious diseases, nurses play crucial roles in rendering care to patients by risking their own lives, going beyond their training and capacities. Frontline nurses are vulnerable for the development of diagnosable and subclinical psychological problems most often triggered with deaths of colleagues, exposure to the mass scale of deaths, perceived inability to save lives despite best efforts, lack of social supports, shift duties, and working in high-risk environment. Aim: The aim of the study is to assess the depression, anxiety, and stress among nurses working in COVID wards versus non-COVID wards. Materials and Methods: A multicenter observational study was done among 176 frontline nurses from seven different referral government hospitals. Psychological ailments were measured on validated instruments of Depression, Anxiety, and Stress Scale-42 items (DASS-42) and Perceived Stress Scale-10 (PSS-10). Results: Using SPSS version 23.0, data were analyzed using Pearson's Chi-square test, with P < 0.055 being considered statistically significant. On PSS, perceived stress was high in 75% of COVID ward nurses and 67% of non-COVID ward ones. On DASS-42 scale, depression, anxiety, and stress levels among nurses in the COVID-19 wards were 21.64% (n = 21), 32.98% (n = 32), and 20.61% (n = 20) compared to 17.72% (n = 14), 24.05% (n = 19), and 15.18%, (n = 12) in the non-COVID wards, respectively. Conclusions: The amount of reported stress, anxiety, and depression was higher in both groups of nurses, i.e., those working in COVID-19 wards and general wards as compared to general population.

This paper proposes a new method for assigning distribution system (DS) losses to consumers and distributed generations in the framework of a liberalized energy market. The architecture of the proposed method is based on a synthesis of electrical network analytical theory and cooperative game theory (CGT). Weighted shapely value (WSV) is a solution idea that is regarded to be efficient in CGT for n-player games with characteristic function form. By retaining the notion of electrical networks, the axioms of game theory will be preserved while employing the WSV in loss allocation theory. To allocate system losses, the proposed method estimates the average marginal contribution of each participant to all conceivable coalitions and also determines the weight of the marginal contribution of each participant based on its power rating. Rather than a sequential branch-oriented approach, the proposed methodology is node-oriented. Additionally, a majority rule game is used to constrain the population of a coalition without jeopardizing the efficacy of the solutions to achieve a fair allocation of losses. The results of the proposed method are presented and contrasted with those obtained using the conventional pro-rata method and the well-known Shapley value solution concept in CGT.

The pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently the biggest healthcare issue worldwide. This study aimed to develop a monoclonal antibody against SARS-CoV-2 from B cells of recovered COVID-19 patients, which might have beneficial therapeutic purposes for COVID-19 patients. We successfully generated human monoclonal antibodies (hmAbs) against the receptor binding domain (RBD) protein of SARS-CoV-2 using developed hybridoma technology. The isolated hmAbs against the RBD protein (wild-type) showed high binding activity and neutralized the interaction between the RBD and the cellular receptor angiotensin-converting enzyme 2 (ACE2) protein. Epitope binning and crystallography results displayed target epitopes of these antibodies in distinct regions beneficial in the mix as a cocktail. The 3D2 binds to conserved epitopes among multi-variants. Pseudovirion-based neutralization results revealed that the antibody cocktail, 1D1 and 3D2, showed high potency in multiple variants of SARS-CoV-2 infection. In vivo studies showed the ability of the antibody cocktail treatment (intraperitoneal (i.p.) administration) to reduce viral load (Beta variant) in blood and various tissues. While the antibody cocktail treatment (intranasal (i.n.) administration) could not significantly reduce the viral load in nasal turbinate and lung tissue, it could reduce the viral load in blood, kidney, and brain tissue. These findings revealed that the efficacy of the antibody cocktail, 1D1 and 3D2, should be further studied in animal models in terms of timing of administration, optimal dose, and efficacy to mitigate inflammation in targeted tissue such as nasal turbinate and lung.

Candida auris, the youngest Candida species, is known to cause candidiasis and candidemia in humans and has been related to several hospital outbreaks. Moreover, Candida auris infections are largely resistant to the antifungal drugs currently in clinical use, necessitating the development of novel medications and approaches to treat such infections. Following up on our previous studies that demonstrated eugenol tosylate congeners (ETCs) to have antifungal activity, several ETCs (C1-C6) were synthesized to find a lead molecule with the requisite antifungal activity against C. auris. Preliminary tests, including broth microdilution and the MUSE cell viability assay, identified C5 as the most active derivative, with a MIC value of 0.98 g/mL against all strains tested. Cell count and viability assays further validated the fungicidal activity of C5. Apoptotic indicators, such as phosphatidylserine externalization, DNA fragmentation, mitochondrial depolarization, decreased cytochrome c and oxidase activity and cell death confirmed that C5 caused apoptosis in C. auris isolates. The low cytotoxicity of C5 further confirmed the safety of using this derivative in future studies. To support the conclusions drawn in this investigation, additional in vivo experiments demonstrating the antifungal activity of this lead compound in animal models will be needed.

Down syndrome arises from chromosomal non-disjunction during gametogenesis, resulting in an additional chromosome. This anomaly presents with intellectual impairment, growth limitations, and distinct facial features. Positive correlation exists between maternal age, particularly in advanced cases, and the global annual incidence is over 200,000 cases. Early interventions, including first and second-trimester screenings, have improved DS diagnosis and care. The manifestations of Down syndrome result from complex interactions between genetic factors linked to various health concerns. To explore recent advancements in Down syndrome research, we focus on the integration of artificial intelligence (AI) and machine learning (ML) technologies for improved diagnosis and management. Recent developments leverage AI and ML algorithms to detect subtle Down syndrome indicators across various data sources, including biological markers, facial traits, and medical images. These technologies offer potential enhancements in accuracy, particularly in cases complicated by cognitive impairments. Integration of AI and ML in Down syndrome diagnosis signifies a significant advancement in medical science. These tools hold promise for early detection, personalized treatment, and a deeper comprehension of the complex interplay between genetics and environmental factors. This review provides a comprehensive overview of neurodevelopmental and cognitive profiles, comorbidities, diagnosis, and management within the Down syndrome context. The utilization of AI and ML represents a transformative step toward enhancing early identification and tailored interventions for individuals with Down syndrome, ultimately improving their quality of life.

Introduction: Vitiligo is a chronic depigmentary disorder having an autoimmune aetiology. Vitiligo has a severe impact on the quality of life of the individual in our country. Considering the changing socio-economic trend and the arrival of new medicines in the therapeutic armamentarium of dermatologists, there is a need to know whether Vitiligo, which had high social stigmata in the past, still continues to have the same impact on Quality Of Life (QOL). Aim: To find out the correlation of Dermatology Life Quality Index (DLQI) in cases of vitiligo using Vitiligo Extent Tensity Index (VETI) score Materials and Methods: A questionnaire-based cross-sectional study was conducted in Department of Dermatology at Military Hospital, Bareilly, Uttar Pradesh, India, from January 2021 to March 2022. The study involved all patients with vitiligo, aged between 15-60 years including both sexes. The patients were given the DLQI questionnaire and DLQI scores were calculated. The DLQI score was correlated with the VETI score established clinically. The statistical analysis was done using Statistical Package for Social Sciences (SPSS) version 20.0. Results: A total of 108 patients which included 41 males and 67 females were included. The VETI score ranged from 0.5 to 25.7 (mean 5.8±4.187) whereas DLQI score ranged from 1 to 19 (mean 6.73±3.751). Patients with high VETI score (mean was 8.84±7.74) had high DLQI score (11-20), thereby correlating the extent of the quality of life compromised. Conclusion: The VETI positively correlated with the DLQI (r-value=0.476; p-value=0.001). Patients with a high VETI have a severely compromised quality of life.

The ongoing enormous loss of human life owing to Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), has led to a global crisis ranging from the collapse of health – care systems to socio-economic instability. As SARS-CoV-2 is a novel virus, very little information is available from researchers and therefore, a rigorous effort is required to decode its pathogenicity. There are no licenced treatment options available for treating SARS-CoV-2 infections and the development of a new antiviral drug targeting coronavirus cannot happen soon. Consequently, drug repurposing is a promising solution for combating the present pandemic. In this review, we have thoroughly discussed all the proteins encoded by the SARS-CoV-2 genome; their importance in pathogenicity and their potential role in drug discovery. Also, the budding threat of co-infections by other pathogenic microbes has been highlighted. Furthermore, the advances made in the medicinal field for the treatment and prevention of this viral infection is explained. Altogether, this review will provide some insightful discussions about this infectious disease and will meet certain of the knowledge gaps which exist by presenting an exhaustive and extensive scientific report on the ongoing mission for COVID-19 drug discovery.

Chronic exposure to low concentrations of volatile organic compounds (VOCs), such as chlorobenzene, is not being monitored in industrializing countries, although VOC exposure is associated with carcinogenic, organ-toxic, and endocrine-disrupting effects. Current VOC-sensing technologies are inaccessible due to high cost, size, and maintenance or are ineffective due to poor sensitivity or reliability. In particular, marginalized individuals face barriers to traditional prescription VOC treatments due to cost, lack of transportation, and limited access to physicians; thus, alternative treatments are needed. Here, we created a novel cumulative wearable color-changing VOC sensor with a paper-based polydiacetylene sensor array for chlorobenzene. With a single smartphone picture, the sensor displays 14 days of logged chlorobenzene exposure data, interpreted by machine-learning (ML) techniques, including principal component analysis. Further, we explored the efficacy of affordable and accessible treatment options to mitigate a VOC’s toxic effects. Vitamin D and sulforaphane are naturally found in cruciferous vegetables, like broccoli, and can be used to treat chlorobenzene-mediated bone degradation. Our platform combines these components into a smartphone app that photographs the sensor’s colorimetric data, analyzes the data via ML techniques, and offers accessible treatments based on exposure data.