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Background: The outbreak of coronavirus disease 2019 (COVID-19) pandemic is currently an international public health crisis. The Indian government had announced the suspension of colleges indefinitely across India. Students of medical colleges are facing distress due to the abrupt disruption of academic routine. Objectives: The study aimed to assess the prevalence and correlates of stress, anxiety, and depression among undergraduate medical students of a medical college of North India during COVID-19 pandemic. Materials and Methods: A cross-sectional study was conducted among medical students of a medical college in North India. Data were collected via a prevalidated, semi-structured questionnaire consisting of sociodemographic and behavioral questions and three standard psychometric scales, i.e., Perceived Stress Scale, Coronavirus Anxiety Scale, and Major Depression Inventory Scale for assessing stress, anxiety, and depression, respectively. Data were analyzed using the Statistical Package for the Social Sciences (version 27.0; SPSS Inc., Chicago, IL, USA). Results: We reported that majority of students (86.5%) have increased social media usage. The level of stress is moderate (72.5%) to high (19%), and depression is also reported in many students from moderate (11%) to severe (17.8%) levels. A small proportion of students showed anxiety though anxiety scores are significantly higher for males than females and for 1st- and 2nd-year subjects than final-year subjects. More than 60% of students are physically active who also showed a low level of stress, depression, and anxiety than sedentary subjects. Conclusion: Our results show that stress, anxiety, and depression were high in medical students during lockdown. Increased food intake and social media usage were risk factors for developing stress, anxiety, and depression. Physical activity has a positive impact on mental health of the students.

Abstract Lead (Pb) contamination in water poses a significant threat to public health across the globe which requires effective remediation strategies. The main objective of this study is to achieve a removal efficiency of Pb(II) ions from aqueous media using activated Syzygium cumini (java plum) stem biochar. It was prepared by slow pyrolysis at 400 °C after treating the biomass with potassium hydroxide for activation. These biochars were characterized thoroughly (SEM, SEM–EDX, TEM, FTIR, XRD, elemental analyses, and SBET) to conduct a set of batch experiments. The effect of several parameters such as pH, temperature, contact time, adsorbent dosage, initial lead concentrations, and co-existing ions were explored. The decrease in starting lead concentrations from 50 to 10 mg L−1 resulted in improved removal efficiency. The removal effectiveness of activated biochar was 97%, but non-activated biochar was just 19%. Lead adsorption increased considerably as pH increased from 3 to 5. Further, the activated biochar was optimized through various isotherms models, kinetic equations, and thermodynamic analysis. It was observed that the pseudo-second-order kinetic model and Temkin isotherms were the best-fitted models that identify the mechanism of chemisorption and monolayer sorption. According to this study, activated biochar is a promising biosorbent for removing lead from an aqueous solution. Graphical Abstract

Abstract Modern deep learning training procedures rely on model regularization techniques such as data augmentation methods, which generate training samples that increase the diversity of data and richness of label information. A popular recent method, mixup, uses convex combinations of pairs of original samples to generate new samples. However, as we show in our experiments, mixup can produce undesirable synthetic samples, where the data is sampled off the manifold and can contain incorrect labels. We propose $$\zeta $$ ζ -mixup, a generalization of mixup with provably and demonstrably desirable properties that allows convex combinations of $${T} \ge 2$$ T ≥ 2 samples, leading to more realistic and diverse outputs that incorporate information from $${T}$$ T original samples by using a p-series interpolant. We show that, compared to mixup, $$\zeta $$ ζ -mixup better preserves the intrinsic dimensionality of the original datasets, which is a desirable property for training generalizable models. Furthermore, we show that our implementation of $$\zeta $$ ζ -mixup is faster than mixup, and extensive evaluation on controlled synthetic and 26 diverse real-world natural and medical image classification datasets shows that $$\zeta $$ ζ -mixup outperforms mixup, CutMix, and traditional data augmentation techniques. The code will be released at https://github.com/kakumarabhishek/zeta-mixup .

Abstract Pneumonia is a widespread and acute respiratory infection that impacts people of all ages. Early detection and treatment of pneumonia are essential for avoiding complications and enhancing clinical results. We can reduce mortality, improve healthcare efficiency, and contribute to the global battle against a disease that has plagued humanity for centuries by devising and deploying effective detection methods. Detecting pneumonia is not only a medical necessity but also a humanitarian imperative and a technological frontier. Chest X-rays are a frequently used imaging modality for diagnosing pneumonia. This paper examines in detail a cutting-edge method for detecting pneumonia implemented on the Vision Transformer (ViT) architecture on a public dataset of chest X-rays available on Kaggle. To acquire global context and spatial relationships from chest X-ray images, the proposed framework deploys the ViT model, which integrates self-attention mechanisms and transformer architecture. According to our experimentation with the proposed Vision Transformer-based framework, it achieves a higher accuracy of 97.61%, sensitivity of 95%, and specificity of 98% in detecting pneumonia from chest X-rays. The ViT model is preferable for capturing global context, comprehending spatial relationships, and processing images that have different resolutions. The framework establishes its efficacy as a robust pneumonia detection solution by surpassing convolutional neural network (CNN) based architectures.

Healthcare 4.0 is considered the most promising technology for gathering data from humans and strongly couples with a communication system for precise clinical and diagnosis performance. Though sensor-driven devices have largely made our everyday lives easier, these technologies have been suffering from various security challenges. Because of data breaches and privacy issues, this heightens the demand for a comprehensive healthcare solution. Since most healthcare data are sensitive and valuable and transferred mostly via the Internet, the safety and confidentiality of patient data remain an important concern. To face the security challenges in Healthcare 4.0, Web 3.0 and blockchain technology have been increasingly deployed to resolve the security breaches due to their immutability and decentralized properties. In this research article, a Web 3.0 ensemble hybrid chaotic blockchain framework is proposed for effective and secure authentication in the Healthcare 4.0 industry. The proposed framework uses the Infura Web API, Web 3.0, hybrid chaotic keys, Ganache interfaces, and MongoDB. To allow for more secure authentication, an ensemble of scroll and Henon maps is deployed to formulate the high dynamic hashes during the formation of genesis blocks, and all of the data are backed in the proposed model. The complete framework was tested in Ethereum blockchain using Web 3.0, in which Python 3.19 is used as the major programming tool for developing the different interfaces. Formal analysis is carried out with Burrows–Abadi–Needham Logic (BAN) to assess the cybersecurity reliability of the suggested framework, and NIST standard tests are used for a thorough review. Furthermore, the robustness of the proposed blockchain is also measured and compared with the other secured blockchain frameworks. Experimental results demonstrate that the proposed model exhibited more defensive characteristics against multiple attacks and outperformed the other models in terms of complexity and robustness. Finally, the paper gives a panoramic view of integrating Web 3.0 with the blockchain and the inevitable directions of a secured authentication framework for Healthcare 4.0.

Background: Crouch Gait is a fairly common pathological gait adaptation seen in children with Cerebral Palsy, characterized by inadequacy of plantar flexion, knee extension and hip extension in the stance phase of gait. From a kinetic perspective crouch gait severely impairs the ability to generate thrusting forces through the extensor muscles of stance extremity which is necessary for forward progression. Broader goal of this study was to develop and test a wearable robotic exoskeletal device for children with crouch gait, where a knee actuator provides extensor thrust. Artificial Intelligence (AI) models which control the torque output of the knee actuator, needed to be trained to activate, control, or organize the output. Objectives: Rhythmical/repetitive patterns, like the phases of normal gait, needed to be identified in crouch gait, which could be used to train AI models Methodology: A total of 30 children with cerebral palsy were screened of which 7 children aged between 7 and 14 years, who presented with classical crouch gait were recruited. Kinesiological data was collected through accelerometric and goniometric placed bilaterally on thigh, leg, and knee, as well as pressure sensors embedded on to bilateral insoles at the fore-foot and hind foot, while the children walked over a 5 meter course. All sensor data was relayed through an Arduino micro-controller unit for analog to digital conversion and stored on a laptop computer. All Accelerometer and goniometric data was plotted against time on MS Excel to identify specific patterns or motifs. Results: Key kinematic motifs could be identified in crouch gait, which bestowed semblance of rhythmicity, and cyclical pattern, which was difficult to identify under normal observation. Conclusion: Accelerometric, as well as goniometric data suggest the presence of inherent kinematic patterns in the same. These could be used for training AI models for robotic exoskeletal devices.

Abstract The majority of adoptive cellular therapies are produced from peripheral mononuclear cells obtained via leukapheresis and further enriched for the cells of interest (e.g., T cells). Here, we present a first‐of‐its‐kind closed system, which effectively removes ~85% of monocytes and ~88% of platelets, while recovering ~88% of concentrated T cells in a separate output stream, as the leukapheresis sample flows through a microfluidic device at 5 mL/min. The system is driven by a common peristaltic pump, enabled by a novel pressure wave dampener, and operates in a closed bag‐to‐bag configuration, without requiring any specialized, dedicated equipment. When compared to standard density gradient centrifugation on paired samples, the new system demonstrated a 1.5‐fold increase in T cell recovery and a 2‐fold reduction in inter‐sample variability for this separation outcome. The T cell‐to‐monocyte ratio of the leukapheresis sample was increased to 20:1, whereas with density gradient processing it decreased to 2:1. As a result of superior purity and/or gentler processing, T cells enriched by the system showed a 2.7‐times higher fold expansion during subsequent culture, and an overall 3.5‐times higher cumulative yield. This centrifugation‐free and label‐free closed system for enriching lymphocytes could significantly simplify and standardize the manufacturing of life‐saving cellular therapies.

Abstract The geographical location of any region, as well as large-scale environmental changes caused by a variety of factors, invite a wide range of disasters. Floods, droughts, earthquakes, cyclones, landslides, tornadoes, and cloudbursts are all common natural disasters that destroy property and kill people. On average, 0.1% of the total deaths globally in the past decade have been due to natural disasters. The National Disaster Management Authority (NDMA), a branch of the Ministry of Home Affairs, plays an important role in disaster management in India by taking responsibility for risk mitigation, response, and recovery from all natural and man-made disasters. This article presents an ontology-based disaster management framework based on the NDMA’s responsibility matrix. This ontological base framework is named as Disaster Management Ontology (DMO). It aids in task distribution among necessary authorities at various stages of a disaster, as well as a knowledge-driven decision support system for financial assistance to victims. In the proposed DMO, ontology has been used to integrate knowledge as well as a working platform for reasoners, and the Decision Support System (DSS) ruleset is written in Semantic Web Rule Language (SWRL), which is based on the First Order Logic (FOL) concept. In addition, OntoGraph, a class view of taxonomy, is used to make taxonomy more interactive for users.

Background and Aims: Sepsis-induced immunosuppression appears to be reversible with immunomodulatory drugs. Mycobacterium indicus pranii (MIP) stimulates the Th1 type of immune response. This systematic review and meta-analysis of randomised controlled trials (RCTs) was aimed to find out if MIP is effective at improving clinical outcomes in sepsis patients. Methods: The databases (PubMed, Google Scholar, Web of Science, and Cochrane Library), along with preprint servers until June 2023, were searched. The methodology was evaluated using the 'Cochrane Collaboration risk of bias-2 tool' for RCT. The study included patients more than 18 years of age with sepsis within 48 h of first organ dysfunction. The primary outcome was 28-day mortality, and secondary outcomes were the length of stay in the intensive care unit (ICU), days on vasopressor support, ventilator-associated pneumonia (VAP), secondary infections, catheter-related bloodstream infections (CRBSI), and the delta sequential organ failure assessment (SOFA) score. Results: The meta-analysis included two studies with 252 participants. In a pooled analysis, mortality in the MIP group was 43% lower than in the control (RR: 0.57, 95%CI: 0.33–1); however, this difference was statistically not significant. We observed the days on a vasopressor day (standardised mean difference [SMD]: 0.38; 95%CI: −1.20 to 0.44), length of ICU stay (SMD: 0.46; 95%CI: −1.44 to 0.51), secondary infection (RR: 0.75; 95%CI: 0.19–3.01), VAP (RR: 0.6; 95%CI: 0.28–1.56), CRBSI (RR: 0.97, 95%CI: 0.14–6.98), delta SOFA score (SMD: 0.88, 95%CI: −1.66 to − 0.10) between the two groups. Conclusions: Our findings observed preliminary evidence in the trends for a positive association of MIP with better outcomes in sepsis patients.

This paper presents a flexible virtual inertia and damping control strategy for a virtual synchronous generator (VSG) for the effective utilization of energy storage. Due to their low inertia and low kinetic energy, power electronics-based renewable energy sources are becoming more and more prevalent, which poses major dependability issues for the grid. However, the typical synchronous generator has the ability to adjust to changes in the grid frequency and voltage thanks to load frequency management and an automatic voltage regulator. So, emulating the synchronous machine characteristics in the power electronics-based converter system increases the stability of the grid and further increases the renewable energy penetration in the grid. However, VSG power and frequency oscillates when a sudden disturbance in the power demand occurs in the distributed generation system. This article focuses on minimizing the frequency oscillations as well as power oscillations of VSG during transient conditions or disturbances. Initially, the impact of varying inertia and damping on the active power control loop of VSG is investigated. In addition, the parameters of the VSG are designed in order to minimize the requirement of discharge/charge from the energy storage. So, in this paper, a control strategy with flexible virtual inertia and damping coefficient is designed for optimizing the energy storage unit to support frequency stability. The proposed control method is verified in the developed experimental prototype. In addition, proposed control algorithm is compared with the currently available adaptive control approaches like adaptive inertia control (adaptive $J$ control) and alternating inertia and damping (alternating $J$ and $D_{P}$ control) based VSG and observed that the energy storage requirement and the oscillations in the active power and frequency are minimum in the proposed control method as compared to the other two methods.

Energy consumption has become an essential factor in designing modern computer system architecture. Because of physical limits, the termination of Moore’s law and Dennard’s scaling has forced the computer design community to investigate new approaches to meet the requirements for computing resources. Approximate computing has emerged as a promising method for reducing energy consumption while trading a controllable quality loss. This paper asserts that an approximated cache coherence protocol preserves overall energy for computation. We can approximate the cache coherence protocol by adding approximated cache lines to a certain level without hindering the output. This paper introduces an enhanced approximated version of the MSI (Modified Shared Invalid) cache coherence protocol MSI-A (Modified Shared Invalid-Approx). We have verified MSI-A and MSI by employing LTL specifications in the NuSMV model checker. To illustrate the benefits of MSI-A, we have added DTMC (Discrete-Time Markov Chain) with PCTL (Probabilistic Computational Tree Logic). Although the PCTL proves the theory of approximation, we have also simulated the MSI-A in the TEJAS hardware simulator on PARSEC 3.0 to investigate the energy gains and cycle gains of MSI-A in varied applications. The cache lines considered to be approx are between 10 and 30 percent. Each application benefited from approximation according to its nature, and VIPS has indicated a total energy gain of 30.18 percent.

In this paper, a wind energy conversion system (WECS) is presented for the electrification of rural areas with wind energy availability. A three-phase AC-DC converter based on a bridgeless Cuk converter is used for power extraction from the permanent magnet synchronous generator (PMSG). The bridgeless topology enables the elimination of the front-end diode bridge rectifier (DBR). Moreover, the converter has fewer components, simple control, and high efficiency, making it suitable for a small-scale WECS. A squirrel cage induction motor (SCIM) is used to emulate a MOD-2 wind turbine to implement the PMSG-based WECS. A direct-drive eight-pole PMSG is used in this study; thus, a low-input-voltage system is designed. The converter is designed to operate in the discontinuous inductor current mode (DICM) for inherent power factor correction (PFC) and the maximum power point tracking (MPPT) is achieved through the tip-speed ratio (TSR) following. The performance of the developed system is analyzed through simulation, and a 500 W hardware prototype is developed and tested in different wind speed conditions.

Abstract According to a report published by Business Wire, the market value of e-commerce reached US$ 13 trillion and is expected to reach US$ 55.6 trillion by 2027. In this rapidly growing market, product and service reviews can influence our purchasing decisions. It is challenging to manually evaluate reviews to make decisions and examine business models. However, users can examine and automate this process with Natural Language Processing (NLP). NLP is a well-known technique for evaluating and extracting information from written or audible texts. NLP research investigates the social architecture of societies. This article analyses the Amazon dataset using various combinations of voice components and deep learning. The suggested module focuses on identifying sentences as ‘Positive‘, ‘Neutral‘, ‘Negative‘, or ‘Indifferent‘. It analyses the data and labels the ‘better’ and ‘worse’ assumptions as positive and negative, respectively. With the expansion of the internet and e-commerce websites over the past decade, consumers now have a vast selection of products within the same domain, and NLP plays a vital part in classifying products based on evaluations. It is possible to predict sponsored and unpaid reviews using NLP with Machine Learning. This article examined various Machine Learning algorithms for predicting the sentiment of e-commerce website reviews. The automation achieves a maximum validation accuracy of 79.83% when using Fast Text as word embedding and the Multi-channel Convolution Neural Network.

Biodegradable polymer nanocomposites have gained attention in recent years owing to their antimicrobial activity. The article summarizes recent developments in improving antimicrobial, mechanical and barrier properties of biodegradable polymers chitosan, cellulose, gelatin and starch. ZnO, TiO2, reduced graphene oxide and silver reinforced biodegradable polymer nanocomposites exhibit improved tensile strength due to intercalation of nanomaterials into the polymer matrices. Silver nanoparticle reinforced polymer nanocomposites have shown significant antimicrobial properties against various strains of bacteria and fungi. Although, development of antimicrobial nanomaterials embedded packaging films has helped to augment shelf-life of food, leakage of nanomaterials into the packaged food remains an area of concern.

Reducing costs associated with inventory is the primary goal of conventional inventory models like the models of economic order quantity and economic production quantity. However, these models fall short of addressing defective goods or revising them. Imperfections in the manufacturing process result in flawed products alongside the final goods. To convert these flawed components into finished goods, rework is necessary. In manufacturing and reworking process produces carbon emissions which are harmful for the earth. To determine the optimal quantity for a single product manufactured in a single-stage manufacturing system that yields partially defective products that are reworked in the same cycle, is determined in real-life situations, where the inventory characteristics and objectives are not exact. Such a type of uncertainty may be characterized by fuzzy numbers. A pentagonal fuzzy number has been used to define the cost parameters. Due to fuzzy parameters, the model becomes a fuzzy quantity, and it is defuzzied by the sign distance method. this article formulates a model of manufacturing inventories with planned backorders. Furthermore, a closed form for the inventory’s system total cost is determined, and a range of actual values for defective products for which an appropriate method exists is also provided. A proper mathematical model is created to accomplish the goal, and the manufacturing lot size that reduces the overall cost is determined. The ideal amount of a production batch to reduce total cost is established in to attain this goal using an appropriate mathematical model. While formulating and solving the relevant model, the necessary and sufficient conditions for a single globally optimal solution have been determined. Examples used as visualizations are given and confirmed by data.

In this study, the functions and threats of Suraha Tal Wetland are identified by the stated preference method and weightage is given according to their rank. The objective of the study is to determine the total economic value of Suraha Tal Wetland. The direct value can be drawn from the market price and from a survey of the stakeholders. Suraha Tal Wetland is also famous for the presence of the Jai Prakash Narayan Birds Sanctuary, which makes it a biodiversity-enriched area. The indirect value has been drawn from a review of the literature on Suraha Tal Wetland and the relevance of this literature is justified through the comprehensive meta-analysis (CMA) software. The total valuation of the wetland has been calculated. The paper concludes with suggestions for a few management strategies for better wetland management.

The present experimental investigation aims to analyse the effect of various machining parameters, such as pulse peak current (Ion), pulse on time (Ton), pulse off time (Toff) and spark voltage (SV) on the surface roughness (SR) and material removal rate(MRR) by using continuous traveling of both wire electrode (i.e. brass wire and zinc-coated brass wire). The present work also analyses the effect of types of wires, such as brass wire and zinc-coated brass wire used during machining of Titanium alloy (Ti-6Al-4V) on Surface roughness (SR) and material removal rate (MRR). This work studies the correlation between various response parameter such SR and MRR by using same machining parameter by for both wires.

ABSTRACTCr(VI) compounds have important industrial applications and are used in various sectors like tanning, chrome plating, anti-corrosion agents and wood preservation. The Cr(VI) contamination in the wastewater is generally due to several natural and anthropogenic sources. Anthropogenic activities like several industrial operations play a major role in the Cr(VI) contamination in the aquatic ecosystem. Cr(VI) well-known toxic metal ion and its exposure in humans causes several health issues. Cr(VI) enters the cells and gradually reduces into a lower oxidation state and generates oxidative stress in the cell which damages cell organelles. The Cr(VI) mediated genotoxicity has been described as damaging the DNA base pairing, sugar-phosphate backbone, histone modification and chromosomal damage. Water and wastewater must be treated to remove Cr(VI) due to its high toxicity. There are several, physiochemical methods used for Cr(VI) remediation but these approaches are expensive and produce hazardous sludge during the treatment process. Therefore, a suitable environmentally friendly and effective Cr(VI) removal approach is urgently needed. Microbial removal of Cr(VI) is considered an eco-friendly and cost-effective process. In this, the authors focused on sources, genotoxicity and microbial remediation approaches of Cr(VI).

BackgroundAmid all public health measures to contain COVID-19, the most challenging has been how to break the transmission chain. This has been even more challenging in low- and middle-income countries (LMICs). A public health emergency warrants a public health perspective, which comes down to prevention. Rapid mass testing has been advocated throughout the pandemic as a way to promptly deal with asymptomatic infections, but its usefulness in LMICs is yet to be fully understood. ObjectiveThe study objectives of this paper are to (1) investigate the impact of the different rapid mass testing options for SARS-CoV-2 that have been delivered at point of care in LMICs and (2) evaluate the diagnostic safety (accuracy) of rapid mass testing for SARS-CoV-2 in LMICs. MethodsThis review will systematically search records in PubMed, EBSCOhost, Cochrane library, Global Index Medicus COVID-19 Register, and Scopus. Records will be managed using Mendeley reference manager and SWIFT-Review. Risk of bias for randomized controlled trials will be assessed using the RoB 2 assessment tool, while nonrandomized interventions will be assessed using the tool developed by the Evidence Project. A narrative approach will be used to synthesize data under the first objective, and either a meta-analysis or synthesis without meta-analysis for the second objective. Tables, figures, and textual descriptions will be used to present findings. The overall body of evidence for the first objective will be assessed using the Grading of Recommendations Assessment, Development, and Evaluation–Confidence in the Evidence from Reviews of Qualitative research (GRADE-CERQual) approach, and for the second objective using GRADE. ResultsThe screening of records has been finalized. We hope to finalize the synthesis by the end of February 2023 and to prepare the manuscript for publication by April 2023. The study will be reported in accordance with standard guidelines for the reporting of systematic reviews. Review results will be disseminated through conferences and their peer-reviewed publication in a relevant journal. ConclusionsThis review highlights the role of a preventive approach in infection control using rapid mass testing. It also flags the overriding need to involve users and providers in the evaluation of such tests in the settings for which they are intended. This will be the first review to the best of our knowledge to generate both qualitative and quantitative evidence regarding rapid mass testing specific to LMICs. Trial RegistrationPROSPERO CRD42022283776; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=283776 International Registered Report Identifier (IRRID)PRR1-10.2196/41132

Abstract Automated machine learning approaches to skin lesion diagnosis from images are approaching dermatologist-level performance. However, current machine learning approaches that suggest management decisions rely on predicting the underlying skin condition to infer a management decision without considering the variability of management decisions that may exist within a single condition. We present the first work to explore image-based prediction of clinical management decisions directly without explicitly predicting the diagnosis. In particular, we use clinical and dermoscopic images of skin lesions along with patient metadata from the Interactive Atlas of Dermoscopy dataset (1011 cases; 20 disease labels; 3 management decisions) and demonstrate that predicting management labels directly is more accurate than predicting the diagnosis and then inferring the management decision ( $$13.73 \pm 3.93\%$$ 13.73 ± 3.93 % and $$6.59 \pm 2.86\%$$ 6.59 ± 2.86 % improvement in overall accuracy and AUROC respectively), statistically significant at $$p < 0.001$$ p < 0.001 . Directly predicting management decisions also considerably reduces the over-excision rate as compared to management decisions inferred from diagnosis predictions (24.56% fewer cases wrongly predicted to be excised). Furthermore, we show that training a model to also simultaneously predict the seven-point criteria and the diagnosis of skin lesions yields an even higher accuracy (improvements of $$4.68 \pm 1.89\%$$ 4.68 ± 1.89 % and $$2.24 \pm 2.04\%$$ 2.24 ± 2.04 % in overall accuracy and AUROC respectively) of management predictions. Finally, we demonstrate our model’s generalizability by evaluating on the publicly available MClass-D dataset and show that our model agrees with the clinical management recommendations of 157 dermatologists as much as they agree amongst each other.

The pandemic of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has affected millions of people and claimed thousands of lives. Starting in China, it is arguably the most precipitous global health calamity of modern times. The entire world has rocked back to fight against the disease and the COVID-19 vaccine is the prime weapon. Even though the conventional vaccine development pipeline usually takes more than a decade, the escalating daily death rates due to COVID-19 infections have resulted in the development of fast-track strategies to bring in the vaccine under a year’s time. Governments, companies, and universities have networked to pool resources and have come up with a number of vaccine candidates. Also, international consortia have emerged to address the distribution of successful candidates. Herein, we summarize these unprecedented developments in vaccine science and discuss the types of COVID-19 vaccines, their developmental strategies, and their roles as well as their limitations.

Abstract The prevalent use of the diode bridge rectifier in three phase wind energy conversion systems decreases the efficiency of the power generation. In standalone wind energy generation, bridgeless converters can address this problem by eliminating the front‐end diode bridge rectifier. This work presents a novel single‐stage three‐phase bridgeless AC to DC c̀uk converter for wind energy conversion systems. The proposed converter is designed to work in discontinuous inductor current mode because of its inherent power factor correction and less harmonic distortion. Using body‐diodes of the MOSFETs per phase component reduction is also done in this converter. A unique maximum power point tracking technique is developed for this converter using three‐phase voltages and currents, instead of the DC link states. A wind energy emulation system using a DC motor and self excited induction generator is used to test the laboratory prototype of the proposed converter.

Albeit, all forms of tuberculosis (TB) are endemic in India, spinal intramedullary TB and tubercular longitudinally extensive transverse myelitis (LETM) is deemed extremely rare. With recent advances in the field of neurology, autoimmune astrocytopathy (neuromyelitis optica spectrum disorders, NMOSD), myelin-oligodendrocyte glycoprotein associated encephalomyelitis (MOG-EM), metabolic myelopathy, connective tissue diseases and viral infections have gained considerable focus in the list of differentials of LETM whereas tubercular association is often forgotten. This report presents a rare case of acute transverse myelopathy which unveiled previously undiagnosed pulmonary tuberculosis in an adult rural Indian male. The patient responded well to anti-tubercular therapy and corticosteroids. Exact pathogenesis of LETM in TB remains elusive. Association of TB with MOG-EM has been one of the recent hot-cakes. However, an ill-defined immune-inflammatory response to the infectious agent is the likely cause of tubercular LETM. Hence, the primary care physicians who are the first medical contacts of acute LETM cases and in most cases due to diagnostic dilemma there is an unavoidable delay in accurate diagnosis and initiation of therapy. Primary care doctors should nurture a high index of suspicion to diagnose this potentially lifetime-debilitating yet absolutely treatable clinical condition i.e. tubercular LETM.

Coughing analysis stays a region that has gotten meager consideration from AI scientists. This can be credited to a few factors, for example, wasteful auxiliary frameworks, high costs in getting databases, or trouble in building classifiers. The current paper classifies and audits the advancement on coughing sound investigation, AI models, and the information assortment strategies through IoT (Internet of Things) for the grouping of pulmonary sicknesses. Moreover, it proposes a Multi-layered Convolutional Neural Network (Deep Convolutional Neural Network-DCNN) for the arrangement of eight pneumonic infections. The DCNN utilizes otherworldly highlights, cepstral coefficients, chroma highlights, and spectrograms from coughing sound for preparing. To test the viability of the model, a similar report with four standard models was directed on a database of 112 patients gathered from a pediatric office in India through a cloud server and wearable electronic sensors. Results demonstrated that the proposed model accomplished an accuracy of 0.4 on the test segment, which was practically equivalent to recent models proposed in the writing overviewed.

This investigation additionally recognizes partner cooperation issue and furthermore to moderate the real issue through subjective and quantitative appraisal of riverine wetland. Considering a floodplain wetland in rustic West Bengal, the concentration was extended to perceive the type of wetland capacities as per the idea of individuals’ contribution by group examination. Be that as it may, NDVI was connected to ponder the total weeds condition into the wetland to decide the connection between Water Quality Index (WQI) with Normalized Difference Vegatation Index (NDVI) and its impact on valuation. In ANOVA, which is computed by MINITAB programming, centrality level was lower than 0.05 for each case.

Recently, substituted 1,8-dioxodecahydroacridine derivatives have been investigated and found to possess a wide variety of biological and pharmacological activities. Two of these biologically relevant N-heterocyclic scaffolds, 2-(9-(4-methoxyphenyl)-3,3,6,6-tetramethyl-1,8-dioxo-1,2,3,4,5,6,7,8- -octahydroacridin-10(9H)-yl)succinic acid (MTDOSA) and 2-(3,3,6,6-tetramethyl- 9-(4-nitrophenyl)-1,8-dioxo-1,2,3,4,5,6,7,8-octahydroacridin-10(9H)-yl)- succinic acid (NTDOSA), have been studied in ground and first excited state using DFT method employing B3LYP/6-311++G(d,p) level of theory. Quantum chemical calculations of geometrical structure and vibrational wavenumbers of MTDOSA and NTDOSA were carried out using DFT method. The experimental FT-IR spectra of the compounds were recorded in the range 4000–400 cm-1 and comprehensively interpreted on the basis of potential energy distribution. The global reactivity descriptors are calculated and discussed. Moreover, 1H- and 13C-NMR spectral data have been calculated using the gauge independent atomic orbital method and compared with experimental spectra. The docking studies reveal that the compounds MTDOSA and NTDOSA have strong binding affinity toward the target protein 5KLH. Thus, the compounds have a possible use as an antileishmanial drug.

Nowadays, finding genetic components and determining the likelihood that treatment would be helpful for patients are the key issues in the medical field. Medical data storage in a centralized system is complex. Data storage, on the other hand, has recently been distributed electronically in a cloud-based system, allowing access to the data at any time through a cloud server or blockchain-based ledger system. The blockchain is essential to managing safe and decentralized transactions in cryptography systems such as bitcoin and Ethereum. The blockchain stores information in different blocks, each of which has a set capacity. Data processing and storage are more effective and better for data management when blockchain and machine learning are integrated. Therefore, we have proposed a machine-learning–blockchain-based smart-contract system that improves security, reduces consumption, and can be trusted for real-time medical applications. The accuracy and computation performance of the IoHT system are safely improved by our system.

Summary StatementLeishmania secretes over 151 proteins during in vitro cultivation. Cellular functions of one such novel protein: mevalonate kinase is discussed here; signifying its importance in Leishmania infection.Visceral Leishmaniasis is a persistent infection, caused by Leishmania donovani in Indian subcontinent. This persistence is partly due to phagocytosis and evasion of host immune response. The underlying mechanism involves secretory proteins of Leishmania parasite; however, related studies are meagre. We have identified a novel secretory Leishmania donovani glycoprotein, Mevalonate kinase (MVK), and shown its importance in parasite internalization and immuno-modulation. In our studies, MVK was found to be secreted maximum after 1 h temperature stress at 37°C. Its secretion was increased by 6.5-fold in phagolysosome-like condition (pH ~5.5, 37°C) than at pH ~7.4 and 25°C. Treatment with MVK modulated host immune system by inducing interleukin-10 and interleukin-4 secretion, suppressing host’s ability to kill the parasite. Peripheral blood mononuclear cell (PBMC)-derived macrophages infected with mevalonate kinase-overexpressing parasites showed an increase in intracellular parasite burden in comparison to infection with vector control parasites. Mechanism behind the increase in phagocytosis and immunosuppression was found to be phosphorylation of mitogen-activated protein (MAP) kinase pathway protein, Extracellular signal-regulated kinases-1/2, and actin scaffold protein, cortactin. Thus, we conclude that Leishmania donovani Mevalonate kinase aids in parasite engulfment and subvert the immune system by interfering with signal transduction pathways in host cells, which causes suppression of the protective response and facilitates their persistence in the host. Our work elucidates the involvement of Leishmania in the process of phagocytosis which is thought to be dependent largely on macrophages and contributes towards better understanding of host pathogen interactions.