Search

Repositioning of FDA approved/clinical phase drugs has recently opened a new opportunity for rapid approval of drugs, as it shortens the overall process of drug discovery and development. In previous studies, we predicted the possibility of better activity profiles of flavopiridol, the FDA approved orphan drug with better fit value 2.79 using a common feature pharmacophore model for anti-adipogenic compounds (CFMPA). The present study aimed to investigate the effect of flavopiridol on adipocyte differentiation and to determine the underlying mechanism. Flavopiridol inhibited adipocyte differentiation in different cell models like 3T3-L1, C3H10T1/2, and hMSCs at 150 nM. Flavopiridol was around 135 times more potent than its parent molecule rohitukine. The effect was mediated through down-regulation of key transcription factors of adipogenesis i.e. Peroxisome proliferator-activated receptor gamma (PPARγ), CCAAT/enhancer-binding protein alpha (C/EBPα), and their downstream targets, including adipocyte protein −2 (aP2) and fatty acid synthase (FAS). Further, results revealed that flavopiridol arrested the cell cycle in G1/S phase during mitotic clonal expansion by suppressing cell cycle regulatory proteins i.e. Cyclins and CDKs. Flavopiridol inhibited insulin-stimulated signalling in the early phase of adipocyte differentiation by downregulation of AKT/mTOR pathway. In addition, flavopiridol improved mitochondrial function in terms of increased oxygen consumption rate (OCR) in mature adipocytes. In the mouse model of diet-induced obesity, flavopiridol attenuated obesity-associated adipose tissue inflammation and improved serum lipid profile, glucose tolerance as well as insulin sensitivity. In conclusion, the FDA approved drug flavopiridol could be placed as a potential drug candidate for the treatment of cancer and obesity comorbid patients.

Abstract The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision spaces. To reach target properties efficiently, these platforms are increasingly paired with intelligent experimental design. However, current optimizers show limitations in maintaining sufficient exploration/exploitation balance for problems dealing with multiple conflicting objectives and complex constraints. Here, we devise an Evolution-Guided Bayesian Optimization (EGBO) algorithm that integrates selection pressure in parallel with a q-Noisy Expected Hypervolume Improvement (qNEHVI) optimizer; this not only solves for the Pareto Front (PF) efficiently but also achieves better coverage of the PF while limiting sampling in the infeasible space. The algorithm is developed together with a custom self-driving lab for seed-mediated silver nanoparticle synthesis, targeting 3 objectives (1) optical properties, (2) fast reaction, and (3) minimal seed usage alongside complex constraints. We demonstrate that, with appropriate constraint handling, EGBO performance improves upon state-of-the-art qNEHVI. Furthermore, across various synthetic multi-objective problems, EGBO shows significative hypervolume improvement, revealing the synergy between selection pressure and the qNEHVI optimizer. We also demonstrate EGBO’s good coverage of the PF as well as comparatively better ability to propose feasible solutions. We thus propose EGBO as a general framework for efficiently solving constrained multi-objective problems in high-throughput experimentation platforms.

Abstract Gut microbiome dysbiosis contributes to the pathophysiology of both gestational diabetes mellitus (GDM) and its associated adverse outcomes in the woman and offspring. Even though GDM prevalence, complications, and outcomes vary among different ethnic groups, limited information is available about the influence of ethnicity on gut microbiome dysbiosis in pregnancies complicated by GDM. This pilot prospective cohort study examined the impact of ethnicity on gut dysbiosis in GDM among three Asian ethnic groups (Chinese, Malay, Indian) living in Singapore, and investigated the potential modulatory roles of diet and lifestyle modifications on gut microbiome post-GDM diagnosis. Women with GDM (n = 53) and without GDM (n = 16) were recruited. Fecal samples were collected at 24–28- and 36–40-weeks’ gestation and analyzed by targeted 16S rRNA gene-based amplicon sequencing. Permutational multivariate analysis of variance (PERMANOVA) analysis was performed to evaluate differences between groups. Differentially abundant taxa were identified by DeSeq2 based analysis. Functional prediction was performed using the phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt2). Among women with GDM, gut microbiome from different ethnicities harbored common microbial features. However, among those without GDM, there was contrasting microbiome composition between ethnic groups. Microbial members such as Collinsella, Blautia, Ruminococcus, Ruminococcus gnavus, Ruminococcus torques, and Eubacterium hallii groups were differentially enriched (p

Tropical fasciolosis is one of the neglected diseases affecting a large proportion of dairy animals in developing countries like India and its economic assessment has revealed huge production losses in affected animals. Reliability of the traditional diagnostic methods lacking sensitivity in prepatent and chronic cases stipulates early and accurate diagnosis. Serodiagnosis is a fast and reliable diagnostic approach for fasciolosis and improved sensitivity, specificity, and accuracy have been obtained with the purification and use of cysteine proteinases as antigens even in naturally infected animals. The present study has been designed to standardize the cysteine proteinase-based ELISA for the assessment of serological prevalence of tropical fasciolosis in dairy animals of Punjab, India. The study highlights the use of cysteine proteinase over coprological approaches for early detection of fasciolosis in dairy animals to minimize the production losses in dairy animals of the state.

This paper studies a 2D, spring-loaded inverted pendulum (SLIP) passive dynamic biped with two degrees of freedom (stance leg only) to find the existence of symmetric periodic walking and jogging gaits. These gaits are differentiated by vertical ground reaction force (vGRF) patterns. The double-support phase is considered to be instantaneous, and jogging has no airborne phase. We consider the initial leg compression an additional model parameter compared to earlier studies with symmetric gaits. Different gaits are obtained through numerical optimization with various combinations of model parameters -- initial leg compression, spring stiffness, and touchdown angle; with the gait speed as the resultant parameter. Initial compression of the leg spring is found to be an important parameter in transitioning between walking and jogging gaits. This approach can capture the vertical ground reaction force and center of mass (CoM) trajectories of human gaits for both walking and jogging while providing additional control over peak vGRF and the amplitude of CoM oscillation. In comparison, existing SLIP walking models employ an asymmetric single support phase (SSP), and jogging gaits incorporate an aerial phase. Overall, we present a common approach to finding both slow walking and jogging gaits without significant changes in resulting gait speeds.

The SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus-2) virus and the resulting COVID-19 pandemic have had devastating and lasting impact on the global population. Although the main target of the disease is the respiratory tract, clinical outcomes, and research have also shown significant effects of infection on other organ systems. Of interest in this review is the effect of the virus on the cardiovascular system. Complications, including hyperinflammatory syndrome, myocarditis, and cardiac failure, have been documented in the context of COVID-19 infection. These complications ultimately contribute to worse patient outcomes, especially in patients with pre-existing conditions such as hypertension, diabetes, or cardiovascular disease (CVD). Importantly and interestingly, reports have demonstrated that COVID-19 also causes myocardial injury in adults without pre-existing conditions and contributes to systemic complications in pediatric populations, such as the development of multisystem inflammatory syndrome in children (MIS-C). Although there is still a debate over the exact mechanisms by which such complications arise, understanding the potential paths by which the virus can influence the cardiovascular system to create an inflammatory environment may clarify how SARS-CoV-2 interacts with human physiology. In addition to describing the mechanisms of disease propagation and patient presentation, this review discusses the diagnostic findings and treatment strategies and the evolution of management for patients presenting with cardiovascular complications, focusing on disease treatment and prevention.

Introduction: India is now the diabetes capital of the world. The rising burden of Gestational Diabetes Mellitus (GDM) adds to the existing diabetes burden. Euglycemia is achieved once the baby is delivered. The first line of management of GDM is mainly through lifestyle modification with diet and physical activity. There is a dearth of information from Lucknow city about how well pregnant women with GDM adhere to dietary changes and exercise recommendations, or how counselling helps them deal with their GDM. Aim: To assess the self-care behaviour related to diet, physical activity, and Self-Monitoring of Blood Glucose (SMBG) of pregnant women diagnosed with GDM. Materials and Methods: A total of 188 pregnant women diagnosed with GDM were selected for present quasi-experimental study. The study participants were recruited from the antenatal Outpatient Department (OPD) at the Department of Obstetrics and Gynaecology of Queen’s Mary Hospital, King George Medical University (KGMU), Lucknow, India. The total study duration was from November 2019 to November 2022. Pregnant women diagnosed with GDM according to Diabetes in Pregnancy Study Group in India (DIPSI) criteria, up to 28 weeks of gestation, who gave their written consent to participate in the study and were living within a 15-kilometer radius of the study Institute, were included in the study. One group of the study participants received one-to-one counselling, an individualised diet plan, along with usual GDM care, and the other group received usual GDM care during their antenatal visits. The summary of diabetes self-care was adapted to assess adherence to the recommended dietary and physical activity modifications, as well as self-monitoring of blood glucose. Data were analysed using R software version 4.1.1 (R Core Team, 2021). All categorical data were presented using frequency and percentages. The comparison of baseline demographic and clinical parameters of pregnant women between the two groups was done using the Chi-square test or Fisher’s-exact test for categorical observations based on the expected frequency. The independent sample t-test or Mann-whitney U test for continuous measurements was used after checking the normality assumption using the Shapiro-wilk test. The change in scores of general diet, specific diet, physical activity, and SMBG throughout all follow-ups was assessed by repeated measures Analysis of Variance (ANOVA). The change from the first follow-up visit to subsequent follow-up visit was compared using a mixed linear model with follow-up visits. The p-value was considered significant at a 5% level of significance for all comparisons. Results: The mean age of the pregnant women with GDM was 27.6±3.7 years in the intervention group and 27.9±3.9 years in the control group (p=0.451). The pregnant women with GDM in the two groups did not show significant differences in terms of socio-demographic variables such as age, religion, education, socio-economic status, occupation, type of family, and family history of diabetes mellitus. A significant difference was observed in the dietary and physical activity scores between the two groups of pregnant women with GDM. However, no difference was observed in the case of self-monitoring of blood glucose scores of the two groups. Conclusion: The study concluded that counseling plays an important role in helping pregnant women with GDM adhere to the recommended dietary modifications and physical activity.

This paper considers the change point detection problem under dependent samples. In particular, we provide performance guarantees for the MMD-CUSUM test under exponentially $\alpha$, $\beta$, and fast $\phi$-mixing processes, which significantly expands its utility beyond the i.i.d. and Markovian cases used in previous studies. We obtain lower bounds for average-run-length ($ {\mathtt {ARL}}$) and upper bounds for average-detection-delay ($ {\mathtt {ADD}}$) in terms of the threshold parameter. We show that the MMD-CUSUM test enjoys the same level of performance as the i.i.d. case under fast $\phi$-mixing processes. The MMD-CUSUM test also achieves strong performance under exponentially $\alpha$/$\beta$-mixing processes, which are significantly more relaxed than existing results. The MMD-CUSUM test statistic adapts to different settings without modifications, rendering it a completely data-driven, dependence-agnostic change point detection scheme. Numerical simulations are provided at the end to evaluate our findings.

Background & objectives: In urban areas, upsurge in population has resulted in more breeding sites for malaria vectors, and hence this scenario potentially undermine malaria elimination and control programs. The change in land use due to urbanization may result in the presence and distribution of malaria vectors. Understanding potential malaria vectors is essential for current and future malaria transmission control strategies. This study investigated the effects of rapid urbanization on malaria vectors An. culicifacies s.l. and An. stephensi L. in Ghaziabad district. Methods: Ghaziabad district which presents several levels of urbanization was selected for this study. Entomological investigations were conducted seasonally from 2014-2016 in the rural, urban, and peri-urban regions. Vector incrimination study was done using ELISA (confirmation by PCR) on suspected Anopheles vectors viz. An. culicifacies, An. stephensi, An. annularis and An. subpictus. Results: An. culicifacies showed alteration in distribution influenced by rural and agricultural land whereas An. stephensi was found to be influenced by artificial habitats and population growth. Interpretation & conclusion: The study also confirms the association between the abundance of malaria vectors and land use change.

Background: Homemakers are the backbones of families, but in rural India, females suffer from many musculoskeletal problems due to excessive workload in their houses. The objective of the present study is to compare body composition parameters as predictors of low back pain (LBP) in nonworking rural homemakers of North India. Materials and Methods: The study was conducted among 296 homemakers from rural areas of Lucknow district in Uttar Pradesh. Details of LBP and body composition parameters (body mass index, body fat, visceral fat) were taken. Results: The prevalence of LBP among homemakers was found to be 15.54%. BMI was found to be a better predictor of LBP than body fat and visceral fat. The risk of LBP is 7.24 times higher in BMI ≥23 than in women with BMI

Autonomous smart waste management services are becoming an essential component of sustainable urbanization. However, the lack of data and insights from current service-providers impedes a reliable transition from labor-intensive to autonomous services. Deploying information gathering devices makes services expensive and resource-demanding. In project MARBLE (Mobile Autonomous RoBot for Litter Emptying) we are currently investigating the implementation of a fleet of service robots. In this framework, we could show that the absence of filling data of litter bins (LBs) hinders the possibility of providing an energy-efficient and time-effective service. Hence, we introduce an approach where machine learning-based predictions for filling levels of LBs, derived from our extensive data gathering, are used to effectively manage the autonomous emptying process. The novel Simulated Rebalancing approach in route-planning combined with the Knapsack algorithm ensures efficient service in comparison to the Nearest Neighbor algorithm. A promising 82% filling level prediction accuracy was achieved with the XGBoost binary classifier, as compared to the 59% baseline accuracy. Through incorporating the predicted filling level data in the Simulated Rebalancing approach, a reduction of 26% in operational time and 31% in energy consumption was achieved for our simulated tests for service-event-area (SEA) James-Simon-Monbijoupark in Berlin with 49 LBs.

Primary retroperitoneal masses include a diverse and uncommon group of lesions that arise within the retroperitoneal space, but do not originate from any retroperitoneal organs. The majority of the lesions are malignant and imaging plays a pivotal role in the detection, staging, and preoperative planning. The evaluation of primary retroperitoneal masses is often challenging owing to the unfamiliarity with the common imaging features of various diseases affecting it. This article describes the multidetector computed tomography appearance of some primary retroperitoneal masses.

Drone-assisted radio communication is revolutionizing future wireless networks, including sixth-generation (6G) and beyond, by providing unobstructed, line-of-sight links from air to terrestrial vehicles, enabling robust cellular cehicle-to-everything (C-V2X) communication networks. However, addressing communication latency is imperative, especially when considering autonomous vehicles. In this study, we analyze different types of delay and the factors impacting them in drone-assisted C-V2X networks. We specifically investigate C-V2X Mode 4, where multiple vehicles utilize available transmission windows to communicate the frequently collected sensor data with an embedded drone server. Through a discrete-time Markov model, we assess the medium access control (MAC) layer performance, analyzing the trade-off between data rates and communication latency. Furthermore, we compare the delay between cooperative perception messages (CPMs) and periodically transmitted basic safety messages (BSMs). Our simulation results emphasize the significance of optimizing BSM and CPM transmission intervals to achieve lower average delay as well as utilization of drones’ battery power to serve the maximum number of vehicles in a transmission time interval (TTI). The results also reveal that the average delay heavily depends on the packet arrival rate while the processing delay varies with the drone occupancy and state-transition rates for both BSM and CPM packets. Furthermore, an optimal policy approximates a threshold-based policy in which the threshold depends on the drone utilization and energy availability.

Abstract Introduction Type 1 diabetes mellitus (T1DM) occurs in genetically susceptible individuals due to certain environmental triggers causing destruction of insulin secreting beta cells. One of the environmental triggers studied recently in the pathogenesis and progression of T1DM is the role of gut microbiome. Objectives (1) To compare the gut microbiome profile of T1DM children with healthy age, gender, and body mass index (BMI) matched controls. (2) To assess the relationship of abundance of genera with glycemic control in children with T1DM. Methods Cross‐sectional, case–control study. Sixty‐eight children with T1DM and 61 age‐, gender‐, and BMI‐matched healthy controls were enrolled. QIAamp Fast DNA Stool Mini kit protocol and reagents were used for DNA isolation and Miseq sequencing platform for targeted gene sequencing. Results Alpha and beta diversity analysis showed no significant differences in the abundance of microbes between the groups. At phylum level, Firmicutes was the dominant phylum followed by Actinobacteria and Bacteroidota in both groups. Analysis of microbiome at the genera level showed that percentage abundance for Parasutterella was higher in children with T1DM as compared to the healthy group (p

The breakage of brittle particulate materials into smaller particles under compressive or impact loads can be modelled as an instantiation of the population balance integro-differential equation. In this paper, the emerging computational science paradigm of physics-informed neural networks is studied for the first time for solving both linear and nonlinear variants of the governing dynamics. Unlike conventional methods, the proposed neural network provides rapid simulations of arbitrarily high resolution in particle size, predicting values on arbitrarily fine grids without the need for model retraining. The network is assigned a simple multi-head architecture tailored to uphold monotonicity of the modelled cumulative distribution function over particle sizes. The method is theoretically analyzed and validated against analytical results before being applied to real-world data of a batch grinding mill. The agreement between laboratory data and numerical simulation encourages the use of physics-informed neural nets for optimal planning and control of industrial comminution processes.

Background: Females in developing countries face brunt of lack of access to affordable menstrual supplies and inequitable distribution of menstrual health education and are victims of period poverty. The objective of the present study was to estimate prevalence of period poverty and to determine association of period poverty with socio-demographic determinants, menstrual knowledge, menstrual hygiene management practices, and reproductive health. Materials and Methods: This was an analytical cross-sectional study, conducted at an urban primary health care centre. Reproductive age group (20–49 years) females attending the primary health care centre from December 2022 to May 2023 were selected for study. Data were collected on semi-structured, pre-tested questionnaires. The collected data were entered in M.S Excel and analysed in STATA version 17.0. Chi-square test was used for comparison of qualitative data, and for quantitative data, unpaired t-test was used. Results: The prevalence of period poverty in the present study was 92 (29.7%). The mean age of females with period poverty was 29.72 ± 7.13 years, and it was 26.45 ± 6.62 years with no period poverty (P 0.001). Period poverty was significantly associated with socio-demographic variables. Period poverty had significant association with menstrual knowledge, menstrual hygiene management practices, and reproductive health. Conclusion: Many females cannot afford menstrual health products to meet their monthly needs, and this may affect their education and socio-economic status. Improved access to affordable sanitary products is requisite of females.

Resistance to commonly used anthelmintics stipulate alternative control methods. The use of natural herbal products with anthelmintic properties can address the issue. In this regard, the present study was conducted to determine the in vitro antiparasitic activities of aqueous and methanolic extracts of Aegle marmelos by using different in vitro assays at various concentrations of extracts. The evaluation was done by egg hatch assay (EHA) and larval development assay (LDA) on Strongyle-type eggs and larval stages of Haemonchus contortus isolated from the goat fecal samples respectively. In EHA, 74.34% and 79.71% were the mean inhibition percent against Strongyle type eggs from hatching at the highest concentration of 50 mg/ml tested and the change was 7.2 and 8.24 fold higher than negative PBS control while 77.45% and 85.78% was mean inhibition percentage in terms of Haemonchus contortus L3 larvae development at the highest concentration 1 mg/ml tested and the change was 14.5 and 18.3 fold higher than negative control by aqueous and methanolic extract of A. marmelos, respectively. Both extracts showed dose-dependent inhibition at all the concentrations tested and were similar to positive control-like treatment with 0.125 mg/ml albendazole for EHA and LDA. The study concluded that the aqueous and methanolic extracts of A. marmelos have antiparasitic potential and can be used to control parasitic infection.

Phosphatase of regenerating liver 3 (PRL3) is a specific tumor antigen overexpressed in a broad range of adult cancer types. However, its physiological expression in pediatric embryonal and mesenchymal tumors and its association with clinical outcomes in children is unknown. We sought to profile the expression of PRL3 in pediatric tumors in relation to survival outcomes, expression of angiogenesis markers, and G-protein-coupled receptor (GPCR)-mitogen-activated protein kinase (MAPK) signaling targets. PRL3-zumab, a first-in-class humanized antibody, was administered in a dose escalation schedule in a first-in-child clinical trial to study toxicity, pharmacokinetics, and clinical outcomes. Among 64 pediatric tumors, PRL3 was most frequently expressed in neuroblastoma (100%), rhabdomyosarcoma and non-rhabdomyosarcoma soft tissue sarcomas (71%), and renal sarcomas (60%) but absent in paired normal tissues. PRL3 was expressed in 75% of relapsed tumors and associated with shorter median event-free survival. Microarray profiling of PRL3-positive tumors showed elevation of angiogenin, TIMP1 and TIMP2, and GPCR-MAPK signaling proteins that commonly interacted with PRL3. The first use of PRL3-zumab in a pediatric patient saw no adverse events. A 28.6% reduction in maximum target lesion diameter was achieved when PRL3-zumab was administered concurrently with hypofractionated radiation. These findings support wider exploration of PRL3 expression in embryonal and mesenchymal tumors and further clinical application of PRL3-zumab in pediatric patients.

This paper applies federated reinforcement learning (FRL) in cellular vehicle-to-everything (C-V2X) communication to enable vehicles to learn communication parameters in collaboration with a parameter server that is embedded in an unmanned aerial vehicle (UAV). Different sensors in vehicles capture different types of data, contributing to data heterogeneity. C-V2X communication networks impose additional communication overhead in order to converge to a global model when the sensor data are not independent-and-identically-distributed (non-i.i.d.). Consequently, the training time for local model updates also varies considerably. Using FRL, we accelerated this convergence by minimizing communication rounds, and we delayed it by exploring the correlation between the data captured by various vehicles in subsequent time steps. Additionally, as UAVs have limited battery power, processing of the collected information locally at the vehicles and then transmitting the model hyper-parameters to the UAVs can optimize the available power consumption pattern. The proposed FRL algorithm updates the global model through adaptive weighing of Q-values at each training round. By measuring the local gradients at the vehicle and the global gradient at the UAV, the contribution of the local models is determined. We quantify these Q-values using nonlinear mappings to reinforce positive rewards such that the contribution of local models is dynamically measured. Moreover, minimizing the number of communication rounds between the UAVs and vehicles is investigated as a viable approach for minimizing delay. A performance evaluation revealed that the FRL approach can yield up to a 40% reduction in the number of communication rounds between vehicles and UAVs when compared to gross data offloading.

The paper studies a game theory model to ensure fairness and improve the communication efficiency in an unmanned aerial vehicle (UAV)-assisted cellular vehicle-to-everything (C-V2X) communication network using Markovian game theory in a federated learning (FL) environment. The UAV and each vehicle in a cluster utilized a strategy-based mechanism to maximize their model completion and transmission probability. We modeled a two-stage zero sum Markovian game with incomplete information to jointly study the utility maximization of the participating vehicles and the UAV in the FL environment. We modeled the aggregating process at the UAV as a mixed strategy game between the UAV and each vehicle. By employing Nash equilibrium, the UAV determined the probability of sufficient updates received from each vehicle. We analyzed and proposed decision-making strategies for several representative interactions involving gross data offloading and federated learning. When multiple vehicles enter a parameter transmission conflict, various strategy combinations are evaluated to decide which vehicles transmit their data to the UAV. The optimal payoff in a transmission window is derived using the Karush–Khun–Tucker (KKT) optimality conditions. We also studied the variation in optimal model parameter transmission probability, average packet delay, UAV transmit power, and the UAV–Vehicle optimal communication probabilities under different conditions.

Background: Inguinal hernia repair is one of the most common surgical conditions. In the absence of other comorbidities, it can be performed as a day-care procedure. Spinal anesthesia is most preferred technique this surgery. Local anesthetics having rapid and smooth onset of action, good intraoperative analgesia, short recovery period, and least side effects are preferred. Aims and Objectives: The objective of the study was to compare the anesthetic efficacy of intrathecal 1% isobaric 2-chloroprocaine (CP) and 0.5% isobaric ropivacaine for inguinal hernia surgeries. Materials and Methods: Sixty patients with ASA grade I/II scheduled for elective inguinal hernia surgeries were randomly allocated into group C (n=30), received 3 mL of 1% isobaric 2- CP and group R (n=30), received 3 mL of 0.5% isobaric ropivacaine intrathecally. Results: Demographic profile, mean time for onset of sensory blockade at T10, highest level of sensory blockade, and maximum motor blockade were comparable among the groups (P>0.05). The highest level of sensory blockade in group C is T7 and group R is T8. The mean time to two segment regression, complete sensory regression, complete motor recovery, recovery of parameters for achieving discharge criteria such as time to unassisted ambulation, drink water, micturition, and first rescue analgesia were significantly shorter in group C (P0.05). Conclusion: CP provides faster block resolution, earlier hospital discharge, and early ambulation with minimal side effects, making it a better alternative to ropivacaine for inguinal hernia surgeries.

Medicinal herbs are being widely accepted as alternative remedies for preventing various diseases especially in India and other Asian countries. However, most plant-based herbal medicines are not yet being scientifically accepted worldwide. “Tinospora cordifolia (Willd.) Miers ex Hook.F. & Thomson”, one of the most promising plant species of Tinospora known as “Giloy” or Guduchi that is used in several traditional medicines in treating diseases e.g., metabolic and immune disorders, diabetes, heart diseases, cancer, and infectious diseases, has been widely investigated. Varieties of bioactive phytochemical constituents isolated from the stem, root and whole plant of T. cordifolia have been identified. In the last two decades, the diverse pharmacological activities of T. cordifolia have been continuously studied. Due to its therapeutic efficacy in immune modulation, it could be effective in viral and other diseases treatment as well. A medicinal plant could be well-suited not only for the treatment of target site but also for boosting the body's immune system. As an alternate source of medication, medicinal herbs are continuously showing better compatibility with the human body with minimal side effects than other therapies. Keeping this in mind, the present review highlights the pharmacological potential of T. cordifolia against various diseases.

Our reliance on agriculture for sustenance, healthcare, and resources has been essential since the dawn of civilization. However, traditional agricultural practices are no longer adequate to meet the demands of a burgeoning population amidst climate-driven agricultural challenges. Microalgae emerge as a beacon of hope, offering a sustainable and renewable source of food, animal feed, and energy. Their rapid growth rates, adaptability to non-arable land and non-potable water, and diverse bioproduct range, encompassing biofuels and nutraceuticals, position them as a cornerstone of future resource management. Furthermore, microalgae’s ability to capture carbon aligns with environmental conservation goals. While microalgae offers significant benefits, obstacles in cost-effective biomass production persist, which curtails broader application. This review examines microalgae compared to other host platforms, highlighting current innovative approaches aimed at overcoming existing barriers. These approaches include a range of techniques, from gene editing, synthetic promoters, and mutagenesis to selective breeding and metabolic engineering through transcription factors.

Abstract In multi-objective optimization, it becomes prohibitively difficult to cover the Pareto front (PF) as the number of points scales exponentially with the dimensionality of the objective space. The challenge is exacerbated in expensive optimization domains where evaluation data is at a premium. To overcome insufficient representations of PFs, Pareto estimation (PE) invokes inverse machine learning to map preferred but unexplored regions along the front to the Pareto set in decision space. However, the accuracy of the inverse model depends on the training data, which is inherently scarce/small given high-dimensional/expensive objectives. To alleviate this small data challenge, this paper marks a first study on multi-source inverse transfer learning for PE. A method to maximally utilize experiential source tasks to augment PE in the target optimization task is proposed. Information transfers between heterogeneous source-target pairs is uniquely enabled in the inverse setting through the unification provided by common objective spaces. Our approach is tested experimentally on benchmark functions as well as on high-fidelity, multidisciplinary simulation data of composite materials manufacturing processes, revealing significant gains to the predictive accuracy and PF approximation capacity of Pareto set learning. With such accurate inverse models made feasible, a future of on-demand human-machine interaction facilitating multi-objective decisions is envisioned.

Background: With increase in incidence of gall bladder stone the laparoscopic cholecystectomy preferred in various hospitals. Laryngoscopic stimulation of oropharyngeal and laryngeal structures leads to hemodynamic stress response. I-gel is a second generation supraglottic airway device with noninflatable cuff, having several advantages such as minimal hemodynamic changes during insertion and easy to insert. Aims and Objectives: The aim of this study was to compare I-gel and endotracheal tube regarding hemodynamic stability, adequacy of ventilation, ease of insertion through number of attempts, and time for insertion and associated complications in patients undergoing laparoscopic cholecystectomy. Materials and Methods: Total 60 patients of either sex, 25–55 years age of ASA Grades I and II were randomly allocated into two groups of 30 patients each; Group A (endotracheal tube) and Group B (I-gel). Ease of insertion in terms of number of attempts and time for insertion, hemodynamic variation and their complications were recorded. Results: Significantly less time was required for insertion of I-gel as compared to endotracheal tube (P=0.04), while number of attempts required for insertion were comparable, highly significant difference in heart rate, systolic blood pressure, mean arterial pressure just after insertion and at extubation of endotracheal tube than I-gel group (P

Background & Aims: Lymphatic vessels (LVs) are crucial for maintaining abdominal fluid homoeostasis and immunity. In cirrhosis, mesenteric LVs (mLVs) are dilated and dysfunctional. Given the established role of vascular endothelial growth factor-C (VEGF-C) in improving LVs, we hypothesised that VEGF-C treatment could ameliorate the functions of mLVs in cirrhosis. Methods: In this study, we developed a nanoformulation comprising LV-specific growth factor, recombinant human VEGF-C (Cys156Ser) protein (E-VEGF-C) and delivered it orally in different models of rat cirrhosis to target mLVs. Cirrhotic rats were given nanoformulation without VEGF-C served as vehicles. Drainage of mLVs was analysed using tracer dye. Portal and systemic physiological assessments and computed tomography were performed to measure portal pressures and ascites. Gene expression and permeability of primary mesenteric lymphatic endothelial cells (LyECs) was studied. Immune cells in mesenteric lymph nodes (MLNs) were quantified by flow cytometry. Endogenous and exogenous gut bacterial translocation to MLNs was examined. Results: In cirrhotic rats, mLVs were dilated and leaky with impaired drainage. Treatment with E-VEGF-C induced proliferation of mLVs, reduced their diameter, and improved functional drainage. Ascites and portal pressures were significantly reduced in E-VEGF-C rats compared with vehicle rats. In MLNs of E-VEGF-C animals, CD8+CD134+ T cells were increased, whereas CD25+ regulatory T cells were decreased. Both endogenous and exogenous bacterial translocation were limited to MLNs in E-VEGF-C rats with reduced levels of endotoxins in ascites and blood in comparison with those in vehicle rats. E-VEGF-C treatment upregulated the expression of vascular endothelial-cadherin in LyECs and functionally improved the permeability of these cells. Conclusions: E-VEGF-C treatment ameliorates mesenteric lymph drainage and portal pressure and strengthens cytotoxic T-cell immunity in MLNs in experimental cirrhosis. It may thus serve as a promising therapy to manage ascites and reduce pathogenic gut bacterial translocation in cirrhosis. Impact and Implications: A human recombinant pro-lymphangiogenic growth factor, VEGF-C, was encapsulated in nanolipocarriers (E-VEGF-C) and orally delivered in different models of rat liver cirrhosis to facilitate its gut lymphatic vessel uptake. E-VEGF-C administration significantly increased mesenteric lymphatic vessel proliferation and improved lymph drainage, attenuating abdominal ascites and portal pressures in the animal models. E-VEGF-C treatment limited bacterial translocation to MLNs only with reduced gut bacterial load and ascitic endotoxins. E-VEGF-C therapy thus holds the potential to manage ascites and portal pressure and reduce gut bacterial translocation in patients with cirrhosis.

Background Programmed death ligand 1 (PD-L1), expressed on cancer cells, shows varied results in the prognosis of breast cancer. This study was conducted to study the expression of PD-L1 in breast carcinoma and to correlate it with pathological, molecular classification and prognostic factors. Materials and Methods PD-L1 expression was correlated with tumor size, histopathological grade, necrosis, lymphovascular, perineurial invasion, lymph node metastasis, molecular classification, and survival in breast carcinoma cases. Results Fifty cases were included which showed statistically significant difference of PD-L1 with mean age, tumor size, histopathological grade, lymphovascular emboli, and lymph node metastasis (p

Ambient air quality of Rajkot city is analyzed along with chemical characterization such as anions, cations, metals and non-sea salts (nss) to understand the chemical footprints on a regional scale. Two sites, rural and urban areaare selected for sampling of PM10,PM2.5,SO2 and NO2, but no major differences are found in ambient air quality.The concentration of ions is found to be high during summer and winter, that is, months of May and December. The concentration of anions and cations in chronological order is Cl−

Wireless sensors are backed by their battery life. The battery depletion creates a communication hole in the network. The replacement of certain energy-depleted sensor nodes necessitates their detection. The work in this article presents a novel solution for the detection of coverage hole in the WSN. An undirected graph was employed in this study to divide the WSN region into two parts: the outer and inner nodes. The Gaussian Mixture Model (GMM) clustering detects the outer/edge nodes at the coverage hole area. Node degree, closeness, betweenness, and page rank are the four graph metrics that GMM uses for clustering in this work. GMM’s clustering performance is governed by the static mean and covariance. A novel combination of social spider optimization and GMM is then leveraged to fine-tune the cluster to decrease false detection rates of holes. Distinct holes, such as round, E-shape, and rectangular, are used to conduct the analysis. Using the SSO-EM GMM approach, the comparison shows that it outperforms the current methods of coverage hole identification.

Background: Skeletal muscle relaxation is integral part of general anesthesia. Non-depolarizing neuromuscular blocking drugs like cisatracurium are devoid of any signs of histamine release and can improve the quality of intubating conditions. Aims and Objectives: We decided to assess the efficacy of Cisatracurium when compared to atracurium in terms of intubating conditions and hemodynamic parameters in patients undergoing surgeries under general anesthesia. Materials and Methods: In this study, 80 patients in the age group of 20–65 years were randomly allocated into Group A received Atracuruium 0.5 mg/kg and Group B received Cisatracurium 0.15 mg/kg for intubation after administration of induction agent. Intuabating conditions were assessed by Cooper et al., score and hemodynamic parameters such as heart rate and blood pressure were recorded at 1, 3, 5, 10, 15, and 30 min of intubation. Any signs of histamine release were noted. Results: Demographic profile was comparable between the two groups. As per Cooper et al., score intubating conditions were excellent with Cisatracurium group as compared to the atracurium group which was statistically significant. Hemodynamic parameters during intubation and after intubation were comparable between two groups. No associated signs of histamine release were noted in either of the groups. Conclusion: From our clinical comparative study, we have observed that Cisatracurium 0.15 mg/kg provides better intubating conditions and stable hemodynamic profile with no signs of histamine release as compared to atracurium 0.5 mg/kg dose.

Key Clinical Message Inflammatory papillary hyperplasia can be seen in a non‐denture‐wearing patient also therfore other etiologies need to be explored as well. Abstract Inflammatory papillary hyperplasia (IPH) is a benign lesion of the palatal mucosa, usually found in denture wearers. This case history report describes an example of the dentate patients with no history of wearing maxillary prostheses and highlights the importance of professional awareness to diagnose IPH among non‐denture‐wearing patients.

Delivery systems for biologically active substances such as proanthocyanidins (PCANs), produced in the form of electrospun nonwoven through the electrospinning method, were designed using a polymeric blend of poly(L-lactide-co-glycolide) (PLGA)and poly[(R,S)-3-hydroxybutyrate] ((R,S)-PHB). The studies involved the structural and thermal characteristics of the developed electrospun three-dimensional fibre matrices unloaded and loaded with PCANs. In the next step, the hydrolytic degradation tests of these systems were performed. The release profile of PCANs from the electrospun nonwoven was determined with the aid of UV–VIS spectroscopy. Approximately 30% of the PCANs were released from the tested electrospun nonwoven during the initial 15–20 days of incubation. The chemical structure of water-soluble oligomers that were formed after the hydrolytic degradation of the developed delivery system was identified through electrospray ionization mass spectrometry. Oligomers of lactic acid and OLAGA oligocopolyester, as well as oligo-3-hydroxybutyrate terminated with hydroxyl and carboxyl end groups, were recognized as degradation products released into the water during the incubation time. It was also demonstrated that variations in the degradation rate of individual mat components influenced the degradation pattern and the number of formed oligomers. The obtained results suggest that the incorporation of proanthocyanidins into the system slowed down the hydrolytic degradation process of the poly(L-lactide-co-glycolide)/poly[(R,S)-3-hydroxybutyrate] three-dimensional fibre matrix. In addition, in vitro cytotoxicity and antimicrobial studies advocate the use of PCANs for biomedical applications with promising antimicrobial activity.

Although traditional opioids such as morphine and oxycodone are commonly used in the management of acute postoperative pain, novel opioids may play a role as alternatives that provide potent pain relief while minimizing adverse effects. In this review, we discuss the mechanisms of action, findings from preclinical studies and clinical trials, and potential advantages of several novel opioids. The more established include oliceridine (biased ligand activity to activate analgesia and downregulate opioid-related adverse events), tapentadol (mu-opioid agonist and norepinephrine reuptake inhibitor), and cebranopadol (mu-opioid agonist with nociceptin opioid peptide activity)—all of which have demonstrated success in the clinical setting when compared to traditional opioids. On the other hand, dinalbuphine sebacate (DNS; semi-synthetic mu partial antagonist and kappa agonist), dual enkephalinase inhibitors (STR-324, PL37, and PL265), and endomorphin-1 analog (CYT-1010) have shown good efficacy in preclinical studies with future plans for clinical trials. Rather than relying solely on mu-opioid receptor agonism to relieve pain and risk opioid-related adverse events (ORAEs), novel opioids make use of alternative mechanisms of action to treat pain while maintaining a safer side-effect profile, such as lower incidence of nausea, vomiting, sedation, and respiratory depression as well as reduced abuse potential.

The problem of designing a rebalancing algorithm for a large-scale ridehailing system with asymmetric (unbalanced) demand is considered here. We pose the rebalancing problem within a semi Markov decision problem (SMDP) framework with closed queues of vehicles serving stationary, but asymmetric demand, over a large city with multiple stations (representing neighborhoods). We assume that the passengers queue up at every station until they are matched with a vehicle. The goal of the SMDP is to minimize a convex combination of the waiting time of the passengers and the total empty vehicle miles traveled. The resulting SMDP appears to be difficult to solve yielding closed-form expression for the optimal rebalancing strategy. Consequently, we use a deep reinforcement learning algorithm to determine the approximately optimal solution to the SMDP. We show through extensive Monte Carlo simulations that the trained policy outperforms other well-known state-dependent rebalancing strategies.

This study encompasses the proposal of a novel hybrid-security model that incorporates the benefits of both the centralised data mining system as well the distributed data mining system. The hybrid model provides two levels of security. The first security level perturbs the individual datasets by transforming it into a non-understandable form using the four-dimensional rotation transformation, and the second security level helps in performing secure distributed data mining using the ratio of secure summation protocol. The trivial data mining techniques, such as k-means clustering technique and naïve Bayes classification technique, verifies the efficiency and accuracy of the hybrid security model. The hybrid security model provides security to sensitive data without compromising the quality of the data. The accuracy obtained in classification task and clustering task using naïve Bayes and k-means technique is high when different datasets in a privacy-enhanced distributed data mining environment verify the working of the hybrid security model.

Abstract The typical feature is the autosomal dominant heritance and clinically dormant, non‐scarpalble, white diffuse, soft, thickened white plaques with a corrugated surface affecting mostly the buccal mucosa bilaterally which can substantially lead to the diagnosis of white sponge nevus.

Background: Obstructive sleep apnea (OSA) and atrial fibrillation (AF) are known to often coexist together. However, whether all patients with AF should be screened for sleep abnormalities is not clear. No previous study has examined the association of asymptomatic OSA with AF. Objective: This study sought to determine the prevalence of asymptomatic OSA in patients with persistent AF and whether asymptomatic OSA is an independent risk factor for atrial fibrillation. Method: Patients with persistent AF without a prior diagnosis of OSA and asymptomatic for sleep abnormalities were prospectively enrolled over 12 months. All patients underwent polysomnography after informed consent. Patients without AF or OSA who underwent polysomnography during the same period served as controls. Results: A total of 97 patients were studied; 50 were in the case group (patients with persistent AF) and 47 were in the control group (patients in sinus rhythm). Asymptomatic OSA was diagnosed on polysomnography in 72% of patients in the AF group and 17% of the control population. Multivariable analysis of factors including diabetes, hypertension, coronary artery disease, hypothyroidism, prior MI, and asymptomatic OSA, suggested asymptomatic OSA as an independent factor associated with AF. Conclusion: A significant proportion (72%) of patients with persistent AF have underlying asymptomatic OSA. Sleep abnormality thus has a strong association with AF even in patients who are asymptomatic for OSA. Screening for OSA may be advised for all patients with AF, as this may have significant implications for management.

Many service robots have to operate in a variety of different Service Event Areas (SEAs). In the case of the waste collection robot MARBLE (Mobile Autonomous Robot for Litter Emptying) every SEA has characteristics like varying area and number of litter bins, with different distances between litter bins and uncertain filling levels of litter bins. Global positions of litter bins and garbage drop-off positions from MARBLEs after reaching their maximum capacity are defined as task-performing waypoints. We provide boundary delimitation for characteristics that describe the SEA. The boundaries interpolate synergy between individual SEAs and the developed algorithms. This helps in determining which algorithm best suits an SEA, dependent on the characteristics. The developed route-planning methodologies are based on vehicle routing with simulated annealing (VRPSA) and knapsack problems (KSPs). VRPSA uses specific weighting based on route permutation operators, initial temperature, and the nearest neighbor approach. The KSP optimizes a route’s given capacity, in this case using smart litter bins (SLBs) information. The game-theory KSP algorithm with SLBs information and the KSP algorithm without SLBs information performs better on SEAs lower than 0.5 km2, and with fewer than 50 litter bins. When the standard deviation of the fill rate of litter bins is ≈10%, the KSP without SLB is preferred, and if the standard deviation is between 25 and 40%, then the game-theory KSP is selected. Finally, the vehicle routing problem outperforms in SEAs with an area of 0.5≤5 km2, 50–450 litter bins, and a fill rate of 10–40%.

The IPCC’s (Intergovernmental Panel on Climate Change) special report highlights the urgent necessity of limiting global warming to 1.5 °C, prompting a vital exploration of decarbonization methods. Carbon capture and sequestration (CCS) play a pivotal role in reducing carbon dioxide emissions from industrial processes and power generation, helping to combat climate change and meet global decarbonization goals. This article focuses on the economic prospects and market potential of carbon capture technologies in India, specifically in utilizing captured CO2 in the power, petrochemicals, and fertilizer sectors. It also emphasizes decarbonization through carbon sequestration involving geological storage to extract carbon dioxide from the environment, ultimately reducing greenhouse gas emissions. This article stresses the need to develop new technologies for carbon capture, utilization, and sequestration to overcome technical and financial barriers. It highlights the importance of improving efficiency, reducing costs, and scaling up these technologies for widespread adoption. Additionally, this study delves into the essential policy and regulatory frameworks for CCUS implementation, emphasizing the need for standards and laws to ensure safety, environmental protection, and effective monitoring in the Indian context. The research findings and recommendations provide valuable insights for future CCUS implementation, advancing sustainable decarbonization efforts in India and globally.

Receding horizon optimal control problems compute the solution at each time step to operate the system on a near-optimal path. However, in many practical cases, the boundary conditions, such as external inputs, constraint equations, or the objective function, vary only marginally from one time step to the next. In this case, recomputing the optimal solution at each time represents a significant burden for real-time applications. This paper proposes a novel algorithm to approximately solve a perturbed constrained dynamic program that significantly improves the computational burden when the objective function and the constraints are perturbed slightly. The method hinges on determining closed-form expressions for first-order perturbations in the optimal strategy and the Lagrange multipliers of the perturbed constrained dynamic programming problem. This information can be used to initialize any algorithm (such as the method of Lagrange multipliers, or the augmented Lagrangian method) to solve the perturbed dynamic programming problem with minimal computational resources.

In this work, we analyze the finite sample complexity bounds for offline reinforcement learning with general state, general function space and state-dependent action sets. The algorithm analyzed does not require the knowledge of the data-collection policy as compared to earlier works. We show that one can compute an $\epsilon$-optimal Q function (state-action value function) using $O(1/\epsilon ^{4})$ i.i.d. samples of state-action-reward-next state tuples.

Background: Infertility is a major psychosocial problem having an impact on their emotional and marital life. Among infertile couples, females show higher levels of distress than their male partners. Aim: The aim of this study is to assess the prevalence of anxiety and depression in female patients with infertility and to assess its association with various coping strategies used by them. Methods: A total of 85 females in the reproductive age group, having primary infertility, drug-naive, and willing to participate in the study were included in the study after obtaining informed consent and institutional ethics committee approval. Those with preexisting psychiatric or medical illnesses which can add to psychological distress were excluded from the study. The patients were assessed for anxiety, depression, and stress coping behavior using the Hamilton Anxiety, the Center for Epidemiologic Studies Depression, and Stress Coping Behavior scales, respectively. Results: The prevalence of anxiety and depression was observed to be 27.1% and 55.3%, respectively. Common coping styles used included “active coping” (94.1%), “religion” (91.8%), “acceptance” (90.5%), and “planning” (88.3%) whereas “substance use” and “humor” were rarely used. Females with anxiety significantly used “self blame” and among females with depression, significant association was observed with “positive reframing” (P = 0.001) and “behavioral disengagement” (P = 0.004). Conclusions: Anxiety and depression are common and are influenced by coping strategies used by female with infertility. Structured psychological support is essential to improve their emotional well-being and coping with infertility.

This paper used fusion of Hilbert transform (HT), Stockwell transform (ST) and Alienation coefficient to design a protection scheme for utility network with high concentration of renewable energy (RE) generation. A hybrid fault detection index (HFDI) is designed which detects the fault events utilizing the features extracted from both current and voltage signals. Peak magnitude of HFDI is compared with a threshold magnitude (HTM) to detect fault events and discriminate such events from operational events. Faults are classified based on faulty phase numbers and a hybrid ground fault index (HGFI). Zero sequence voltage and zero sequence current are processed by application of ST to compute HGFI which effectively detects presence of ground during fault event. Performance of protection scheme is validated using utility grid of IEEE-13 bus test system where 50% RE penetration is used. Further, performance is also tested to detect faults incident on a practical utility network with RE penetration of 57%. Performance of algorithm is evaluated to recognize different fault events and operational events. Protection scheme is effectively tested to detect faults with high noise levels of 20 dB SNR (signal to noise ratio). Proposed protection scheme perform better compared to Alienation coefficient based protection scheme reported in literature.

Orthogonal time-frequency space (OTFS) scheme, which transforms a time and frequency selective channel into an almost non-selective channel in the delay-Doppler domain, establishes reliable wireless communication for high-speed moving devices. This work designs and analyzes low-complexity zero-forcing (LZ) and minimum mean square error (LM) receivers for multiple-input multiple-output (MIMO)-OTFS systems with perfect and imperfect receive channel state information (CSI). The proposed receivers provide exactly the same solution as that of their conventional counterparts, and reduce the complexity by exploiting the doubly-circulant nature of the MIMO-OTFS channel matrix, the block-wise inverse, and Schur complement. We also derive, by exploiting the Taylor expansion and results from random matrix theory, a tight approximation of the post-processing signal-to-noise-plus-interference-ratio (SINR) expressions in closed-form for both LZ and LM receivers. We show that the derived SINR expressions, when averaged over multiple channel realizations, accurately characterize their respective bit error rate (BER) with both perfect and imperfect receive CSI. We numerically show the lower BER and lower complexity of the proposed designs over state-of-the-art exiting solutions.

Introduction: Edentulousness (partial or complete) is a sequel of tooth loss and is an indicator of the oral health status of a population. Edentulousness has a series of deleterious consequences for oral and general health. The aim of this study was to find out the prevalence of edentulousness among patients visiting the dental unit of a tertiary care centre. Methods: A descriptive cross-sectional study was carried out based on hospital records of patients visiting the Department of Oral Medicine and Prosthodontics of a tertiary care centre from 1 January 2019 to 30 December 2019 to see the prevalence of edentulousness. Ethical approval was obtained from the Institutional Review Committee (Reference number: 077/ 078 /40). A convenience sampling method was used. Point estimate and 95% Confidence Interval were calculated. Results: Among 4697 patients, edentulousness was found in 403 (8.58%) (7.78-9.38, 95% Confidence Interval). Partial edentulous were 263 (65.30%) and complete edentulous were 140 (34.70%). Of the total partial edentulous patient, Kennedy’s class III found in 200 (76.05%) was the most common pattern followed by Kennedy’s class I in 32 (12.17%), class II in 21 (7.98%) and class IV in 10 (3.80%) patients respectively. Conclusions: The prevalence of edentulousness was similar to other studies done in similar settings. Since edentulousness is a preventable problem, it should be addressed with high priority.

Erythema multiforme (EM) is an acute inflammatory, mucocutaneous, psychosomatic, and vesiculobullous condition that varies from minor to major forms. The acral distribution of target lesions is a characteristic of this condition. The aetiology of erythema multiforme is multifactorial. 90% of the cases are triggered by a herpes infection, whereas 10% occur secondary to drug intake. The offending drugs include nonsteroidal anti-inflammatory drugs, antibiotics, and anticonvulsants. The present case series discusses four cases of drug-induced erythema multiforme and their management.

Abstract This paper introduced an advanced algorithm making hybrid use of Stockwell transform (ST), Hilbert transform (HT) and Alienation coefficient (ACF) for identification, classification and to locate faulty events on transmission line. Signals of Current are processed by application of ST, HT and ACF for computing S‐index, H‐index and A‐index, respectively. These indices are multiplied element by element to compute proposed fault index (FI). A threshold magnitude is decided after testing the algorithm during different fault scenarios and faulty events are recognized when FI exceeds this threshold magnitude. Faults are categorized by identifying the number of phases which are faulty in nature and a ground fault index (GFI). GFI is designed by processing the zero sequence current using ST and used to identify involvement of ground during fault event. A mathematical formulation is framed to estimate location of faults on transmission line. Fault location has been estimated with a mean error less than 1%. Investigated faults include phase to ground (PGF), double phase (PPF), double phase to ground (PPGF) and three phase to ground (TPGF). Algorithm is found effective for faulty scenario such as fault impedance variations, fault incidence angle (FIA) variations, reverse power flow, effect of line loading, effect of noise, transient faults, off‐nominal frequency, and presence of harmonic components. Algorithm is also effective for discriminating switching transients from faulty conditions. Effective performance of the algorithm is established by comparing with fault detection and classification approach based on alienation coefficients, discrete Fourier transform (DFT) and time‐frequency approach. Study is performed on a two terminal transmission line in MATLAB/Simulink environment. Effectiveness of the algorithm is also established on a real time transmission grid of Rajasthan state of India.

Background: Japanese encephalitis (JE) is an important cause of viral encephalitis in children in South East Asian countries including India. Aims and Objective: We have done this study in an JE endemic district of India to know the demographic profile, clinical presentation, seasonal variation, outcome and about the vaccination status of the JE cases. Materials and Methods: This observational, cross sectional study was done for a period of one year in the Pediatric department of Burdwan Medical College, West Bengal in children up to the age of 12 year, presented with acute encephalitis syndrome (AES). Demographic data, clinical presentation and JE vaccination status were recorded and confirmed JE cases were detected by either serum or CSF JE IgM antibody. Outcome of the JE cases were noted as discharge, death or leave against medical advice (LAMA). Results: We have found confirmed JE in 18 children out of total 125 AES cases (14.4%). 61.1% of them were within 6 to 12 year age, mostly from low socioeconomic status and 100% case occurred during monsoon and post monsoon period. History, was of JE vaccination were present in only 11.1% case of JE. Most common presenting symptoms were high fever (100%), convulsion (94.4%) and altered sensorium (72.2%). 77.8% JE case were discharged and 11.1% expired. Conclusion: We must consider JE whenever a child from any endemic district comes with AES and we should try to bring every child under the JE vaccination coverage in the endemic regions globally to reduce the burden of this preventable encephalitis.

Thura and colleagues report that PRL3 promotes PGCCs, which are found to be enriched in relapsed tumours. The authors demonstrated that this relapse can be prevented using PRL3-zumab for use in adjuvant immunotherapy to target PRL3-expressing PGCCs, thus preventing metastasis and relapse.