Search

From the last few years power demanding is increasing as per load requirement. The condition of power demand is not accompanied by only generation and transmission of electricity. Many industrial power plants are running on higher capacity to fulfill load requirement. There are many issues such as voltage instability and power quality which led to many power plants. In distributed generation generators are distributed asynchronous type and mostly affected by the reactive power. They basically cannot generate reactive power but draws away from the power system. It causes the voltage drop to the consumer side which may be not acceptable. Then reactive power management is essential for reducing those problems and provides continuity of electricity to the consumers. Distributed generation systems and FACTS devices are converting conventional power grid into smart grid. Reactive power compensation is now challenging issue to preserve adequate power quality and improve the performance of distribution system. There are many FACTS devices such as we can control power factor of the circuit and reactive power compensation. Many researchers have focused on behavior of FACTS device in an unbalanced condition and majority work is done only with DSTATCOM. This paper deals with different FACTS devices in grid systems with analysis and overview of D-FACTS devices. Hence in this paper, applications of various D-FACTS devices have been described to find location of compensation devices for reliable operation.

Thromboembolic events (TE) are a common complication in patients with metastatic renal cell carcinoma (mRCC) and are associated with poorer clinical outcomes. However, the incidence of TE and clinical and genomic characteristics of patients with mRCC who develop this complication are poorly understood. Herein, we describe the incidence and clinical features of patients with mRCC with or without TE at our institution, and examine their association with the underlying genomic and transcriptomic characteristics of the tumor. This retrospective study included all consecutive cases of mRCC seen at our institution. A CLIA-certified lab performed tumor genomics and transcriptomics. Patients were classified based on the presence of a TE within the first year of diagnosis. Three hundred and seventy patients with mRCC were included in the study. TE was seen in 11% (42) of the patients. Patients with favorable International mRCC Database Consortium (IMDC) risk were less likely to develop a TE. In contrast, patients receiving combination treatment with a tyrosine kinase inhibitor (TKI) and an immune checkpoint inhibitor were more likely to develop a TE. No difference in overall survival among patients with or without TE was observed (52 vs. 55 months; HR 0.85, 95% CI 0.5574–1.293, p = 0.24). The most upregulated pathways in mRCC with TEs versus those without were the xenobiotic metabolism and mTORC1 signaling pathways. Our findings suggest potential biomarkers that, after external validation, could be used to better select patients who would benefit from prophylactic anticoagulation.

Abstract Variability of ice microphysical properties like crystal size and density in cirrus clouds is important for climate through its impact on radiative forcing, but challenging to represent in models. For the first time, recent laboratory experiments of particle growth (tied to crystal morphology via deposition density) are combined with a state‐of‐the‐art Lagrangian particle‐based microphysics model in large‐eddy simulations to examine sources of microphysical variability in cirrus. Simulated particle size distributions compare well against balloon‐borne observations. Overall, microphysical variability is dominated by variability in the particles' thermodynamic histories. However, diversity in crystal morphology notably increases spatial variability of mean particle size and density, especially at mid‐levels in the cloud. Little correlation between instantaneous crystal properties and supersaturation occurs even though the modeled particle morphology is directly tied to supersaturation based on laboratory measurements. Thus, the individual thermodynamic paths of each particle, not the instantaneous conditions, control the evolution of particle properties.

The Lightweight, Multi-speaker, Multi-lingual Indic Text-to-Speech (LIMMITS'23) challenge is organized as part of the ICASSP 2023 Signal Processing Grand Challenge. LIMMITS'23 aims at the development of a lightweight, multi-speaker, multi-lingual Text to Speech (TTS) model using datasets in Marathi, Hindi, and Telugu, with at least 40 hours of data released for each of the male and female voice artists in each language. The challenge encourages the advancement of TTS in Indian Languages as well as the development of techniques involved in TTS data selection and model compression. The 3 tracks of LIMMITS'23 have provided an opportunity for various researchers and practitioners around the world to explore the state-of-the-art techniques in TTS research.

The burden of cardiovascular disease (CVD) in patients with metabolic dysfunction-associated steatohepatitis (MASH) is poorly characterized, particularly vs other liver diseases including metabolic dysfunction-associated steatotic liver disease (MASLD). To identify available evidence, Embase, MEDLINE, and Cochrane database searches (main search: 2011–September 6, 2021; additional ad hoc search [MEDLINE only]: September 7, 2021–February 15, 2023), plus manual searches (2019–September 2021), were performed. Studies reporting CVD outcomes (angina, coronary artery disease [CAD], heart failure, myocardial infarction, peripheral artery disease, stroke, venous thromboembolic disease, and CV mortality) in adults with histologically confirmed MASH and MASLD or other liver diseases were identified, with studies of MASLD without confirmed MASH excluded. Of 8732 studies, 21 were included. An increased incidence or prevalence of CVD in patients with MASH vs other conditions was reported in 12 studies; odds ratios (OR), where reported, ranged from 3.12 (95 % CI: 1.33–5.32) to 4.12 (95 % CI: 1.91–8.90). The risk of CAD was increased in people with MASH in 6 of 7 studies, while the risk of stroke was increased in 6 of 6 studies, and heart failure in 2 of 4 studies. Three of 6 studies provided evidence of increased CVD-related mortality in patients with MASH vs those without. In conclusion, this literature review suggests that CVD is prevalent in patients with MASH and may contribute to increased mortality. Accordingly, cardiovascular risk factors should be aggressively managed in this population. Whether the CVD burden in patients with MASH is a direct consequence of MASH itself requires further study.

BackgroundSmartphones have become integral to people’s lives, with a noticeable increase in the average screen time, both on a global scale and, notably, in India. Existing research links mobile consumption to sleep problems, poor physical and mental health, and lower subjective well-being. The comparative effectiveness of monetary incentives given for self-selected versus assigned targets on reducing screen time and thereby improving mental health remains unanswered. ObjectiveThis study aims to assess the impact of monetary incentives and target selection on mobile screen time reduction and mental health. MethodsWe designed a 3-armed randomized controlled trial conducted with employees and students at an educational institution in India. The study is conducted digitally over 12 weeks, including baseline (2 weeks), randomization (1 week), intervention (5 weeks), and postintervention (4 week) periods. We emailed the employees and students to inquire about their interest in participation. Those who expressed interest received detailed study information and consent forms. After securing consent, participants were asked to complete the initial survey and provide their mobile screen time during the baseline period. At the beginning of the intervention period, the participants were randomly allocated into 1 of 3 study groups in a 2:2:1 ratio (self-selected vs assigned vs control). Participants in the self-selected group were presented with 3 target options: 10%, 20%, and 30%, and they were asked to self-select a target to reduce their mobile screen time from their baseline average mobile screen time. Participants in the assigned group were given a target to reduce their mobile screen time from their baseline average mobile screen time. The assigned target was set as the average of the targets selected by participants in the self-selected group. During the intervention period, participants in the self-selected and assigned group were eligible to receive a monetary incentive of INR (Indian Rupee) 50 (US $0.61) per day for successfully attaining their target. Participants in the control group neither received nor selected a target for reducing their mobile screen time and did not receive any monetary incentives during the intervention period. All participants received information regarding the advantages of reducing mobile screen time. As an incentive, all participants would receive INR 500 (US $6.06) upon completion of the study and a chance to win 1 of 2 lotteries valued at INR 5000 (US $60.55) for consistently sharing their mobile screen time data. ResultsCurrently, the study intervention is being rolled out. Enrollment occurred between August 21, 2023, and September 2, 2023; data collection concluded in November 2023. We expect that results will be available by early 2024. ConclusionsThe monetary incentives and self-selected versus assigned targets might be effective interventions in reducing mobile screen time among working professionals and students. Trial RegistrationAsPredicted 142497; https://aspredicted.org/hr3nn.pdf International Registered Report Identifier (IRRID)DERR1-10.2196/53756

Background: Vaccines are a key strategy to stop the COVID19 pandemic. The present study was conducted to assess the incidence of Adverse Drug Reactions to the Covishield vaccine among healthcare workers. Materials and Methods: A cross-sectional, observational, questionnaire-based study was carried out on healthcare workers of R.U.H.S. College of Medical Sciences, Jaipur, Rajasthan. The study tool consisted of a digital questionnaire. Results: The present study was carried out among 316 healthcare workers who received the first dose of the Covishield vaccine. 83 (26.26%) participants complained of side effects after receiving the first dose of the Covishield vaccine. Fatigue (64), fever (52), body ache (40), swelling at the vaccination site (35), headache (25), and pain in the limb (18) were the most prevalent symptoms. Most post-vaccination symptoms were found to be mild. 67 participants showed side effects of vaccination within 24 hours while 16 showed side effects after 24 hours. Conclusion: This study reflects that one fourth of participants complained of side effects after receiving the first dose of the Covishield vaccine. These side effects are not severe and should not be an obstacle to the successful control of the Covid-19 pandemic in India.

Abstract Birds perform significant ecosystem services in the environment. Nevertheless, they have been facing threats to their survival globally. This special collection assembles diverse articles on various aspects of birds’ life, their interactions with the environment, their adaptations, and threats they have been facing along with conservation measures.

In groundwater, excessive fluoride concentration has been evidenced in more than 20 developing and developed countries including India, wherein 19 states are confronting serious problems owing to fluorosis. A total 71% of earth's surface constitutes water however, only some amount is drinkable and suitable for household activities. Groundwater present below the earth's surface is a vital source of water for human intake. Numerous scientists have reported that a small amount of fluoride is beneficiary for strengthening the bone and preventing dental caries, instead, its high dose causes severe effects on human health such as skeletal fluorosis, dental fluorosis, increase in bone fracture, decrease in birth rates, impaired thyroid functions, increased rates of Urolithiasis and lower IQ in children. Fluoride exposure may lead to high frequency of chronic irritative respiratory disease, chronic bronchitis in particular. Due to high intake of sodium fluoride pesticides and dental products, severe fluoride toxicity and death have also been witnessed. Although fluorine is present in all four spheres of earth i.e., lithosphere, atmosphere, hydrosphere and biosphere, however the volcanic rocks are also one of major sources of large quantity of fluorine. Through numerous natural processes like rock dissolution, volcanic eruptions and man-induced activities (ore processing, coal burning, use of industrial plants and fertilizers), fluorine enters to the environment. Fluoride is a highly reactive chemical entity naturally found as CaF2 being a vital component in minerals (fluorite, topaz, cryolite, fluorapatite, theorapatite and phosphorite etc.). Entrance of fluoride in the soil occurs through rocks weathering, precipitation or waste runoff. In cases where water is not polluted by external sources, the surface water does not constitute more than 0.3 Mg/l of fluoride. Drinking water is the foremost supplier (75–90% of day-to-day consumption) of fluoride whereas, other sources are intoxication like food, drugs, cosmetics and industrial exposure etc.

Background: Ovarian tumors are common, ranking third in frequency after cervical and uterine cancer. According to the World Ovarian Cancer Coalition Atlas 2018, India has the second-highest incidence of ovarian carcinoma worldwide. In Chhattisgarh, ovarian malignancy accounts for 5.21% of all malignancies in females. Materials and Methods: The present descriptive observational cross-sectional study encompassed 108 cases of ovarian tumors. A histopathological examination was done, and data was compiled and analyzed. Observations and Results: Among the 108 ovarian tumors, 59.25% were noted in women aged 40–59 years. The primary presenting symptom was abdominal pain, reported by 57.40% of the cases. The majority of ovarian tumors were unilateral, the right side being more common. Primary ovarian tumors constituted 91.66%, while 8.33% were classified as secondary or metastatic ovarian tumors. Of 108 cases, 55.5% were malignant, 40.7% were benign, and 3.7% were borderline tumors. Surface epithelial tumors accounted for 73.14% of the cases; germ cell tumors constituted 9.25%, sex cord-stromal tumors were 7.4%, and mesenchymal tumors were 1.85%. Among surface epithelial tumors, serous cystadenocarcinoma was most common. Mature cystic teratoma was the most common germ cell tumor, and granulosa cell tumor was the most common sex cord-stromal tumor. Conclusion: Ovarian tumors were more commonly seen in the peri and postmenopausal age group. Primary malignant epithelial tumors were the most common type of ovarian tumor. The histopathological evaluation of ovarian tumors remains the gold standard for definitive diagnosis.

Edge-Based Online Learning (EBOL), a technique that combines the practical, hands-on approach of EBOL with the convenience of Online Learning (OL), is growing in popularity. But accurately monitoring student engagement to enhance teaching methodologies and learning outcomes is one of the difficulties of OL. To determine this challenge, this paper has put forth an Edge-Based Student Attentiveness Analysis System (EBSAAS) method, which uses a Face Detection (FD) algorithm and a Deep Learning (DL) model known as DLIP to extract eye and mouth landmark features. Images of the eye and mouth are used to extract landmarks using DLIP or Deep Learning Image Processing. Landmark Localization pre-trained models for Facial Landmark Localization (FLL) are one well-liked DL model for facial landmark recognition. The Visual Geometry Group-19 (VGG-19) learning model then uses these features to classify the student's level of attentiveness as fatigued or focused. Compared to a server-based model, the proposed model is developed to execute on an Edge Device (ED), enabling a swift and more effective analysis. The EBOL achieves 95.29% accuracy and attains 2.11% higher than existing model 1 and 4.41% higher than existing model 2. The study's findings have shown how successful the proposed method is at assisting teachers in changing their teaching methodologies to engage students better and enhance learning outcomes.

An affective state of a learner in E-learning has gained enormous interest. The prediction of the emotional state of a learner can enhance the outcome of learning by including designated mediation. Many techniques are developed for anticipating emotional states using video, audio, and bio-sensors. Still, examining video, and audio will not confirm secretiveness and is exposed to security issues. Here the creator devises a fusion technique, to be specific Squirrel Search and Rider optimization-grounded Deep LSTM for affect prediction.The Deep LSTM is trained to exercise the new fusion SS-ROA. Then, the SS-ROA-grounded Deep LSTM classifies the states like frustration, confusion, engagement, wrathfulness, and so on. It is based on the interaction log data of the E-learner. In conclusion, the course and student ID, predicted state, test marks, and course completion status are taken as result information to find out the correlations. The new algorithm gives the best performance in comparison to other present methods with the highest prediction accurateness of 0.962 and the most noteworthy connection of 0.379 respectively. After discovering affective states, students may get the advantage of getting real comments from a teacher for improving one's performance during learning. However, such systems should also give feedback about the learner's affective state or passion because it greatly affects the student's encouragement toward better learning.

IntroductionAlmost 250 million children fail to achieve their full growth or developmental potential, trapping them in a cycle of continuing disadvantage. Strong evidence exists that parent-focussed face to face interventions can improve developmental outcomes; the challenge is delivering these on a wide scale. SPRING (Sustainable Programme Incorporating Nutrition and Games) aimed to address this by developing a feasible affordable programme of monthly home visits by community-based workers (CWs) and testing two different delivery models at scale in a programmatic setting. In Pakistan, SPRING was embedded into existing monthly home visits of Lady Health Workers (LHWs). In India, it was delivered by a civil society/non-governmental organisation (CSO/NGO) that trained a new cadre of CWs.MethodsThe SPRING interventions were evaluated through parallel cluster randomised trials. In Pakistan, clusters were 20 Union Councils (UCs), and in India, the catchment areas of 24 health sub-centres. Trial participants were mother-baby dyads of live born babies recruited through surveillance systems of 2 monthly home visits. Primary outcomes were BSID-III composite scores for psychomotor, cognitive and language development plus height for age z-score (HAZ), assessed at 18 months of age. Analyses were by intention to treat.Results1,443 children in India were assessed at age 18 months and 1,016 in Pakistan. There was no impact in either setting on ECD outcomes or growth. The percentage of children in the SPRING intervention group who were receiving diets at 12 months of age that met the WHO minimum acceptable criteria was 35% higher in India (95% CI: 4–75%, p = 0.023) and 45% higher in Pakistan (95% CI: 15–83%, p = 0.002) compared to children in the control groups.DiscussionThe lack of impact is explained by shortcomings in implementation factors. Important lessons were learnt. Integrating additional tasks into the already overloaded workload of CWs is unlikely to be successful without additional resources and re-organisation of their goals to include the new tasks. The NGO model is the most likely for scale-up as few countries have established infrastructures like the LHW programme. It will require careful attention to the establishment of strong administrative and management systems to support its implementation.

IntroductionThere is limited evidence from low and middle-income settings on the effectiveness of early child development interventions at scale. To bridge this knowledge-gap we implemented the SPRING home visiting program where we tested integrating home visits into an existing government program (Pakistan) and employing a new cadre of intervention workers (India). We report the findings of the process evaluation which aimed to understand implementation.Methods and materialsWe collected qualitative data on acceptability and barriers and facilitators for change through 24 in-depth interviews with mothers; eight focus group discussions with mothers, 12 with grandmothers, and 12 with fathers; and 12 focus group discussions and five in-depth interviews with the community-based agents and their supervisors.ResultsImplementation was sub-optimal in both settings. In Pakistan issues were low field-supervision coverage and poor visit quality related to issues scheduling supervision, a lack of skill development, high workloads and competing priorities. In India, issues were low visit coverage - in part due to employing new workers and an empowerment approach to visit scheduling. Coaching caregivers to improve their skills was sub-optimal in both sites, and is likely to have contributed to caregiver perceptions that the intervention content was not new and was focused on play activities rather than interaction and responsivity - which was a focus of the coaching. In both sites caregiver time pressures was a key reason for low uptake among families who received visits.DiscussionPrograms need feasible strategies to maximize quality, coverage and supervision including identifying and managing problems through monitoring and feedback loops. Where existing community-based agents are overstretched and system strengthening is unlikely, alternative implementation strategies should be considered such as group delivery. Core intervention ingredients such as coaching should be prioritized and supported during training and implementation. Given that time and resource constraints were a key barrier for families a greater focus on communication, responsivity and interaction during daily activities could have improved feasibility.

Machine recognition of the human activities is an active research area in computer vision. In previous study, either one or two types of modalities have been used to handle this task. However, the grouping of maximum information improves the recognition accuracy of human activities. Therefore, this paper proposes an automatic human activity recognition system through deep fusion of multi-streams along with decision-level score optimization using evolutionary algorithms on RGB, depth maps and 3d skeleton joint information. Our proposed approach works in three phases, 1) space-time activity learning using two 3D Convolutional Neural Network (3DCNN) and a Long Sort Term Memory (LSTM) network from RGB, Depth and skeleton joint positions 2) Training of SVM using the activities learned from previous phase for each model and score generation using trained SVM 3) Score fusion and optimization using two Evolutionary algorithm such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. The proposed approach is validated on two 3D challenging datasets, MSRDailyActivity3D and UTKinectAction3D. Experiments on these two datasets achieved 85.94% and 96.5% accuracies, respectively. The experimental results show the usefulness of the proposed representation. Furthermore, the fusion of different modalities improves recognition accuracies rather than using one or two types of information and obtains the state-of-art results.

In order to provide quick and accurate fault detection in a DC microgrid, a new protection strategy is developed in this study. It is based on the multi-resolution analysis of travelling waves. The multi-resolution analysis of travelling waves is carried out using discrete wavelet transform (DWT). The dyadic filter bank theory is used to obtain the DWT coefficient of the voltage travelling wave. The energy of the DWT coefficient is then determined, and the fault will be identified if this energy content exceeds the predetermined threshold value. Initially, the efficacy of the proposed algorithm is verified on a 500 V ring-type bipolar DC microgrid test model, which is developed with MATLAB/Simulink environment. Then the results obtained were later validated with the real-time simulator Opal-RT. The various simulation results indicate that the proposed algorithm can successfully be implemented in both modes, i.e., grid-integrated and islanded. It can also provide protection against high resistance fault (HRF) up to 900 Ω in grid-integrated mode, and up to 1,300 Ω in islanded mode. The operating time of the proposed algorithm is also quite fast, i.e., 0.64 m.

There has been substantial evolution in the treatment of metastatic renal cell carcinoma with notable changes in the first-line setting. Currently, doublet combination therapy with either two immune checkpoint inhibitors or a combination of an immune checkpoint and tyrosine kinase inhibitor is considered the standard of care. The doublet combination therapies have demonstrated significantly improved clinical outcomes. A recently conducted trial (COSMIC-313) showed superior efficacy with a triplet combination of cabozantinib, nivolumab, and ipilimumab when compared to a placebo, nivolumab, and ipilimumab but at the cost of additional toxicity. Many other combination treatments, such as pembrolizumab plus lenvatinib plus belzutifan (NCT04976634), are being investigated, possibly leading to more options in the first-line setting in the future.

BackgroundBreast cancer is the most common type of cancer in women, and vast research is being conducted throughout the world for the treatment of this malignancy by natural products using various computational approaches. Xanthohumol, a prenylated flavonoid, is known for its anticancer activity; however, the mechanism behind its action is still in the preliminary stage.MethodsThe current study aimed to analyze the efficacy of xanthohumol compared to the currently available anticancer drugs targeting phosphoinositide-3-kinase (PI3K), serine/threonine kinase (AKT) receptors, and human epidermal growth factor receptor 2 (HER2) for breast cancer treatment through in silico analysis.ResultsThe result revealed that the target compound showed significant binding affinity to targets within the PI3K, AKT, and HER2 signaling pathways with a binding energy of −7.5, −7.9, and −7.9 kcal/mol, respectively. Further prediction studies were then made concerning this compound’s absorption, distribution, metabolism, and excretion (ADME) as well as drug-likeness properties, resulting in its oral bioavailability with only a single violation of Lipinski’s rule of five.ConclusionsThe finding revealed the ability of xanthohumol to bind with multiple cancer cell signaling molecules including PI3K, AKT kinase, and HER2. The current novel study opened the door to advancing research into the management and treatment of breast cancer.

The mountain systems of the Himalayan regions are changing rapidly due to climatic change at a local and global scale. The Indian Western Himalaya ecosystem (between the tree line and the snow line) is an underappreciated component. Yet, knowledge of vegetation distribution, rates of change, and vegetation interactions with snow-hydroclimatic elements is lacking. The purpose of this study is to investigate the linkage between the spatiotemporal variability of vegetation (i.e., greenness and forest) and related snow-hydroclimatic parameters (i.e., snow cover, land surface temperature, Tropical Rainfall Measuring Mission (TRMM), and Evapotranspiration (ET)) in Himachal Pradesh (HP) Basins (i.e., Beas, Chandra, and Bhaga). Spatiotemporal variability in forest and grassland has been estimated from MODIS land cover product (MCD12Q1) using Google Earth Engine (GEE) for the last 19 years (2001–2019). A significant inter- and intra-annual variation in the forest, grassland, and snow-hydroclimatic factors have been observed during the data period in HP basins (i.e., Beas, Chandra, and Bhaga basin). The analysis demonstrates a significant decrease in the forest cover (214 ha/yr.) at the Beas basin; however, a significant increase in grassland cover is noted at the Beas basin (459 ha/yr.), Chandra (176.9 ha/yr.), and Bhaga basin (9.1 ha/yr.) during the data period. Spatiotemporal forest cover loss and gain in the Beas basin have been observed at ~7504 ha (6.6%) and 1819 ha (1.6%), respectively, from 2001 to 2019. However, loss and gain in grassland cover were observed in 3297 ha (2.9%) and 10,688 ha (9.4%) in the Beas basin, 1453 ha (0.59%) and 3941 ha (1.6%) in the Chandra basin, and 1185 ha (0.92%) and 773 ha (0.60%) in the Bhaga basin, respectively. Further, a strong negative correlation (r = −0.65) has been observed between forest cover and evapotranspiration (ET). However, a strong positive correlation (r = 0.99) has been recorded between grassland cover and ET as compared to other factors. The main outcome of this study in terms of spatiotemporal loss and gain in forest and grassland shows that in the Bhaga basin, very little gain and loss have been observed as compared to the Chandra and Beas basins. The present study findings may provide important aid in the protection and advancement of the knowledge gap of the natural environment and the management of water resources in the HP Basin and other high-mountain regions of the Himalayas. For the first time, this study provides a thorough examination of the spatiotemporal variability of forest and grassland and their interactions with snow-hydroclimatic factors using GEE for Western Himalaya.

Due to its inherent ability of augmenting power system stability limit while maintaining good electric Power Quality (PQ), Power System Stabilizer (PSS) is envisaged to be an effective device in numerous Distributed Generation (DG) applications. It becomes a paramount work to maintain stable frequency, and alleviate the utility-grid collapse. Owing to this, this paper evaluates the performance of generic, and Multiband (MB)-PSSs for a hybrid 9 MW wind farm connected with mini/micro hydro power plant. The proposed system is interfaced with 200 MVA, 25 kV utility-grid system. A comparative analysis in the performance of a generic-PSS is presented with a MB-PSS at various voltage level points and power flows at various Point of Common Couplings (PCC) are analysed. Transient analysis is carried out during the presence of various unsymmetrical faults. It is envisaged that transients and unbalanced voltage sag-swells are mitigated at various PCCs while maintaining uninterruptable real and reactive power flows. The superiority of MB-PSS is established over generic-PSS in maintaining optimal PQ flow. Consequently, the reactive power requirement of the connected load is compensated by wind and mini/micro hydro controlling schemes using MB-PSS. Overall, the accurateness of the developed simulink models is demonstrated and satisfactory results are obtained in comparative analysis of both stabilizers.

Background: COVID-19 vaccination is one of the pivotal key tools against the ongoing pandemic, but its acceptance relies on efficacy and safety data among various populations, including patients with cancers. However, there is limited data on seroconversion rates, efficacy, and safety of the COVID-19 vaccine in patients with cancer. Breakthrough infections after vaccination have also been reported, which could further strengthen the refusal behavior of specific populations to be immunized. Our objective was to investigate the efficacy and safety of COVID-19 vaccination in real-world patients with advanced genitourinary cancers. Methods and results: A retrospective study of the 738 patients with advanced metastatic genitourinary malignancy was conducted at our genitourinary oncology clinic from October 2020 to September 2021, out of which 462 patients (62.6%) were vaccinated. During the study period, two vaccinated, and six unvaccinated patients tested positive for SARS-CoV-2 (breakthrough infection rate: 0.4% vs. 2.2%, p = 0.027). Vaccine protection against infection was 81.8% (95% CI: 0.04–0.98). One vaccinated and 4 unvaccinated patients were hospitalized due to COVID-19 (0.2% vs. 1.4%, p = 0.048). Vaccine effectiveness in preventing hospitalization was 85.7% (95% CI: 0.02–1.33). Within one month of vaccination, 1.5% of patients (n = 7) had emergency visits, 0.8% (n = 4) were hospitalized for any reason, and of these, 3 (0.6%) experienced a delay in the receipt of their cancer therapy. Conclusion: In our hypothesis-generating data among patients with advanced genitourinary cancers, COVID-19 vaccination was efficacious and safe and was rarely associated with treatment disruptions. These data should help improve the acceptance of the COVID-19 vaccine in the general population and patients with cancer. The vaccine effectiveness in our patients is comparable with existing published data without cancer.

Abstract A state‐of‐the‐art Lagrangian microphysics scheme is used in a large‐eddy simulation to investigate the stratocumulus transition from closed to open cell structure. Processes controlling precipitation development, which is a key to the transition, are analyzed by leveraging unique benefits of Lagrangian microphysics, particularly the ability to track computational drops in the flow. Sufficient time is needed for coalescence growth of cloud drops to drizzle within the updraft‐downdraft cycle of large eddies. This favors broad drop size distributions (DSDs) and drizzle growth in downdrafts, where drops are typically much older than in updrafts. During the closed cell stage, mean cloud drop radius is too small, and the DSDs are too narrow, so that the timescale for coalescence is much longer than the large eddy turnover time and drizzle growth is limited. The closed‐to‐open cell transition occurs when these timescales become comparable and the precipitation flux increases sharply.

Background: Brain dynamics associated with epilepsy remains limited. EEG-based epilepsy diagnosis and seizure detection is still in its infancy. The problem is further amplified for the design and development of automated algorithms, which requires a quantitative parametric representation of the qualitative or visual aspect of the markers. This study proposes an automatic classification system for epilepsy based on neural networks and EEG signals. Material and Method: The present study made use of EEG data from 16 controls and 16 temporal lobe epilepsy (TLE) patients in order to comparatively assess neural dynamics in normal healthy young adults and epileptic patients treated with anti-epileptic drugs in the context of resting state during eye close session. Such tangible differences could be appreciated through artificial neural network (ANN) classifiers. Results: During eye closed session of EEG in order to diagnose temporal lobe epileptic patient, the extracted features of EEG activity are given to the classifier algorithm for training and test performance. Artificial Neural Network (ANN) classifier was used for the diagnosis task. Fractal dimension (Katz, Higuchi and Permission entropy) were analyzed, in which the best results was observed in trained set of data of Katz (93.18%). Conclusion: Non-linear analysis plays an important role in prediction of complex partial seizure during interictal period.

Introduction Presently, there are few population-level strategies to address SARS-CoV-2 infection except preventive measures such as vaccination. Micronutrient deficiency, particularly vitamin D and zinc deficiency, has been associated with dysregulated host responses, and may play an important role in COVID-19.Methods and analysis We have designed a 2×2 factorial, randomised, double-blind, multi-centre placebo-controlled trial to evaluate the effect of vitamin D and zinc on COVID-19 outcomes in Maharashtra, India. COVID-19 positive individuals are recruited from hospitals in Mumbai and Pune. Participants are provided (1) vitamin D3 bolus (180 000 IU) maintained by daily dose of 2000 IU and/or (2) zinc gluconate (40 mg daily), versus placebo for 8 weeks. Participants undergo a detailed assessment at baseline and at 8 weeks, and are monitored daily in hospital or every 3 days after leaving the hospital to assess symptoms and other clinical measures. A final follow-up telephone call occurs 12 weeks post-enrolment to assess long-term outcomes. The primary outcome of the study is to time to recovery, defined as time to resolution of all of fever, cough and shortness of breath. Secondary outcomes include: duration of hospital stay, all-cause mortality, necessity of assisted ventilation, change in blood biomarker levels and individual symptoms duration. Participant recruitment commenced on April 2021.Ethics and dissemination Ethical approval was obtained from institutional ethical committees of all participating institutions. The study findings will be presented in peer-reviewed medical journals.Trial registration numbers NCT04641195, CTRI/2021/04/032593, HMSC (GOI)-2021-0060.

Background: Early adversities negatively impact children’s growth and development, putatively mediated by chronic physiological stress resulting from these adverse experiences. We aimed to estimate the associations between prospectively measured cumulative early adversities with growth and cognition outcomes in rural Indian preschool children and to explore if hair cortisol concentration (HCC), a measure of chronic physiological stress, mediated the above association. Methods: Participants were recruited from the SPRING cRCT in rural Haryana, India. Adversities experienced through pregnancy and the first year of life were measured in 1304 children at 12-months. HCC was measured at 12-months in 845 of them. Outcome measures were height-for-age-z-score (HAZ), weight-for-age-z-score (WAZ) and cognition, measured in 1124 children followed up at 3-years. Cognition was measured using a validated tablet-based gamified tool named DEEP. Results: Cumulative adversities at 12-months were inversely associated with all outcomes measures at 3-years. Each unit increase in adversity score led to a decrease of 0·08 units [95% confidence interval (CI):-0·11,-0·06] in DEEP-z-score; 0·12 units [-0·14,-0·09] in HAZ and 0·11 units [-0·13,-0·09] in WAZ. 12-month HCC was inversely associated with DEEP-z-score (-0·09 [-0·16,-0·01]) and HAZ (-0·12 [-0·20,-0·04]), but the association with WAZ was not significant (p = 0·142). HCC marginally mediated the association between cumulative adversities and HAZ (proportion mediated = 0·06, p = 0·014). No evidence of mediation was found for the cognition outcome. Conclusions: Cumulative early adversities and HCC measured at 12-months have persistent negative effects on child growth and cognition at 3-years. The association between adversities and these two child outcomes were differentially mediated by HCC, with no evidence of mediation observed for the cognitive outcome. Future studies should focus on other stress biomarkers, and alternate pathways such as the immune, inflammation and cellular ageing pathways, to unpack key mechanisms underlying the established relationship between early adversities and poor child outcomes.

Background: N-terminal pro-B-type natriuretic peptide (NT-proBNP) improves emergency room diagnosis and is a prognostic marker of acutely decompensated heart failure (HF) after hospitalization. We tried to assess the prognosis in heart failure with preserved ejection fraction (HFpEF) and heart failure with reduced ejection fraction (HFrEF) using the biomarker. Methods: Patients were categorized into HFpEF (left ventricular ejection fraction [LVEF] ≥50%) (n = 102), HF with midrange EF (LVEF of 40%–49%) (n = 106), and HFrEF (LVEF ≤40%) (n = 112) as per the ESC classification of HF subtypes. Prognosis by absolute and percentage change in NT-proBNP at admission and at the 10th day of treatment was found in 3-month follow-up. Mortality after discharge was also assessed. Results: Among all HF groups, a reduction of NT-proBNP

Background: Coronavirus disease-2019 (COVID-19) pandemic has affected millions worldwide. The various complications of COVID-19 have been discussed in the recent studies. We would like to discuss about one such cardiovascular complication, ST elevation myocardial infarction (STEMI) in COVID-19, and its characteristics in this article. Methodology: An observational study was done from April 1, 2020, to July 15, 2020, selecting all STEMI patients admitted to a tertiary care center in Chennai and treated with tenecteplase were grouped in to COVID-19 positive and negative. Their presenting features and complications and outcome were studied. Results: A total of 300 patients were admitted with STEMI during the study period, of which 11.66% were COVID-19 positive. Atherosclerotic cardiovascular risk factors were much lower in COVID-19-positive STEMI patients when compared to non-COVID STEMI patients. Mean oxygen saturation at time of presentation in COVID-19 positive and negative patients was 85.1% and 97.5%, respectively (P < 0.001). COVID-19 group had significantly higher noncardiac complications than non-COVID patients (P < 0.001). The average duration of hospital stay in COVID-19 STEMI was 13.6 days, whereas in non-COVID group was 6.8 days (P < 0.001). Higher mortality was found in the COVID-19 positive group (14.28%) when compared to non-COVID STEMI (7.9%; P < 0.001). Conclusion: COVID-19 itself is a systemic inflammatory disease, which could increase the risk of coronary plaque rupture. Thrombosis has also been described as a mechanism underlying certain cases causing presentation of STEMI, because of endothelial dysfunction and hypercoagulable state.

Electroencephalography (EEG) provides a non-invasive means to advancing our understanding of the development and function of the brain. However, the majority of the world’s population residing in low and middle income countries has historically been limited from contributing to, and thereby benefiting from, such neurophysiological research, due to lack of scalable validated methods of EEG data collection. In this study, we establish a standard operating protocol to collect approximately 3 min each of eyes-open and eyes-closed resting-state EEG data using a low-cost portable EEG device in rural households through formative work in the community. We then evaluate the acceptability of these EEG assessments to young children and feasibility of administering them through non-specialist workers. Finally, we describe properties of the EEG recordings obtained using this novel approach to EEG data collection. The formative phase was conducted with 9 families which informed protocols for consenting, child engagement strategies and data collection. The protocol was then implemented on 1265 families. 977 children (Mean age = 38.8 months, SD = 0.9) and 1199 adults (Mean age = 27.0 years, SD = 4) provided resting-state data for this study. 259 children refused to wear the EEG cap or removed it, and 58 children refused the eyes-closed recording session. Hardware or software issues were experienced during 30 and 25 recordings in eyes-open and eyes-closed conditions respectively. Disturbances during the recording sessions were rare and included participants moving their heads, touching the EEG headset with their hands, opening their eyes within the eyes-closed recording session, and presence of loud sounds in the testing environment. Similar to findings in laboratory-based studies from high-income settings, the percentage of recordings which showed an alpha peak was higher in eyes-closed than eyes-open condition, with the peak occurring most frequently in electrodes at O1 and O2 positions, and the mean frequency of the alpha peak was found to be lower in children (8.43 Hz, SD = 1.73) as compared to adults (10.71 Hz, SD = 3.96). We observed a deterioration in the EEG signal with prolonged device usage. This study demonstrates the acceptability, feasibility and utility of conducting EEG research at scale in a rural low-resource community, while highlighting its potential limitations, and offers the impetus needed to further refine the methods and devices and validate such scalable methods to overcome existing research inequity.

Background: Early adversities negatively impact children’s growth and development, putatively mediated by chronic physiological stress resulting from these adverse experiences. We aimed to estimate the associations between prospectively measured cumulative early adversities with growth and cognition outcomes in rural Indian preschool children and to explore if hair cortisol concentration (HCC), a measure of chronic physiological stress, mediated the above association. Methods: Participants were recruited from the SPRING cRCT in rural Haryana, India. Adversities experienced through pregnancy and the first year of life were measured in 1304 children at 12-months. HCC was measured at 12-months in 845 of them. Outcome measures were height-for-age-z-score (HAZ), weight-for-age-z-score (WAZ) and cognition, measured in 1124 children followed up at 3-years. Cognition was measured using a validated tablet-based gamified tool named DEEP. Results: Cumulative adversities at 12-months were inversely associated with all outcomes measures at 3-years. Each unit increase in adversity score led to a decrease of 0·08 units [95% confidence interval (CI):-0·11,-0·06] in DEEP-z-score; 0·12 units [-0·14,-0·09] in HAZ and 0·11 units [-0·13,-0·09] in WAZ. 12-month HCC was inversely associated with DEEP-z-score (-0·09 [-0·16,-0·01]) and HAZ (-0·12 [-0·20,-0·04]), but the association with WAZ was not significant (p = 0·142). HCC marginally mediated the association between cumulative adversities and HAZ (proportion mediated = 0·06, p = 0·014). No evidence of mediation was found for the cognition outcome. Conclusions: Cumulative early adversities and HCC measured at 12-months have persistent negative effects on child growth and cognition at 3-years. The association between adversities and these two child outcomes were differentially mediated by HCC, with no evidence of mediation observed for the cognitive outcome. Future studies should focus on other stress biomarkers, and alternate pathways such as the immune, inflammation and cellular ageing pathways, to unpack key mechanisms underlying the established relationship between early adversities and poor child outcomes.

In this work, a novel 1-Benzylpyridazinium bromide (BzPyBr) was synthesized using microwave irradiation and characterized by proton, carbon-13 and mass spectroscopy. The corrosion inhibition behavior was investigated in 0.5 M sulfuric acid on mild steel (MS) via experimental and theoretical methods. BzPyBr exhibits the best efficiency of 97.52% at 1000 ppm (at 303K). PDP studies revealed it behaves as a mixed-type corrosion inhibitor whereas impedance data confirmed its adsorption on the MS surface. Surface topography as examined by AFM and SEM along with EDS depicted a smooth surface in presence of the studied ionic liquid. Langmuir isotherm model was found to be acceptable for the adsorption phenomenon exhibited by the inhibitor. ΔGads was found to be suggestive of physical and chemical adsorption of the studied inhibitor on the mild steel surface. In addition, Quantum studies based on the DFT method supported the experimental data.

COVID-19 has sparked a global pandemic, with a variety of inflamed instances and deaths increasing on an everyday basis. Researchers are actively increasing and improving distinct mathematical and ML algorithms to forecast the infection. The prediction and detection of the Omicron variant of COVID-19 brought new issues for the health fraternity due to its ubiquity in human beings. In this research work, two learning algorithms, namely, deep learning (DL) and machine learning (ML), were developed to forecast the Omicron virus infections. Automatic disease prediction and detection have become crucial issues in medical science due to rapid population growth. In this research study, a combined Extended CNN-RNN research model was developed on a chest CT-scan image dataset to predict the number of +ve and −ve cases of Omicron virus infections. The proposed research model was evaluated and compared against the existing system utilizing a dataset of 16,733-sample training and testing CT-scan images collected from the Kaggle repository. This research article aims to introduce a combined ML and DL technique based on the combination of an Extended Convolutional Neural Network (ECNN) and an Extended Recurrent Neural Network (ERNN) to diagnose and predict Omicron virus-infected cases automatically using chest CT-scan images. To overcome the drawbacks of the existing system, this research proposes a combined research model that is ECNN-ERNN, where ECNN is used for the extraction of deep features and ERNN is used for exploration using extracted features. A dataset of 16,733 Omicron computer tomography images was used as a pilot assessment for this proposed prototype. The investigational experiment results show that the projected prototype provides 97.50% accuracy, 98.10% specificity, 98.80% of AUC, and 97.70% of F1-score. To the last, the study outlines the advantages being offered by the proposed model with respect to other existing models by comparing different parameters of validation such as accuracy, error rate, data size, time complexity, and execution time.

Background: Clostridioides difficile infection (CDI) is a considerable healthcare burden, and now identified as the leading cause of acquired diarrheal illness in patients receiving antibiotics. Patients with malignancies are more prone to acquire CDI, owing to their frequent exposure to risk factors. Objective: This study aims to investigate the factors affecting the outcome of Clostridioides Difficile Infection in patients with solid tumors at our community healthcare center. Methods: This is a retrospective study that included a total of 59 patients with solid tumors who were hospitalized for Clostridioides difficile infection. Results: The median age of the study population was 79 years with 39 males and 20 females. The patients had a diagnosis of a malignancy involving the following sites: prostate (25), lung (19), colon (7), bladder (4), breast (3), and renal (1). There were 52 cases of first time and 7 cases of recurrent CDI admissions. 40 patients were detected to have CDI at presentation while 19 patients were diagnosed with CDI after admission. CDI was categorized as follows: non-severe (29), severe (28), and very severe (2). There were 33 patients on chemotherapy and 20 patients undergoing radiotherapy. Twenty-seven patients had a recent history of cancer care-related procedures or interventions. Twenty-nine patients were from either a rehabilitation center or a long-term nursing care facility. There were 39 recent hospitalizations with 29 patients receiving antibiotics. Almost half of the patients were on proton pump inhibitors (29) and 12 were on steroids (20.3%) at the time of developing CDI. Patients with a high-risk qSOFA score of 2 or more (p-value = 0.008) or a high white blood cell count of >15 × 109/L (p-value = 0.016) at the time of admission were found to have higher in-hospital mortality. Critical care data suggested that 9 patients required intensive care, 7 patients required vasopressor support, and 6 needed mechanical ventilation. Patients were treated with either vancomycin alone (13), or metronidazole alone (25), or combination therapy with vancomycin + metronidazole (21). The median duration of hospital stay was 6 days with 11 fatalities (18.64%). Conclusions: CDI causes significant morbidity in patients with malignancies. A high qSOFA score and leukocytosis are significantly associated with high morbidity and thus should be used to prioritize and intensify inpatient care of these patients.

Background: Poly (ADP-ribose) polymerase (PARP) inhibitors are approved for patients with metastatic castration-resistant prostate cancer harboring deleterious or suspected deleterious BRCA1 and/or 2 mutations. Identifying patients with prostate cancer harboring these mutations may be challenging. Circulating cell-free DNA (cfDNA) provides an avenue for an easier detection of these mutations. Herein, we aimed to evaluate the concordance of BRCA mutations in the tumor tissue and cfDNA in patients with metastatic prostate cancer in the real-world setting. Methods: Somatic genomic profiling results were obtained from a clinical cohort of patients at our institution who had at least two samples tested. One of the samples needed to be from either primary or metastatic tissue. Concordance was adjusted to not include mutation types that the cfDNA platforms were not designed to detect. Results: The presence or absence of mutations in the BRCA gene was assessed in a total of 589 samples, including 327 cfDNA samples, from 260 patients with metastatic prostate cancer. The median time between the first test and any subsequent test was 22.8 (0.0–232) months. BRCA mutation was present in the patient’s original prostate tissue in 23 samples (3.9%) of patients. The adjusted concordance between prostate tumor tissue and cfDNA was 97.9% [95% CI, 95.3–99.1%]. The adjusted concordance between metastatic samples and cfDNA was 93.5% [95% CI, 86.4–97.3%]. Of the patients who had a BRCA mutation detected in their prostate tissue, there was a 70% probability of detecting a BRCA mutation in the patient’s cfDNA as well. For patients who did not have a detectable BRCA mutation in their primary prostate tissue, the probability of detecting a subsequent one later in the disease course was less than 0.9%. Conclusion: There is a high level of concordance between tissue and blood for BRCA mutations. Testing cfDNA can provide reliable information on BRCA mutational status and is a viable alternative to solid tissue sequencing when unavailable. The development of a new BRCA mutation later in the disease course is a rare event.

Brugada syndrome, a rare genetic disease increases the risk of arrhythmias and sudden cardiac death. Various trigger factors has been associated with Brugada syndrome. Arrhythmias have been reported in COVID 19. We report a patient with transient Brugada like pattern in ECG triggered by COVID 19.

The latest threat to global health is the form of the ongoing Coronavirus Disease 2019 (COVID-19) pandemic. This new coronavirus (SARS-COV-2) started as a local outbreak in Wuhan, China but soon tightened its grip on human lives around the globe. So far, we do not have a particularly effective anti-SARS-COV-2 vaccine or antiviral agent against COVID-19. Across the globe, many research organizations such as the National Institutes of Health (NIH), United States are studying and testing various drugs and vaccines for their effectiveness against SARS-COV-2. Currently, the principle fighting tool being promoted by the World Health Organization (WHO) is the prevention of acquiring SARS-COV-2 infection by following basic health hygiene rules and social distancing. We hereby discuss major non-pharmacological and pharmacological interventions.

Currently, the world is facing its 3rd coronavirus outbreak of the 21st century, which has turned to a pandemic recently. Starting on December 2019, coronavirus disease (COVID-19) was first detected in Wuhan City, Hubei province, China. As of 31st March, 2020, more than 900,000 COVID-19 cases have been reported across the globe involving more than 200 countries. The first case of the United States (US) was confirmed on 20th January, 2020 in a 35-year-old male who had a travel history to Wuhan, China, before returning to the U.S. Since then, the SARS-CoV-2 virus has spread to all the 50 states of US, with more cases being reported every day. New York, New Jersey, Michigan, and California are the worst-hit states. As COVID-19 is growing, every day has been marked by novel developments and updates. We hereby talk about how the U.S. is leading the multiagency effort to fight against this pandemic and the steps that have been taken so far.

Background: The bone health of patients with locally advanced and metastatic prostate cancer is at risk from treatment-related bone density loss and skeletal-related events from metastatic disease in bones. Evidence-based guidelines recommend using denosumab or zoledronic acid at bone metastasis-indicated dosages in the setting of castration-resistant prostate cancer with bone metastases and at the osteoporosis-indicated dosages in the hormone-sensitive setting in patients with a significant risk of fragility fracture. For the concerns of jaw osteonecrosis, a dental evaluation is recommended before starting bone-modifying agents. The literature review suggests a limited evidence-based practice for bone health with prostate cancer in the real world. Both under-treatment and inappropriate dosing of bone remodeling therapies place additional risks to bone health. An incomplete dental work up before starting bone-modifying agents increases the risk of jaw osteonecrosis. Methods: We created an algorithm-based clinical practice tool to minimize the deviation from evidence-based guidelines at our center and provide appropriate bone health care to our patients by ensuring indication-appropriate dosing and dental screening rates. This order set was incorporated into the electronic medical record system for ordering a bone remodeling agent for prostate cancer. The tool prompts the clinicians to follow the appropriate algorithm in a stepwise manner to ensure a pretreatment dental evaluation and use of the correct dosage of drugs. Results: We analyzed the data from Sept 2019 to April 2022 following the incorporation of this tool. 0/35 (0%) patients were placed on inappropriate bone modifying agent dosing, and dental health was addressed in every patient before initiating treatment. We compared the change in the practice of prescribing and noted a significant difference in the clinician’s practice while prescribing denosumab/zoledronic acid before and after implementation of this tool [incorrect dosing: 24/41 vs. 0/35 (p < 0.00001)]; and an improvement in pretreatment dental checkup before and after implementation of the tool was noted to be [missed dental evaluation:12/41 vs. 0/35 (p < 0.00001)]. Conclusion: We found that incorporating an evidence-based algorithm in the order set while prescribing bone remodeling agents significantly improved our institutional clinical practice of indication-appropriate dosing and dental screening rates, and facilitated high-quality, evidence-based care to our patients with prostate cancer.

Introduction: Acute myeloid leukemia (AML) is the commonest leukemia in adults. Mortality in thew first 30-days ranges from 6% to 43%, while infections account for 30–66% of early deaths. We aim to present our experience of infections in newly-diagnosed AML. Method: This prospective, observational study, was undertaken at a tertiary care hospital in Northern India. Patients with confirmed AML (bone marrow morphology and flow cytometry) and who had developed febrile neutropenia (FN), were included. Result: A total of fifty-five patients were included in the study. The median age of the patients was 47.1 years (12–71) and 28 (50.9%) were males. Fever (33, 60%) was the commonest presentation at the time of diagnosis. One or more comorbid conditions were present in 20 patients (36.36%). Infection at presentation was detected in 17 patients (30.9%). The mean duration to develop febrile neutropenia since the start of therapy was 11.24 days. With each ten-thousand increase in white blood cell (WBC) count, the mean number of days of FN development decreased by 0.35 days ( p = 0.029). Clinical and/or radiological localization was possible in 23 patients (41.81%). Thirty-four blood samples (34/242, 14.04%) from 26 patients (26/55, 47.3%) isolated one or more organisms. Gram negative bacilli (GNB) were isolated in 24 (70.58%) samples. Burkholderia cepacia (8/34, 23.52%) was the commonest organism. The number of days required to develop febrile neutropenia was inversely associated with overall survival (OS). However, when compared, there was no statistically significant difference in OS between patients developing fever on day-10 and day-25 ( p = 0.063). Thirteen patients (23.63%) died during the study period. Discussion: Low percentage of blood culture positivity and high incidence of MDR organisms are a matter of concern. Days to develop febrile neutropenia were inversely associated with overall survival (OS), emphasizing the importance of preventive measures against infections. Conclusion: Infections continues to be a major cause of morbidity and mortality among AML patients.

Background: There is an urgent need to fill the gap of scalable cognitive assessment tools for preschool children to enable identification of children at-risk of sub-optimal development and to support their timely referral into interventions. We present the associations between growth in early childhood, a well-established marker of cognitive development, and scores on a novel digital cognitive assessment tool called DEvelopmental Assessment on an E-Platform (DEEP) on a sample of 3-year old pre-schoolers from a rural region in north India. Methods: Between February 2018 and March 2019, 1359 children from the Sustainable Programme Incorporating Nutrition and Games (SPRING) programme were followed up at 3-years age and data on DEEP, anthropometry and a clinical developmental assessment, the Bayley's Scale of Infant and Toddler Development, 3rd edition (BSID-III) was collected. DEEP data from 200 children was used to train a machine learning algorithm to predict their score on the cognitive domain of BSID-III. The DEEP score of the remaining 1159 children was then predicted using this algorithm to examine the cross-sectional and prospective association of growth with the DEEP score. Findings: The magnitude of the concurrent positive association between height-for-age and cognitive z-scores in 3-year olds was similar when cognition was measured by BSID-III (0.20 standard deviations increase for every unit change in specifically age-adjusted height (HAZ), 95% CI = 0.06–0.35) and DEEP (0.26 CI, 0.11–0.41). A similar positive prospective relationship was found between growth at 18 (0.21 CI, 0.17–0.26) and 12-months (0.18 CI, 0.13–0.23) and DEEP score measured at 3-years. Additionally, the relationship between growth and cognitive development was found to be dependant on socioeconomic status (SES). Interpretation: In this study, we suggest the utility of DEEP, a scalable, digital cognitive assessment tool, to measure cognition in preschool children. Further validation in different and larger datasets is necessary to confirm our findings. Funding: The SPRING Programme was funded through a Wellcome Trust programme grant and the follow-up study by the Corporate Social Responsibility initiative grant from Madura Microfinance Ltd.

For the last few months, various geographical regions and health sectors have been facing challenges posed by the current COVID-19 pandemic. COVID-19 has led to significant disruption in the normal functioning of potentially life-saving therapies of hematopoietic cell transplant and chimeric antigen receptor therapy. As transplant physicians are gaining more information and experience regarding the undertaking of these complex procedures during the ongoing COVID-19 pandemic, we believe it is important to discuss the challenges faced, prognostic risk factors, and outcomes of COVID-19 in post-hematopoietic stem cell transplantation patients based on the available real-world data.

The Human Immunodeficiency Virus-1 (HIV-1) nucleocapsid protein (NC) as a mature protein or as a domain of the Gag precursor plays important roles in the early and late phases of the infection. To better understand its roles, we searched for new cellular partners and identified the RNA-binding protein Unr/CSDE1, Upstream of N-ras, whose interaction with Gag and NCp7 was confirmed by co-immunoprecipitation and FRET-FLIM. Unr interaction with Gag was found to be RNA-dependent and mediated by its NC domain. Using a dual luciferase assay, Unr was shown to act as an ITAF (IRES trans-acting factor), increasing the HIV-1 IRES-dependent translation. Point mutations of the HIV-1 IRES in a consensus Unr binding motif were found to alter both the IRES activity and its activation by Unr, suggesting a strong dependence of the IRES on Unr. Interestingly, Unr stimulatory effect is counteracted by NCp7, while Gag increases the Unr-promoted IRES activity, suggesting a differential Unr effect on the early and late phases of viral infection. Finally, knockdown of Unr in HeLa cells leads to a decrease in infection by a non-replicative lentivector, proving its functional implication in the early phase of viral infection.

BK virus (BKV)-related haemorrhagic cystitis (HC) is an important cause of morbidity following allogeneic haematopoietic stem cell transplantation (HSCT). The various risk factors include high-level BKV viruria and/or viremia, myeloablative conditioning, acute graft versus host disease (GVHD), cytomegalovirus viremia, and unrelated or HLA-mismatched donor. The presence of high plasma BK viral load and cytopenias have been implicated as important predictors for protracted disease course. These patients frequently require hospitalisation which may extend for several weeks. Supportive measures in the form of analgesics, intravenous hydration, bladder irrigation, and transfusion support remain the mainstay of management. Various drugs have been used with limited success in this setting. These include antiviral drugs, fluoroquinolones, leflunomide, growth factors, clotting factors, estrogens, and prostaglandins. The role of adoptive cellular immunotherapy has also been explored but lacks clinical validation. The strategies aimed at expediting urothelial repair like hyperbaric oxygen therapy (HBOT), intravesical fibrin glue and platelet-rich plasma (PRP) are emerging. Some patients with severe disease do require surgical intervention to relieve urinary obstruction. The frequent co-occurrence of acute GVHD and CMV disease further complicates the management in such patients. There is an unmet need for effective and evidence-based options for the prevention and management of this disease. Due to lack of robust data supported by randomised trials, the acceptability of the available guidelines to simplify the treatment is expected to be low. Despite the availability of various treatment options, the management of BKV-related HC in day-to-day practice continues to be a challenge. The aim of this article is to put forward an up-to-date review of the preventive and therapeutic strategies for BKV-related HC.

Esophageal perforation is a fatal entity if not diagnosed in a timely fashion. Once diagnosed, it requires collaborative effort of team of doctors including radiologists, thoracic surgeons and general surgeons. We share hereby our experience with esophageal perforation and successful outcome.

Microbial fuel cells (MFCs) are the rising modern equipment for the generation of bioelectricity from organic matters. In this study, MFCs in two formats are assembled and concurrently operated for a 30-day period in a batch mode manner. Natural biowaste cattle dung slurry with mediators is used as a substrate persistently for the enhancement of electron transfer rate and additionally for the augmentation of required electrical parameters. Under similar conditions, the MFC setups are experimented with a variety of anode-cathode material combinations, namely carbon-carbon, copper-carbon, and zinc-carbon. The performance of these MFCs during the testing period is evaluated independently and compared by plotting polarization data generated by them. It is revealed that maximum current and power densities are achieved from all these MFCs and the best attained values are 1858 mA/m2 and 1465 mW/m2, respectively, for the novel single-chamber zinc-carbon electrode MFC. The corresponding findings present that the MFC with zinc-carbon electrodes has the better power density than other MFCs. Being conductive and higher standard potential metal electrodes have improved the capability to act in place of carbon family electrodes for MFC-based power applications. Although the MFC power generation is low, but modifications in configurations, electrodes, microbe-rich biowaste, mediators, and power management may enhance the power output to a significant level for commercialization of this technology. The unique feature of this research is to explore the pertinent use of conductive metal electrodes to enhance the power generation capability of MFCs through biowaste as an alternative power source for small applications. The novelty of this research is presented through usage of conductive metal electrodes for the performance analysis of MFCs.