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Background: Medical interns have 2 weeks compulsory internship in psychiatry. Aim: We tried to explore their learning and views about psychiatry during these 2 weeks. To document verbatim their account about the knowledge gained and their experiences during the psychiatry internship posting and derive prominent themes. Methods: Focus group discussion was conducted at the end of the posting using a semistructured questionnaire for seven consecutive batches of interns posted in psychiatry. To enhance trustworthiness, we utilized member checking, data triangulation, and peer debriefing. Their routine during posting, competencies, expectations from the posting, and frustrations were the main points discussed. The interns willingly participated and discussed their views about psychiatry posting. Results: They were aware of psychiatry as an important medical discipline and had a fair degree of knowledge about it. Most of them found the posting useful. The interns had not attained the required competencies in handling emergencies and use of psychiatric drugs. Conclusion: Involvement of senior faculty and formative assessments will be necessary in the training of medical interns. This can ensure that they gain the required competencies.

Biodiesel was made in this experiment by transesterifying corn oil. The engine's performance and emissions from fossil fuels were evaluated by adjusting the fuel mixture proportions. This investigation examined various parameters, including hydrocarbon emissions, CO, NOx emissions, brake power, mechanical efficiency, brake thermal efficiency, and indicated thermal efficiency. It had a 5.2-kilowatt diesel engine. Making biodiesel is possible by disposing of corn oil. Biodiesel is a product of blending 20% corn oil with diesel. The methyl ester of corn oil satisfied the criteria set by ASTM for fuel. Through the application of different loads to a single-cylinder four-stroke diesel engine, the experimental findings were derived. Although indicated thermal efficiency declined, the B20 blend with 100 ppm titanium oxide nanoparticles led to improvements in brake power, mean effective pressure, mechanical efficiency, and brake thermal efficiency. While the addition of TiO2 nanoparticles to B20 decreased HC and CO emissions, it increased NOx emissions. The BD20+100ppm TiO2 sample provided the most favourable outcomes, all things considered.

Abstract Advancements in clinical treatment are increasingly constrained by the limitations of supervised learning techniques, which depend heavily on large volumes of annotated data. The annotation process is not only costly but also demands substantial time from clinical specialists. Addressing this issue, we introduce the S4MI (Self-Supervision and Semi-Supervision for Medical Imaging) pipeline, a novel approach that leverages advancements in self-supervised and semi-supervised learning. These techniques engage in auxiliary tasks that do not require labeling, thus simplifying the scaling of machine supervision compared to fully-supervised methods. Our study benchmarks these techniques on three distinct medical imaging datasets to evaluate their effectiveness in classification and segmentation tasks. Notably, we observed that self-supervised learning significantly surpassed the performance of supervised methods in the classification of all evaluated datasets. Remarkably, the semi-supervised approach demonstrated superior outcomes in segmentation, outperforming fully-supervised methods while using 50% fewer labels across all datasets. In line with our commitment to contributing to the scientific community, we have made the S4MI code openly accessible, allowing for broader application and further development of these methods. The code can be accessed at https://github.com/pranavsinghps1/S4MI .

The experiment yielded significant findings. Transesterification was used to turn waste cooking oil into biodiesel, a renewable energy source. The fuel mixture ratios were adjusted to measure engine performance and lower fossil fuel emissions. The study examined power generation, torque, consumption per hour, and emissions of unburnt hydrocarbons and carbon dioxide. The diesel engine's power output was 5.2 kilowatts. Biodiesel, derived from discarded leftover cooking oil, was synthesized by blending it with diesel fuel at a concentration of 20%. Engine performance tests revealed no statistically significant variations in power or torque across the various commercial diesel mixes. Notably, the consumption of diesel petroleum for commercial purposes increased by 15% and 20% per hour, respectively. The main advantage was the reduction in carbon dioxide emissions, which was 20% for all combinations of commercial fuel and biodiesel, compared to only commercial diesel.

Underwater cameras are crucial in marine ecology, but their data management needs automatic species identification. This study proposes a two-stage deep learning approach. First, the Unsharp Mask Filter (UMF) preprocesses images. Then, an enhanced region-based fully convolutional network (R–FCN) detects fish using two-order integrals for position-sensitive score maps and precise region of interest (PS-Pr-RoI) pooling for accuracy. The second stage integrates ShuffleNetV2 with the Squeeze and Excitation (SE) module, forming the Improved ShuffleNetV2 model, enhancing classification focus. Hyperparameters are optimized with the Enhanced Northern Goshawk Optimization Algorithm (ENGO). The improved R–FCN model achieves 99.94 % accuracy, 99.58 % precision and recall, and a 99.27 % F-measure on the Fish4knowledge dataset. Similarly, the ENGO-based ShuffleNetV2 model, evaluated on the same dataset, shows 99.93 % accuracy, 99.19 % precision, 98.29 % recall, and a 98.71 % F-measure, highlighting its superior classification accuracy.

The layer-by-layer deposition of molten polymer filament in fused deposition modeling (FDM) has evolved as a disruptive technology for building complex parts. This technology has drawbacks such as the anisotropic property of the printed parts resulting in lower strength for parts printed in the vertical Z direction compared with the other two planes. In this manuscript, we attempt to address these challenges as well as the lack of standardization in sample preparation and mechanical testing of the printed parts. The paper focuses on process parameters and design optimization of the ZX build orientation. Type I tensile bars in ZX orientation were printed as per the ASTM D638 standard using two (2B) and four (4B) tensile bar designs. The proposed design reduces material loss and post-processing to extract the test coupons. Printing a type I tensile bar in the ZX orientation is more challenging than type IV and type V due to the increased length of the specimen and changes in additional heat buildup during layer-by-layer deposition. Three different polymer composite systems were studied: fast-crystallizing nanofiller-based high-temperature nylon (HTN), slow-crystallizing nanofiller-based polycyclohexylene diethylene terephthalate glycol-modified (PCTG), and amorphous carbon fiber-filled polyetherimide (PEI-CF). For all the polymer composite systems, the 2B showed the highest strength properties due to the shorter layer time aiding the diffusion in the interlayers. Further, rheological studies and SEM imaging were carried out to understand the influence of the two designs on fracture mechanics and interlayer bonding, providing valuable insights for the field of additive manufacturing and material science.

Livestock farming is a significant source of ammonia emissions, which can have adverse effects on human health and the environment. Understanding the distribution and dispersion of ammonia within a livestock farm is crucial for developing effective mitigation strategies. In this study, computational fluid dynamics (CFD) simulations were employed to assess the spatial distribution of ammonia within a representative livestock farm. A detailed 3D model of the farm was created, incorporating the structural layout and ventilation systems. The CFD simulations were conducted using an approach validated by several other researcher groups, considering factors such as ammonia release rate, temperature, and ventilation rates. The results of the CFD simulations provided valuable insights into the distribution patterns of ammonia within the farm. The concentration contours revealed areas of high ammonia concentration, highlighting potential hotspots and vulnerable locations. To investigate the effect of air velocity and temperature on ammonia convection and to save electricity cost, the duty cycle was determined using transient analysis. We have studied the costs and benefits of agricultural ammonia emission abatement options for compliance with air quality regulations. The simulations allowed for the evaluation of different ventilation rates to mitigate ammonia concentration. The findings contribute to the understanding of ammonia emission sources and provide valuable guidance for the design and optimization of livestock farming systems, enabling the development of sustainable practices that mitigate the environmental impact of ammonia emissions.

Abstract The virus life cycle depends on host-virus protein-protein interactions, which often involve a disordered protein region binding to a folded protein domain. Here, we used proteomic peptide phage display (ProP-PD) to identify peptides from the intrinsically disordered regions of the human proteome that bind to folded protein domains encoded by the SARS-CoV-2 genome. Eleven folded domains of SARS-CoV-2 proteins were found to bind 281 peptides from human proteins, and affinities of 31 interactions involving eight SARS-CoV-2 protein domains were determined (K D ∼ 7-300 μM). Key specificity residues of the peptides were established for six of the interactions. Two of the peptides, binding Nsp9 and Nsp16, respectively, inhibited viral replication. Our findings demonstrate how high-throughput peptide binding screens simultaneously identify potential host-virus interactions and peptides with antiviral properties. Furthermore, the high number of low-affinity interactions suggest that overexpression of viral proteins during infection may perturb multiple cellular pathways.

Abstract Background All cancers harbor somatic mutations in their genomes. In principle, mutations affecting between one and fifty base pairs are generally classified as small mutational events. Conversely, large mutational events affect more than fifty base pairs, and, in most cases, they encompass copy-number and structural variants affecting many thousands of base pairs. Prior studies have demonstrated that examining patterns of somatic mutations can be leveraged to provide both biological and clinical insights, thus, resulting in an extensive repertoire of tools for evaluating small mutational events. Recently, classification schemas for examining large-scale mutational events have emerged and shown their utility across the spectrum of human cancers. However, there has been no computationally efficient bioinformatics tool that allows visualizing and exploring these large-scale mutational events. Results Here, we present a new version of SigProfilerMatrixGenerator that now delivers integrated capabilities for examining large mutational events. The tool provides support for examining copy-number variants and structural variants under two previously developed classification schemas and it supports data from numerous algorithms and data modalities. SigProfilerMatrixGenerator is written in Python with an R wrapper package provided for users that prefer working in an R environment. Conclusions The new version of SigProfilerMatrixGenerator provides the first standardized bioinformatics tool for optimized exploration and visualization of two previously developed classification schemas for copy number and structural variants. The tool is freely available at https://github.com/AlexandrovLab/SigProfilerMatrixGenerator with an extensive documentation at https://osf.io/s93d5/wiki/home/ .

Abstract Gemcitabine (Gem) has been a standard first-line drug for pancreatic cancer (PCa) treatment; however, Gem's rapid metabolism and systemic instability (short half-life) limit its clinical outcome. The objective of this study was to modify Gem into a more stable form called 4-(N)-stearoyl-gemcitabine (4NSG) and evaluate its therapeutic efficacy in patient-derived xenograft (PDX) models from PCa of Black and White patients. Methods 4NSG was synthesized and characterized using nuclear magnetic resonance (NMR), elemental analysis, and high-performance liquid chromatography (HPLC). 4NSG-loaded solid lipid nanoparticles (4NSG-SLN) were developed using the cold homogenization technique and characterized. Patient-derived pancreatic cancer cell lines labeled Black (PPCL-192, PPCL-135) and White (PPCL-46, PPCL-68) were used to assess the in vitro anticancer activity of 4NSG-SLN. Pharmacokinetics (PK) and tumor efficacy studies were conducted using PDX mouse models bearing tumors from Black and White PCa patients. Results 4NSG was significantly stable in liver microsomal solution. The effective mean particle size (hydrodynamic diameter) of 4NSG-SLN was 82 ± 6.7 nm, and the half maximal inhibitory concentration (IC50) values of 4NSG-SLN treated PPCL-192 cells (9 ± 1.1 µM); PPCL-135 (11 ± 1.3 µM); PPCL-46 (12 ± 2.1) and PPCL-68 equaled to 22 ± 2.6 were found to be significantly lower compared to Gem treated PPCL-192 (57 ± 1.5 µM); PPCL-135 (56 ± 1.5 µM); PPCL-46 (56 ± 1.8 µM) and PPCL-68 (57 ± 2.4 µM) cells. The area under the curve (AUC), half-life, and pharmacokinetic clearance parameters for 4NSG-SLN were 3–fourfold higher than that of GemHCl. For in-vivo studies, 4NSG-SLN exhibited a two-fold decrease in tumor growth compared with GemHCl in PDX mice bearing Black and White PCa tumors. Conclusion 4NSG-SLN significantly improved the Gem's pharmacokinetic profile, enhanced Gem's systemic stability increased its antitumor efficacy in PCa PDX mice bearing Black and White patient tumors.

Abstract Viruses mimic host short linear motifs (SLiMs) to hijack and deregulate cellular functions. Studies of motif-mediated interactions therefore provide insight into virus-host dependencies, and reveal targets for therapeutic intervention. Here, we describe the pan-viral discovery of 1712 SLiM-based virus-host interactions using a phage peptidome tiling the intrinsically disordered protein regions of 229 RNA viruses. We find mimicry of host SLiMs to be a ubiquitous viral strategy, reveal novel host proteins hijacked by viruses, and identify cellular pathways frequently deregulated by viral motif mimicry. Using structural and biophysical analyses, we show that viral mimicry-based interactions have similar binding strength and bound conformations as endogenous interactions. Finally, we establish polyadenylate-binding protein 1 as a potential target for broad-spectrum antiviral agent development. Our platform enables rapid discovery of mechanisms of viral interference and the identification of potential therapeutic targets which can aid in combating future epidemics and pandemics.

Abstract Image templates are a common tool for neuroscience research. Often, they are used for spatial normalization of magnetic resonance imaging (MRI) data, which is a necessary procedure for analyzing brain morphology and function via voxel-based analysis. This allows the researcher to reduce individual shape differences across images and make inferences across multiple subjects. Many templates have a small field-of-view typically focussed on the brain, limiting the use for applications requiring detailed information about other extra-cranial structures in the head and neck area. However, there are several applications where such information is important, for example source reconstruction of electroencephalography (EEG) and/or magnetoencephalography (MEG). We have constructed a new template based on 225 T1w and FLAIR images with a big field-of-view that can serve both as target for across subject spatial normalization as well as a basis to build high-resolution head models. This template is based on and iteratively re-registered to the MNI152 space to provide maximal compatibility with the most commonly used brain MRI template.

Abstract Nanofluids made up of propylene glycol, and water and graphene nanopowder dispersed throughout them are the primary focus of our study. Nanofluids were created by mixing propylene glycol and water in quantities of 100:0, 75:25, and 50:50. The essential fluids used in this experiment were propylene glycol and water. Graphene was dispersed in these three different base fluids at percentages of 0.25 and 0.5, respectively. This body of work's fundamental objective is to explore nanofluids' tribological behavior. This behavior was observed with a pin-on-disc device, and the impact for load on wear, coefficient of friction, and frictional force was investigated. The tests were conducted with weights ranging from 1 to 3 kg. It was revealed that as the load ascended, there was a reduction in the amount of wear, the coefficient of friction, and the frictional force for the most of the samples tested. Still, there was an increase in the amount of wear and friction coefficient, including the frictional force for some of the samples.

Introduction: Alcohol is a common substance that is legally available in India. Its prevalence varies across geographical and cultural boundaries. There were sudden restrictions on the supply of alcohol use during the COVID-19 lockdown. We conducted this study to understand the pattern of alcohol withdrawal among COVID-19-positive patients admitted for isolation. Methodology: A cross-sectional study was conducted from March 2020 to May 2020 at the District Government General Hospital, Nizamabad, Telangana, India. Seventy consenting patients admitted to the COVID isolation ward with a history of alcohol use were included in the study. A structured pro forma was used to collect sociodemographic and clinical profiles from the case sheets. The Alcohol Use Disorders Identification Test (AUDIT) score was calculated to understand the problems with alcohol use. Results: Eighty-eight percent of the patients consumed local adulterated toddy. The majority were male (88%), with a mean age of 45 years. The median AUDIT score was high (30). They presented with a significantly higher incidence of complications related to alcohol withdrawal including seizures, delirium, and psychosis. The mortality rate was 11%. Conclusions: There was a high prevalence of alcohol use and related complications in the study population. Adulteration of toddy with long-acting benzodiazepines may have been a reason for the higher incidence of complicated withdrawals. Primary care and family physicians must be cognizant of the problem and must take adequate capacity-building measures to manage the complications.

Abstract People have been shown to be optimistically biased when their future outcome expectancies are assessed. In fact, we display optimism bias (OB) toward our own success when compared to a rival individual’s (personal OB [POB]). Similarly, success expectancies for social groups we like reliably exceed those we mention for a rival group (social OB [SOB]). Recent findings suggest the existence of neural underpinnings for OB. Mostly using structural/functional MRI, these findings rely on voxel-based mass-univariate analyses. While these results remain associative in nature, an open question abides whether MRI information can accurately predict OB. In this study, we hence used predictive modelling to forecast the two OBs. The biases were quantified using a validated soccer paradigm, where personal (self versus rival) and social (in-group versus out-group) forms of OB were extracted at the participant level. Later, using gray matter cortical thickness, we predicted POB and SOB via machine-learning. Our model explained 17% variance (R2 = 0.17) in individual variability for POB (but not SOB). Key predictors involved the rostral-caudal anterior cingulate cortex, pars orbitalis and entorhinal cortex—areas that have been associated with OB before. We need such predictive models on a larger scale, to help us better understand positive psychology and individual well-being.

Abstract The failure of current chemotherapeutic agents for pancreatic cancer (PCa) makes it the most aggressive soft tissue tumor with a 5-year survival of slightly above 10% and is estimated to be the second leading cause of cancer death by 2030. Objective: The main aim was to synthesize, characterize and evaluate the anticancer activity of 1,3-bistetrahydrofuran-2yl-5FU (MFU). Methods: MFU was synthesized by using 5-fluorouracil (5-FU) and tetrahydrofuran acetate, and characterized by nuclear magnetic resonance (NMR), micro-elemental analysis, high-performance liquid chromatography (HPLC), and liquid chromatography with mass spectrophotometry (LC-MS). MFU and Gemcitabine hydrochloride (GemHCl) were tested for antiproliferative activity against MiaPaca-2 and Panc-1 cell lines. Results: The half-minimum inhibitory concentration (IC50) of MFU was twice lower than that of GemHCl when used in both cell lines. MiaPaca-2 cells (MFU-IC50 = 4.5 ± 1.2 μM vs. GemHCl-IC50 = 10.3 ± 1.1 μM); meanwhile similar trend was observed in Panc-1 cells (MFU-IC50 = 3.0 ± 1 μM vs. GemHCl-IC50 = 6.1 ± 1.03 μM). The MFU and GemHCl effects on 3D spheroids showed a similar trend (IC50-GemHCl = 14.3 ± 1.1 μM vs. IC50-MFU = 7.2 ± 1.1 μM) for MiaPaca-2 cells, and (IC50-GemHCl = 16.3 ± 1.1 μM vs. IC50-MFU = 9.2 ± 1.1 μM) for Panc-1 cells. MFU significantly inhibited clonogenic cell growth, and induced cell death via apoptosis. Cell cycle data showed mean PI for GemHCl (48.5–55.7) twice higher than MFU (24.7 to 27.9) for MiaPaca-2 cells, and similarly to Panc-1 cells. The in-vivo model showed intensely stained EGFR (stained brown) in all control, GemHCl and MFU-treated mice bearing subcutaneous PDX tumors, however, HER2 expression was less stained in MFU-treated tumors compared to GemHCl-treated tumors and controls. Mean tumor volume of MFU-treated mice (361 ± 33.5 mm3) was three-fold lower than GemHCl-treated mice (1074 ± 181.2 mm3) bearing pancreatic PDX tumors. Conclusion: MFU was synthesized with high purity and may have potential anticancer activity against PCa.

Photovoltaic power is most important cause of energy subsequently it is simultaneously clean and limitless. PV power conversion devices must be maintained at MPP there to maximize the energy production of Solar array. To get the most energy out of the Solar array, MPPT monitoring is necessary. In recent times, a slew of strategies for controlling the voltage was presented. Power electronic devices developments for the combination of wind and solar power generation are discussed. Based on the dependability and development of each technology, conversations concerning developments and trends in sustainable source systems are provided. The consumption of electricity generated is increasing exponentially, and as a result, the incorporation of photovoltaic panels into distribution companies is increasing rapidly as well, even though it has a substantial impact on the show’s voltage stability. The purpose of this report is to look at how solar PV integration affects distribution transmission energy reliability. The analysis is carried out using RSCAD software, with one of the radial distribution networks with the lowest voltage profile accessible during the maximum of linked loads. Furthermore, the influence of current and voltage on the transmission or distribution system is compared by analyzing distribution system configuration with various solar PV system penetration rates. The simulated findings show that as the penetrating capability of the PV system increases, large harmonic distortion levels are injected, indicating that the solar photo system should only integrate corresponding to the highest capability the connection can sustain. When a PV technology is connected further than this maximum penetration of renewable energy sources, it produces considerable harmonic components, which harms the system that improves. Total voltage output frequency and current consumption distortion are determined to be 4.98 percent and 14.99 percent, correspondingly, at the maximum diffusion way that enables the permissible harmonic distortion limitation. Thus, the research presents the uses of Grid Integration to estimate the performance and the quality of the solar PV model.

Composite materials are increasingly replacing synthetic fiber combinations in various applications. However, certain extreme environments on Earth and in space require structures to operate under low temperatures, specifically cryogenic conditions, which can significantly affect material reactions. Therefore, the main focus of this study is to develop and evaluate hybridized biocomposites, specifically assessing their tensile, bending, and impact strengths in a controlled liquid nitrogen environment (77 K). Utilizing the Taguchi optimization method, the statistical analysis of wearing characteristics was carried out utilizing cryogenic treatment hours, load, sliding distance, and weight percentage of abaca and sisal fibers. When 20 percent abaca and sisal were mixed, tensile performance increased from 28.96 to 36.58 MPa. Likewise, the same mixture increased bending strength from 59.63 to 75.68 MPa, and impact strength improved from 59.36 to 71.25 J/m. The cryogenic treatment of composite materials for 15–30 min improved the mechanical characteristics of the materials by enhancing the binding between reinforcements and substrate. The Taguchi 27 test outcomes showed a decreased friction coefficient of 7.79 × 105 mm3/Nm in the 10th trial with 30 min of cold working, 10% hybrid fibers, 600 m slide distance, and a 4 N load combination. Frictional coefficient data indicated the lowest rate during the third experiment with 15 min of cryogenic treatment, 10% hybrid fibers, 1,500 m slide length, and a 12 N load combination. The microstructural analysis of the fractured specimen was evaluated by scanning electron microscopy. Finally, such composite materials are employed in liquid propellant tanks, satellites, spaceships, rocket constructions, aeroplane components at cruising altitudes, and other applications.

There is a rapid evolution in highway investigation due to the increased usage of vehicles. Recently, various investigators have huge attention to the Intelligent Transportation System (ITS). Predicting a particular image plays a crucial role in digital image processing. However, this process is more challenging due to its changing viewpoints, illumination, colour, shape, etc. This work concentrates on modelling a novel deep learning (DL) approach. Initially, a deep-Convolutional Neural Network (D–CNN)–based sample-level classifier is proposed iteratively to measure the sampling uncertainty. Here, the uncertainty evaluation method is integrated with the sample learning process for training the robust sample classifier. The proposed D-CNN model has the competency to identify the abnormal activities and feature representation simultaneously. Subsequently, Deep Recurrent Neural Network (D-RNN) is proposed to examine the feature representation from the bag of samples and generates the final classification output by considering the local and global sampling aggregately. The simulation is performed using MATLAB 2020a and the proposed D-CNN and D-RNN model provides better trade-off with the classification accuracy compared to other approaches. The model accuracy is 92.9%, sensitivity is 76.40%, and specificity is 100% which is substantially higher than other approaches.

Epilepsy is a brain network disorder with associated genetic risk factors. Here, the authors show that spatial patterns of transcriptomic vulnerability co-vary with structural brain network alterations in focal and generalized epilepsy.

Abstract: The odontogenic keratocyst be a developmental cyst that's important due to its specific clinical behavior and histopathology. They arise from remnants of the epithelial structures that are related to the event of teeth and occur predominantly intraosseous. However, they occasionally can appear extraosseous in the gingiva as peripheral counterparts. The gingiva is the most common location of peripheral keratocyst, but other sites like mucosal, and intramuscular, epidermal sites have also been reported. The origin of soft tissue OKCs is still under controversy. [1] In this article will be discussing the Oral soft tissue keratocysts reported so far in the literature. Conclusion: The clinical evaluation of soft tissue keratocysts is very difficult, they often present different clinical entities, but however, they have to be evaluated histopathologically to be diagnosed as soft tissue keratocyst.

Abstract We investigated the white matter correlates of personality profiles predictive of subjective well-being. Using principal component analysis to first determine the possible personality profiles onto which core personality measures would load, we subsequently searched for whole-brain white matter correlations with these profiles. We found three personality profiles that correlated with the integrity of white matter tracts. The correlates of an “optimistic” personality profile suggest (a) an intricate network for self-referential processing that helps regulate negative affect and maintain a positive outlook on life, (b) a sustained capacity for visually tracking rewards in the environment and (c) a motor readiness to act upon the conviction that desired rewards are imminent. The correlates of a “short-term approach behavior” profile was indicative of minimal loss of integrity in white matter tracts supportive of lifting certain behavioral barriers, possibly allowing individuals to act more outgoing and carefree in approaching people and rewards. Lastly, a “long-term approach behavior” profile’s association with white matter tracts suggests lowered sensitivity to transient updates of stimulus-based associations of rewards and setbacks, thus facilitating the successful long-term pursuit of goals. Together, our findings yield convincing evidence that subjective well-being has its manifestations in the brain.

There is high prevalence of psychological distress in patients diagnosed with neurological disorders. A cross-sectional study was done among inpatients from the department of neurology in a tertiary care centre in South India to assess prevalence of anxiety, depression, and barriers to mental health-care utilization. Anxiety and depression were estimated using the Hospital Anxiety and Depression Scale (HADS). There is a high prevalence of psychological distress among the study participants. Proactive inquiry and screening of distress by the treating team may help identify psychological distress. Patients are more conforming and open to treating doctor. Barriers to mental health-care utilization included stigma, patient perception regarding treating doctor's attitude, patients' perception regarding distress, and doctor's perception regarding patient distress. There is a need for measures to alleviate stigma.

Unlike most medical conditions, psychiatric disorders do not have any established tests to diagnose, treat, monitor, or predict prognosis. In this article, we attempt to explain biomarkers, their categorization, and their current status in psychiatry. We explore the technologies that are currently being employed to study various prospective biomarkers in psychiatry. Hitherto, there are no established biomarkers in psychiatry, but with emerging artificial intelligence, there is a reason for hope.

Ochronosis is rarely considered a cause for knee synovitis due to its rare occurrence. It starts affecting the joint from the third decade onward causing premature joint damage. The damaged joint and surrounding tissue present as blackish decolorated mass and can surprise the operating surgeon, unless diagnosed beforehand. Prior diagnosis helps both the anesthetist and operating surgeon to better handle the associated complications of ochronosis during surgery. Diagnosis is essentially based on prior knowledge of the disease and its markers. Here, we report a rare case of ochronosis of the knee that was treated elsewhere previously as tuberculosis of the knee. A history evaluation along with intraoperative findings helped arrive at the possible diagnosis of ochronosis. Total synovectomy was done. Postoperative improvement of visual analog scale score was significant and the patient was satisfied with the pain relief attained through treatment. Early diagnosis and timely management is critical to avoid unwanted complication related to both anesthesia and surgery pertaining to the disease. Knowledge of skin markers and urine darkening helps in identifying the disease early.

Background and objectives: Genetic generalized epilepsy (GGE) is the most common form of generalized epilepsy. Although individual patients with GGE typically present without structural alterations, group differences have been demonstrated in GGE and some GGE subtypes like juvenile myoclonic epilepsy (GGE-JME). Previous studies usually involved only small cohorts from single centers and therefore could not assess imaging markers of multiple GGE subtypes. Methods: We performed a diffusion MRI mega-analysis in 192 participants consisting of 126 controls and 66 patients with GGE from four different cohorts and two different epilepsy centers. We applied whole-brain multi-site harmonization and analyzed fractional anisotropy (FA), as well as mean, radial and axial diffusivity (MD/RD/AD) to assess differences between controls, patients with GGE and the common GGE subtypes, i.e. GGE with generalized tonic-clonic seizures only (GGE-GTCS), GGE-JME and absence epilepsy (GGE-AE). We also analyzed relationships with patients' response to anti-seizure-medication (ASM). Results: Relative to controls, we identified decreased anisotropy and increased RD in patients with GGE. We found no significant effects of disease duration, age of onset or seizure frequency on diffusion metrics. Patients with JME had increased MD and RD when compared to controls, while patients with GGE-GTCS showed decreased MD/AD when compared to controls. Compared to patients with GGE-AE, patients with GGE-GTCS had lower AD/MD. Compared to patients with GGE-GTCS, patients with GGE-JME had higher MD/RD and AD. Moreover, we found lower FA in patients with refractory when compared to patients with non-refractory GGE in the right cortico-spinal tract, but no significant differences in patients with active versus controlled epilepsy. Discussion: We provide evidence that clinically defined GGE as a whole and GGE-subtypes harbor marked microstructural differences detectable with diffusion MRI. Moreover, we found an association between microstructural changes and treatment resistance. Our findings have important implications for future full-resolution multi-site studies when assessing GGE, its subtypes and ASM refractoriness.

It has been demonstrated that B20 biodiesel can be used in diesel engines without modifying their specifications. It is still being developed to reduce dependence on diesel fuel by developing biodiesel with a higher percentage of palm oil. Alcohol is added to biodiesel to reduce problems with fuel injection due to its high viscosity. In this study, the biodiesel properties of diesel-palm cooking oil-methanol/ethanol/butanol blends are investigated in relation to diesel engine performance and potential exhaust emissions. The percentages of palm cooking oil used were 30%, 40%, 50%, 60%, 70%, 80%, and 85%. A 15% concentration of methanol, ethanol, and butanol alcohol was used. According to chemical and physical tests, biodiesel with a higher palm oil content had poorer properties. A biodiesel blend that combines diesel, palm oil, and methanol can perform almost as well as diesel fuel in terms of exhaust emissions and engine performance.

Background: Clozapine has well-documented inter-ethnic variations in pharmacokinetics. There is a paucity of data about clozapine use and associated adverse events such as seizures, obsessive compulsive symptoms, neutropenia, and agranulocytosis, from India. Methods: This retrospective cohort study followed up 228 patients initiated on clozapine in a tertiary care referral center in India for an average of 10 years. We calculated incidence rates of new-onset seizures, new-onset obsessive compulsive symptoms, agranulocytosis, and neutropenia. We collected data on doses of clozapine used and serum assays and calculated concentration-to-dose (C/D) ratios. We also collected relevant clinical details about clozapine-induced seizures. Results: In the sample, 16.8% had new-onset seizures, 12.3% had new-onset OC symptoms, 2.7% had neutropenia, and 0.9% had agranulocytosis. The mean C/D ratio was 2.09 (SD = 1.8). Almost half (46.3%) of available serum assays were in the supra-therapeutic range. Seizures were associated with a higher clozapine dose at one year (OR = 1.003; 95%CI = 1.000–1.006; P value = 0.045) and the presence of positive psychotic symptoms at one year (OR = 4.214; 95%CI = 1.894–9.373; P < 0.001). Conclusion: Compared to existing literature, Indians have a higher rate of clozapine- related seizures and need lower doses to reach therapeutic serum levels.

Many interactions between viral and host proteins are mediated by short peptide motifs. Here, using a phage-based viral peptide library, the authors identify 269 peptide-based interactions for 18 coronaviruses, including an interaction between SARS-CoV-2 N and G3BP1/2 that affects stress granules.

Summary: The post-synaptic density (PSD) is a phase-separated membraneless compartment of proteins including PSD-95 that undergoes morphological alteration in response to synaptic activity. Here, we investigated the interactome of a three-domain supramodule, PDZ3-SH3-GK (PSG) from PSD-95 using bioinformatics to identify potential binding partners, and biophysical methods to characterize the interaction with peptides from these proteins. PSG and the single PDZ3 domain bound similar peptides, but with different specificity. Furthermore, we found that the protein ADGRB1 formed liquid droplets with the PSG supramodule, extending the model for PSD formation. Moreover, certain mutations, introduced outside of the binding pocket in PDZ3, increased the affinity and specificity of the interaction and the size of liquid droplets. Other mutations within the ligand binding pocket lead to a new binding motif specificity. Our results show how the context in terms of supertertiary structure modulates affinity, specificity, and phase separation, and how these properties can evolve by point mutation.

Abstract The India Hypertension Control Initiative (IHCI) is a multi‐partner initiative, implementing and scaling up a public health hypertension control program across India. A cohort of 21,895 adult hypertension patients in 24 IHCI sentinel site facilities in four Indian states (Punjab, Madhya Pradesh, Maharashtra, and Telangana), registered from January 2018 until June 2019 were assessed at baseline and then followed up for blood pressure (BP) control and antihypertensive medication use. Among all registrations, 11 274 (51%) of the patients returned for a follow‐up visit between July 2019 and September 2019. Among patients returning for follow‐up, 26.3% had BP controlled at registration, and 59.8% had BP controlled at follow‐up (p

The Particle Measurement Programme Informal Working Group (PMP-IWG) coordinated a global interlaboratory study (ILS) on brake wear particle emissions with the participation of 16 testing facilities. Two articles present the main outcomes of the ILS: (I) Particulate matter mass (PM), and (II) Particle Number (PN) emissions. The test matrix covered a wide variety of brake systems and configurations. The tested disc brakes were found to emit PM2.5 and PM10 that varied between 0.8–4.0 mg/km and 2.2–9.5 mg/km per brake, respectively, depending on the type of brake and the applied testing load. The drum brake emitted much lower PM due to its enclosed nature. Almost 37–45% of the emitted PM falls in the fine particle size with this fraction being higher for the drum brake. On the other hand, almost 50–65% of the total brake mass loss falls in particle sizes larger than 10 μm or gets lost before being measured. The most important loss mechanisms for PM in the proposed layout are being discussed. Finally, the PM measurement variability and lab-to-lab reproducibility are investigated.

Vehicles’ exhaust particulate matter (PM) emissions have significantly decreased over the years. On the other hand, non-exhaust emissions, i.e., particle emissions from brakes and tires, have increased due to the increase in the vehicle fleet, traffic congestion, and the distance traveled. As a result, regulatory bodies are investigating the possibility of mitigating non-exhaust emissions. The Euro 7 proposal introduces specific emission limits for both brakes and tires for the first time in a regulation worldwide. The methodology for brake particle emissions sampling and measurement builds on the work of the Particle Measurement Programme (PMP) informal working group of the United Nations Economic Commission for Europe (UNECE). The recently adopted Global Technical Regulation (GTR) on brakes from light-duty vehicles up to 3.5 t prescribes the technical details. In this paper, we present the technical specifications for the measurements of PM. We also evaluate the penetrations for two cases with two setups for minimum and maximum particle losses. This study, using aerosol engineering calculations, estimates the maximum expected differences between the two setups, both of which are compliant with the GTR. This study also discusses the mass ratios of PM2.5 and PM10 as a function of the mass median diameters.

Topic modeling, or identifying the set of topics that occur in a collection of articles, is one of the primary objectives of text mining. One of the big challenges in topic modeling is determining the correct number of topics: underestimating the number of topics results in a loss of information, i.e., omission of topics, underfitting, while overestimating leads to noisy and unexplainable topics and overfitting. In this paper, we consider a semantic-assisted non-negative matrix factorization (NMF) topics model, which we call SeNMFk, based on Kullback-Leibler(KL) divergence and integrated with a method for determining the number of latent topics. SeNMFk involves (i) creating a random ensemble of pairs of matrices whose mean is equal to the initial words-by-documents matrix representing the text corpus and the Shifted Positive Pointwise Mutual Information (SPPMI) matrix, which encodes the context information, respectively, and (ii) jointly factorizing each of these pairs with different number of topics to acquire sets of latent topics that are stable to noise. We demonstrate the performance of our method by identifying the number of topics in several benchmark text corpora, when compared to other state-of-the-art techniques. We also show that the number of document classes in the input text corpus may differ from the number of the extracted latent topics, but these classes can be retrieved by clustering the column-vectors of one of the factor matrices. Additionally, we introduce a software called pyDNMFk to estimate the number of topics. We demonstrate that our unsupervised method, SeNMFk, not only determines the correct number of topics, but also extracts topics with a high coherence and accurately classifies the documents of the corpus.

PURPOSERadiation therapy technologists (RTTs), being the frontline health care workers, are vulnerable to COVID-19 disease and subsequent detrimental consequences of the pandemic. Hence, the study was undertaken for the comprehensive assessment of adverse effects of COVID-19 pandemic on RTTs.MATERIALS AND METHODSThis cross-sectional, survey-based study was carried out among RTTs working in various cancer centers across India. Assessment was carried out by using a questionnaire comprising the demographic profile and a structured instrument to quantify psychological, social, financial, and professional impact of COVID-19 pandemic. Responses were collected between February 1, 2021, and April 31, 2021.RESULTSOf 302 responders, more than two third of the RTTs were concerned for getting COVID-19 disease either outside the hospital (n = 210, 69.5%) or from patients or attendants (n = 220, 72.8%). More than one third of RTTs were very much concerned for increased financial burden of COVID-19 pandemic (n = 94, 34.1%), and one third (n = 92, 30.5%) were very much concerned for newly imposed restrictions. RTTs working in a private setup (P = .000), living in a tier 1 city (P = .028), and lacking the coverage of COVID-19 disease in health insurance (P = .010) faced a significantly higher financial burden. RTTs living in tier 1 city (P = .023) and those who were in profession for > 5 years (P = .013) had significantly higher concern for ensuring proper sanitization of the professional environment. More than half (n = 171, 56.6%) were interested in taking COVID-19 vaccine.CONCLUSIONCOVID-19 pandemic adversely affected psychosocial, financial, and professional well-being of RTTs even after subsidizing the initial wave, and a timely intervention is warranted for their well-being and to sustain oncologic facilities.