Search

Abstract Biological neural systems operate on multiple time scales, enabling real-time interaction with their environment. However, replicating this in electronic systems is challenging, as scalable devices that operate on similar time scales, particularly from seconds to minutes, are lacking. This study addresses this gap by exploiting proton dynamics to achieve volatile resistance changes over long time scales, and developed a neuromorphic system that can predict biomedical data, such as blood glucose levels, in real time. By applying a low voltage (below 1 V) to a two-terminal device, the Pd electrode hydrogenates or dehydrogenates the mixed conductor WO3, resulting in significant changes in electronic resistance. The device is scalable due to the uniform proton distribution, leading to high impedance and minimal power consumption. Utilizing these volatile protons for short-term memory in an Echo State Network (ESN), this approach demonstrates low-power, efficient real-time information processing, paving the way for future neuromorphic computing applications.

Abstract Due to the excessive growth of PM 2.5 in aerosol, the cases of lung cancer are increasing rapidly and are most severe among other types as the highest mortality rate. In most of the cases, lung cancer is detected with least symptoms at its later stage. Hence, clinical records may play a vital role to diagnose this disease at the correct stage for suitable medication to cure it. To detect lung cancer an accurate prediction method is needed which is significantly reliable. In the digital clinical record era with advancement in computing algorithms including machine learning techniques opens an opportunity to ease the process. Various machine learning algorithms may be applied over realistic clinical data but the predictive power is yet to be comprehended for accurate results. This paper envisages to compare twelve potential machine learning algorithms over clinical data with eleven symptoms of lung cancer along with two major habits of patients to predict a positive case accurately. The result has been found based on classification and heat map correlation. K-Nearest Neighbor Model and Bernoulli Naive Bayes Model are found most significant methods for early lung cancer prediction.

The one-dimensional Two-Fluid concept uses an area-average approach to simplify the time and phase-averaged Two-Fluid conservation equations, making it more suitable for addressing difficulties at an industrial scale. Nevertheless, the mathematical framework has inherent weaknesses due to the loss of details throughout the averaging procedures. This limitation makes the conventional model inappropriate for some flow regimes, where short-wavelength perturbations experience uncontrolled amplification, leading to solutions that need to be physically accurate. The critical factor in resolving this problem is the integration of closure relations. These relationships play a crucial function in reintroducing essential physical characteristics, thus correcting the loss that occurs during averaging and guaranteeing the stability of the model. To improve the accuracy of predictions, it is essential to assess the stability and grid dependence of one-dimensional formulations, which are particularly affected by closure relations and numerical schemes. The current research presented in the text focuses on improving the well-posedness of the TFM, specifically within the TRACE code, which is widely utilized for nuclear reactor safety assessments. Incorporating a bubble collision model in the momentum equations is demonstrated to enhance the TFM's resilience, especially in scenarios with high void fractions where conventional TFMs may face challenges. The analysis presents a linear stability analysis performed for the transient one-dimensional Two-Fluid Model of system code TRACE within the framework of vertically dispersed flows. The main emphasis is on evaluating the stability characteristics of the model while also acknowledging its susceptibility to closure relations and numerical techniques.

Introduction and objectives: It is challenging to adopt a policy of ABO identical platelet transfusion in hemato-oncological patients because of the high demand. Moreover, there are no global standards for the management of ABO non-identical platelet transfusions due to limited evidence. The current study compared the impact of dose and storage duration of platelets on percent platelet recovery (PPR) at 1 h and 24 h between ABO identical and ABO non-identical platelet transfusions in hemato-oncological conditions. The other objectives were to assess the clinical efficacy and compare adverse reactions between the two groups. Methods: A total of 130 random donor platelet transfusion episodes (81 ABO identical and 49 ABO non-identical) were evaluated in 60 eligible patients with different malignant, as well as non–malignant, hematological conditions. All analysis was performed using two-sided tests, and p-values

Introduction Pneumococcal diseases pose a significant public health concern in India, with substantial morbidity and mortality, with the elderly and those with coexisting medical conditions being most at risk. Pneumococcus was also seen to be one of the main reasons for co-infection, pneumonia and complications in COVID. Current guidelines recommend vaccination for specific adult populations, but there is a lack of uniformity and guidance on risk stratification, prioritisation and optimal timing. Methods Nation Against Pneumococcal Infections – Expert Panel Opinion (NAP-EXPO) is a panel convened to review and update recommendations for adult pneumococcal vaccination in India. The panel of 23 experts from various medical specialties engaged in discussions and evidence-based reviews, discussed appropriate age for vaccination, risk stratification for COPD and asthma patients, vaccination strategies for post-COVID patients, smokers and diabetics, as well as methods to improve vaccine awareness and uptake. Outcome The NAP-EXPO recommends the following for adults: All healthy individuals 60 years of age and above should receive the pneumococcal vaccine; all COPD patients, regardless of severity, high-risk asthma patients, post-COVID cases with lung fibrosis or significant lung damage, should be vaccinated with the pneumococcal vaccine; all current smokers and passive smokers should be educated and offered the pneumococcal vaccine, regardless of their age or health condition; all diabetic individuals should receive the pneumococcal vaccine, irrespective of their diabetes control. Strategies to improve vaccine awareness and uptake should involve general practitioners (GPs), primary health physicians (PHPs) and physicians treating patients at high risk of pneumococcal disease. Advocacy campaigns should involve media, including social media platforms. Conclusion These recommendations aim to enhance pneumococcal vaccination coverage among high-risk populations in India in order to ensure a reduction in the burden of pneumococcal diseases, in the post-COVID era. There is a need to create more evidence and data to support the recommendations that the vaccine will be useful to a wider range of populations, as suggested in our consensus.

PURPOSEGene fusions are critical oncogenic mutations that drive cancer development and serve as diagnostic and prognostic biomarkers. Despite the increasing cancer burden in India, large-scale analyses of molecular landscapes, particularly gene fusions, have been relatively scarce.MATERIALS AND METHODSThis retrospective study used RNA-exome data from 1,392 Indian patients with cancer across 15 major cancer types to explore gene fusions. The study used a comprehensive framework that integrated open-source and proprietary tools to detect gene fusions from formalin-fixed paraffin-embedded tumor samples. The process involved RNA extraction, RNA-exome library preparation, and analysis using tools such as FastQC, DRAGEN RNA Pipeline, STAR-Fusion, and FusionInspector. We validated and filtered potential false-positive fusion calls using AGFusion and FusionAnnotator to annotate fusion breakpoints and their functional impact through various in silico tools.RESULTSThe study found a notable prevalence of FGFR fusions across cancer types, especially FGFR3, with FGFR3::TACC3 as the most recurrent. Kinase fusions were prevalent in the cohort accounting for 37% of incidence in the patients. We also identified 91 novel potential driver fusions, including those involving FGFR2, MET, ESR1, and PDGFRA.CONCLUSIONThis study underscores the critical role of gene fusions as biomarkers in cancer, extending beyond fusion-driven malignancies to encompass all cancer types. Gene fusions serve as both diagnostic markers and tumor-agnostic therapeutic targets within the current cancer treatment paradigm. Our insights into the prevalence of oncogenic drivers and novel targets expand the understanding of gene fusions, shedding new light on their mechanisms and clinical implications.

Abstract Soluble lead redox flow battery (SLRFB) is an emergent energy storage technology appropriate for integrating solar and wind energy into the primary grid. It is an allied technology of conventional lead‐acid batteries. This appraisal compares lead‐acid batteries and SLRFB apropos their general characteristics. SLRFBs can overcome the inadequate cycle‐life of Lead‐Acid batteries as the electrodes of SLRFB do not participate in the reaction, which helps extending its durability. However, SLRFB has challenges of dendrite formation, oxygen evolution reaction, passivation of PbO2 and shunt current. These problems need to be resolved before SLRFBs can be projected for large‐scale energy storage applications. In this technical update, we have reviewed the recent studies pertinent to dendrite formation, mechanism of the lead electrode, and reversibility of the PbO2 electrode in the state‐of‐art of SLRFB along with progress in advances while developing a 12 V – 250 Wh 8‐cell SLRFB stack.

Emission and performance parameters of a 4-stroke CI engine operated on a blend of catalytic co-pyrolysis oil with pure diesel, produced through Azadirachta indica seed, waste LDPE (low-density polyethylene), and aluminium oxide (Al2O3) as a catalyst, are modelled in the current work using an Artificial Neural Network (ANN). At 500°C temperature, the highest oil output obtained was 93.91 wt%. The produced liquid fuel possessed similar physical features to that of pure diesel, including density (794 kg/m3) and heating value (44.42 MJ/kg), but lower flash and fire points that would assist in a better and complete combustion of the fuel blend resulting in a better performance and combustion characteristics. Using inputs including brake mean effective pressure, load, brake power, and torque, a developed ANN model was applied to forecast the performance (Brake Thermal Efficiency and Brake Specific Fuel Consumption) along with emission characteristics (Smoke and NOx). The Levenberg-Marquardt back-propagation training technique was applied for emissions and performance characteristics prediction having the best accuracy. Regression coefficients (R2) for predicting BTE, BSFC, NOx, and smoke were all very near to 1: 0.99801, 0.9983, 0.95753, and 0.97467. The study determines that the proposed alternative fuel could be utilized in blend with the pure diesel to in an unmodified diesel engine. It has also been found that artificial neural networks (ANN) could prove to be useful to model and forecast the performance or emissions of renewable fuels in diesel engines, with the potential for these fuels to be employed in transportation.

Background: The cabbage webworm, Hellula undalis (Fabricius) (Lepidoptera: Pyralidae), is a significant pest of brassicas and other cruciferous plants in warm regions worldwide. Transcriptome analysis is valuable for investigation of molecular mechanisms underlying the insect development and reproduction. De novo assembly is particularly useful for acquiring complete transcriptome information of insect species when there is no reference genome available. In case of Hellula undalis, only 17 nucleotide records are currently available throughout NCBI nucleotide database. Genes associated with metabolic processes, general development, reproduction, defense and functional genomics were not previously predicted in the Hellula undalis at the genomic level. Methods & Results: To address this issue, we constructed Hellula undalis transcriptome using Illumina NovaSeq6000 technology. Approximately 48 million 150 bp paired-end reads were obtained from sequencing. A total of 30,451 contigs were generated by de novo assembly of sample and were compared with the sequences in the NCBI non-redundant protein database (Nr). In total, 71 % of contigs were matched to known proteins in public databases including Nr, Gene Ontology (GO), and Cluster Orthologous Gene Database (COG), and then, contigs were mapped to 123 via functional annotation against the Kyoto Encyclopedia of Genes and Genomes pathway database (KEGG). In addition, we compared the ortholog gene family of the Hullula undalis, transcriptome to Spodoptera frugiperda, spodotera litura and spodoptera littoralis and found that 391 orthologous gene families are specific to Hullula undalis. A total of 1,913 potential SSRs was discovered in Hullula undalis contigs. Conclusions: This study is the first transcriptome data for Hullula undalis. Additionally, it serves as a valuable resource for identifying target genes and developing effective and environmentally friendly strategies for pest control.