Search

Abstract The high-quality genome of coconut (Cocos nucifera L.) is a crucial resource for enhancing agronomic traits and studying genome evolution within the Arecaceae family. We sequenced the Chowghat Green Dwarf cultivar, which is resistant to the root (wilt) disease, utilizing Illumina, PacBio, ONT, and Hi-C technologies to produce a chromosome-level genome of ~ 2.68 Gb with a scaffold N50 of 174 Mb; approximately 97% of the genome could be anchored to 16 pseudo-molecules (2.62 Gb). In total, 34,483 protein-coding genes were annotated; the BUSCO completeness score was 96.80%, while the k-mer completeness was ~ 87%. The assembled genome includes 2.19 Gb (81.64%) of repetitive sequences, with long terminal repeats (LTRs) constituting the most abundant class at 53.76%. Additionally, our analysis confirms two whole-genome duplication (WGD) events in the C. nucifera lineage. A genome-wide analysis of LTR insertion time revealed ancient divergence and proliferation of copia and gypsy elements. In addition, 1368 RGAs were discovered in the CGD genome. We also developed a web server ‘Kalpa Genome Resource’ ( http://210.89.54.198:3000/ ), to manage and store a comprehensive array of genomic data, including genome sequences, genetic markers, structural and functional annotations like metabolic pathways, and transcriptomic profiles. The web server has an embedded genome browser to analyze and visualize the genome, its genomics elements, and transcriptome data. The in-built BLAST server allows sequence homology searches against genome, annotated transcriptome & proteome sequences. The genomic dataset and the database will support comparative genome analysis and can expedite genome-driven breeding and enhancement efforts for tapping genetic gains in coconut.

Abstract CGGBP1 is a GC-rich DNA-binding protein which is important for genomic integrity, gene expression and epigenome maintenance through regulation of CTCF occupancy and cytosine methylation. It has remained unclear how CGGBP1 integrates multiple diverse functions with its simple architecture of only a DNA-binding domain tethered to a C-terminal tail with low structural rigidity. We have used truncated forms of CGGBP1 with or without the DNA-binding domain (DBD) to assay cytosine methylation and global gene expression. Proximal promoters of CGGBP1-repressed genes, although significantly GC-poor, contain GC-rich transcription factor binding motifs and exhibit base compositions indicative of low C-T transition rates due to prevention of cytosine methylation. Genome-wide analyses of cytosine methylation and binding of CGGBP1 DBD show that CGGBP1 restricts cytosine methylation in a manner that depends on its DBD and its DNA-binding. The CGGBP1-repressed genes show an increase in promoter cytosine methylation alongside a decrease in transcript abundance when the DBD-deficient CGGBP1 is expressed. Our findings suggest that CGGBP1 protects transcription factor binding sites (TFBS) from cytosine methylation-associated loss and thereby regulates gene expression. By analysing orthologous promoter sequences we show that restriction of cytosine methylation is a function of CGGBP1 progressively acquired during vertebrate evolution. A superimposition of our results and evolution of CGGBP1 suggests that mitigation of cytosine methylation is majorly achieved by its N-terminal DBD. Our results position CGGBP1 DNA-binding as a major evolutionarily acquired mechanism through which it keeps cytosine methylation under check and regulates TFBS retention and gene activity.

Abstract Biomedical research is fundamental in developing preventive and therapeutic vaccines, serving as a cornerstone of global public health. This review explores the key concepts, methodologies, tools, and challenges in the vaccine development landscape, focusing on transitioning from basic biomedical sciences to clinical applications. Foundational disciplines such as virology, immunology, and molecular biology lay the groundwork for vaccine creation, while recent innovations like messenger RNA (mRNA) technology and reverse vaccinology have transformed the field. Additionally, it highlights the role of pharmaceutical advancements in translating lab discoveries into clinical solutions. Techniques like CRISPR-Cas9, genome sequencing, monoclonal antibodies, and computational modeling have significantly enhanced vaccine precision and efficacy, expediting the development of vaccines against infectious diseases. The review also discusses challenges that continue to hinder progress, including stringent regulatory pathways, vaccine hesitancy, and the rapid emergence of new pathogens. These obstacles underscore the need for interdisciplinary collaboration and the adoption of innovative strategies. Integrating personalized medicine, nanotechnology, and artificial intelligence is expected to revolutionize vaccine science further. By embracing these advancements, biomedical research has the potential to overcome existing challenges and usher in a new era of therapeutic and prophylactic vaccines, ultimately improving global health outcomes. This review emphasizes the critical role of vaccines in combating current and future health threats, advocating for continued investment in biomedical science and technology.

Abstract Inflammatory bowel disease (IBD) treatment often involves systemic administration of anti-inflammatory drugs or biologics such as anti-TNF-α antibodies. However, current drug therapies suffer from limited efficacy due to unresponsiveness and adverse side effects. To address these challenges, we developed inflammation-targeting nanoparticles (ITNPs) using biopolymers derived from the gum kondagogu (Cochlospermum gossypium) plant. These ITNPs enable selective drug delivery to inflamed regions, offering improved therapeutic outcomes. We designed ITNPs that specifically bind to inflamed regions in both human and mouse intestines, facilitating more effective, uniform, and prolonged drug delivery within the inflamed tissues. Furthermore, we demonstrated that oral administration of ITNPs loaded with urolithin A (UroA), a microbial metabolite or its synthetic analogue UAS03 significantly attenuated chemical- and immune checkpoint inhibitor- induced colitis in pre-clinical models. In conclusion, ITNPs show great promise for delivering UroA or its analogues while enhancing therapeutic efficacy at lower doses and reduced frequency compared to free drug administration. This targeted approach offers a potential solution to enhance IBD treatment while minimizing systemic side effects.

The security of digital communication and information systems is mostly dependent on block ciphers. ARX-based ciphers are widely used due to their simplicity and efficiency. This paper provides an exhaustive cryptanalysis of a subset of ARX-based block ciphers, with particular emphasis on SIMON, SPECK, and IDEA. These ciphers need to be exposed for their weaknesses in algebraic attack resistance and cryptographic properties such as key sensitivity. In addition, we assess the resource utilization and speed of these ciphers, both of which are critical for practical implementation. SMT (Satisfiability Modulo Theories) framework is utilized to tackle constraint fulfillment problems based on first-order logic. The following cryptographic steps use SMT solvers: differential cryptanalysis, collision attack, pre-image attack, modular root-finding, and cryptographic primitive verification. We show that SMT solvers can solve block cipher cryptanalysis constraints. In a cryptanalytic attack, we convert block cipher boolean equations to Z3py. The proposed cryptanalysis method evaluates ARX cipher performance. This method recovers the partial secret key using plaintext and ciphertext pairs, partial key bits, and a predetermined number of rounds. To determine whether SIMON, SPECK, or IDEA are appropriate for distinct security requirements, we conducted a comparative analysis of the results and presented them in tabulated form. This research builds a better understanding of ARX-based block ciphers and allows us to develop more robust and efficient cryptographic algorithms to protect sensitive data in modern communication systems.

Introduction: Care of the patient with shock can be one of the most challenging issues in emergency medicine. The early use of ultrasound at the bedside in such emergency is a very valuable tool to determine the type of shock. Rapid Ultrasound in SHock (RUSH) protocol done by emergency physicians is one such protocol which is being underused. Hence, we conducted this study to assess the effectiveness of the RUSH protocol for patients presenting with undifferentiated hypotension and shock. Materials and methods: This was prospective observational study done on a total of 100 patients presented with hypotension and shock. We performed an early bedside sonographic examination for participants based on RUSH protocol and type of shock was determined. Then the definitive interventions were performed as per the type of shock. The outcome measures were determined either till the survival to discharge from the hospital or till the in-hospital death of the patient. The data was analysed using SPSS version25. Results: In this study, the type of shock most commonly found overall was hypovolemic shock (40%) followed by cardiogenic shock (27%) and distributive shock (17%). Contractility of left ventricle, size of right ventricle, inferior venacava caliber, hemoperitoneum, pneumothorax, hemothorax, pulmonary edema, pleural effusion and lungs consolidation were statistically significant in determining the type of shock except cardiac tamponade, abdominal aortic aneurysm/dissection and deep vein thombosis. Majority of the interventions performed were fluid resuscitation (41%), vasopressors and inotropes (28%) and laparotomy (9%) and were statistically significant (p=0.001). Among all patients, 73% survived and 27% died. The outcome was statistically significant (p=0.002). Conclusion: RUSH protocol used for undifferentiated hypotension and shock is an effective tool at the bedside as an early approach to diagnose the type of shock. It will also help in determining early definitive interventions in shock states and thereby having better patient outcomes.

Abstract In the current work, an innovative nondominated sorting colliding bodies optimization (NSCBO) technique is introduced to tackle multiobjective optimal power flow (MOOPF) challenges within electrical power networks. This method offers a means to generate a diverse array of nondominated solutions in a single iteration by including the nondominated (ND) sorting process and the concept of crowding distance. Additionally, it utilizes a spread indicator to archive the latest nondominated solutions. In the NSCBO method, the mass of each colliding body is determined by its nondominated rank rather than relying on objective function information. Moreover, a fuzzy decision-making strategy is employed to identify a suitable solution from the set of ND solutions. To showcase the scalability and viability of the NSCBO method, experiments are conducted on IEEE 30-bus, considering both bi- and tri-objective models. Comparative analysis with existing methods from recent literature demonstrates the efficacy of the NSCBO technique in handling constraints and deriving nondominated solutions for MOOPF problems.

Abstract Fuel cells are the most promising energy source for the future energy demand. The automobile industry is looking at the integration of fuel cells with electric vehicles (EV). This integration comes with many challenges like dynamic operational behaviors. For operating the fuel cell with maximum efficiency, this work proposes an Adaptive Neuro Fuzzy Inference System (ANFIS) based Maximum Power Point Tracking (MPPT) method. The hydrogen flow rate, pressure and stack temperature are the parameters considered to track the maximum power point of the fuel cell. The ANFIS-MPPT algorithm has been integrated with the 1.26 kW fuel cell in MATLAB/Simulink® and validated in different scenarios like dynamic variation in hydrogen pressure, stack temperature, load variation. The performance has been observed and compared with the conventional MPPT algorithms of Perturb and Observe (P&O) algorithm and Incremental Conductance (InC) algorithm. The proposed ANFIS-MPPT algorithm improves the power stability by 10–15% than the P&O and InC methods. Also, the proposed ANFIS-MPPT has 30% faster response as compared to the P&O algorithm, and 23% than the InC algorithm. From the analysis, it is observed that the ANFIS, P&O and InC methods are having the response time of 2.5 s, 3.6 s and 4.5 s respectively. Also the ANFIS method delivers the maximum power output of 1.26 kW, whereas the P&O and InC deliver 1.13 kW, 1.19 kW respectively. The detailed simulation analysis and results are presented in this paper.

Objective: To identify the clinical, laboratory, and radiological markers that could predict a positive GeneXpert result in patients suspected of tuberculous meningitis. Methods: In this prospective, observational study, patients with tuberculous meningitis were systematically evaluated. Various clinical, laboratory [including cerebrospinal fluid (CSF) microscopy, culture, and GeneXpert], and neuroimaging factors were examined. All participants were administered anti-tuberculous treatment and corticosteroids. A six-month follow-up was done to evaluate the outcome. Results: Of the 116 patients studied, 54 (46.6%) tested positive for GeneXpert, while 62 (53.4%) were negative. Third cranial nerve involvement (OR 3.71, 95% CI 1.052-13.09, P=0.04) and the presence of basal exudates on neuroimaging (OR 5.22, 95% CI 2.03-13.42, P=0.001) emerged as independent predictive factors for a positive GeneXpert result. A positive CSF GeneXpert result (P=0.002) and drug resistance (P=0.004) were significantly linked to adverse outcomes. Additionally, a high score on the Medical Research Council stage III (OR 5.64, 95% CI 1.18-26.87, P=0.03) and elevated CSF cell counts (OR 1.002, 95% CI 1.00-1.001, P=0.03) were identified as independent predictors of poor prognosis. Conclusions: Third cranial nerve involvement and the presence of basal exudates were significant indicators of a positive GeneXpert result. MRC stage III disability and elevated CSF cell counts predicted poor outcomes.

Abstract Covalent organic frameworks designed as chromatic sensors offer opportunities to probe biological interfaces, particularly when combined with biocompatible matrices. Particularly compelling is the prospect of chemical tomography – or the 3D spatial mapping of chemical detail within the complex environment of living systems. Herein, we demonstrate a chromic Covalent Organic Framework (COF) integrated within silk fibroin (SF) microneedles that probe plant vasculature, sense the alkalization of vascular fluid as a biomarker for drought stress, and provide a 3D in-vivo mapping of chemical gradients using smartphone technology. A series of Schiff base COFs with tunable pKa ranging from 5.6 to 7.6 enable conical, optically transparent SF microneedles with COF coatings of 120 to 950 nm to probe vascular fluid and the surrounding tissues of tobacco and tomato plants. The conical design allows for 3D mapping of the chemical environment (such as pH) at standoff distances from the plant, enabling in-vivo chemical tomography. Chromatic COF sensors of this type will enable multidimensional chemical mapping of previously inaccessible and complex environments.

Abstract Background Five distinct forms of violence - Physical, Verbal, Sexual, Cyber, and Relational - persistently afflict sexual minorities worldwide. The presence of feminine attributes in Men who have Sex with Men is inherently linked to a heightened susceptibility to various manifestations of violence, thereby exerting detrimental repercussions on both the physical and mental well-being of these individuals. This correlation stands as a principal contributor to the alarmingly elevated rates of suicide attempts among Men who have Sex with Men, particularly during their early years. Data and Methods This cross-sectional study investigates the risk factors for violence against Men who have Sex with Men in six metropolitan cities across India. Quantitative data were gathered from 300 self-identified Men who have Sex with Men, with 240 providing only quantitative data, while both quantitative and qualitative data were collected from a subset of 60 respondents, ranging in age from 18 to 70 years. Quantitative analysis was conducted using descriptive statistics, chi-square tests, and binary logistic regression to explore the likelihood of different types of violence. Qualitative data collected through in-depth interviews, were analyzed thematically to provide contextual depth and validate the quantitative findings, focusing on specific incidents and key themes associated with violence among Men who have Sex with Men. Results More than 60 per cent of the respondents encountered at least one type of violence due to their sexuality. More than half of the respondents who engaged in the receptive role during sexual activity reported experiencing sexual violence. The likelihood of encountering verbal violence was found to be nearly six times higher among respondents who were receptive than penetrative sexual partners. Respondents of Muslim religion exhibited a 2.6-fold increase in the likelihood of experiencing sexual violence compared to their Hindu counterparts. Conclusion Violence against Men who have Sex with Men is a pervasive issue in the six metropolitan cities studied in India, particularly affecting individuals with lower education and low income. These findings resonate with global research, which identifies similar risk factors for violence against sexual minorities, especially in regions with deep-rooted homophobia and socio-economic inequalities. Insights from this study underscore the urgent need for targeted policies and interventions to create a safer, more inclusive environment for this population in India and other parts of the Global South.

Abstract Dinanath grass (Pennisetum pedicellatum Trin.) is an extensively grown forage grass known for its significant drought resilience. In order to comprehensively grasp the adaptive mechanism of Dinanath grass in response to water deficient conditions, transcriptomic and metabolomics were applied in the leaves of Dinanath grass exposed to two distinct drought intensities (48-hour and 96-hour). Transcriptomic analysis of Dinanath grass leaves revealed that a total of 218 and 704 genes were differentially expressed under 48- and 96-hour drought conditions, respectively. The genes that were expressed differently (DEGs) and the metabolites that accumulated in response to 48-hour drought stress mainly showed enrichment in the biosynthesis of secondary metabolites, particularly phenolics and flavonoids. Conversely, under 96-hour drought conditions, the enriched pathways predominantly involved lipid metabolism, specifically sterol lipids. In particular, phenylpropanoid pathway and brassinosteroid signaling played a crucial role in drought response to 48- and 96-hour water deficit conditions, respectively. This variation in drought response indicates that the adaptation mechanism in Dinanath grass is highly dependent on the intensity of drought stress. In addition, different genes associated with phenylpropanoid and fatty acid biosynthesis, as well as signal transduction pathways namely phenylalanine ammonia-lyase, putrescine hydroxycinnamoyl transferase, abscisic acid 8’-hydroxylase 2, syntaxin-61, lipoxygenase 5, calcium-dependent protein kinase and phospholipase D alpha one, positively regulated with drought tolerance. Combined transcriptomic and metabolomic analyses highlights the outstanding involvement of regulatory pathways related to secondary cell wall thickening and lignin biosynthesis in imparting drought tolerance to Dinanath grass leaves. These findings collectively contribute to an enhanced understanding of candidate genes and key metabolites relevant to drought response in Dinanath grass. Furthermore, they establish a groundwork for the creation of a transcriptome database aimed at developing abiotic stress-tolerant grasses and major crop varieties through both transgenic and genome editing approaches.

Dysplasia epiphysealis hemimelica (DEH), also known as Trevor’s disease, is a rare disease which is skeletal developmental disorder characterized by asymmetrical growth of epiphyseal cartilage. It most frequently affects the ankle joint. Surgical treatment with complete resection is recommended before irreversible joint damage and deformity occurs. Multiple surgeries may be required if resected at an early age because recurrences are common until skeletal maturity. We present a case of DEH at the anteromedial aspect of a left ankle joint and a review of the literature.

Glioblastoma (GBM) is a malignant cancer affecting the brain. As per the WHO classifications, it is a grade IV glioma and is characterized by heterogenous histopathology, high recurrence rates, and a high median age of diagnosis. Most individuals diagnosed with GBM are aged between 50 and 64 years, and the prognosis is often poor. Untreated GBM patients have a median survival of 3 months, while treatments with Temozolomide (TMZ) and radiotherapy can improve the survival to 10–14 months. Tumor recurrence is common, owing to the inefficiency of surgical resection in removing microscopic tumor formations in the brain. A crucial component of GBM-related research is understanding the tumor microenvironment (TME) and its characteristics. The various cellular interactions in the TME contribute to the higher occurrence of malignancy, resistance to treatments, and difficulty in tumor resection and preventative care. Incomplete pictures of the TME have been obtained in 2D cultures, which fail to incorporate the ECM and other crucial components. Identifying the hallmarks of the TME and developing ex vivo and in vitro models can help study patient-specific symptoms, assess challenges, and develop courses of treatment in a timely manner which is more efficient than the current methods. Microfluidic models, which incorporate 3D cultures and co-culture models with various channel patterns, are capable of stimulating tumor conditions accurately and provide better responses to therapeutics as would be seen in the patient. This facilitates a more refined understanding of the potential treatment delivery systems, resistance mechanisms, and metastatic pathways. This review collates information on the application of such microfluidics-based systems to analyze the GBM TME and highlights the use of such systems in improving patient care and treatment options.

A numerical study of 𝐹𝑒3𝑂4−TiO2−𝑁𝑖/C2H6O2 modified nanofluid's flow through a stretched surface is presented in the current work with an applied angled magnetic field. In the subsequent form of hybrid nanofluid, known as modified nanofluid, three distinct suspended nanoparticles in a base fluid are taken into consideration. Iron Oxide, Nical, and Titanium Dioxide nanoparticles are suspended in ethanol glycol, which is used as a base liquid. One way to improve heat transfer rates in MHD flow over a stretched surface with variable viscosity is to utilize modified nanofluids. This is useful in a number of sectors, including energy systems, thermal management in aircraft, and cooling electronic systems. By applying the proper similarity transformations, the Runga-Kutta fourth order technique encounters the mathematical framework of the flow. One important finding is that, in contrast to nanofluids and hybrid nanofluids, the modified nanofluid has a larger capacity for heat transmission. The modified nanofluid's heat transfer capabilities exhibit intriguing behavior that calls for more research on it. There are numerical solutions that are displayed graphically. Increases in the volume fraction parameter and the inclination angle parameter of the magnetic field have been found to cause a drop in the velocity field of the modified nanofluid.

Recent advancements in dentistry have introduced new light-curing units such as 1-second and 5-second LED curing lights to orthodontics. We aimed to evaluate the shear bond strength and Adhesive Remnant Index (ARI) of stainless-steel orthodontic brackets cured with 1-second, and 5-second LED curing lights. Ninety human extracted upper premolars were selected based on the inclusion and exclusion criteria. The selected teeth were randomly divided into 2 groups - Group I and Group II. In Group I, the teeth were bonded with Standard pre-adjusted edgewise upper premolar brackets (Orthox, JJ Orthodontics) using Transbond XT and cured with Woodpecker iLED 1-second curing light. In Group II, the same brackets and composite were used to bond and light-cure with 5-seconds Woodpecker LED-D unit. The shear bond strength and the Adhesive Remnant Index (ARI) of brackets in both groups were evaluated. Student t-test was used for the statistical analysis of the data. In Group-I (1-second LED curing light), the shear bond strength was 6.62 MPa, whereas it was 10.32 MPa in group II (5-second LED curing light). A highly significant difference was observed in the shear bond strength between the groups. The ARI scores further revealed that the 5-second curing light resulted in a safer failure mode, with adhesive remaining mostly on the bracket. We found that the 5-second Woodpecker LED-D curing light demonstrated higher shear bond strength compared to the 1-second iLED curing light, although both sets of bond strength values are clinically acceptable.

Abstract Erythropoietin (EPO) plays a key role in energy metabolism, with EPO receptor (EpoR) expression in white adipose tissue (WAT) mediating its metabolic activity. Here, we show that male mice lacking EpoR in adipose tissue exhibit increased fat mass and susceptibility to diet-induced obesity. Our findings indicate that EpoR is present in WAT, brown adipose tissue, and skeletal muscle. Elevated EPO in male mice improves glucose tolerance and insulin sensitivity while reducing expression of lipogenic-associated genes in WAT, which is linked to an increase in transcription factor RUNX1 that directly inhibits lipogenic genes expression. EPO treatment in wild-type male mice decreases fat mass and lipogenic gene expression and increase in RUNX1 protein in adipose tissue which is not observed in adipose tissue EpoR ablation mice. EPO treatment decreases WAT ubiquitin ligase FBXW7 expression and increases RUNX1 stability, providing evidence that EPO regulates energy metabolism in male mice through the EPO-EpoR-RUNX1 axis.

Background: Interleukin (IL) genes are essential for regulating the immune and inflammatory processes. Epidemiological studies suggest that polymorphisms in IL genes are associated with a higher risk of recurrent pregnancy loss (RPL). Objective: This study aimed to examine the association between RPL risk and IL gene polymorphisms. Materials and Methods: A comprehensive search of the literature was conducted using PubMed, Google Scholar and Embase to identify relevant studies published until May 30, 2024. A total of 58 studies involving 13,696 participants (both RPL cases and controls) were included. Pooled odds ratios and 95% confidence intervals were calculated using fixed- or random-effects models, as appropriate, with ReviewManager version 5.4, Cochrane RoB Tool 2 and G*Power 3.1 software. Results: The meta-analysis revealed significant correlations between RPL risk and genetic variations in IL-1β (-511T > C), IL-6 (-174 G > C) and IL-10 (-1082 A > G and-592 C > A), with statistical significance set at P < 0.05. Ethnic subgroup analysis showed that IL-1β (-511T > C) and IL-10 (-592 C > A) variations were strongly associated with RPL risk in Asian populations. Interpretation: Our research suggests that genetic variations in IL-1β (-511T > C), IL-6 (-174 G > C) and IL-10 (-1082 A > G and-592 C > A) are associated with susceptibility to RPL. Limitation: Our meta-analysis concentrated exclusively on single-factor studies and did not consider potential confounding variables such as age, environmental influences and lifestyle, which could affect susceptibility to RPL. In addition, the lack of individual raw data prevented us from investigating gene–environment and gene–gene interactions or the influence of other polymorphisms and cytokines in our analysis. Protocol Registration: http://www.osf.io/qhbva.

Abstract Accurate determination of sexual dimorphism in skeletal structures is crucial in forensic anthropology and medical diagnostics. This study aimed to assess sexual dimorphism in various indices of the second cervical vertebra (axis) and other associated structures. A comprehensive analysis was conducted on axis dimensions, vertebral foraminal measurements, body diameters, odontoid process parameters, and auricular facet indices in male and female subjects. A total of 122 specimens were examined, comprising 62 male and 62 female specimens.The analysis revealed significant differences between male and female subjects in various morphological indices. In terms of axial dimensions, males exhibited larger average height, length, and width of the axis compared to females, indicating sexual dimorphism. Similarly, significant differences were observed in the maximum length and width of the vertebral foramen, with males demonstrating larger measurements. Additionally, males showed larger transverse and sagittal diameters of the body compared to females. Regarding the odontoid process, males displayed greater sagittal and transverse diameters, as well as maximum height, suggesting sexual dimorphism in this aspect. Furthermore, significant differences were noted in the mean sagittal angle of the dens axis between males and females. Analysis of the superior and inferior auricular facets also indicated notable morphological variations between the sexes.The findings highlight pronounced sexual dimorphism in the morphology of the second cervical vertebra and associated structures. These results underscore the importance of considering sex-related variations in skeletal assessments for forensic and diagnostic purposes. Further research in this area can enhance the accuracy of sex determination in skeletal remains and contribute to the development of new identification methodologies.

The main goal of this research is to assess the antibacterial effectiveness of commonly used UAE natural herbs, including Turmeric, Henna, Sidr, and Myrrh, against a range of bacteria such as Staphylococcus aureus, Streptococcus agalactiae (group B streptococci), Streptococcus pyogenes (group A streptococci), Escherichia coli, Enterococcus faecalis, Proteus mirabilis, Pseudomonas aeruginosa, and Klebsiella pneumoniae. Agar diffusion technique was utilized in this study. The herbs were extracted and prepared in serial dilution, a standardized amount of the test microorganisms were inoculated on the agar plates. Subsequently, extracted herbs were placed in the wells that formed on the surface of the media. The agar plates are then incubated at 37°C under appropriate condition. Typically, the herbs extract diffuses in the media, inhibiting the germination of the bacteria, the zone of inhibition is subsequently measured. Significant inhibitory effects were observed with henna herbal extract against 75% of Gram-positive bacteria, while 25% of the bacteria showed inhibition when using sidr extract. Myrrh exhibited an antibacterial effect against most tested bacteria. Comparatively less of an impact was seen by turmeric extract on both Gram-negative and Gram-positive. The antibacterial efficacy of the four plant extracts suggests that Henna displayed the highest effectiveness, followed by Sidr and Myrrh, with Turmeric showing the least potency. Additionally, strains such as E. coli, E. faecalis, P. aeruginosa, K. pneumoniae, and P. mirabilis demonstrated resistance to the plant extracts, while S. aureus, S. pyogenes, and S. agalactiae appeared to be the most susceptible strains. These findings underscore the potential of plant extracts in treating bacterial infections, offering insights for the development of novel compounds with enhanced activity against both resistant and susceptible bacteria, thereby addressing the limitations of current antibiotic agents.

Food is the primary cause for diseases in humans and carries high risk pathogens. Assessment of the safety in foods is needed to validate the presence of pathogenic bacteria. We used colony PCR for this approach to detect foodborne pathogens such as Escherichia coli, Lactobacillus and Bacillus cereus. Suitable primers were selected based on specific gene 1040 for Escherichia coli, gene S2 for Lactobacillus, and gene NVF for Bacillus cereus. Agarose gel electrophoresis is used for the detection of amplified products against a suitable marker. ImageJ is used for DNA band analysis, enabling precise quantification, normalization, and statistical comparisons. These studies have established a promising role in the detection of pathogens in various environmental samples. The insights gained from this study may serve as the foundation for rapid detection of foodborne diseases in the food industry.

Abstract Renewable energy sources are playing a leading role in today's world. However, integrating these sources into the distribution network through power electronic devices can lead to power quality (PQ) challenges. This work addresses PQ issues by utilizing a shunt active power filter in combination with an Energy Storage System (ESS), a Wind Energy Generation System (WEGS), and a Solar Energy System. While most previous research has relied on complex methods like the synchronous reference frame (SRF) and active-reactive power (pq) approaches, this work proposes a simplified approach by using a neural network (NN) for generating reference signals, along with the design of a five-level reduced switch voltage source converter. The gain values of the proportional-integral controller (PIC), as well as the parameters for the shunt filter, boost, and buck-boost converters in the WEGS and ESS, are optimally selected using the horse herd optimization algorithm. Additionally, the weights and biases for the neural network (NN) are also determined using this method. The proposed system aims to achieve three key objectives: (1) stabilizing the voltage across the DC bus capacitor; (2) reducing total harmonic distortion (THD) and improving the power factor; and (3) ensuring superior performance under varying demand and PV irradiation conditions. The system's effectiveness is evaluated through three different testing scenarios, with results compared against those obtained using the genetic algorithm, biogeography-based optimization (BBO), as well as conventional SRF and pq methods with PIC. The results clearly demonstrate that the proposed method achieves THD values of 3.69%, 3.76%, and 4.0%, which are lower than those of the other techniques and well within IEEE standards. The method was developed using MATLAB/Simulink version 2022b.

The abundantly available waste banana pseudostem fibers have attracted the paper and textile industries, however the stem-sap extracted during fiber processing remains underutilized. Although this sap comprising cellulosic sugars holds potential as a feedstock, its commercial viability in the renewable energy sector remains a challenge. Our study delves into this untapped resource by mechanically extracting sap from banana pseudostems and enhancing its reducing sugar content to approximately 35.5 g/L through acid hydrolysis and detoxification without concentrating the sap. Using separate batch fermentations with pentose and hexose fermenting strains such as Pichia stipitis NCIM 3499 and Saccharomyces cerevisiae ATCC 2601, we achieved bioethanol production efficiencies of 67.5 % and 70.03 %, respectively. Yield and cost analyses confirmed the feasibility of this approach for industrial application in low-economy settings. Furthermore, gene-interaction network and functional enrichment analysis identified 63 key genes involved in carbohydrate metabolism and ethanol conversion pathways within the fermenting organisms. Among these, the PGK1 gene and its direct interactors emerged as promising targets for future biotechnological enhancements aimed at boosting bioethanol production. This study not only underscores the renewable energy potential of unconcentrated banana pseudostem sap but also paves the way for innovative genetic interventions to optimize bioethanol yields.

Subacute sclerosing panencephalitis (SSPE) is a life-threatening and delayed complication of measles. It may rarely follow a fulminant course, with rapid progression to a vegetative state. There have been case reports of this challenging and aggressive form of SSPE in previously immunised children. The cause of the rapid progression in many patients is unknown. However, it is postulated that genetically determined immune dysfunction can prevent cell-mediated immune clearance of the measles virus. This was a retrospective study of five patients who presented with clinical features and laboratory investigations suggestive of fulminant SSPE over the previous year. The diagnosis of SSPE was established using Radermecker’s complexes in electroencephalogram, encephalitis on brain magnetic resonance imaging and elevated titres of immunoglobulin (Ig) M and IgG measles antibodies in the serum and cerebrospinal fluid. Treatment included isoprinosine, intravenous Igs, sodium valproate, levetiracetam and clobazam. The outcome was dismal in all patients, with progression to a bedridden state despite timely intervention. In our series of five cases, the diagnosis of SSPE was often missed because the children were immunised and healthy before the onset of symptoms. However, after excluding other central nervous system viral infections, the clinical presentation and laboratory findings corroborated the fulminant course of SSPE. This article captures the presentation and outcomes of five patients and emphasises the importance of clinicians’ awareness of this condition.

Imaging is essential for managing COVID-19 pneumonia. In resource-limited settings, chest radiography is the only practicable imaging modality. The Brixia radiographic scoring system, an objective method of assessing pulmonary involvement, is yet to be studied in India. (a) To study the association between the clinical severity of COVID-19 patients with their Brixia scores and, (b) to evaluate the utility of the score as a predictor for invasive ventilation. Peripheral rural hospital by Indian Armed Forces; Cross-sectional study. A thousand patients diagnosed with COVID-19 and hospitalized were classified as having a mild, moderate, and severe disease based on their least oxygen saturation (SpO2 >94: mild, between 90-94: moderate, and

Globally, diabetes mellitus (DM) and stroke are two common chronic illnesses that have a substantial impact on rates of morbidity and mortality. There is significant evidence linking diabetes to an increased risk of stroke in terms of incidence, severity, and mortality. This extensive review looks at shared risk factors, underlying pathophysiological mechanisms, epidemiological trends, and evidence-based therapy approaches to give a thorough analysis of the causal relationship between diabetes mellitus and stroke. Studies using epidemiological data regularly show that people with diabetes have a higher incidence of stroke than people without the disease. Furthermore, diabetes is linked to a less favorable outcome following a stroke, as well as an elevated chance of stroke recurrence. Determining the pathophysiological pathways that connect diabetes and stroke is essential to understanding their relationship. Key pathophysiological processes associated with these disorders include endothelial dysfunction, inflammation, oxidative stress, hyperglycemia, and dyslipidemia. Due to microvascular complications, these mechanisms raise the risk of hemorrhagic stroke and predispose diabetics to an increased risk of ischemic stroke by creating a prothrombotic and atherosclerotic milieu. Diabetes and stroke are linked due to shared risk factors like smoking, obesity, dyslipidemia, hypertension, and poor glycemic control. Lifestyle changes, blood pressure control, lipid-lowering therapy, antiplatelet medicines, and a nutritious diet are essential for stroke risk reduction. Reducing the risk of stroke in people with diabetes requires the implementation of management techniques that focus on both diabetes control and stroke prevention. Optimizing results and lowering the frequency of stroke-related complications in diabetics requires multidisciplinary care. The intricate interactions between diabetes mellitus and stroke are highlighted in this review’s conclusion, which also highlights the value of patient education, risk factor treatment, the effect of antidiabetic therapy on stroke, and integrated care in lowering the incidence of stroke in people with diabetes.

Background and Aims: Caudal block is more frequently used in children for postoperative analgesia. However, its disadvantage is its short duration. Erector spinae plane block (ESPB) at the sacral level can potentially block the pudendal nerve. It may prove an alternative to caudal block for hypospadias repair regarding time to first rescue analgesia. Methods: Fifty children of 2–7 years of age were included. After induction of general anaesthesia, Group I (n = 25) was given ultrasound-guided sacral ESPB with 1 ml/kg of 0.25% bupivacaine and Group II (n = 25) was given caudal block with 0.5 ml/kg of 0.25% bupivacaine. Postoperatively at face, leg, activity, cry, consolability (FLACC) score ≥4, rescue analgesia was given using intravenous 15 mg/kg paracetamol. The primary outcome was to compare time to first rescue analgesia, and secondary outcomes were intraoperative haemodynamic parameters, fentanyl consumption, postoperative FLACC score and analgesic consumption in 24 h. Continuous variables were compared using the independent sample t-test or Mann–Whitney test, and categorical variables were compared using the Chi-square test. Results: The mean time to first rescue analgesia was 21.30 (standard deviation [SD]: 3.06) h in Group I and 9.36 (SD: 1.71) h in Group II (P < 0.001) (mean difference −11.94 [95% CI: −13.39, −10.48]). The FLACC score was significantly higher (P < 0.05) postoperatively at 8, 10, 12 and 18 h in Group II. Mean postoperative analgesic consumption was 310.5 (SD: 72.69) mg in Group I and 615.6 (SD: 137.51) mg in Group II (P < 0.001) (mean difference 30.5 [95% CI: 236.41, 373.78]). Conclusion: Sacral ESPB is better regarding time to first rescue analgesia than caudal block in paediatric patients undergoing hypospadias repair.

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by both non-motor and motor symptoms, due to the loss of dopamine-producing neurons in the brain. Monoamine oxidase-B (MAO-B) inhibitors are essential in the treatment of PD, as they increase dopamine levels and could potentially slow down the progression of the disease. MAO-B inhibitors block the ability of the enzyme to degrade dopamine in the brain. MAO-B inhibitors work by inhibiting this enzyme, which raises dopamine levels and helps reduce motor symptoms, such as akinesia and stiffness in the muscles. In addition to their impact on dopamine levels, MAO-B inhibitors may possess neuroprotective properties. Research indicates that these inhibitors can shield neurons from the harmful byproducts of dopamine breakdown, such as dihydroxy acetaldehyde and hydrogen peroxide. This neuroprotective effect could potentially slow the progression of PD and protect against neuronal damage. MAO-B inhibitors are effective in treating both advanced and early stages of PD. They are recommended as initial treatments for individuals with early PD and can also be used as supplementary therapy in advanced PD to assist in managing motor complications. Additionally, MAO-B inhibitors have shown promise for the treatment of non-motor symptoms of PD, such as fatigue and sleep disturbances. MAO-B inhibitors are an important class of drugs for the treatment of PD, offering both symptomatic relief and potential disease-modifying effects. The goal of ongoing research and development of MAO-B inhibitors is to enhance their safety and selectivity profiles, which could lead to improved treatment approaches for PD and other neurodegenerative disorders.

Abstract Underground wastewater collection systems degrade with time, necessitating utility owners to engage in ongoing evaluations and enhancements of their asset management frameworks to preserve the performance of their assets. The inspection and condition assessment of sewer pipes are crucial for the effective operation and maintenance of sewer systems. The closed-circuit television (CCTV) is frequently employed to examine sewer pipes in the United States. This procedure is both costly and laborious because of the extensive number of pipes in a metropolis. Prioritisation of inspection for sanitary sewage pipe segments requiring repair or maintenance can be done in advance depending on their past performance. Hence, the aim of this study is to construct a predictive model for the state of sanitary sewer pipes, utilising data collected from a city located in the southcentral region of the United States. The main contribution is that this study used multiclass classification and predicted PACP scores of the pipes. Condition prediction models were developed using extensively utilised supervised machine learning algorithms including logistic regression (LR), k-nearest neighbors (k-NN), and random forest (RF). However, the bulk of the constructed models were assessed using a limited number of assessment measures, such as the receiver operator characteristic (ROC) curve and the area under the curve (AUC) value. This paper asserts that the assessment of the predictive capacity of these models cannot be determined only by relying on ROC and AUC values. Out of the three models evaluated in this study, the LR model had an AUC value of 0.76. However, this model had a higher number of misclassifications or inaccurate predictions compared to the other models. Consequently, these models were assessed using additional assessment measures, including precision, recall, and F-1 scores (which represent the harmonic mean of precision and recall). Curiously, the LR model achieved an F1-score of 0.28 on a scale ranging from 0 to 1. The RF model yielded an F1-score of 0.45 and an AUC value of 0.86. The existing model can be enhanced before it is employed by asset managers during the inspection phase to assess the state of their sanitary sewers and identify essential sewers that require immediate care.

Abstract The magnet-less switched reluctance motor (SRM) speed-torque characteristics are ideally suited for traction motor drive characteristics and its advantage to minimize the overall cost of on-road EVs. The main drawbacks are torque and flux ripple, which have produced high in low-speed operation. However, the emerging direct torque control (DTC) operated magnitude flux and torque estimation with voltage vectors (VVs) gives high torque ripples due to the selection of effective switching states and sector partition accuracy. On the other hand, the existing model predictive control (MPC) with multiple objective and optimization weighting factors produces high torque ripples due to the system dynamics and constraints. Therefore, existing DTC and MPC can result in high torque ripples. This paper proposed a finite set (FS)-MPC with a single cost function objective without weighting factor: the predicted torque considered to evaluate VVs to minimize the ripples further. The selected optimal VV minimizes the SRM drive torque and flux ripples in steady and dynamic state behaviour. The classical DTC and proposed model were developed, and simulation results were verified using MATLAB/Simulink. The proposed model operated in SRM drives experimental results to prove the effective minimization of torque and flux ripples compared to the existing DTC.

In high seismicity regions, the use of unreinforced masonry (URM) infills in reinforced concrete buildings is massive. In most of them, the openings in URM infills due to architectural requirements cause a variation in lateral stiffness which, in turn, alters the dynamic characteristics of the building. Although much research was developed about the effects of openings on the building performance, studies on assessing functionality reduction and resilience of a building in post-disaster scenarios are very few. This paper gives a contribution to that issue by performing and interpreting a parametric analysis which considers five models of a low-rise R.C. building with different percentages of infills openings. The results are expressed as extreme damage probability percentage, damage loss ratio and loss of resilience. The results show that the case with percentage of opening in in the outer and inner infill walls respectively (referred to as IF20–15%) corresponds to a damage loss ratio and a loss of resilience less than the other cases by 3%. IF20–15% model has the extreme damage probability of 5–7% less when compared with other opening cases. Based on this result, for the considered building the optimum infill opening of IF20–15% can be recommended.

In the field of medical imaging, the increasing demand for advanced computer-aided diagnosis systems is crucial in radiography. Accurate identification of various diseases, such as COVID-19, pneumonia, tuberculosis, and pulmonary lung nodules, holds vital significance. Despite substantial progress in the medical field, a persistent research gap necessitates the development of models that excel in precision and provide transparency in decision-making processes. In order to address this issue, this work introduces an approach that utilizes transfer learning through the EfficientNet-B4 architecture, leveraging a pre-trained model to enhance the classification performance on a comprehensive dataset of lung X-rays. The integration of explainable artificial intelligence (XAI), specifically emphasizing Grad-CAM, contributes to model interpretability by providing insights into the neural network’s decision-making process, elucidating the salient features and activation regions influencing multi-disease classifications. The result is a robust multi-disease classification system achieving an impressive 96% accuracy, accompanied by visualizations highlighting critical regions in X-ray images. This investigation not only advances the progression of computer-aided diagnosis systems but also sets a pioneering benchmark for the development of dependable and transparent diagnostic models for lung disease identification.

Background & objectives: Despite significant progress in malaria control throughout India, Chhattisgarh state continues to be a significant contributor to both malaria morbidity and mortality. This study aims to identify key factors associated with malaria endemicity, with a goal of focusing on these factors for malaria elimination by 2030. Methods: We employed an analysis and narrative review methodology to summarize the existing evidence on malaria epidemiology in Chhattisgarh. Data encompassing environmental conditions, dominant malaria vectors and their distribution, and the impact of previous interventions on malaria control, were extracted from published literature using PubMed and Google Scholar. This information was subsequently correlated with malaria incidence data using appropriate statistical and geographical methods. Results: Much of the malaria burden in Chhattisgarh state is concentrated in a few specific districts. The primary malaria vectors in these regions are Anopheles culicifacies and An. fluviatilis. High transmission areas are found in tribal belts which are challenging to access and are characterized by densely forested areas that provide a conducive habitat for malaria vectors. Interpretation & conclusion: Conducive environmental conditions characterized by high forest cover, community behavior, and insurgency, contribute to high malaria endemicity in the area. Challenges include insecticide resistance in malaria vectors and asymptomatic malaria. Allocating additional resources to high-endemic districts is crucial. Innovative and focused malaria control programs of the country, such as DAMAN and Malaria Mukt Abhiyan, hold immense importance.

INTRODUCTION: Vitamin D and HDL (high density lipoprotein) levels have potential to be used as an early and simple predictor of morbidity and mortality in critically ill patients. The aim of the study was to determine the predictive capacity of vitamin D and HDL levels alone and in combination for mortality in critically ill patients. MATERIALS AND METHODS: Prospective observational study involving 302 patients admitted to an intensive care unit (ICU). Blood samples were obtained within the initial 24 hours of admission to assess vitamin D and HDL levels. Vitamin D levels of >20 ng/ml were considered non-deficient and ≤ 20 ng/mL considered deficient. HDL levels >30 mg/dl were considered normal HDL and

Abstract The study investigates the heat transfer and friction factor properties of ethylene glycol and glycerol-based silicon dioxide nanofluids flowing in a circular tube under continuous heat flux circumstances. This study tackles the important requirement for effective thermal management in areas such as electronics cooling, the automobile industry, and renewable energy systems. Previous research has encountered difficulties in enhancing thermal performance while handling the increased friction factor associated with nanofluids. This study conducted experiments in the Reynolds number range of 1300 to 21,000 with particle volume concentrations of up to 1.0%. Nanofluids exhibited superior heat transfer coefficients and friction factor values than the base liquid values. The highest enhancement in heat transfer was 5.4% and 8.3% for glycerol and ethylene glycol -based silicon dioxide Nanofluid with a relative friction factor penalty of ∼30% and 75%, respectively. To model and predict the complicated, nonlinear experimental data, five machine learning approaches were used: linear regression, random forest, extreme gradient boosting, adaptive boosting, and decision tree. Among them, the decision tree-based model performed well with few errors, while the random forest and extreme gradient boosting models were also highly accurate. The findings indicate that these advanced machine learning models can accurately anticipate the thermal performance of nanofluids, providing a dependable tool for improving their use in a variety of thermal systems. This study's findings help to design more effective cooling solutions and improve the sustainability of energy systems.

Tuberculosis (TB) is a serious disease that has been observed since ancient times. In the mid-to late-20th century, the main clinical approach to this disease involved focusing on its diagnosis, prevention, and treatment. However, in the 21st century, the focus has shifted toward the diagnosis and treatment of drug-resistant TB. With the use of the Xpert MTB/RIF assay at the frontlines in India, interpreting indeterminate results to treatment with rifampicin, an antitubercular drug, can be challenging. This is further exacerbated by a lack of knowledge regarding mutation frequency in antitubercular drug-resistant genes in this region. Among antitubercular drugs, rifampicin is the most potent and effective drug for the treatment of tuberculosis; hence, understanding the pattern of rifampicin resistance (rpoB) gene mutations will provide insights into the genetic basis of this resistance, which may help in the prevention and treatment of TB. This retrospective observational study presents sociodemographic details, sample types, Mycobacterium tuberculosis load, types of probe mutations detected, and rifampicin indeterminate results from the Xpert MTB/RIF assay. Of the 314 samples analyzed, 258 showed rifampicin resistance as detected by MTB, with 56 samples of MTB-detected rifampicin indeterminate results. Type E probe mutation (58.9%) was the most common type, while the least frequent mutation was Type C probe (1.5%). No missing probe was observed in approximately 8.9% of samples. Among the 56 rifampicin indeterminate results, the maximum Cycle threshold value did not cross 34.5 in six samples.

Abstract Here, we explore the application of Raman spectroscopy for the assessment of plant biodiversity. Raman spectra from 11 vascular plant species commonly found in forest ecosystems, specifically angiosperms (both monocots and eudicots) and pteridophytes (ferns), were acquired in vivo and in situ using a Raman leaf-clip. We achieved an overall accuracy of 91% for correct classification of a species within a plant group and identified lignin Raman spectral features as a useful discriminator for classification. The results demonstrate the potential of Raman spectroscopy in contributing to plant biodiversity assessment.

Abstract Farmers from South Asian countries spray insecticides without protective gear, which leads to insecticide exposure through dermal and nasal routes. Acetylcholinesterase plays a crucial role in controlling neuromuscular function. Organophosphate and carbamate insecticides inhibit acetylcholinesterase, which leads to severe neuronal/cognitive dysfunction, breathing disorders, loss of endurance, and death. To address this issue, an Oxime-fabric is developed by covalently attaching silyl-pralidoxime to the cellulose of the fabric. The Oxime-fabric, when stitched as a bodysuit and facemask, efficiently deactivates insecticides (organophosphates and carbamates) upon contact, preventing exposure. The Oxime-fabric prevents insecticide-induced neuronal damage, neuro-muscular dysfunction, and loss of endurance. Furthermore, we observe a 100% survival rate in rats when repeatedly exposed to organophosphate-insecticide through the Oxime-fabric, while no survival is seen when organophosphate-insecticide applied directly or through normal fabric. The Oxime-fabric is washable and reusable for at least 50 cycles, providing an affordable solution to prevent insecticide-induced toxicity and lethality among farmers.

Abstract Background Cancer is a medical condition where some cells of the body reproduce uncontrollably and metastasize to other parts of the body. The burden of the disease is significantly high both at the global and national levels. In UAE, cancer was found to be the third leading cause of death. Breast cancer has been ranked first due to its prevalence, incidence, and mortality in UAE. Breast cancer survivors have significantly poor cardiovascular tolerance which affects their quality of life (QoL), even after the carcinoma has been treated or removed. Thus, the protocol aims to analyze the changes in cardiovascular endurance and QoL domains for breast cancer survivors in the United Arab Emirates using a long-term 2-month physical rehabilitation. Methods A total of 60 breast cancer survivors would be included in the study using a randomized controlled allocation of a 2-month physical rehabilitation intervention program with 3 months of follow-up. The intervention would target the cardiovascular endurance component of the participants to improve their physical well-being and quality of life ultimately. Discussion The findings of the study would have high clinical significance among breast cancer survivors in the UAE. The proposed physical rehabilitation program could be beneficial in improving cardiovascular endurance and thereby reduce the risk of mortality among breast cancer survivors. In addition, the physiological benefits of the exercise program could improve their quality-of-life domains including physical, mental, and social well-being. On a larger view, it could also help to reduce the economic burden on the health system due to associated complications. Trial registration ClinicalTrials.gov NCT06013527. Registered on 28 August 2023.

Background: Thyroid disease is one of the most common illnesses in the UAE, which could be linked to vast numbers of people suffering from vitamin D deficiency. This study aimed to explore the association between serum 25(OH)D levels and thyroid function parameters in men and women with diagnosed hypothyroidism. Methods and Results: This cross-sectional observational study included 86 patients (78[90.7%] women and 8[9.3%] men) with diagnosed hypothyroidism. The patients were divided into two groups, male and female. These two groups were compared in terms of age, TSH, free-T4 (FT4), vitamin D, free-T3 (FT3), and body mass index (BMI). In addition, the correlation between levels of vitamin D and TSH was also examined in these two groups. The mean age of the patients was 27.5 years, and BMI was 28.00 kg/m2, indicating overweight. Vitamin D deficiency was found in 61(70.9%) patients and severe vitamin D deficiency in 10(11.6%) patients with hypothyroidism. 25(OH)D levels were significantly low in patients with high TSH levels, showing a weak negative correlation (r=-0.132, P=0.043). A negligible positive correlation was identified between 25(OH)D levels and FT4 r=0.089, P>0.05) and FT3 (r=0.071, P>0.05), and a negligible negative correlation with BMI (r=-0.059, P>0.05). Conclusion: There is a clear indication that vitamin D deficiency is prevalent in hypothyroid patients and that these subjects have lower levels of serum 25(OH)D. Suggesting that lower serum 25(OH)D is related to hypothyroidism and the deficiency in vitamin D plays a role in the development of the disease.

Pinus roxburghii commonly known as Chir Pine is a coniferous tree native to the Himalayas. Pine needles shed from pine trees decay very slowly, form a carpet on the forest floor and are a major cause of frequent forest fires. The abundantly available unutilized pine needles are rich source of lignocellulosic biomass, goes waste, and its management through sustainable utilization has been a challenge.In the present study, pine needles were fibrillated by using laboratory disc refiner without any chemical treatment under different refiner passes from 0.10 to 0.40 mm to prepare pinepeat. The biomasses under different refiner passes were characterized for pH (6.2–6.5), freeness (481–821 ml), water absorbency (350–580 %), drainage time (9.10–16.99 sec), carbon (45.08 %), nitrogen (0.93 %), hydrogen (6.49 %), sulfur (0.13 %) content and C/N ratio (50.86 %). Anatomical characteristics of pine needle and pinepeat were also examined. The study reported significant change in morphology of pine needles after refining treatment wherein tissues turned into flattened, fibrillated and separated in short groups, increasing the surface area of the fragmented biomass. Application of processed pinepeat as amendments in nursery substrates was further assessed. The significantly higher growth parameters in pinepeat in comparison to conventional growing media suggests pinepeat as an additional environmentally sustainable choice for nursery operations in near future. The utility of pinepeat in this way is imperative for environment waste management and would turn into an alternative, renewable, and reliable growing media as amendments in nursery substrates.

Protein lysine methyltransferases (PKMTs) methylate histone and non-histone proteins to regulate biological outcomes such as development and disease including viral infection. While PKMTs have been extensively studied for modulating the antiviral responses via host gene regulation, their role in methylation of proteins encoded by viruses and its impact on host-pathogen interactions remain poorly understood. In this study, we discovered distinct nucleo-cytoplasmic form of euchromatic histone methyltransferase 1 (EHMT1N/C), a PKMT, that phase separates into viral inclusion bodies (IBs) upon cytoplasmic RNA-virus infection (Sendai Virus). EHMT1N/C interacts with cytoplasmic EHMT2 and methylates SeV-Nucleoprotein upon infection. Elevated nucleoprotein methylation during infection correlated with coalescence of small IBs into large mature platforms for efficient replication. Inhibition of EHMT activity by pharmacological inhibitors or genetic depletion of EHMT1N/C reduced the size of IBs with a concomitant reduction in replication. Additionally, we also found that EHMT1 condensation is not restricted to SeV alone but was also seen upon pathogenic RNA viral infections caused by Chandipura and Dengue virus. Collectively, our work elucidates a new mechanism by which cytoplasmic EHMT1 acts as proviral host factor to regulate host-pathogen interaction.

A study was carried out during 2021 to 2022 at Assam Agricultural University, Jorhat, Assam to study the effect of long-term integrated nutrient management on productivity, profitability, soil microbial activity and nutrient content of rice (Oryza sativa L.) in Assam. The experiment was conducted in a randomized block design (RBD) comprised of 5 nutrient management practices, viz. T1, Absolute control; T2, 100% recommended dose (RD) through chemical fertilizers (CF); T3, 50% RD through CF + liquid biofertilizers; T4, 50% RD through CF + solid biofertilizers; and T5, 50% RD through CF + enriched compost (EC) (@1 t/ha), with 4 replications. The results showed higher grain yield and profitability in treatment T2 (5.23 t/ha and $1472.29/ha) followed by T5 (5.23 t/ha and $1446.29/ha), T3 (4.90 t/ha and $1362.29/ha), T4 (4.87 t/ha and $1346.69/ha), and the lowest in T1 (3.21 t/ha and $897.85/ha). Notably, treatment T5 delivered comparable yield and returns to T2. However, treatment T5 significantly increased the essential nutrients (N, P, K, Zn, Cu, Mn, Fe) in grain compared to T2 or T3/T4. In addition, soil microbial activity such as dehydrogenase activity, phospho-monoesterase activity, microbial biomass carbon, fluorescein diacetate hydrolase, and arylsulphatase activity were significantly higher under treatment T5. These findings highlight that treatment T5 i.e. 50% CF + EC (@1 t/ha) not only matches conventional 100% fertilizer application in yield and returns but also enhances soil microbial activity. Study highlights the potential of integrated nutrient management practices with 50% RD through CF + enriched compost (EC) (@1 t/ha) to address micronutrient deficiencies, improve crop productivity, profitability, and promote sustainable agriculture in the region.

Positive and unlabeled (PU) learning is a type of semi-supervised binary classification where the machine learning algorithm differentiates between a set of positive instances (labeled) and a set of both positive and negative instances (unlabeled). PU learning has broad applications in settings where confirmed negatives are unavailable or difficult to obtain, and there is value in discovering positives among the unlabeled (e.g., viable drugs among untested compounds). Most PU learning algorithms make the selected completely at random (SCAR) assumption, namely that positives are selected independently of their features. However, in many real-world applications, such as healthcare, positives are not SCAR (e.g., severe cases are more likely to be diagnosed), leading to a poor estimate of the proportion, α, of positives among unlabeled examples and poor model calibration, resulting in an uncertain decision threshold for selecting positives. PU learning algorithms vary; some estimate only the proportion, α, of positives in the unlabeled set, while others calculate the probability that each specific unlabeled instance is positive, and some can do both. We propose two PU learning algorithms to estimate α, calculate calibrated probabilities for PU instances, and improve classification metrics: i) PULSCAR (positive unlabeled learning selected completely at random), and ii) PULSNAR (positive unlabeled learning selected not at random). PULSNAR employs a divide-and-conquer approach to cluster SNAR positives into subtypes and estimates α for each subtype by applying PULSCAR to positives from each cluster and all unlabeled. In our experiments, PULSNAR outperformed state-of-the-art approaches on both synthetic and real-world benchmark datasets.

The robust rotor structure and fault-tolerance characteristics of the Switched Reluctance Motors (SRMs) are the best choice for next-generation Electric Vehicle (EV) applications. This machine has few restraints like high torque and flux ripples. However, the existing Model Predictive Control (MPC) using multiple control objectives and maximum sectors in the switching table results in high torque ripples due to the improper sector partition, Voltage Vectors (VVs) and weight factor (K1) selection. This paper proposes a Finite Set-Model Predictive Control (FS-MPC) for an analytical model of a non-linearity SRM machine to analyze the torque ripple performance. The proposed VVs are derived using sector partition based on the rotor position. The control is designed as a single cost function with the weighting factor contributing to smooth torque by selecting optimal control signals. Simulation studies and experiments with a four-phase 8/6 SRM drive verifies the enhanced FS-MPC for real-time implementation. The dynamic speed and ripple values of SRM Drives are measured using a mixed signal oscilloscope and the sensor probes. The laboratory outcomes calculate the analytical equations to validate the findings. The calculated value of torque ripple is 9 % through this FS-MPC. The study reveals that the proposed method is well suited for torque ripple reduction than flux ripples.

This comprehensive review covers the latest EV technologies, charging methods, and optimization strategies. Electric and hybrid vehicles are compared, explaining their operation and effects on energy, efficiency, and the environment. The review covers new EV charging technologies. Conductive charging (CC), the most popular method due to its simplicity and cost, is tested. Wireless power transfer (WPT) systems, which charge without cables, and their integration into urban infrastructure are also examined. Battery swap stations (BSS), which quickly recharges depleted batteries, are also reviewed for their viability and adoption issues. Electric vehicle standards like charging rate and system configuration are covered in this paper. These standards simplify electric mobility across regions and manufacturers by ensuring charging infrastructure and vehicle technology compatibility. The review evaluates algorithms and mathematical models that maximize efficiency, reduce costs, and improve charging resource accessibility. Urban planners and policymakers need these optimization strategies to improve EV infrastructure. Article ends with research ideas. It identifies knowledge gaps and suggests research to improve EV technologies and charging systems. To promote electric vehicle adoption and innovation, the recommendations address technical and socioeconomic barriers. Researchers, engineers, and decision-makers use this review to develop and implement electric vehicle technologies and infrastructure.

Background: Parry-Romberg Syndrome (PRS), characterized by progressive hemifacial atrophy, presents unique challenges in oral health management due to its rare nature and varied clinical manifestations. While cutaneous and craniofacial features are well-documented, gingival involvement is less commonly recognized. Case Presentation: We present a case of gingival manifestation of PRS in a 26-year-old female, emphasizing the progressive nature of the condition and its impact on oral health. Clinical examination revealed significant gingival recession on the affected side, leading to tooth sensitivity and compromised periodontal health. Diagnosis: The diagnosis of PRS was established based on characteristic facial asymmetry, radiographic findings, and absence of systemic involvement. Gingival recession was confirmed through periodontal assessment. Management: Management strategies included symptomatic relief with desensitizing toothpaste, preservation of periodontal health through oral hygiene instructions and periodontal therapy, and supportive care tailored to the patient's needs. Multidisciplinary collaboration was essential to optimize patient care and address the complex clinical manifestations of PRS. Outcome: While complete resolution of gingival recession was not achieved due to the progressive nature of PRS, symptomatic relief and preservation of periodontal health were achieved through comprehensive management strategies. Long-term follow-up is essential to monitor disease progression and adjust treatment as needed. Conclusion: Gingival involvement in PRS underscores the importance of a multidisciplinary approach to oral health management. Recognition of gingival manifestations and implementation of individualized treatment plans are crucial for optimizing oral health outcomes and enhancing the quality of life for affected individuals. Update Dent. Coll. j: 2024; 14(2): 27-30

Ensuring safe and effective blood transfusion is essential for improving healthcare and preventing the spread of blood-borne diseases worldwide. Medical students are a potential key resource for voluntary blood donation, particularly in medical college hospitals, which can help alleviate the shortage of blood products. Additionally, they have the potential to inspire a wider segment of the population to donate blood, thereby narrowing the gap between blood supply and demand. This study aims to evaluate the knowledge, perceptions, and practices related to voluntary blood donation among undergraduate medical students at Gulf Medical University in Ajman, United Arab Emirates, and to identify the factors associated with these aspects. A cross-sectional, institution-based study was conducted among undergraduate medical students at Gulf Medical University. Data were gathered through a self-administered structured questionnaire and analyzed using the Statistical Package for Social Sciences (SPSS) version 27. Knowledge, perceptions, and practices related to blood donation among 301 participants were assessed. The findings revealed no significant association between knowledge levels and demographics such as gender, marital status, residence, college, or academic year. While most participants were aware of blood donation procedures and benefits, actual donation practices were low, with 84.4% rarely donating blood. Key barriers included fear of needle pain and time constraints. Health professionals and social media were the primary sources of information, indicating the need for targeted educational interventions to improve blood donation rates. Students at Gulf Medical University are well-informed about blood donation, with many willing to donate. However, fears like needle pain and time constraints limit participation. Educational efforts could help overcome these barriers.