Harnessing solar power for enhanced photocatalytic degradation of coloured pollutants using novel Mg-doped-ZnFe2O4/S@g-C3N4 heterojunction: A facile hydrothermal synthesis approach.

The ability of heterogeneous photocatalysis to effectively remove organic pollutants from wastewater has shown great promise as a tool for environmental remediation. Pure zinc ferrites (ZnFe2O4) and magnesium-doped zinc ferrites (Mg@ZnFe2O4) with variable percentages of Mg (0.5, 1, 3, 5, 7, and 9 mol%) were synthesized via hydrothermal route and their photocatalytic activity was checked against methylene blue (MB) taken as a model dye. FTIR, XPS, BET, PL, XRD, TEM, and UV-Vis spectroscopy were used for the identification and morphological characterization of the prepared nanoparticles (NPs) and nanocomposites (NCs). The 7% Mg@ZnFe2O4 NPs demonstrated excellent degradation against MB under sunlight. The 7% Mg@ZnFe2O4 NPs were integrated with diverse contents (10, 50, 30, and 70 wt.%) of S@g-C3N4 to develop NCs with better activity. When the NCs were tested to degrade MB dye, it was revealed that the 7%Mg@ZnFe2O4/S@g-C3N4 NCs were more effective at utilizing solar energy than the other NPs and NCs. The synergistic effect of the interface formed between Mg@ZnFe2O4 and S@g-C3N4 was primarily responsible for the boosted photocatalytic capability of the NCs. The fabricated NCs may function as an effective new photocatalyst to remove organic dyes from wastewater.

Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality.

Tools for predicting COVID-19 outcomes enable personalized healthcare, potentially easing the disease burden. This collaborative study by 15 institutions across Europe aimed to develop a machine learning model for predicting the risk of in-hospital mortality post-SARS-CoV-2 infection. Blood samples and clinical data from 1286 COVID-19 patients collected from 2020 to 2023 across four cohorts in Europe and Canada were analyzed, with 2906 long non-coding RNAs profiled using targeted sequencing. From a discovery cohort combining three European cohorts and 804 patients, age and the long non-coding RNA LEF1-AS1 were identified as predictive features, yielding an AUC of 0.83 (95% CI 0.82-0.84) and a balanced accuracy of 0.78 (95% CI 0.77-0.79) with a feedforward neural network classifier. Validation in an independent Canadian cohort of 482 patients showed consistent performance. Cox regression analysis indicated that higher levels of LEF1-AS1 correlated with reduced mortality risk (age-adjusted hazard ratio 0.54, 95% CI 0.40-0.74). Quantitative PCR validated LEF1-AS1's adaptability to be measured in hospital settings. Here, we demonstrate a promising predictive model for enhancing COVID-19 patient management.

An Integrated Framework for Analysis and Prediction of Impact of Single Nucleotide Polymorphism Associated with Human Diseases.

Single nucleotide polymorphisms are most common type of genetic variation in human genome. Analyzing genetic variants can help us better understand the genetic basis of diseases and develop predictive models which are useful to identify individuals who are at increased risk for certain diseases. Several SNP analysis tools have already been developed. For running these tools, the user needs to collect data from various databases. Secondly, often researchers have to use multiple variant analysis tools for cross validating their results and increase confidence in their findings. Extracting data from multiple databases and running multiple tools at a time, increases complexity and time required for analysis. There are some web-based tools that integrate multiple genetic variant databases and provide variant annotations for a few tools. These approaches have some limitations such as retrieving annotation information, filtering common pathogenic variants. The proposed web-based tool, namely IPSNP: An Integrated Platform for Predicting Impact of SNPs is written in Django which is a python-based framework. It uses RESTful API of MyVariant.info to extract annotation information of variants associated with a given gene, rsID, HGVS format variants specified in a VCF file for 29 tools. The results are in the form of a CSV file of predictions (1) derived from the consensus decision, (2) a file having annotations for the variants associated with the given gene, (3) a file showing variants declared as pathogenic commonly by the selected tools, and (4) a CSV file containing chromosome coordinates based on GRCh37 and GRCh38 genome assemblies, rsIDs and proteomic data, so that users may use tools of their choice and avoiding manual parameter collection for each tool. IPSNP is a valuable resource for researchers and clinicians and it can help to save time and effort in discovering the novel disease-associated variants and the development of personalized treatments.

Biopolymer Materials in Triboelectric Nanogenerators: A Review.

In advancing the transition of the energy sector toward heightened sustainability and environmental friendliness, biopolymers have emerged as key elements in the construction of triboelectric nanogenerators (TENGs) due to their renewable sources and excellent biodegradability. The development of these TENG devices is of significant importance to the next generation of renewable and sustainable energy technologies based on carbon-neutral materials. This paper introduces the working principles, material sources, and wide-ranging applications of biopolymer-based triboelectric nanogenerators (BP-TENGs). It focuses on the various categories of biopolymers, ranging from natural sources to microbial and chemical synthesis, showcasing their significant potential in enhancing TENG performance and expanding their application scope, while emphasizing their notable advantages in biocompatibility and environmental sustainability. To gain deeper insights into future trends, we discuss the practical applications of BP-TENG in different fields, categorizing them into energy harvesting, healthcare, and environmental monitoring. Finally, the paper reveals the shortcomings, challenges, and possible solutions of BP-TENG, aiming to promote the advancement and application of biopolymer-based TENG technology. We hope this review will inspire the further development of BP-TENG towards more efficient energy conversion and broader applications.

The prevalent dynamic and genetic characterization of mcr-1 encoding multi-drug resistant Escherichia coli strains recovered from poultry in Hebei, China.

OBJECTIVES: Colistin is known as the last resort antibiotic to treat the infections caused by multi-drug resistant (MDR) foodborne pathogens. The emergence and widespread dissemination of plasmid-mediated colistin resistance gene mcr-1 in the E. coli incurs potential threat to public health. Here, we investigated the epidemiology, transmission dynamics, and genetic characterization of mcr-1 harboring E. coli isolates from poultry origin in Hebei province, China. METHODS: A total of 297 fecal samples were collected from the two large poultry farms in Hebei province, China. The samples were processed for E. coli identification by MALDI-TOF-MS and 16S rD4A sequencing. Then, mcr-1 gene harboring E. coli strains were identified by PCR and subjected to antimicrobial susceptibility testing by broth microdilution assay. The genomic characterization of the isolates was done by whole genome sequencing using the various bioinformatics tools, and multi-locus sequence typing (MLST) was done by sequence analysis of the seven housekeeping genes. The conjugation experiment was done to check the transferability of mcr-1 along with the plasmid stability testing. RESULTS: A total of six mcr-1 E. coli isolates with MIC of 4 µg/mL were identified from 297 samples (2.02%). The mcr-1 harboring E. coli were identified as MDR and belonged to ST101 (n=4) and ST410 (n=2). The genetic environment of mcr-1 presented its position on IncHI2 plasmid in four isolates and p0111 in two isolates which is rarely reported plasmid type for mcr-1. Moreover, both type of plasmids was transferable to recipient J53, and mcr-1 was flanked by three mobile elements ISApl1, Tn3, and IS26 forming a novel backbone Tn3-IS26-mcr-1- pap2-ISApl1 on p0111 plasmid. The phylogenetic analysis shared a common lineage with mcr-1 harboring isolates from the environment, human and animals which indicate its horizontal spread among the diverse sources, species, and hosts. CONCLUSION: This study recommends the one health approach for future surveillance across multiple sources and bacterial species to adopt relevant measures and reduce global resistance crises.

Evolutionary trends of respiratory syncytial viruses: Insights from large-scale surveillance and molecular dynamics of G glycoprotein.

Human respiratory syncytial virus (RSV) is an underlying cause of lower respiratory illnesses in children, elderly and immunocompromised adults. RSV contains multiple structural and non-structural proteins with two major glycoproteins that control the initial phase of infection, fusion glycoprotein and the attachment (G) glycoprotein. G protein attaches to the ciliated cells of airways initiating the infection. The hypervariable G protein plays a vital role in evolution of RSV strains. We employed multiple bioinformatics tools on systematically accessed large-scale data to evaluate mutations, evolutionary history, and phylodynamics of RSV. Mutational analysis of central conserved region (CCR) on G protein-coding sequences between 163 and 189 positions revealed frequent mutations at site 178 in human RSV (hRSV) A while arginine to glutamine substitutions at site 180 positions in hRSV B, remained prevalent from 2009 to 2014. Phylogenetic analysis indicates multiple signature mutations within G protein responsible for diversification of clades. The USA and China have highest number of surveillance records, followed by Kenya. Markov Chain Monte Carlo Bayesian skyline plot revealed that RSV A evolved steadily from 1990 to 2000, and rapidly between 2003 and 2005. Evolution of RSV B continued from 2003 to 2022, with a high evolution stage from 2016 to 2020. Throughout evolution, cysteine residues maintained their strict conserved states while CCR has an entropy value of 0.0039(±0.0005). This study concludes the notion that RSV G glycoprotein is continuously evolving while the CCR region of G protein maintains its conserved state providing an opportunity for CCR-specific monoclonal antibodys (mAbs) and inhibitors as potential candidates for immunoprophylaxis.

Unrevealing the therapeutic potential of artesunate against emerging zoonotic Babesia microti infection in the murine model.

Babesiosis, a zoonotic blood protozoal disease, threatens humans and animals and is difficult to treat due to growing antimicrobial resistance. The study aimed to investigate the therapeutic efficacy of artesunate (AS), a well-known derivative of artemisinin, against Babesia microti (B. microti) using a murine infection model. Male BALB/c mice (6 weeks old; 15 per group) were chosen and randomly divided into 1) the control group, 2) the B. microti group, and 3) the B. microti + artesunate treatment groups. AS treatment at 2 mg/kg, 4 mg/kg, and 8 mg/kg of body weight significantly (p < 0.05) reduced the B. microti load in blood smears in a dose-dependent manner. Additionally, AS treatment mitigated the decrease in body weight and restored the normal state of the liver and spleen viscera index compared to the B. microti-infected group after 28 days. Hematological analysis revealed significant increases in RBC, WBC, and PLT counts post-AS treatment compared to the B. microti-infected group. Furthermore, AS administration resulted in significant reductions in total protein, bilirubin, ALT, AST, and ALP levels, along with reduced liver and spleen inflammation and lesions as observed through histopathological analysis. AS also elicited dose-dependent changes in mRNA and protein expression levels of apoptotic, proinflammatory, and anti-inflammatory cytokines in the liver compared to the control and B. microti-infected groups. Immunolabeling revealed decreased expression of apoptotic and inflammation-related proteins in AS-treated hepatic cytoplasm compared to the B. microti-infected group. AS also in dose-dependent manner decreased apoptotic protein and increased Bcl-2. Overall, these findings underscore the potential of AS as an anti-parasitic candidate in combating B. microti pathogenesis in an in vivo infection model, suggesting its promise for clinical trials as a treatment for babesiosis.

Growth phase-dependent ribonucleic acid production dynamics.

Transcriptional events play a crucial role in major cellular processes that specify the activity of an individual cells and influences cell population behavior in response to environment. Active (ON) and an inactive (OFF) states controls the transcriptional burst. Yet, the mechanism and kinetics of ON/OFF-state across the different growth phases of Escherichia coli remains elusive. Here, we have used a single mRNA detection method in live-cells to comprehend the ON/OFF mechanism of the first transcriptional (TF) and consecutive events (TC) controlled by lactose promoters, Plac and Plac/ara1. We determined that the duration of TF ON/OFF has different modes, exhibiting a close to inverse behavior to that of TC ON/OFF. Dynamics of ON/OFF states in fast and slow-dividing cells were affected by the promoter region during the initiation of transcription. Period of TF ON-state defines the behavior of TC by altering the number and the frequency of mRNAs formed. Furthermore, we have shown that delayed OFF-time in TF affects the dynamics of TC in both states, which is mainly determined by the upstream promoter region. Furthermore, using elongation arrest experiments, we independently validate that mRNA noise in TC is governed by the delayed OFF-period in TF. We have identified the position of the regulatory regions that plays a crucial role in noise (Fano) modulation. Taken together, our results suggest that the dynamics of the first transcriptional event, TF, pre-defines the diversity of the population.

Design of a novel intelligent computing framework for predictive solutions of malaria propagation model.

The paper presents an innovative computational framework for predictive solutions for simulating the spread of malaria. The structure incorporates sophisticated computing methods to improve the reliability of predicting malaria outbreaks. The study strives to provide a strong and effective tool for forecasting the propagation of malaria via the use of an AI-based recurrent neural network (RNN). The model is classified into two groups, consisting of humans and mosquitoes. To develop the model, the traditional Ross-Macdonald model is expanded upon, allowing for a more comprehensive analysis of the intricate dynamics at play. To gain a deeper understanding of the extended Ross model, we employ RNN, treating it as an initial value problem involving a system of first-order ordinary differential equations, each representing one of the seven profiles. This method enables us to obtain valuable insights and elucidate the complexities inherent in the propagation of malaria. Mosquitoes and humans constitute the two cohorts encompassed within the exposition of the mathematical dynamical model. Human dynamics are comprised of individuals who are susceptible, exposed, infectious, and in recovery. The mosquito population, on the other hand, is divided into three categories: susceptible, exposed, and infected. For RNN, we used the input of 0 to 300 days with an interval length of 3 days. The evaluation of the precision and accuracy of the methodology is conducted by superimposing the estimated solution onto the numerical solution. In addition, the outcomes obtained from the RNN are examined, including regression analysis, assessment of error autocorrelation, examination of time series response plots, mean square error, error histogram, and absolute error. A reduced mean square error signifies that the model's estimates are more accurate. The result is consistent with acquiring an approximate absolute error close to zero, revealing the efficacy of the suggested strategy. This research presents a novel approach to solving the malaria propagation model using recurrent neural networks. Additionally, it examines the behavior of various profiles under varying initial conditions of the malaria propagation model, which consists of a system of ordinary differential equations.

Study of Cation Distribution and Photocatalytic Activity of Nonthermal Plasma-Modified NiZnFe2O4 Magnetic Nanocomposites.

In this study, NiZnFe2O4 composite was synthesized using a sol-gel route and subjected to nonthermal plasma treatment for tailoring their cations' distribution and physicochemical, magnetic, and photocatalytic properties. Microwave plasma treatment was given to the composites for 60 min in support of postsynthesis sintering at 700 °C for 5 h. X-ray diffraction (XRD) analysis was conducted on pre- and postplasma-modified ferrite composites to identify phase-pure cubic spinel structure and cations' distribution. The cation distributions were measured from the ratio of XRD intensity peaks corresponding to (220), (311), (422) and (440) planes. The intensity ratio of plasma-treated ferrite composites decreased compared to that of pristine composites. The crystallite size and lattice constant were increased on plasma treatment of the composite. The morphological analysis showed nanoflower-like structures of the particles with an increased surface area in the plasma-treated composites. The plasma oxidation and sputtering effects caused a reduction in the nanoflower size. The energy bandgap increased with a decrease in particle size due to plasma treatment. The rhodamine B dye solution was then irradiated with a light source in the presence of the nanocomposites. The dye degradation efficiency of the composite photocatalyst increased from 80 to 96% after plasma treatment.

Association of polymorphism in the promotor area of the caprine BMPR1B gene with litter size and body measurement traits in Damani goats.

This study aimed to explore polymorphisms in the promoter region of the caprine BMPR1B (Bone morphogenetic protein receptor 1 beta) gene and its association with body measurement and litter size traits in Damani does. A total of 53 blood samples were collected to analyze the association between the BMPR1B gene polymorphism and 11 phenotypic traits in Damani female goats. The results revealed that three novel SNPs were identified in the promoter region of the caprine BMPR1B gene, including g.67 A > C (SNP1), g.170 G > A(SNP2), and g.501A > T (SNP3), among which the SNP1 and SNP2 were significantly (p < 0.05) associated with litter size and body measurement traits in Damani goats. In SNP1 the AC genotype could be used as a marker for litter size, and the CC genotype for body weight in Damani goats. In SNP2, the genotype GG was significantly (p < 0.05) associated with ear and head length. Therefore, we can conclude from the present study, that genetic variants AC and CC of the caprine BMPR1B gene could be used as genetic markers for economic traits through marker-assisted selection for the breed improvement program of the Damani goat.

Investigating ischemia and reperfusion-induced organ damage in severe cardiac arrest: A comprehensive proteomics perspective.

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A framework for the analysis of skin sores disease using evolutionary intelligent computing approach.

The most common and contagious bacterial skin disease i.e. skin sores (impetigo) mostly affects newborns and young children. On the face, particularly around the mouth and nose area, as well as on the hands and feet, it typically manifests as reddish sores. In this study, a neuro-evolutionary global algorithm is introduced to solve the dynamics of nonlinear skin sores disease model (SSDM) with the help of an artificial neural network. The global genetic algorithm is integrated with local sequential quadratic programming (GA-LSQP) to obtain the optimal solution for the proposed model. The designed differential model of skin sores disease is comprised of susceptible (S), infected (I), and recovered (R) categories. An activation function based neural network modeling is exploited for skin sores system through mean square error to achieve best trained weights. The integrated approach is validated and verified through the comparison of results of reference Adam strategy with absolute error analysis. The absolute error results give accuracy of around 10-11 to 10-5, demonstrating the worthiness and efficacy of proposed algorithm. Additionally, statistical investigations in form of mean absolute deviation, root mean square error, and Theil's inequality coefficient are exhibited to prove the consistency, stability, and convergence criteria of the integrated technique. The accuracy of the proposed solver has been examined from the smaller values of minimum, median, maximum, mean, semi-interquartile range, and standard deviation, which lie around 10-12 to 10-2.

Advanced detection of coronary artery disease via deep learning analysis of plasma cytokine data.

The 2017 World Health Organization Fact Sheet highlights that coronary artery disease is the leading cause of death globally, responsible for approximately 30% of all deaths. In this context, machine learning (ML) technology is crucial in identifying coronary artery disease, thereby saving lives. ML algorithms can potentially analyze complex patterns and correlations within medical data, enabling early detection and accurate diagnosis of CAD. By leveraging ML technology, healthcare professionals can make informed decisions and implement timely interventions, ultimately leading to improved outcomes and potentially reducing the mortality rate associated with coronary artery disease. Machine learning algorithms create non-invasive, quick, accurate, and economical diagnoses. As a result, machine learning algorithms can be employed to supplement existing approaches or as a forerunner to them. This study shows how to use the CNN classifier and RNN based on the LSTM classifier in deep learning to attain targeted "risk" CAD categorization utilizing an evolving set of 450 cytokine biomarkers that could be used as suggestive solid predictive variables for treatment. The two used classifiers are based on these "45" different cytokine prediction characteristics. The best Area Under the Receiver Operating Characteristic curve (AUROC) score achieved is (0.98) for a confidence interval (CI) of 95; the classifier RNN-LSTM used "450" cytokine biomarkers had a great (AUROC) score of 0.99 with a confidence interval of 0.95 the percentage 95, the CNN model containing cytokines received the second best AUROC score (0.92). The RNN-LSTM classifier considerably beats the CNN classifier regarding AUROC scores, as evidenced by a p-value smaller than 7.48 obtained via an independent t-test. As large-scale initiatives to achieve early, rapid, reliable, inexpensive, and accessible individual identification of CAD risk gain traction, robust machine learning algorithms can now augment older methods such as angiography. Incorporating 65 new sensitive cytokine biomarkers can increase early detection even more. Investigating the novel involvement of cytokines in CAD could lead to better risk detection, disease mechanism discovery, and new therapy options.

ADH-Enhancer: an attention-based deep hybrid framework for enhancer identification and strength prediction.

Enhancers play an important role in the process of gene expression regulation. In DNA sequence abundance or absence of enhancers and irregularities in the strength of enhancers affects gene expression process that leads to the initiation and propagation of diverse types of genetic diseases such as hemophilia, bladder cancer, diabetes and congenital disorders. Enhancer identification and strength prediction through experimental approaches is expensive, time-consuming and error-prone. To accelerate and expedite the research related to enhancers identification and strength prediction, around 19 computational frameworks have been proposed. These frameworks used machine and deep learning methods that take raw DNA sequences and predict enhancer's presence and strength. However, these frameworks still lack in performance and are not useful in real time analysis. This paper presents a novel deep learning framework that uses language modeling strategies for transforming DNA sequences into statistical feature space. It applies transfer learning by training a language model in an unsupervised fashion by predicting a group of nucleotides also known as k-mers based on the context of existing k-mers in a sequence. At the classification stage, it presents a novel classifier that reaps the benefits of two different architectures: convolutional neural network and attention mechanism. The proposed framework is evaluated over the enhancer identification benchmark dataset where it outperforms the existing best-performing framework by 5%, and 9% in terms of accuracy and MCC. Similarly, when evaluated over the enhancer strength prediction benchmark dataset, it outperforms the existing best-performing framework by 4%, and 7% in terms of accuracy and MCC.

A prospective observational study of estimating drug related problems and clinical outcomes in subtypes of stroke patients.

BACKGROUND: Stroke is a neurological disease and a leading cause of mortality worldwide. Strokes mainly consist of two types: hemorrhage and ischemia. Stroke patients are being administered multiple drug therapy and are at risk of drug-related problems. AIM: To estimate drug-related problems (DRPs) and clinical end outcomes in hospitalized stroke patients. METHODS: Current study was a multicenter, cross-sectional prospective observational study including 250 stroke patients admitted to tertiary care hospitals in Karachi, Pakistan. The study included all clinical subtypes of stroke patients i.e. Stroke, Ischemic stroke, Hemorrhagic stroke, CVA, and TIA. Associations among patient-clinical end outcomes and drug therapy-related variables like DRPs, mortality, and morbidity rates were estimated using Pearson's chi-squared test. Statistical analysis was done by using SPSS software, version 25. RESULTS: A total of 250 patients participated in this study suffering from different clinical subtypes of stroke i.e. Ischemic stroke, hemorrhagic stroke, TIA, and CVA, including 46% male and 54% female patients. The majority of patients' stay at the hospital was between 1-10 days. The overall mortality rate in stroke patients was 51%. HAIs were observed in 70% of patients, HAIs faced by patients were SAP, CAP, UTI, sepsis, and VAP. Drugs were assessed according to NEML i.e. access group antibiotics, watch group antibiotics, reserve group antibiotics, statins, antiepileptics, and proton pump inhibitors. Majorly ceftriaxone was administered to 79% of patients, piperacillin-tazobactam to 52%, and cefixime to 48%, whereas meropenem was administered to 42% of patients along with vancomycin to 39% of total patients. A high mortality rate was observed in the case of Klebsiella pneumoniae and Staphylococcus aureus i.e. 78% and in the case of streptococcus pneumoniae 61% mortality rate was observed. Due to the presence of DRPs and various other clinical factors like comorbidities, DDIs, HAIs, administration of potentially nephrotoxic drugs, and administration of antibiotics without having CST, hospitalized stroke patients faced many problems. CONCLUSION: This study helped determine DRPs along with various clinical factors affecting the clinical end outcomes of patients suffering from any clinical subtype of stroke. Due to the enhancement in the evidence of the incidence of DRPs in tertiary care hospitals, pharmacist-led drug therapy review by interfering with doctors and other medical professionals at the patient bed site is needed and should be done to avoid any negative end outcomes and serious issues related to DRPs.

Facile-Solution-Processed Silicon Nanofibers Formed on Recycled Cotton Nonwovens as Multifunctional Porous Sustainable Materials.

Limited efficiency, lower durability, moisture absorbance, and pest/fungal/bacterial interaction/growth are the major issues relating to porous nonwovens used for acoustic and thermal insulation in buildings. This research investigated porous nonwoven textiles composed of recycled cotton waste (CW) fibers, with a specific emphasis on the above-mentioned problems using the treatment of silicon coating and formation of nanofibers via facile-solution processing. The findings revealed that the use of an economic and eco-friendly superhydrophobic (contact angle higher than 150°) modification of porous nonwovens with silicon nanofibers significantly enhanced their intrinsic characteristics. Notable improvements in their compactness/density and a substantial change in micro porosity were observed after a nanofiber network was formed on the nonwoven material. This optimized sample exhibited a superior performance in terms of stiffness, surpassing the untreated samples by 25-60%. Additionally, an significant enhancement in tear strength was observed, surpassing the untreated samples with an impressive margin of 70-90%. Moreover, the nanofibrous network of silicon fibers on cotton waste (CW) showed significant augmentation in heat resistance ranging from 7% to 24% and remarkable sound absorption capabilities. In terms of sound absorption, the samples exhibited a performance comparable to the commercial standard material and outperformed the untreated samples by 20% to 35%. Enhancing the micro-roughness of fabric via silicon nanofibers induced an efficient resistance to water absorption and led to the development of inherent self-cleaning characteristics. The antibacterial capabilities observed in the optimized sample were due to its superhydrophobic nature. These characteristics suggest that the proposed nano fiber-treated nonwoven fabric is ideal for multifunctional applications, having features like enhanced moisture resistance, pest resistance, thermal insulation, and sound absorption which are essential for wall covers in housing.

Identification of potential natural products derived from fungus growing termite, inhibiting Pseudomonas aeruginosa quorum sensing protein LasR using molecular docking and molecular dynamics simulation approach.

Pseudomonas aeruginosa, the most common opportunistic pathogen, is becoming antibiotic-resistant worldwide. The fate of P. aeruginosa, a multidrug-resistant strain, can be determined by multidrug efflux pumps, enzyme synthesis, outer membrane protein depletion, and target alterations. Microbial niches have long used quorum sensing (QS) to synchronize virulence gene expression. Computational methods can aid in the development of novel P. aeruginosa drug-resistant treatments. The tripartite symbiosis in termites that grow fungus may help special microbes find new antimicrobial drugs. To find anti-quorum sensing natural products that could be used as alternative therapies, a library of 376 fungal-growing termite-associated natural products (NPs) was screened for their physicochemical properties, pharmacokinetics, and drug-likeness. Using GOLD, the top 74 NPs were docked to the QS transcriptional regulator LasR protein. The five lead NPs with the highest gold score and drug-like properties were chosen for a 200-ns molecular dynamics simulation to test the competitive activity of different compounds against negative catechin. Fridamycin and Daidzein had stable conformations, with mean RMSDs of 2.48 and 3.67 Å, respectively, which were similar to Catechin's 3.22 Å. Fridamycin and Daidzein had absolute binding energies of -71.186 and -52.013 kcal/mol, respectively, which were higher than the control's -42.75 kcal/mol. All the compounds within the active site of the LasR protein were kept intact by Trp54, Arg55, Asp67, and Ser123. These findings indicate that termite gut and fungus-associated NPs, specifically Fridamycin and Daidzein, are potent QS antagonists that can be used to treat P. aeruginosa's multidrug resistance.Communicated by Ramaswamy H. Sarma.

5-Fluorouracil-Loaded Hyaluronic Acid-Coated Niosomal Vesicles: Fabrication and Ex Vivo Evaluation for Skin Drug Delivery.

5-Fluorouracil (5-FU) is one of the most potent drugs against solid tumors. However, its parenteral administration is associated with systemic toxicity, while its topical application has limited percutaneous absorption. To overcome these limitations, the current study undertakes the formulation of 5-FU as niosomal vesicles that were coated with hyaluronic acid to improve its targeting efficiency for cancer cells. The niosomes were prepared by the thin-film hydration method using cholesterol as physiological lipid and nonionic surfactants (Tween 80 and Span 80) in the ratio of 1:1. The niosomal vesicles were characterized for their size, size distribution, viscosity, surface tension, density, and drug entrapment efficiency. The vesicles were within the particle size range of 337-478 nm with relatively homogeneous particle size distribution (PDI ≤ 0.5). The ζ-potential and drug entrapment efficiency of coated formulations (F2 and F4) were comparatively higher than corresponding noncoated formulations (F1 and F3). The release behavior of 5-FU from niosomal vesicles using a dialysis membrane depicts that initial burst drug release was higher for F1 and F3 due to their smaller particle size in comparison to their coated counterparts. However, the release was more controlled for F4 due to the larger particle size, higher viscosity, and entrapped fraction of the formulation. The permeation of the drug through the rat's skin was comparatively higher in the case of noncoated formulations than their coated counterparts (p ≤ 0.05). This could be attributed to their small particle size and lower surface tension. In the case of coated formulations, the hydrophilic hyaluronic acid hinders the permeation of the drug through the lipid bilayer membrane of the skin. The retention of the drug in the skin was found to be in the range of 20-40%, which is sufficient to achieve optimum drug concentration in the tumorous tissue. Overall, the study successfully designed novel niosomal carrier systems for improved 5-FU delivery after topical application.

Assistive technology-based solutions in learning mathematics for visually-impaired people: exploring issues, challenges and opportunities.

In the absence of vision, visually impaired and blind people rely upon the tactile sense and hearing to obtain information about their surrounding environment. These senses cannot fully compensate for the absence of vision, so visually impaired and blind people experience difficulty with many tasks, including learning. This is particularly true of mathematical learning. Nowadays, technology provides many effective and affordable solutions to help visually impaired and blind people acquire mathematical skills. This paper is based upon a systematic review of technology-based mathematical learning solutions for visually impaired people and discusses the findings and objectives for technological improvements. It analyses the issues, challenges and limitations of existing techniques. We note that audio feedback, tactile displays, a supportive academic environment, digital textbooks and other forms of accessible math applications improve the quality of learning mathematics in visually impaired and blind people. Based on these findings, it is suggested that smartphone-based solutions could be more convenient and affordable than desktop/laptop-based solutions as a means to enhance mathematical learning. Additionally, future research directions are discussed, which may assist researchers to propose further solutions that will improve the quality of life for visually impaired and blind people.