Theoretical insight into physical characteristics of lead-free perovskites Rb2TlSbX6 (X = Cl, Br, I) for optoelectronic devices.

CONTEXT: Novel optoelectronic and thermoelectric properties with broad compositional range, non-toxic nature and structural stability make halide-based double perovskites fascinating for flexible optoelectronic devices. In this work, the structural electronic, optical and transport properties of Rb2TlSbX6 (X = Cl, Br, I) were studied using density functional theory for optoelectronic devices. The elastic analysis demonstrates ductile nature, mechanical stability, anisotropic behaviour and feasibility for flexible optoelectronic devices. The band structure study using Tran-Blaha-modified Becke-Johnson (TB-mBJ) potential shows that all studied materials have direct bandgap. In addition, the bandgap of Rb2TlSbCl6 is more appropriate for optoelectronic devices. The small loss and maximum absorption in visible regions make these materials prime candidates for optoelectronic devices. The transport features indicate that all the studied double perovskites reflect p-type semiconducting behaviour as highlighted by positive Seebeck coefficient values. Furthermore, the high power factor values of Rb2TlSbX6 (X = Cl, Br, I) double perovskites make them suitable for thermoelectric device applications at high temperatures. Based on electronic optical and thermoelectric properties Rb2TlSbCl6 is the best candidate for flexible optoelectronic devices. METHODS: In this paper, structural optimization of Rb2TlSbX6 (X = Cl, Br, I) double perovskites was conducted utilizing the Wien2k software based on first principle calculations with Perdew-Burke-Ernzerhof's generalized-gradient approximation (PBE-sol approximation). The TB-mBJ potential was employed to compute the accurate band gap of studied materials. The thermoelectric properties are evaluated with BoltzTraP code, showing a predominance of P-type charge carriers in all studied perovskites. This methodological strategy verifies the material's remarkable stability and optical properties and offers a solid framework for examining its potential in optoelectronic devices.

Effects of smokeless nicotine on blood physiology, biochemical, and histological alterations using Labeo rohita as a model organism.

Objective: The present research was conducted to evaluate the negative effects of nicotine powder on the blood physiology, and biochemical and histological alterations of Labeo rohita. Materials and Methods: Fish were divided into four groups (1-4). Fish groups 2, 3, and 4 were exposed to different concentrations of nicotine, such as 0.75, 1.25, and 1.75 mg/l, while group 1 acted as a control. To find out the long-term impact of nicotine on body physiology, we conducted a 42-day experiment. After the completion of the experiment, hematology, biochemical assays, and histology were done. Results: Results revealed a considerable increase in HGB, red blood cells, WBCs, hematocrit, mean corpuscular volume, red cell distribution width -SD, procalcitonin, neutrophils, lymphocytes, monocytes, triglycerides, total cholesterol, low-density lipoprotein, very low-density lipoprotein, alanine transaminase, aspartate aminotransferase, globulin, thyroid stimulating hormone, BUN, creatinine, and blood glucose levels, whereas mean corpuscular hemoglobin concentration, mean corpuscular hemoglobin, RDW, platelet, high-density lipoprotein, albumin, total proteins, and T3 levels were significantly (p ≤ 0.05) decreased in exposed fish as compared to control group fish. Histological alterations showed that exposure to smokeless nicotine causes deleterious and degenerative effects in the liver, kidney, and gills of exposed fish. Conclusion: Nicotine administration in fish results in adverse effects on different biochemical and hematological parameters and causes histological alterations in some vital organs of exposed fish.

Fabrication and evaluation of nanoemulsion based insulin loaded microneedles for transdermal drug delivery.

Aim: Insulin therapy require self-administration of subcutaneous injection leading to painful and inconvenient drug therapy. The aim is to fabricate nanoemulsion (NE) based insulin loaded microneedles with improved bioavailability and patient compliance. Materials & methods: Different ratios of polyvinyl alcohol and polyvinylpyrrolidone as polymers were prepared through micro-molding technique for microneedles. Characterization of were performed using scanning electron microscope, differential scanning calorimetry, Fourier-transform infrared spectroscopy and circular dichroism. Mechanical strength, hygroscopicity and pain perception of these microneedles were also evaluated. In vitro release, permeation and in vivo PK/PD study of NE-based microneedles were conducted. Results: NE-based microneedles of insulin have improved bioavailability and quick response. Conclusion: Microneedles loaded with insulin can be effectively delivered insulin transdermally to treat diabetes with increased convenience and patient compliance.

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Cellulose ether and carbopol 971 based gastroretentive controlled release formulation design, optimization and physiologically based pharmacokinetic modeling of ondansetron hydrochloride minitablets.

This study aims to design and optimize ondansetron (OND) gastro-retentive floating minitablets for better and prolonged control of postoperative nausea and vomiting (PONV) with improved patient compliance. Minitablets were directly compressed and encapsulated in a size 2 capsule shell with an overall dose of 24 mg. Central composite design (CCD) was applied keeping one cellulose ether derivative HPMC K15M and Carbopol 971 as variable and used as swelling and rate retarding agents. The other cellulose derivative i.e. sodium carboxymethyl cellulose, along with mannitol, sodium bicarbonate, and talc, were used in fixed quantities. The floating lag time, total floating time, swelling index, in-vitro drug release, and zero-order (RSQ value), were critical quality parameters. The optimized formulation (Fpred) was evaluated for all critical parameters, along with surface morphology, thermal stability, chemical interaction, and accelerated stability. The in silico PBPK modeling was applied to compare the bioavailability of Fpred with reference OND immediate-release tablets. The numerical optimization model predicted >90 % drug release with zero-order at 12 h. In silico PBPK modeling revealed comparable relative bioavailability of Fpred with the reference formulation. The gastroretentive floating minitablets of OND were successfully designed for prolonged emesis control in patients receiving chemotherapeutic agents.

Bridging therapy-induced phenotypes and genetic immune dysregulation to study interleukin-2-induced immunotoxicology.

Interleukin-2 (IL-2) holds promise for the treatment of cancer and autoimmune diseases, but its high-dose usage is associated with systemic immunotoxicity. Differential IL-2 receptor (IL-2R) regulation might impact function of cells upon IL-2 stimulation, possibly inducing cellular changes similar to patients with hypomorphic IL2RB mutations, presenting with multiorgan autoimmunity. Here, we show that sustained high-dose IL-2 stimulation of human lymphocytes drastically reduces IL-2Rß surface expression especially on T cells, resulting in impaired IL-2R signaling which correlates with high IL-2Rα baseline expression. IL-2R signaling in NK cells is maintained. CD4+ T cells, especially regulatory T cells are more broadly affected than CD8+ T cells, consistent with lineage-specific differences in IL-2 responsiveness. Given the resemblance of cellular characteristics of high-dose IL-2-stimulated cells and cells from patients with IL-2Rß defects, impact of continuous IL-2 stimulation on IL-2R signaling should be considered in the onset of clinical adverse events during IL-2 therapy.

Identification of most representative hub-genes for diagnosis, prognosis, and therapies of hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths globally. To reduce HCC-related mortality, early diagnosis and therapeutic improvement are essential. Hub differentially expressed genes (HubGs) may serve as potential diagnostic and prognostic biomarkers, also offering therapeutic targets for precise therapies. Therefore, we aimed to identify top-ranked hub genes for the diagnosis, prognosis, and therapy of HCC. METHODS: Through a systematic literature review, 202 HCC-related HubGs were derived from 59 studies, yet consistent detection across these was lacking. Then, we identified top-ranked HubGs (tHubGs) by integrated bioinformatics analysis, highlighting their functions, pathways, and regulators that might be more representative of the diagnosis, prognosis, and therapies of HCC. RESULTS: In this study, eight HubGs (CDK1, AURKA, CDC20, CCNB2, TOP2A, PLK1, BUB1B, and BIRC5) were identified as the tHubGs through the protein-protein interaction (PPI) network and survival analysis. Their differential expression in different stages of HCC, validated using The Cancer Genome Atlas (TCGA) Program database, suggests their potential as early HCC markers. The enrichment analyses revealed some important roles in HCC-related biological processes (BPs), molecular functions (MFs), cellular components (CCs), and signaling pathways. Moreover, the gene regulatory network analysis highlighted key transcription factors (TFs) and microRNAs (miRNAs) that regulate these tHubGs at transcriptional and post-transcriptional. Finally, we selected three drugs (CD437, avrainvillamide, and LRRK2-IN-1) as candidate drugs for HCC treatment as they showed strong binding with all of our proposed and published protein receptors. CONCLUSIONS: The findings of this study may provide valuable resources for early diagnosis, prognosis, and therapies for HCC.

Recurrent neural network for the dynamics of Zika virus spreading.

Recurrent Neural Networks (RNNs), a type of machine learning technique, have recently drawn a lot of interest in numerous fields, including epidemiology. Implementing public health interventions in the field of epidemiology depends on efficient modeling and outbreak prediction. Because RNNs can capture sequential dependencies in data, they have become highly effective tools in this field. In this paper, the use of RNNs in epidemic modeling is examined, with a focus on the extent to which they can handle the inherent temporal dynamics in the spread of diseases. The mathematical representation of epidemics requires taking time-dependent variables into account, such as the rate at which infections spread and the long-term effects of interventions. The goal of this study is to use an intelligent computing solution based on RNNs to provide numerical performances and interpretations for the SEIR nonlinear system based on the propagation of the Zika virus (SEIRS-PZV) model. The four patient dynamics, namely susceptible patients S(y), exposed patients admitted in a hospital E(y), the fraction of infective individuals I(y), and recovered patients R(y), are represented by the epidemic version of the nonlinear system, or the SEIR model. SEIRS-PZV is represented by ordinary differential equations (ODEs), which are then solved by the Adams method using the Mathematica software to generate a dataset. The dataset was used as an output for the RNN to train the model and examine results such as regressions, correlations, error histograms, etc. For RNN, we used 100% to train the model with 15 hidden layers and a delay of 2 seconds. The input for the RNN is a time series sequence from 0 to 5, with a step size of 0.05. In the end, we compared the approximated solution with the exact solution by plotting them on the same graph and generating the absolute error plot for each of the 4 cases of SEIRS-PZV. Predictions made by the model appeared to be become more accurate when the mean squared error (MSE) decreased. An increased fit to the observed data was suggested by this decrease in the MSE, which suggested that the variance between the model's predicted values and the actual values was dropping. A minimal absolute error almost equal to zero was obtained, which further supports the usefulness of the suggested strategy. A small absolute error shows the degree to which the model's predictions matches the ground truth values, thus indicating the level of accuracy and precision for the model's output.

Dietary supplementation of protease and organic acid in poultry by-product meal-based diet in broilers.

Objective: This study investigated the impact of supplementation of protease and organic acid on growth performance and other biological parameters in broilers fed poultry by-product meal (PBM) based diet. Methods: Five hundred-day-old broiler chicks (Ross 308) were distributed into five treatments with 5 replicates, each pen having 20 birds, and fed each group one of five isocaloric and isonitrogenous diets in two phases: stater phase (1-21 days) ME 3000 kcal/kg; CP 22%, and a finisher phase (22-35 days) ME 3200 kcal/kg; CP 19.5%. The dietary treatments were: 1) standard broiler ration (Cont); 2) The control diet with 25% of the soybean meal replaced by poultry by-product meal (PBM) on an equivalent protein basis (PBM); 3) PBM diet supplemented with 0.5 g/kg of protease (PBMP); 4) PBM diet supplemented with 1 g/kg organic acid (PBMO); and 5) PBM diet addition with 0.5 g/kg protease and 1 g/kg organic acid (PBMPO). Results: The overall data showed that FCR was improved (P<0.05) in the PBMP group. Apparent crude protein digestibility was higher (P<0.05) in both Cont and PBMP groups. Jejunal villus height (VH) increased (P<0.05) in PBMP and PBMPO groups, while only the PBMO group exhibited a higher (P<0.05) crypt depth (CD). Lipase activity was increased (P<0.05) in the PBMP, PBMO and PBMPO dietary treatments. However, trypsin activity showed a significant increase (P<0.05) in the PBMP and PBMO groups. Serum biochemistry increased (P<0.05) globulin and total protein levels in the PBMP group. Conclusion: PBM could partially replace the soybean meal with supplementation of either protease or organic acid in broiler diets without impairing overall growth performance. Furthermore, careful optimization must be considered when combining protease and organic acids.

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.

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.

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.

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.

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.

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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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.