First-principle calculations to investigate mechanical and acoustical properties of predicted stable halide Perovskite ABX3

This work examines the predicted stable halide perovskites' elastic, acoustical, and thermal characteristics. The work uses the Full Potential-Linearized Augmented Plane Wave (FP-LAPW) technique through PBE-GGA to compute compounds in the WIEN2K algorithm. The ELATE program for the evaluation of elastic tensors to plot 2D and 3D graphs was also used. The bulk modulus, Young's modulus, shear modulus, anisotropy factors, Cauchy pressure, Pugh's ratio, Poisson's ratio, Kleinman's parameter, Lame's coefficient, Vicker's hardness, sound velocities, Gruneisen parameter and even melting and Debye temperature were computed. The mechanical and elastic properties are reported for the first time for most of the compounds, demonstrating that the investigated HPs-aside from TlBeF3, BaAgBr3, and CsTcl3-are mechanically stable and exhibit weaker resistance against shear distortion than they do to unidirectional compression. The results of Poisson's, Pugh's, and Frantsevich's ratios data prove that all materials are ductile except SrLiF3. The estimated Poisson's ratio data indicates the metallic bonding nature of HPs, whereas only SrLiF3 exhibits covalent behavior with ν = 0.23. Debye temperature for SrLiF3, ZnLiF3, ZnScF3, CsRhCl3, CsRuCl3, and CsBeCl3 is greater than 200 K which signifies their hardness, thermal conductivity, and high sound velocities. The large melting temperature values, make them suitable for high-temperature industrial applications. The anharmonicity effect is highest for CaCuBr3 (3.265) and lowest for SrLiF3 (1.402). The current approach calculates elastic and mechanical properties, providing a practical understanding of various physical processes and enabling technology developers to utilize compounds in diverse applications.

Genome wide identification and characterization of Bax inhibitor-1 gene family in cucumber (Cucumis sativus) under biotic and abiotic stress

In plants, the BAX inhibitor-1 (BI-1) gene plays a crucial part in controlling cell death under stress conditions. This mechanism of Programmed Cell Death (PCD) is genetically regulated and is crucial for the elimination of unwanted or damaged cells in a controlled manner, which is essential for normal development and tissue maintenance. A study on cucumber identified and characterized five BI-1 genes: CsBI1, CsBI2, CsBI3, CsBI4, and CsBI5. These genes share conserved domains, indicating common evolutionary history and function. Physicochemical analysis revealed their molecular weights and isoelectric points, while subcellular localization showed their presence in different cellular compartments. The phylogenetic analysis highlighted evolutionary relationships with related crops. Chromosomal distribution and synteny analysis suggested segmental or tandem duplications within the gene family. Protein-protein interaction analysis revealed extensive interactions with other cucumber proteins. Cis-regulatory elements in the promoter regions provided insights into potential functions and transcriptional regulation. miRNAs showed diverse regulatory mechanisms, including mRNA cleavage and translational inhibition. The CsBI3, CsBI4 and CsBI5 genes exhibit elevated expression levels during cold stress, suggesting their vital involvement in cucumber plant defense mechanisms. The application of chitosan oligosaccharides externally confirms their distinct expression patterns. The qRT-PCR confirms the upregulation of CsBI genes in ToLCNDV-infected plants, indicating their potential to mitigate biotic and abiotic stresses. The comprehensive genome-wide exploration provides opportunities for the development of cold-tolerant and virus-resistant cucumber variants by traditional breeding or gene.

Composite superplastic aerogel scaffolds containing dopamine and bioactive glass-based fibers for skin and bone tissue regeneration

Multifunctional bioactive biomaterials with integrated bone and soft tissue regenerability hold great promise for the regeneration of trauma-affected skin and bone defects. The aim of this research was to fabricate aerogel scaffolds (GD-BF) by blending the appropriate proportions of short bioactive glass fiber (BGF), gelatin (Gel), and dopamine (DA). Electrospun polyvinyl pyrrolidone (PVP)-BGF fibers were converted into short BGF through calcination and homogenization. Microporous GD-BF scaffolds displayed good elastic deformation recovery and promoted neo-tissue formation. The DA could enable thermal crosslinking and enhance the mechanical properties and structural stability of the GD-BF scaffolds. The BGF-mediated release of therapeutic ions shorten hemostatic time (<30 s) in a rat tail amputation model and a rabbit artery injury model alongside inducing the regeneration of skin appendages (e.g., blood vessels, glands, etc.) in a full-thickness excisional defect model in rats (percentage wound closure: GD-BF2, 98 % vs. control group, 83 %) at day 14 in vitro. Taken together, these aerogel scaffolds may have significant promise for soft and hard tissue repair, which may also be worthy for the other related disciplines.

Integrative metagenomic dissection of last-resort antibiotic resistance genes and mobile genetic elements in hospital wastewaters

Hospital wastewater is a critical source of antimicrobial resistance (AMR), which facilitates the proliferation and spread of clinically significant antimicrobial resistance genes (ARGs) and pathogenic bacteria. This study utilized metagenomic approaches, including advanced binning techniques, such as MetaBAT2, MaxBin2, and CONCOCT, which offer significant improvements in accuracy and completeness over traditional binning methods. These methods were used to comprehensively assess the dynamics and composition of resistomes and mobilomes in untreated wastewater samples taken from two general hospitals and one cancer hospital. This study revealed a diverse bacterial landscape, largely consisting of Proteobacteria, Firmicutes, Bacteroidetes, and Actinobacteria, with notable variations in microbial composition among hospitals. Analysis of the top 15 genera showed unique microbial pattern distribution in each hospital: Aeromonas was predominant in 1stHWTS (49.39 %), Acidovorax in the CAHWTS at 16.85 %, and Escherichia and Bacteroides in the 2ndHWTS at 11.44 % and 11.33 %, respectively. A total of 114 pathogenic bacteria were identified, with drug-resistant Aeromonas caviae and Escherichia coli being the most prevalent. The study identified 34 types and 1660 subtypes of ARGs, including important last-resort antibiotic resistance genes (LARGs), such as blaNDM, mcr, and tet(X). Using metagenomic binning, this study uncovered distinct patterns of host-resistance associations, particularly with Proteobacteria and Firmicutes. Network analysis highlighted the complex interactions among ARGs, mobile genetic elements (MGEs), and bacterial species, all contributing to the dissemination of AMR. These findings emphasize the intricate nature of AMR in hospital wastewater and the influence of hospital-specific factors on microbial resistance patterns. This study provides support for implementing integrated management strategies, including robust surveillance, advanced wastewater treatment, and strict antibiotic stewardship, to control the dissemination of AMR. Understanding the interplay among bacterial communities, ARGs, and MGEs is important for developing effective public health measures against AMR.

Ecological consequences of antimicrobial residues and bioactive chemicals on antimicrobial resistance in agroecosystems

BACKGROUND

The widespread use of antimicrobials in agriculture, coupled with bioactive chemicals like pesticides and growth-promoting agents, has accelerated the global crisis of antimicrobial resistance (AMR). Agroecosystems provides a platform in the evolution and dissemination of antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs), which pose significant threats to both environmental and public health.

AIM OF REVIEW

This review explores the ecological consequences of antimicrobial residues and bioactive chemicals in agroecosystems, with a focus on their role in shaping AMR. It delves into the mechanisms by which these substances enter agricultural environments, their interactions with soil microbiomes, and the subsequent impacts on microbial community structure.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Evidence indicates that the accumulation of antimicrobials promotes resistance gene transfer among microorganisms, potentially compromising ecosystem health and agricultural productivity. By synthesizing current research, we identify critical gaps in knowledge and propose strategies for mitigating the ecological risks associated with antimicrobial residues. Moreover, this review highlights the urgent need for integrated management approaches to preserve ecosystem health and combat the spread of AMR in agricultural settings.

Unraveling the dynamics of ChatGPT adoption and utilization through Structural Equation Modeling

ChatGPT, an advanced Artificial Intelligence tool, is getting considerable attention in higher education. ChatGPT significantly changes the student learning experience through its AI-aided support, personalized study assistance and effective educational experiences, and it has become an object of particular interest in this context. This research aimed to build a technology acceptance and usage model that encapsulates the elements influencing students' adoption and utilization of ChatGPT, drawing on constructs from the 'Unified Theory of Acceptance and Use of Technology' and 'Flow Theory'. The proposed model was found valid and prolific, with the credibility of the results relying on the self-reported surveys of 505 students from three universities in Pakistan. Structural Equation Modelling (SEM) was used to analyze data that confirmed the robustness and validity of the proposed model of the study. The study findings supported nine out of the ten proposed hypotheses. Perceived playfulness was declared the paramount predictor of behavioral intention, while perceived values and performance expectancy were the next-level predictors. Additionally, behavioral attention was a high and inspiring determinant of ChatGPT usage behavior, followed by attention focus. This analysis demonstrates a need for a thorough investigation of AI tools like ChatGPT in higher education.

Exploring the Gut Microbiota Landscape in Cow Milk Protein Allergy: Clinical Insights and Diagnostic Implications in Pediatric Patients

Cow milk protein allergy (CMPA) is a significant health concern characterized by adverse immune reactions to cow milk proteins. Biomarkers for the accurate diagnosis and prognosis of CMPA are lacking. This study analyzed the clinical features of CMPA, and 16S RNA sequencing was used to investigate potential biomarkers through fecal microbiota profiling. Children with CMPA exhibit a range of clinical symptoms, including gastrointestinal (83% of patients), skin (53% of patients), and respiratory manifestations (26% of patients), highlighting the complexity of this condition. Laboratory analysis revealed significant differences in red cell distribution width (RDW) and inflammatory markers between the CMPA and control groups, suggesting immune activation and inflammatory responses in CMPA. Microbial diversity analysis revealed higher specific diversity indices in the CMPA group compared with those in control group, with significant differences at the genus and species levels. Bacteroides were more abundant in the CMPA group, whereas Bifidobacterium, Ruminococcus, Faecalibacterium, and Parabacteroides were less abundant. The control group exhibited a balanced microbial profile, with a predominant presence of Bifidobacterium bifidum and Akkermansia muciniphila. The significant abundance of Bifidobacterium in the control group (23.19% vs 9.89% in CMPA) was associated with improved growth metrics such as height and weight, suggesting its potential as a probiotic to prevent CMPA and enhance gut health. Correlation analysis linked specific microbial taxa such as Coprococcus and Bifidobacterium to clinical parameters such as family allergy history, weight and height, providing insights into CMPA pathogenesis. Significant differences in bacterial abundance suggested diagnostic potential, with a panel of 6 bacteria achieving high predictive accuracy (area under curve (AUC) = 0.8708). This study emphasizes the complex relationship between the gut microbiota and CMPA, offering valuable insights into disease mechanisms and diagnostic strategies.

Droplet Microfluidics Powered Hydrogel Microparticles for Stem Cell-Mediated Biomedical Applications

Stem cell-related therapeutic technologies have garnered significant attention of the research community for their multi-faceted applications. To promote the therapeutic effects of stem cells, the strategies for cell microencapsulation in hydrogel microparticles have been widely explored, as the hydrogel microparticles have the potential to facilitate oxygen diffusion and nutrient transport alongside their ability to promote crucial cell-cell and cell-matrix interactions. Despite their significant promise, there is an acute shortage of automated, standardized, and reproducible platforms to further stem cell-related research. Microfluidics offers an intriguing platform to produce stem cell-laden hydrogel microparticles (SCHMs) owing to its ability to manipulate the fluids at the micrometer scale as well as precisely control the structure and composition of microparticles. In this review, the typical biomaterials and crosslinking methods for microfluidic encapsulation of stem cells as well as the progress in droplet-based microfluidics for the fabrication of SCHMs are outlined. Moreover, the important biomedical applications of SCHMs are highlighted, including regenerative medicine, tissue engineering, scale-up production of stem cells, and microenvironmental simulation for fundamental cell studies. Overall, microfluidics holds tremendous potential for enabling the production of diverse hydrogel microparticles and is worthy for various stem cell-related biomedical applications.

Predominance of blaNDM- and blaIMP-Harboring Escherichia coli Belonging to Clonal Complexes 131 and 23 in a Major University Hospital

Background and Objectives: Carbapenem resistance is a growing global challenge for healthcare, and, therefore, monitoring its prevalence and patterns is crucial for implementing targeted interventions to mitigate its impact on patient outcomes and public health. This study aimed to determine the prevalence of carbapenem resistance among Escherichia coli (E. coli) strains in the largest tertiary care hospital of the capital territory of Pakistan and to characterize the isolates for the presence of antimicrobial resistance genes. Additionally, the most prevalent sequence types were analyzed. Materials and Methods: A total of 15,467 clinical samples were collected from November 2020 to May 2022, underwent antimicrobial susceptibility testing, and were analyzed for antimicrobial resistance genes through conventional PCR and sequence typing using MLST. Results: In carbapenem-resistant E. coli (CR-EC), 74.19% of isolates harbored the blaNDM gene, with blaNDM-1 (66.96%), blaNDM-5 (12.17%), and blaNDM-7 (20.87%) variants detected. Additionally, blaIMP was found in 25.81% and blaOXA-48 in 35.48% of isolates. The presence of blaCTX-M15 and blaTEM was identified in 83.87% and 73.55% of CR-EC isolates, respectively, while armA and rmtB were detected in 40% and 65.16% of isolates, respectively. Colistin and tigecycline were the most effective drugs against CR-EC isolates, with both showing an MIC50 of 0.5 µg/mL. The MIC90 for colistin was 1 µg/mL, while for tigecycline, it was 2 µg/mL. MLST analysis revealed that the CR-EC isolates belonged to ST131 (24.52%), ST2279 (23.87%), ST3499 (16.13%), ST8051 (15.48%), ST8900 (9.68%), ST3329 (7.10%), ST88 (1.94%), and ST6293 (1.29%). The ST131 complex (70.97%) was the most prevalent, harboring 95.65% of the blaNDM gene, while the ST23 complex (18.06%) harbored 62.50% of the blaIMP gene. Conclusions: Implementing large-scale surveillance studies to monitor the spread of specific pathogens, along with active infection control policies, is crucial for the effective containment and prevention of future epidemics.

A Novel Method for the Enhancement of Sunflower Growth from Animal Bones and Chicken Feathers

The present study aimed at converting meat industry waste, particularly waste bones and chicken feathers, into biochar to recycle valuable nutrients present in it, which ultimately become part of the municipal waste. The bone biochar (BB) and feathers biochar (FB) were prepared at 550 °C, and their potential was evaluated as an organic amendment for the growth of sunflower. The ash content (AC) and fixed carbon (FC) improved significantly in prepared biochars as compared to raw feedstock. Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX) analyses signaled the occurrence of various functional groups viz. amide group and hydroxyapatite, porosity, and multiple nutrients. Application of BB and FB in potted soil alone as well as in composites (1:1, 1:2, 2:1) at 1%, 3%, and 5% (w/w) and synthetic fertilizer significantly increased soil pH, electrical conductivity (ECe), organic matter (OM) and water holding capacity (WHC), while reducing the bulk density (BD). The growth of plants grown in soil treated with a 2:1 composite of feathers and bone biochar at 5% application rate showed significantly greater differences in plant height, total chlorophyll content, and plant dry weight than the control but was comparable to growth with chemical fertilizer, rendering it a potential alternative to chemical-based synthetic fertilizer.

A novel Skin lesion prediction and classification technique: ViT-GradCAM

BACKGROUND

Skin cancer is one of the highly occurring diseases in human life. Early detection and treatment are the prime and necessary points to reduce the malignancy of infections. Deep learning techniques are supplementary tools to assist clinical experts in detecting and localizing skin lesions. Vision transformers (ViT) based on image segmentation classification using multiple classes provide fairly accurate detection and are gaining more popularity due to legitimate multiclass prediction capabilities.

MATERIALS AND METHODS

In this research, we propose a new ViT Gradient-Weighted Class Activation Mapping (GradCAM) based architecture named ViT-GradCAM for detecting and classifying skin lesions by spreading ratio on the lesion's surface area. The proposed system is trained and validated using a HAM 10000 dataset by studying seven skin lesions. The database comprises 10 015 dermatoscopic images of varied sizes. The data preprocessing and data augmentation techniques are applied to overcome the class imbalance issues and improve the model's performance.

RESULT

The proposed algorithm is based on ViT models that classify the dermatoscopic images into seven classes with an accuracy of 97.28%, precision of 98.51, recall of 95.2%, and an F1 score of 94.6, respectively. The proposed ViT-GradCAM obtains better and more accurate detection and classification than other state-of-the-art deep learning-based skin lesion detection models. The architecture of ViT-GradCAM is extensively visualized to highlight the actual pixels in essential regions associated with skin-specific pathologies.

CONCLUSION

This research proposes an alternate solution to overcome the challenges of detecting and classifying skin lesions using ViTs and GradCAM, which play a significant role in detecting and classifying skin lesions accurately rather than relying solely on deep learning models.

Genome wide identification of phenylalanine ammonia-lyase (PAL) gene family in Cucumis sativus (cucumber) against abiotic stress

Phenylalanine ammonia lyase (PAL) is a widely studied enzyme in plant biology due to its role in connecting primary metabolism to secondary phenylpropanoid metabolism, significantly influencing plant growth, development, and stress response. Although PAL genes have been extensively studied in various plant species but their exploration in cucumber has been limited. This study successfully identified 11 CsPAL genes in Cucumis sativus (cucumber). These CsPAL genes were categorized based on their conserved sequences revealing patterns through MEME analysis and multiple sequence alignment. Interestingly, cis-elements related to stress were found in the promoter regions of CsPAL genes, indicating their involvement in responding to abiotic stress. Furthermore, these gene's promoters contained components associated with light, development and hormone responsiveness. This suggests that they may have roles in hormone developmental processes. MicroRNAs were identified as a key regulators for the CsPAL genes, playing a crucial role in modulating their expression. This discovery underscores the complex regulatory network involved in the plant's response to various stress conditions. The influence of these microRNAs further highlights the complicated mechanisms that plants use to manage stress. Gene expression patterns were analyzed using RNA-seq data. The significant upregulation of CsPAL9 during HT3h (heat stress for 3 h) and the heightened upregulation of both CsPAL9 and CsPAL7 under HT6h (heat stress for 6 h) in the transcriptome study suggest a potential role for these genes in cucumber's tolerance to heat stress. This comprehensive investigation aims to enhance our understanding of the PAL gene family's versatility, offering valuable insights for advancements in cucumber genetics.

The contribution of quality management practices to student performance: Mediated by school culture

School management is responsible and accountable for implementing educational policies into practice effectively and efficiently to provide quality education. Simultaneously, school management can grasp the core features of the whole school process and identify the relationship among three variables: quality management practices, school culture, and student performance. The current study aims to explore the school principals' perception about quality management practices and its relationship with school culture and student performance in the public secondary schools of Punjab province, Pakistan. In order to achieve the objectives of the study, the study adopted an exploratory sequential mix-methods research design. The researcher conducted a systematic literature review of sixty-three previous studies and interviews with eleven school principals for the qualitative data. Based on results obtained from the qualitative phase, a questionnaire was prepared and dispatched to 150 school principals to get quantitative data. Successively 120 valid responses were received. SEM analysis was performed to get quantitative results. The study's preliminary conclusion demonstrated a positive connection between quality management and student performance in public secondary schools, and quality management was also a significant predictor of school culture. Further, school culture served as a complete mediator between quality management and student performance.

Genome wide identification of the NPR1 gene family in plant defense mechanisms against biotic stress in chili (Capsicum annuum L.)

Chili pepper cultivation in the Indian subcontinent is severely affected by viral diseases, prompting the need for environmentally friendly disease control methods. To achieve this, it is essential to understand the molecular mechanisms of viral resistance in chili pepper. The NONEXPRESSOR OF PATHOGENESIS-RELATED GENES 1 (NPR1) genes are known to provide broad-spectrum resistance to various phytopathogens by activating systemic acquired resistance (SAR). An in-depth understanding of NPR1 gene expression during begomovirus infection and its correlation with different biochemical and physiological parameters is crucial for enhancing resistance against begomoviruses in chili pepper. Nevertheless, limited information on chili CaNPR genes and their role in biotic stress constrains their potential in breeding for biotic stress resistance. By employing bioinformatics for genome mining, we identify 5 CaNPR genes in chili. The promoter regions of 1,500 bp of CaNPR genes contained cis-elements associated with biotic stress responses, signifying their involvement in biotic stress responses. Furthermore, these gene promoters harbored components linked to light, development, and hormone responsiveness, suggesting their roles in plant hormone responses and development. MicroRNAs played a vital role in regulating these five CaNPR genes, highlighting their significance in the regulation of chili genes. Inoculation with the begomovirus "cotton leaf curl Khokhran virus (CLCuKV)" had a detrimental effect on chili plant growth, resulting in stunted development, fibrous roots, and evident virus symptoms. The qRT-PCR analysis of two local chili varieties inoculated with CLCuKV, one resistant (V1) and the other susceptible (V2) to begomoviruses, indicated that CaNPR1 likely provides extended resistance and plays a role in chili plant defense mechanisms, while the remaining genes are activated during the early stages of infection. These findings shed light on the function of chili's CaNPR in biotic stress responses and identify potential genes for biotic stress-resistant breeding. However, further research, including gene cloning and functional analysis, is needed to confirm the role of these genes in various physiological and biological processes. This in-silico analysis enhances our genome-wide understanding of how chili CaNPR genes respond during begomovirus infection.

A deep learning-based theoretical protocol to identify potentially isoform-selective PI3Kα inhibitors

Phosphoinositide 3-kinase alpha (PI3Kα) is one of the most frequently dysregulated kinases known for their pivotal role in many oncogenic diseases. While the side effects linked to existing drugs against PI3Kα-induced cancers provide an avenue for further research, the significant structural conservation among PI3Ks makes it extremely difficult to develop new isoform-selective PI3Kα inhibitors. Embracing this challenge, we herein designed a hybrid protocol by integrating machine learning (ML) with in silico drug-designing strategies. A deep learning classification model was developed and trained on the physicochemical descriptors data of known PI3Kα inhibitors and used as a screening filter for a database of small molecules. This approach led us to the prediction of 662 compounds showcasing appropriate features to be considered as PI3Kα inhibitors. Subsequently, a multiphase molecular docking was applied to further characterize the predicted hits in terms of their binding affinities and binding modes in the targeted cavity of the PI3Kα. As a result, a total of 12 compounds were identified whereas the best poses highlighted the efficiency of these ligands in maintaining interactions with the crucial residues of the protein to be targeted for the inhibition of associated activity. Notably, potential activity of compound 12 in counteracting PI3Kα function was found in a previous in vitro study. Following the drug-likeness and pharmacokinetic characterizations, six compounds (compounds 1, 2, 3, 6, 7, and 11) with suitable ADME-T profiles and promising bioavailability were selected. The mechanistic studies in dynamic mode further endorsed the potential of identified hits in blocking the ATP-binding site of the receptor with higher binding affinities than the native inhibitor, alpelisib (BYL-719), particularly the compounds 1, 2, and 11. These outcomes support the reliability of the developed classification model and the devised computational strategy for identifying new isoform-selective drug candidates for PI3Kα inhibition.

Quantification and polymeric characterization of microplastics in composts and their accumulation in lettuce

Organic fertilizers have become a vector for the transport of microplastics (MPs), which pose human health concerns through the food chain. This study aimed to quantify and characterize MPs in eight different compost samples of various raw materials and their subsequent translocation to lettuce (Lacuta sativa) grown on contaminated composts. The results revealed that the MP abundance ranged from 3810 to 16530 MP/kg. Municipal solid waste compost (MSWC) had highest abundance (16082 ± 632 MP/kg), followed by leaf compost (LC) and organic compost (OC) (6299 ± 1011 and 3680 ± 419 MP/kg, respectively). MPs of <100 μm in size were most dominant in MSWC and LC. Fragments and fibers were the prevalent shape types, with white/transparent colored MPs being more abundant. Polyethylene (PE), polypropylene (PP) and polyethylene terephthalate (PET) were the dominant polymers. MPs accumulation in the lettuce leaves was greatest in the lettuce plants grown on MSWC, followed by those grown on LC and OC, indicating that MSWC grown lettuce is not suitable for human consumption. The decrease in the growth (leaf length, number of leaves, leaf fresh and weights) and physiological (membrane stability index, relative water contents) parameters of lettuce was in line with the trend of MP accumulations. Hence, it is highly important to regulate the plastic contents in compost because it is a threat to ecosystems and human health.

Precision medicine in practice: unravelling the prevalence and antibiograms of urine cultures for informed decision making in federal tertiary care- a guide to empirical antibiotics therapy

Background and Objectives

Urinary tract infections (UTIs), one of the most prevalent bacterial infections, are facing limited treatment options due to escalating concern of antibiotic resistance. Urine cultures significantly help in identification of etiological agents responsible for these infections. Assessment of antibiotic susceptibility patterns of these bacteria aids in tackling the emerging concern of antibiotic resistance and establishment of empirical therapy guidelines. Our aim was to determine various agents responsible for urinary tract infections and to assess their antibiotic susceptibility patterns.

Materials and Methods

This cross-sectional study was performed over a period of six months from January 2023 to July 2023 in Department of Microbiology of Pakistan Institute of Medical Sciences (PIMS).

Results

Out of 2957 positive samples, Gram negative bacteria were the most prevalent in 1939 (65.6%) samples followed by Gram positive bacteria in 418 (14.1%) and Candida spp. in 269 (9.1%) samples. In gram negative bacteria, Escherichia coli (E. coli) was the most prevalent bacteria isolated from 1070 samples (55.2%) followed by Klebsiella pneumoniae in 397 samples (20.5%). In Gram positive bacteria, Enterococcus spp. was the most common bacteria in 213 samples (51%) followed by Staphylococcus aureus in 120 samples (28.7%). Amikacin was the most sensitive drug (91%) for Gram negative bacteria. Gram positive bacteria were most susceptible to linezolid (97%-100%).

Conclusion

The generation of a hospital tailored antibiogram is essential for the effective management of infections and countering antibiotic resistance. By adopting antimicrobial stewardship strategies by deeper understanding of sensitivity patterns, we can effectively combat antibiotic resistance.

Genome-wide analysis and identification of Carotenoid Cleavage Oxygenase (CCO) gene family in coffee (coffee arabica) under abiotic stress

The coffee industry holds importance, providing livelihoods for millions of farmers globally and playing a vital role in the economies of coffee-producing countries. Environmental conditions such as drought and temperature fluctuations can adversely affect the quality and yield of coffee crops.Carotenoid cleavage oxygenases (CCO) enzymes are essential for coffee plants as they help break down carotenoids contributing to growth and stress resistance. However, knowledge about the CCO gene family in Coffee arabica was limited. In this study identified 21 CCO genes in Coffee arabica (C. arabica) revealing two subfamilies carotenoid cleavage dioxygenases (CCDs) and 9-cis-epoxy carotenoid dioxygenases (NCED) through phylogenic analysis. These subfamilies exhibited distribution patterns in terms of gene structure, domains, and motifs. The 21 CaCCO genes, comprising 5 NCED and 16 CCD genes were found across chromosomes. Promoter sequencing analysis revealed cis-elements that likely interact with plant stress-responsive, growth-related, and phytohormones, like auxin and abscisic acid. A comprehensive genome-wide comparison, between C. arabica and A. thaliana was conducted to understand the characteristics of CCO genes. RTqPCR data indicated that CaNCED5, CaNCED6, CaNCED12, and CaNCED20 are target genes involved in the growth of drought coffee plants leading to increased crop yield, in a conditions, with limited water availability. This reveals the role of coffee CCOs in responding to abiotic stress and identifies potential genes useful for breeding stress-resistant coffee varieties.

Prudent Use of Blood Cultures for Hospitalized Patients With Cirrhosis

Background No reliable risk stratification method is available to guide the extent of infectious work-up among hospitalized patients with cirrhosis. Therefore, we aimed to create a risk stratification method for obtaining blood cultures from hospitalized patients with cirrhosis. Methods This was a retrospective cohort study using the Healthcare Cost and Utilization Project - National Readmission Database 2019. Adult patients who were not immunocompromised comprised the final cohort. The primary outcome was the incidence of bacteremia among hospitalized patients with cirrhosis. Secondary outcomes included length of hospital stay, inpatient mortality, and 30-day readmission rate among cirrhosis patients with and without bacteremia. After propensity score matching, the χ2 test was used to assess the primary outcome and inpatient mortality. The Wilcoxon signed-rank test was used to compare the length of hospital stay. Readmission rates were compared via survival analysis. Concomitant bacterial infection, cirrhosis causes, and complications were assessed as potential risk factors for bacteremia using binomial regression. Results The risk ratio (RR) of bacteremia was 1.66 (95% confidence interval (CI): 1.55-1.78) among patients with cirrhosis compared to those without cirrhosis. A concomitant bacterial infection was found to have a strong association with bacteremia in patients with cirrhosis (RR: 3.3, 95% CI: 3.03-3.59). Among cirrhosis patients without concomitant bacterial infection, the incidence of bacteremia was 0.76% (<1%). Among the causes of cirrhosis, primary sclerosing cholangitis was found to have a strong association with bacteremia (RR: 3.88, 95% CI: 2.3-6.04, P < 0.001). Patients with cirrhosis who had bacteremia were hospitalized three days longer than those without bacteremia. There was no difference in inpatient mortality or 30-day readmission rates between cirrhotic patients with and without bacteremia. Conclusion This study suggests that, in the absence of another concomitant bacterial infection and primary sclerosing cholangitis, we can avoid unnecessary blood cultures among immunocompetent patients with cirrhosis. However, given some inherent limitations associated with the database (such as the unavailability of vitals or laboratory values), additional studies are needed to validate its findings.

Multi-Resolution Wavelet Fractal Analysis and Subtask Training for Enhancing Few-Shot Noisy Brainwave Recognition

The integration of healthcare monitoring with Internet of Things (IoT) networks radically transforms the management and monitoring of human well-being. Portable and lightweight electroencephalography (EEG) systems with fewer electrodes have improved convenience and flexibility while retaining adequate accuracy. However, challenges emerge when dealing with real-time EEG data from IoT devices due to the presence of noisy samples, which impedes improvements in brainwave detection accuracy. Moreover, high inter-subject variability and substantial variability in EEG signals present difficulties for conventional data augmentation and subtask learning techniques, leading to poor generalizability. To address these issues, we present a novel framework for enhancing EEG-based recognition through multi-resolution data analysis, capturing features at different scales using wavelet fractals. The original data can be expanded many times after continuous wavelet transform (CWT) and recombination, alleviating insufficient training samples. In the transfer stage of deep learning (DL) models, we adopt a subtask learning approach to train the recognition model to generalize efficiently. This incorporates wavelets at various scales instead of exclusively considering average prediction performance across scales and paradigms. Through extensive experiments, we demonstrate that our proposed DL-based method excels at extracting features from small-scale and noisy EEG data. This significantly improves healthcare monitoring performance by mitigating the impact of noise introduced by the external environment.

Advances in biodegradable materials: Degradation mechanisms, mechanical properties, and biocompatibility for orthopedic applications

Mg-based and Zn-based biodegradable materials have the potential to become the next-generation implant materials to treat bone diseases, because of their desired degradation and mechanical properties. This article reviews the status of these implant materials. The required properties of biodegradable materials such as biodegradability, mechanical properties, and biocompatibility for performance evaluation were briefly discussed. The influence of fabrication techniques, microstructure, alloying elements, and post-processing techniques on the properties of Mg and Zn-based materials was addressed. The degradation mechanism by dissolution, oxidation, and interaction with human body cells was discussed. The biocompatibility of Mg and Zn-based biodegradable materials was analyzed. The significance of in vitro and in vivo biocompatibility testing was highlighted, emphasizing the superiority of in vivo results over cell line studies. This article identifies the many Mg and Zn-based biodegradable materials and summarizes the key findings.

Mesoporous Silicon with Strontium-Powered Poly(Lactic-Co-Glycolic acid)/Gelatin-Based Dressings Facilitate Skin Tissue Repair

Purpose

Functional inorganic nanomaterials (NMs) are widely exploited as bioactive materials and drug depots. The lack of a stable form of application of NMs at the site of skin injury, may impede the removal of the debridement, elevate pH, induce tissue toxicity, and limit their use in skin repair. This necessitates the advent of innovative wound dressings that overcome the above limitations. The overarching objective of this study was to exploit strontium-doped mesoporous silicon particles (PSiSr) to impart multifunctionality to poly(lactic-co-glycolic acid)/gelatin (PG)-based fibrous dressings (PG@PSiSr) for excisional wound management.

Methods

Mesoporous silicon particles (PSi) and PSiSr were synthesized using a chemo-synthetic approach. Both PSi and PSiSr were incorporated into PG fibers using electrospinning. A series of structure, morphology, pore size distribution, and cumulative pH studies on the PG@PSi and PG@PSiSr membranes were performed. Cytocompatibility, hemocompatibility, transwell migration, scratch wound healing, and delineated angiogenic properties of these composite dressings were tested in vitro. The biocompatibility of composite dressings in vivo was assessed by a subcutaneous implantation model of rats, while their potential for wound healing was discerned by implantation in a full-thickness excisional defect model of rats.

Results

The PG@PSiSr membranes can afford the sustained release of silicon ions (Si4+) and strontium ions (Sr2+) for up to 192 h as well as remarkably promote human umbilical vein endothelial cells (HUVECs) and NIH-3T3 fibroblasts migration. The PG@PSiSr membranes also showed better cytocompatibility, hemocompatibility, and significant formation of tubule-like networks of HUVECs in vitro. Moreover, PG@PSiSr membranes also facilitated the infiltration of host cells and promoted the deposition of collagen while reducing the accumulation of inflammatory cells in a subcutaneous implantation model in rats as assessed for up to day 14. Further evaluation of membranes transplanted in a full-thickness excisional wound model in rats showed rapid wound closure (PG@SiSr vs control, 96.1% vs 71.7%), re-epithelialization, and less inflammatory response alongside skin appendages formation (eg, blood vessels, glands, hair follicles, etc.).

Conclusion

To sum up, we successfully fabricated PSiSr particles and prepared PG@PSiSr dressings using electrospinning. The PSiSr-mediated release of therapeutic ions, such as Si4+ and Sr2+, may improve the functionality of PLGA/Gel dressings for an effective wound repair, which may also have implications for the other soft tissue repair disciplines.

Exploring the mechanism of action of spirooxindoles as a class of CDK2 inhibitors: a structure-based computational approach

Cyclin-dependent kinase 2 (CDK2) regulates cell cycle checkpoints in the synthesis and mitosis phases and plays a pivotal role in cancerous cell proliferation. The activation of CDK2, influenced by various protein signaling pathways, initiates the phosphorylation process. Due to its crucial role in carcinogenesis, CDK2 is a druggable hotspot target to suppress cancer cell proliferation. In this context, several studies have identified spirooxindoles as an effective class of CDK2 inhibitors. In the present study, three spirooxindoles (SOI1, SOI2, and SOI3) were studied to understand their inhibitory mechanism against CDK2 through a structure-based approach. Molecular docking and molecular dynamics (MD) simulations were performed to explore their interactions with CDK2 at the molecular level. The calculated binding free energy for the spirooxindole-based CDK2 inhibitors aligned well with experimental results regarding CDK2 inhibition. Energy decomposition (ED) analysis identified key binding residues, including I10, G11, T14, R36, F82, K89, L134, P155, T158, Y159, and T160, in the CDK2 active site and T-loop phosphorylation. Molecular mechanics (MM) energy was identified as the primary contributor to stabilizing inhibitor binding in the CDK2 protein structure. Furthermore, the analysis of binding affinity revealed that the inhibitor SOI1 binds more strongly to CDK2 compared to the other inhibitors under investigation. It demonstrated a robust interaction with the crucial residue T160 in the T-loop phosphorylation site, responsible for kinase activation. These insights into the inhibitory mechanism are anticipated to contribute to the development of potential CDK2 inhibitors using the spirooxindole scaffold.

Uncovering PPAR-γ agonists: An integrated computational approach driven by machine learning

Peroxisome proliferator-activated receptor gamma (PPAR-γ) serves as a nuclear receptor with a pivotal function in governing diverse facets of metabolic processes. In diabetes, the prime physiological role of PPAR-γ is to enhance insulin sensitivity and regulate glucose metabolism. Although PPAR-γ agonists such as Thiazolidinediones are effective in addressing diabetes complications, it is vital to be mindful that they are associated with substantial side effects that could potentially give rise to health challenges. The recent surge in the discovery of selective modulators of PPAR-γ inspired us to formulate an integrated computational strategy by leveraging the promising capabilities of both machine learning and in silico drug design approaches. In pursuit of our objectives, the initial stage of our work involved constructing an advanced machine learning classification model, which was trained utilizing chemical information and physicochemical descriptors obtained from known PPAR-γ modulators. The subsequent application of machine learning-based virtual screening, using a library of 31,750 compounds, allowed us to identify 68 compounds having suitable characteristics for further investigation. A total of four compounds were identified and the most favorable configurations were complemented with docking scores ranging from -8.0 to -9.1 kcal/mol. Additionally, the compounds engaged in hydrogen bond interactions with essential conserved residues including His323, Leu330, Phe363, His449 and Tyr473 that describe the ligand binding site. The stability indices investigated herein for instance root-mean-square fluctuations in the backbone atoms indicated higher mobility in the region of orthosteric site in the presence of agonist with the deviation peaks in the range of 0.07-0.69 nm, signifying moderate conformational changes. The deviations at global level revealed that the average values lie in the range of 0.25-0.32 nm. In conclusion, our identified hits particularly, CHEMBL-3185642 and CHEMBL-3554847 presented outstanding results and highlighted the stable conformation within the orthosteric site of PPAR-γ to positively modulate the activity.

BC-QNet: A quantum-infused ELM model for breast cancer diagnosis

The timely and accurate diagnosis of breast cancer is pivotal for effective treatment, but current automated mammography classification methods have their constraints. In this study, we introduce an innovative hybrid model that marries the power of the Extreme Learning Machine (ELM) with FuNet transfer learning, harnessing the potential of the MIAS dataset. This novel approach leverages an Enhanced Quantum-Genetic Binary Grey Wolf Optimizer (Q-GBGWO) within the ELM framework, elevating its performance. Our contributions are twofold: firstly, we employ a feature fusion strategy to optimize feature extraction, significantly enhancing breast cancer classification accuracy. The proposed methodological motivation stems from optimizing feature extraction for improved breast cancer classification accuracy. The Q-GBGWO optimizes ELM parameters, demonstrating its efficacy within the ELM classifier. This innovation marks a considerable advancement beyond traditional methods. Through comparative evaluations against various optimization techniques, the exceptional performance of our Q-GBGWO-ELM model becomes evident. The classification accuracy of the model is exceptionally high, with rates of 96.54 % for Normal, 97.24 % for Benign, and 98.01 % for Malignant classes. Additionally, the model demonstrates a high sensitivity with rates of 96.02 % for Normal, 96.54 % for Benign, and 97.75 % for Malignant classes, and it exhibits impressive specificity with rates of 96.69 % for Normal, 97.38 % for Benign, and 98.16 % for Malignant classes. These metrics are reflected in its ability to classify three different types of breast cancer accurately. Our approach highlights the innovative integration of image data, deep feature extraction, and optimized ELM classification, marking a transformative step in advancing early breast cancer detection and enhancing patient outcomes.