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Defected ground structure antenna array with metasurface inspired interlinked CSRR for 5G millimeter wave applications
Investigating the electronic structure and adsorption of OCN− and CN− on Si12C12 Fullerene-like cages using DFT and MD simulations
Author Correction: Topology meets time-reversal symmetry breaking in FeSe1−xTex superconductors
Heat shock proteins related signature characterizes immune status and predicts prognosis of gliomas
The RING finger E3 ligase RNF25 protects DNA replication forks independently of its canonical roles in ubiquitin signaling
Depressive and anxiety symptoms mediate the relationship between family support and alcohol relapse in transplant recipients with alcohol-associated liver disease
Promoting implant osseointegration via the osteoblast-selective β-amino acid polymer strategy
Epidemiology of potential source, risk attribution of Clostridium perfringens from Egyptian broiler farms and genetic diversity of multidrug resistance strains
Abstract Clostridium perfringens induced necrotic enteritis (NE) became a persistent problem that had a major financial impact on the poultry business worldwide. Nevertheless, no prior research has assessed the related risk factors in Egyptian broiler farms. Thus, the purpose of this study is to perform an epidemiological analysis of C. perfringens occurrence, toxinotyping, and risk factors in El-Dakhlia and Damietta provinces, Egypt as well as their characteristics of antimicrobial resistance and molecular typing. From 25 broiler farms, 1100 samples were gathered. Antimicrobial resistance profiles and molecular typing were used to characterize the isolates. The findings revealed an overall prevalence of 26.3% (289/1100) from chickens’ clinical samples (30.3%; 273/900) and farm environmental samples (8%; 16/200). Toxinotyping of 289 isolates showed that 165 (57.1%) isolates were C. perfringens type A, harboring only alpha toxin gene (cpa) while 124 (42.9%) isolates were C. perfringens type G, containing netB gene. The cpb2 genes were found in 66 (22.8%) isolates with the highest positive rate from dead birds. Additionally, the study found a number of possible risk variables that were substantially linked to the prevalence of C. perfringens, including location in Damietta, winter season, history of coccidia infection, use of antimicrobial growth promoters, birds older than 22 days, wet litter type, and biosecurity strategy. Univariate and multivariate analyses showed a significant association between C. perfringens infection and grower chickens (OR 2.09, 95% CI 1.05–4.21, p = 0.037) compared to starter chickens. The isolates displayed their highest resistance rate to sulfamethoxazole-trimethoprim (94.5%), erythromycin, imipenem (94.1% each), penicillin, ampicillin, streptomycin, gentamycin (90.7% each), ampicillin/sulbactam (89.9%), cefuroxime and cefepime (85.8%), nalidixic acid (85.1%), and tetracycline (78.9%). Remarkably, none of the strains were resistant to meropenem. Multidrug-resistant was observed in 94.5% strains with MAR index of 0.32–0.79. The resistance genes carried by different strains were obviously different, among which the detection rate of aminoglycoside resistance gene aphA1, was the highest (100%), followed by bla TEM (85.8%), ermB, qnrS (85.1%), sul1 (80.6%), tetA(78.9%), drfA-1 (75.1%), qnrD, sul2 (60.2%), catA (57.4%), aadA (57.4%), ampC, mefA, qnrA (50.2%), bla CTX (46%), and tetM (42.2%). Enterobacterial repetitive intergenic consensus polymerase chain reaction (ERIC-PCR) was used to classify these isolates into eight different genotypes according to sampling place and sample type. The epidemiological information from this study was helpful in determining the danger of clostridial infection linked to Egyptian broiler farms. Our results also show that in order to combat multidrug resistance, new medications and antibiotic substitutes are required. The importance of conducting more surveys to better understand the prevalence of C. perfringens infection under strict management circumstances for various flock purposes cannot be overstated.
Machine learning models highlight environmental and genetic factors associated with the Arabidopsis circadian clock
Abstract The circadian clock of plants contributes to their survival and fitness. However, understanding clock function at the transcriptome level and its response to the environment requires assaying across high resolution time-course experiments. Generating these datasets is labour-intensive, costly and, in most cases, performed under tightly controlled laboratory conditions. To overcome these barriers, we have developed ChronoGauge: an ensemble model that can reliably estimate the endogenous circadian time of Arabidopsis plants using the expression of a handful of time-indicating genes within a single time-pointed transcriptomic sample. ChronoGauge can predict a plant’s circadian time with high accuracy across unseen Arabidopsis bulk RNA-seq and microarray samples, and can be further applied to make non-random predictions across samples in non-model species, including field samples. Finally, we demonstrate how ChronoGauge can be applied to generate hypotheses regarding the response of the circadian transcriptome to specific genotypes or environmental conditions.
Linking carbon footprint methodologies and environmental protection via sustainable product development with a moderating role of regulatory compliance
Thor: a platform for cell-level investigation of spatial transcriptomics and histology
Abstract Spatial transcriptomics links gene expression with tissue morphology, however, current tools often prioritize genomic analysis, lacking integrated image interpretation. To address this, we present Thor, a comprehensive platform for cell-level analysis of spatial transcriptomics and histological images. Thor employs an anti-shrinking Markov diffusion method to infer single-cell spatial transcriptome from spot-level data, effectively combining gene expression and cell morphology. The platform includes 10 modular tools for genomic and image-based analysis, and is paired with Mjolnir, a web-based interface for interactive exploration of gigapixel images. Thor is validated on simulated data and multiple spatial platforms (ISH, MERFISH, Xenium, Stereo-seq). Thor characterizes regenerative signatures in heart failure, screens breast cancer hallmarks, resolves fine layers in mouse olfactory bulb, and annotates fibrotic heart tissue. In high-resolution Visium HD data, it enhances spatial gene patterns aligned with histology. By bridging transcriptomic and histological analysis, Thor enables holistic tissue interpretation in spatial biology.
Left to dry: unseen risks lurking on reusable medical devices
3D printing of salt-like granular polyacrylamide as sacrificial molds for shaping versatile materials
Disc deformation as a potential biomarker of nonspecific low back pain
Abstract Nonspecific low back pain (nLBP) lacks clear biomarkers, making diagnosis challenging. The disorder is commonly associated with annular fissures, which are hypothesized to trigger nerve endings to signal pain during disc deformation. Although symptoms worsen with load, MRI is typically performed in a relaxed state. Herein, a novel loading-based MRI method for non-invasive quantification of disc deformation was applied on 76 discs from 28 nLBP-patients to determine whether specific deformation patterns reflect fissures and pain using computer tomography and low-pressure discography during the same day as references. Discs without fissures and those with posterior fissures only displayed compression of annulus fibrosus posteriorly and expansion anteriorly, while discs with fissures both anteriorly and posteriorly and those with severe fissuring displayed elevated deformation and larger within-group variance (p = 0.001–0.04). Notably, discs with elevated anterior and low posterior deformation were more likely pain-signaling, whereas those with elevated posterior and low anterior deformation were often non-pain-signaling. At model probability thresholds below 0.38, all discs were correctly identified as non-painful and above 0.65, 86% were correctly identified as pain-signaling. Conclusively, this study provides in vivo evidence of a close interplay between disc deformation, fissures and pain, supporting the hypothesis that disc deformation contributes to nLBP.