Microbial pathogenesis最新文献

Bacteremia, meningitis, endocarditis, and pneumonia are some difficult-to-treat nosocomial infections caused by drug-resistant Acinetobacter baumannii. This study aimed to investigate Salvia abrotanoides essential oils against multidrug-resistant (MDR) and extensively drug-resistant (XDR) strains of A. baumannii, which have not previously been reported. Essential oils extracted from Salvia abrotanoides leaf (L. EO) and flower (F. EO) were analyzed for the antimicrobial, antioxidant, and cell cytotoxicity activity. GC/MS identified 19 new compounds not previously reported in the studied EOs. The IC50 values from the DPPH assay were 0.22 mg/mL for L. EO and 0.015 mg/mL for F. EO. The essential oils showed antimicrobial activity, with concentrations ranging from 1.4 to 1.75 mg/mL for MIC and from 2.8 to 7 mg/mL for MBC, effectively inhibiting growth and killing the tested MDR and XDR strains, respectively. Furthermore, at sub-MIC concentrations, both essential oils downregulated the expression of several key genes in A. baumannii, including bap, csuD, abaI, abaR, and barB, based on qRT-PCR results. ADMET analysis demonstrated that all studied plant ligands complied with Lipinski's rule of five and Veber's rule, indicating their potential as oral bioavailable drugs. Based on these findings, the essential oils of S. abrotanoides (F. EO and L. EO) are promising for the development of antimicrobial agents targeting the MDR and XDR strains of A. baumannii.

Background: The causal links between gut microbiota, inflammatory cytokines, and chronic rhinosinusitis are unclear.

Methods: A Mendelian randomization study used data from the MiBioGen consortium (211 microbiota taxa, n=18,340), genome-wide association studies of 91 inflammatory cytokines, and chronic rhinosinusitis data from the FinnGen consortium.

Results: Five microbiota taxa were causally linked to chronic rhinosinusitis. The genera Ruminococcaceae NK4A214 group and Victivallis were risk factors, while Lachnospiraceae NC2004 group, Ruminococcus2, and Subdoligranulum were protective. Elevated levels of axin-1, C-X-C motif chemokine 10, interleukin-18 receptor 1, interleukin-1-alpha, and vascular endothelial growth factor A increased risk, whereas C-C motif chemokine 19, CD40L receptor, and Fractalkine were protective. The Ruminococcaceae NK4A214 group id.11358 increased risk through reduced Fractalkine and elevated vascular endothelial growth factor A levels.

Conclusions: The study supports a causal link between Ruminococcaceae NK4A214 group id.11358 and chronic rhinosinusitis, mediated by Fractalkine and vascular endothelial growth factor A levels.

The cotton sector has recently encountered various obstacles, and traditional methods persist in the identification of cotton leaf diseases. This study has established an automated approach for diagnosing cotton leaf blast disease via deep learning methodologies and image processing. The research included deep learning architectures like Convolutional Neural Network, InceptionV3, ResNet50, VGG16, VGG19, and Xception. The extensive collection consists of over 4200 images, including around 3000 depicting cotton leaf blight and 1200 representing healthy leaves. The results demonstrated that the Convolutional Neural Network models InceptionV3, ResNet50, VGG16, and VGG19 attained final validation accuracies of 92.92%, 64.1%, 96.81%, 95.42%, and 95.97%, respectively. The ResNet50 approach has exhibited greater accuracy than previous models, whereas the VGG19 model has achieved the second-highest accuracy. This research enhances precision agriculture by delivering a reliable and precise automated approach for predicting cotton diseases. Subsequent inquiries have been undertaken to enhance the precision and efficacy of deep learning models by the incorporation of cutting-edge technologies, including ResNet50, RegNet, EfficientNetB, and Vision Transformers. This study has resulted in a significant enhancement of cotton leaf diseases through identification with the model of surpassing existing leading methodologies in accuracy, complexity, and inference speed. Thus, the creation of these reliable and precise automated diagnostic tools for cotton leaf diseases markedly enhances precision agriculture. The current investigation could equip farmers with a dependable and effective method to detect and mitigate cotton leaf diseases prior to inflicting significant harm on cotton crops.

Pediatric severe pneumonia represents a significant global infectious illness, characterized by elevated morbidity and mortality rates. In light of antibiotic misuse and bacterial biofilm resistance, various metal-based compounds have been established. Nevertheless, the elevated oxygen levels in the lungs enable certain aerobic pathogenic bacteria to exhibit significant tolerance to oxygen and reactive oxygen species (ROS), rendering metal-based materials reliant on ROS potentially ineffective in therapeutic applications. Motivated by the susceptibility to cuproptosis in aerobic respiratory cells, we developed an antibacterial copper nanocomposite. We demonstrated that it can efficiently induce cuproptosis-like mortality in the lungs of aerobic bacteria. To overcome the challenges of in vivo cuproptosis, manganese dioxide was initially used to reduce protective glutathione, which binds copper and thereby prevents its interaction with proteins, thereby facilitating antibacterial action through immunological improvement. Cuproptosis-like cell death necessitates a substantial quantity of copper ions. To satisfy this demand, we provide positively hydrophilic altered CM nanoparticles that efficiently traverse the lung mucus layer via local administration, with copper ions subsequently released rapidly by the acidic conditions at the infection site, thereby enhancing the destruction of bacterial biofilms in conjunction with manganese. This drug-delivery method can efficiently address pediatric severe pneumonia, while mitigating systemic toxicity associated with high dosages of copper.

Human breast milk is increasingly recognized not only as nourishment but as a dynamic medium of biological information-delivering nutrients, immune molecules, and microbial ecosystems critical to early development. While each mother transmits a unique immunological and microbiota profile, the developmental impact of amplifying this diversity remains unexplored. Here, we introduce the Vyarna Booster: a novel, shelf-stable powder formed by combining lyophilized breast milk from multiple verified providers to intentionally exceed the microbial diversity found in any single sample. Using 16S rRNA gene sequencing, we analyzed bacterial composition across fresh (F), individually lyophilized (L), and mixed composite (M) samples from 13 mothers. The composite formulation exhibited greater microbial richness and evenness than any source sample, with consistently elevated abundance of commensals such as Streptococcus salivarius and Lactococcus raffinolactis. This increased diversity was reproducible across independently assembled mixes. While we do not claim viability or clinical outcomes, the observed taxonomic amplification suggests a previously untested mechanism for engineering postnatal microbial exposure. We propose the Vyarna Booster as a new class of infant nutritional supplement-one that augments the natural limits of human milk through deliberate bioinformational combination, and raises foundational questions about the boundaries of maternal transmission, microbial design, and early life programming.