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Objective Bloodstream infections caused by carbapenem-resistant Acinetobacter baumannii (CRAB) are a significant public health problem, with high morbidity and mortality. Detection and identification of CRAB is essential for early diagnosis and treatment. Therefore, a rapid and economical method for the detection of CRAB-associated Bloodstream infections (BSIs) is urgently needed. Methods A triple PCR-UV reaction system has been developed for the detection of the antibiotic resistance genes OXA23 , OXA51 and AB-specific gene. Primer specificity, limit of detection (LOD), reproducibility, and accuracy of the assay were evaluated. The PCR products were directly analyzed using UV and ImageJ analysis, which provided a quickly interpretation of the results. Furthermore, the established assay was validated on clinical isolates and compared with blood culture and drug susceptibility testing. Results The triple PCR-UV method established in this study demonstrated strong primer specificity and discriminated CRAB among 23 common clinical pathogens. The results of this PCR method were validated by electrophoresis and showed good accuracy and reproducibility, with a limit of detection (LOD) of 3.0 × 10 –1 ng/uL. Meanwhile, the optimal annealing temperature for the triple method has been optimized to 56.4 ℃. The result of PCR amplification could be judged by the result of the gray value of the tube to be tested / the gray value of the blank control of the same lot. The ratio > 1.3 is CRAB, the ratio between 1.1–1.3 is carbapenem-sensitive Acinetobacter baumannii (CSAB), the ratio < 1.1 is negative result. When applied to detect 30 patients with BSIs of AB, the results were consistent with clinical blood culture identification and drug susceptibility testing. Conclusion The triple PCR-UV assay developed in this study is a UV-visual, rapid, and cost-effective method for the detection of Acinetobacter baumannii (AB ) and identification of CRAB in bloodstream infections. The assay could be particularly useful in community settings where expensive molecular instrumentation is not readily available and could help in the diagnosis and management of CRAB infections in BSIs.

Background Nasopharyngeal carcinoma (NPC) is a malignant tumor with high incidence in Southeast Asia and Southern China, characterized by difficulties in early diagnosis and high recurrence rates after treatment. Metabolic reprogramming plays a crucial role in the development and progression of tumors. In-depth studies on the metabolic characteristics and molecular mechanisms of NPC are essential to identify novel diagnostic and therapeutic targets. Objectives This study aimed to systematically reveal the metabolic characteristics and molecular mechanisms of NPC cell lines by integrating untargeted metabolomics, transcriptomics, and confocal micro-Raman spectroscopy (CMRS), and to explore potential biomarkers for prognostic evaluation and precision treatment of NPC. Methods: We performed an integrated analysis of transcriptomic, metabolomic, and Raman spectral data on five NPC cell lines (CNE1, CNE2, 5–8 F, 6-10B, and SUNE1) and the immortalized nasopharyngeal epithelial cell line NPEC1-BMI1. The analysis included association analysis of differentially expressed metabolites (DEMs) and differentially expressed genes (DEGs), pathway enrichment analysis, and network analysis to elucidate the interplay between gene expression and metabolic alterations. Furthermore, we employed machine learning models to achieve efficient discrimination between NPC cell lines and NPEC1-BMI1 using Raman spectroscopy. Finally, we validated the expression levels of selected DEGs using quantitative polymerase chain reaction (qPCR), Western blotting (WB), and immunohistochemistry (IHC). Results Significant differences in metabolic and gene expression profiles were observed between NPC cells and normal cells. CMRS analysis, combined with a multilayer perceptron (MLP) model, achieved high-precision discrimination between NPC cells and normal cells (accuracy 99.3%, AUC = 1.00). Further integrated analysis revealed significant correlations between KYNU and other DEGs, multiple DEMs, and specific Raman spectral features, suggesting their potential as diagnostic and prognostic biomarkers. Validation using the The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases showed high KYNU expression in head and neck squamous cell carcinoma (HNSCC) and NPC tissues. Consistent high expression of KYNU was confirmed in NPC cell lines and tissues by qPCR, WB, and IHC. Conclusions This study elucidated the unique metabolic characteristics and molecular signatures of NPC, clarified how molecular changes regulate gene expression, and provided new potential targets for prognostic evaluation and precision treatment of NPC. Graphical abstract

Background: The global pandemic of COVID-19 is caused by the rapidly evolving severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The clinical presentation of SARS-CoV-2 Omicron variant infection varies from asymptomatic to severe disease with diverse symptoms. However, the underlying mechanisms responsible for these symptoms remain incompletely understood. Methods: Transcriptome datasets from peripheral blood mononuclear cells (PBMCs) of COVID-19 patients infected with the Omicron variant and healthy volunteers were obtained from public databases. A comprehensive bioinformatics analysis was performed to identify hub genes associated with the Omicron variant. Hub genes were validated using quantitative RT-qPCR and clinical data. DSigDB database predicted potential therapeutic agents. Results: Seven hub genes (IFI44, IFI44L, MX1, OAS3, USP18, IFI27, and ISG15) were potential biomarkers for Omicron infection’s symptomatic diagnosis and treatment. Type I interferon-related hub genes regulated Omicron-induced symptoms, which is supported by independent datasets and RT-qPCR validation. Immune cell analysis showed elevated monocytes and reduced lymphocytes in COVID-19 patients, which is consistent with retrospective clinical data. Additionally, ten potential therapeutic agents were screened for COVID-19 treatment, targeting the hub genes. Conclusions: This study provides insights into the mechanisms underlying type I interferon-related pathways in the development and recovery of COVID-19 symptoms during Omicron infection. Seven hub genes were identified as promising biological biomarkers for diagnosing and treating Omicron infection. The identified biomarkers and potential therapeutic agent offer valuable implications for Omicron’s clinical manifestations and treatment strategies.

Carbonate mud represents one of the most important geochemical archives for reconstructing ancient climatic, environmental, and evolutionary change from the rock record. Mud also represents a major sink in the global carbon cycle. Yet, there remains no consensus about how and where carbonate mud is formed. Here, we present stable isotope and trace-element data from carbonate constituents in the Bahamas, including ooids, corals, foraminifera, and algae. We use geochemical fingerprinting to demonstrate that carbonate mud cannot be sourced from the abrasion and mixture of any combination of these macroscopic grains. Instead, an inverse Bayesian mixing model requires the presence of an additional aragonite source.We posit that this source represents a direct seawater precipitate. We use geological and geochemical data to show that “whitings” are unlikely to be the dominant source of this precipitate and, instead, present a model for mud precipitation on the bank margins that can explain the geographical distribution, clumped-isotope thermometry, and stable isotope signature of carbonate mud. Next, we address the enigma of why mud and ooids are so abundant in the Bahamas, yet so rare in the rest of the world: Mediterranean outflow feeds the Bahamas with the most alkaline waters in themodern ocean (>99.7th-percentile). Such high alkalinity appears to be a prerequisite for the nonskeletal carbonate factory because, when Mediterranean outflow was reduced in the Miocene, Bahamian carbonate export ceased for 3-million-years. Finally, we show how shutting off and turning on the shallow carbonate factory can send ripples through the global climate system.

Background Delayed diagnosis of inflammatory bowel disease (IBD) is common, there is still no effective imaging system to distinguish Crohn's Disease (CD) and Ulcerative Colitis (UC) patients. Methods This multicenter retrospective study included IBD patients at three centers between January 2012 and May 2022. The intestinal and perianal imagin g signs were evaluated. Visceral fat information from CT images was extracted, including the ratio of visceral to subcutaneous fat volume (VSR), fat distribution, and attenuation values. The valuable indicators were screened out in the derivation cohort by binary logistic regression and receiver working curve (ROC) analysis to construct an imaging report and data system for IBD (IBD-RADS), which was tested in the validation cohort. Results The derivation cohort included 606 patients (365 CD, 241 UC), and the validation cohort included 155 patients (97 CD, 58 UC). Asymmetric enhancement (AE) (OR = 87.75 [28.69, 268.4]; P  < 0.001), perianal fistula (OR = 4.968 [1.807, 13.66]; P  = 0.002) and VSR (OR = 1.571 [1.087, 2.280]; P  = 0.04) were independent predictors of CD. VSR improved the efficiency of imaging signs (AUC: 0.929 vs. 0.901; P  < 0.001), with a threshold greater than 0.97 defined as visceral fat predominance (VFP). In IBD-RADS, AE was the major criterion, VFP and perianal fistula were auxiliary criteria, and intestinal fistula, limited small bowel disease, and skip distribution were special favoring items as their 100% specificity. Grade 3 to 5 correctly classified most CD patients (derivation: 96.5% (352/365), validation: 98.0% (95/97)), and 98% of those were eventually diagnosed with CD (derivation: 97.8% (352/360), validation: 98.0% (95/97)). Conclusions IBD-RADS can help radiologists distinguish between CD and UC in patients with suspected IBD.

This investigation was designed to analyze alterations in functional connectivity across brain networks associated with cognitive fatigue through electroencephalogram (EEG) data analysis. Through the application of both global and local graph-theoretical metrics to characterize the topology of brain networks, this study establishes a conceptual framework supporting enhanced detection of cognitive fatigue manifestations while facilitating examination of its neurophysiological substrates. The study cohort comprised neurologically intact individuals aged 20-35 years, recruited from Beijing Rehabilitation Hospital, Capital Medical University between February 6 and September 30, 2024 for participation in a cognitive fatigue induction task. Following acquisition of written informed consent, data before and after the task were obtained, including both subjective fatigue assessments using the Visual analog scale for fatigue (VAS-F) scores and EEG data. The preprocessed EEG signals were segmented into three frequency bands: θ (4-8 Hz),α (8-13 Hz), and β (13-30 Hz). To determine the frequency band exhibiting maximal sensitivity to cognitive fatigue, cross-band comparative power spectral density (PSD) was implemented. The selected frequency band subsequently served as the basis for weighted Phase Lag Index (wPLI) computation, yielding a functional connectivity matrix derived from wPLI measurements. Network topology was evaluated through application of five global graph theory metrics (global efficiency [Eg], local efficiency [Eloc], clustering coefficient [Cp], shortest path length [Lp], and small-world property [Sigma]) complemented by two local graph theory metrics (nodal efficiency [NE] and degree centrality [DC]). This analytical framework enabled systematic comparison of connectivity patterns and topological characteristics between before and after cognitive fatigue states. Statistical analysis revealed significant post-fatigue elevations in global average PSD across all examined frequency bands: α (p < 0.001), θ (p < 0.001), and β (p = 0.004). The α band demonstrated the most pronounced effect size (Cohen's d = 4.23, r = 0.90). Topological analysis of α-band wPLI networks showed enhanced Eg (p = 0.005), Eloc (p < 0.001), and Cp (p < 0.001), whereas Lp displayed significant reduction (p = 0.005). Regional analysis revealed preferential enhancement of NE, particularly in central and anterior cortical regions. The experimental data indicated that α-band activity exhibited the highest sensitivity to cognitive fatigue induced by the sustained Stroop task, establishing a framework for accurate identification of fatigue states. Cognitive fatigue compensatory mechanisms manifested as concurrent improvements in both local and global neural information processing efficiency. Although such adaptive reorganization may compromise overall network efficiency, these findings implied an inherent balance between adaptive network reconfiguration and system efficiency. These results elucidated novel neurophysiological mechanisms underlying cognitive fatigue, substantially advancing our understanding of brain network dynamics during prolonged cognitive demand.

Diesel engines show poor performance and high emissions under cold-start conditions. The intake manifold burner is an effective method to increase the intake air temperature and improve engine performance. In this paper, a visualization system was employed to investigate the combustion process of the intake manifold burner. The effects of diesel flow rate and airflow velocity on combustion performance were investigated. The combustion process of the intake manifold burner showed four stages: preparing stage A, rapid development stage B, steady-development stage C, and stable stage D. Flame stripping was found in stages C and D, presenting the instability of the combustion process. With the increase in air flow velocity from 1.4 m/s to 3.0 m/s, the flame stripping was enhanced, leading to the increasing combustion instability and regular flame penetration fluctuations. The average temperature rise and combustion efficiency increased with the increasing diesel flow rate, indicating the combustion enhancement. Comparison of temperature rise and combustion efficiency under 2.0 m/s and 10.0 m/s showed that stronger cross wind enhances the heat convection, improving the temperature uniformity and combustion efficiency.

We aim to investigate the changes in brain functional connectivity induced by cognitive fatigue from a whole-brain perspective and multiple angles to uncover the underlying mechanisms in healthy individuals, thereby enhancing high-quality cognitive activities and the effectiveness of cognitive rehabilitation training. This study involved 48 healthy adults aged 20–35 and was conducted in two phases: cognitive fatigue validation and whole-brain connectivity feature analysis. We used fMRI to compare Amplitude of Low-Frequency Fluctuations (ALFF), Regional Homogeneity (ReHo), Functional Connectivity (FC), and graph theory metrics before and after cognitive fatigue. The results indicated that changes in activity within the bilateral thalamus and Front-Parietal network were significantly correlated with subjective fatigue levels. Additionally, the left precuneus, cerebellum, and certain frontal regions may contribute to the neural regulation of cognitive fatigue through various mechanisms. These findings provide a reliable theoretical basis for developing new strategies to mitigate cognitive fatigue and offer valuable insights for optimizing rehabilitation plans for patients with cognitive impairments.

Introduction Effective immunotherapeutic treatment of solid tumors has been greatly challenged by the complex hostile tumor immunosuppressive microenvironment (TIME), which typically involves hypoxia and immunosuppression. Methods Herein, a multifunctional biomimetic gold nano-modulator (denoted as GNR-SNO@MMT ) was developed to realize the efficient second near-infrared (NIR-II) photothermal immunotherapy via tumor targeting and deep penetration, vascular normalization and immune reprogramming. NIR-II photothermal agent gold nanorods (GNR) were grafted with thermosensitive S-nitrosothiol (SNO) donors and camouflaged with the tumor-penetrating peptide tLyp-1-modified macrophage membrane (MM) to yield GNR-SNO@MMT . Results The engineered membrane coating increased the capacity for tumor inflammatory tropism and deep penetration, which aided GNR-SNO@MMT in ablating tumors together with NIR-II laser irradiation. Moreover, hyperthermia-stimulated nitric oxide (NO) release in situ acted as a gas immunomodulator to effectively enhance blood perfusion and reprogram the TIME via multiple functions (e.g., decreasing PD-L1, repolarizing tumor-associated macrophages, and revitalizing cytotoxic T cells). Ultimately, the inhibition rate against 4T1 mouse mammary tumor model mediated by GNR-SNO@MMT plus NIR-II laser was 94.7% together with 2.4-fold CD8 + T cells infiltrated into tumors than that of the untreated counterpart. Conclusions The engineered biomimetic nano-modulator of GNR-SNO@MMT provides an effective and novel photoimmunotherapy candidate against deep-sited solid tumors through immune reconfiguration via NO-involved nanomedicine and external NIR-II laser assistance. Graphical abstract