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Exploring the molecular mechanisms of subarachnoid hemorrhage and potential therapeutic targets: insights from bioinformatics and drug prediction
Enhanced individual difference of functional brain network induced by volitional eyes closing
Untargeted metabolomic profiling for identifying systemic signatures of helicobacter pylori infection in a guinea pig model
Abstract Infections caused by the Gram-negative bacterium Helicobacter pylori (H. pylori) can lead to gastritis, gastric or duodenal ulcers, and even gastric cancer in humans. Investigating quantitative changes in soluble biomarkers associated with H. pylori infection offers a promising method for monitoring the progression of the infection, inflammatory response and potentially systemic consequences. This study aimed to identify, using an experimental model of H. pylori infection in guinea pigs, the specific metabolomic biomarkers in the serum of H. pylori-infected (32) versus uninfected (32) animals. The H. pylori status was confirmed through histological, molecular, and serological examinations. Metabolomic profiling was conducted using UPLC-QTOF/MS methods. The metabolomic biomarkers significantly associated with H. pylori infection were selected based on volcano plots and traditional univariate receiver operating characteristics (ROC). This study identified 12 unique metabolites significantly differentiating H. pylori-infected guinea pigs from uninfected ones. In summary, the metabolomic profiling of serum samples, in combination with ROC characteristics of the data, enhances the monitoring of H. pylori infection and related inflammatory responses in guinea pigs experimentally infected with these bacteria, with potential applications in humans for prediction the infection course and its systemic effects.
Retrospective study on NIPT or NIPT plus combined with ultrasound in screening fetal chromosomal abnormalities
Surrogate modeling of electrospun PVA/PLA nanofibers using artificial neural network for biomedical applications
Abstract Blending poly (lactic acid) (PLA) with poly (vinyl alcohol) (PVA) improves the strength and hydrophilicity of nanofibers, making them suitable for biomedical applications like wound dressings. This study explores how electrospinning parameters—applied voltage, flow rate, and needle-to-collector distance—affect PVA/PLA nanofiber properties, optimizing them using a Taguchi design of experiment (DoE) approach to enhance their mechanical and surface properties for clinical use. Given the high costs and time associated with conducting extensive experimental tests, an artificial neural network based surrogate model is developed to predict experimental outcomes more efficiently, facilitating faster identification of optimal design configurations. Analysis of Variance reveals flow rate as the most significant determinant of fiber diameter. The optimal electrospinning configuration yields nanofibers with an average diameter of 127.6 ± 19.8 nm. These fibers exhibit exceptional tensile strength, flexibility, and a water contact angle of 37°, demonstrating superior hydrophilicity conducive to cell adhesion and proliferation—key factors in promoting wound healing. Comparative analyses confirm that the optimized scaffold (18 cm needle-to-collector distance, 0.6 ml/h flow rate, and 18 kV applied voltage) significantly outperforms alternative configurations, such as 10 cm needle-to-collector distance, 1.2 ml/h flow rate, and 22 kV applied voltage, which display larger diameters, reduced hydrophilicity (contact angle of 72°), and diminished suitability for medical use. Validation experiments affirm the accuracy and reproducibility of the Taguchi optimization, substantiating the methodological rigor and reliability of the findings. This work contributes novel insights into the tunable design of electrospun nanofibers, providing a pathway to developing advanced wound dressings that facilitate tissue integration and accelerate healing. The optimized PVA/PLA nanofibers have the potential to revolutionize wound care by offering a cost-effective and clinically viable solution for enhancing patient recovery, reducing treatment durations, and improving global healthcare outcomes.
Evaluation of urinary metabolites as biomarkers for occupational p-chloronitrobenzene exposure: a pilot study
Abstract We explored the feasibility of using urinary metabolites of p-chloronitrobenzene (p-CNB) as exposure biomarkers of occupational p-CNB exposure. Forty-two workers exposed to p-CNB during their jobs at a chemical enterprise in Shaoxing, Zhejiang Province, China were included in the exposure group, while administrative personnel who do not come into contact with p-CNB at work were included in the control group. Each worker in the exposure group was equipped with a personal air sampler to collect airborne p-CNB samples, and urine samples were collected at the conclusion of each shift. After sample collection, the airborne p-CNB concentrations and urinary metabolite concentrations were detected by gas chromatography-tandem mass spectrometry and ultra-performance liquid chromatography - quadrupole - orbitrap high resolution mass spectrometry, respectively. The urinary metabolite concentrations were corrected by the content of urinary creatinine. Subsequently, the correlations between the urinary metabolite concentrations and the p-CNB time-weighted average (TWA) concentrations were assessed using correlation analysis. All p-CNB TWA concentrations measured in this study were below the occupational exposure limit in the Chinese national standards. In the exposure group, N-acetyl-S-(4-nitrophenyl)-L-cysteine (NANPC), 2-chloro-5-nitrophenol (2C5NP), p-chloroacetanilide (p-CAA), p-chlorooxanilic acid (p-COA), 2-amino-5-chlorophenol (2A5CP), and p-chloroaniline (p-CA) were detected at varying levels. The percentages of NANPC, 2C5NP, p - CAA, p - COA, 2A5CP, and p - CA were 64.1%, 5.1%, 0.3%, 15.1%, 5.1%, and 10.3%, respectively. We found extremely significant positive relationships (p < 0.01) between the urinary metabolite concentrations (p-CA, 2C5NP, 2A5CP, NANPC, and p-COA) and the p-CNB TWA concentrations, with respective correlation coefficients of 0.827, 0.673, 0.790, 0.714, and 0.741. Thus, these five metabolites may be used as exposure biomarkers of occupational p-CNB exposure. Moreover, among these metabolites, NANPC was identified as the most suitable exposure biomarker because it had the highest correlation coefficient and the highest content in urine.
Epidemiological characteristics of patients with invasive pulmonary aspergillosis infected with Aspergillus fumigatus from a tertiary hospital in Ningxia, China
Nomogram for thromboembolic events in primary membranous nephropathy associated with PLA2R antibody
Anterior tenting vs. whole wrapping technique for acellular dermal matrix in breast reconstruction under post-mastectomy radiotherapy in rats
Dogs’ olfactory resting-state functional connectivity is modulated by age and brain shape
Abstract Humans have long applied canine olfaction in various contexts. Dog olfactory brain networks have recently been mapped by anatomical measures, but functional connections remain unexplored. Also, whereas individual characteristics, including age, sex, and brain shape, are known to affect olfactory performance, their covariation with olfactory functional networks is unknown. To address these, we investigated dogs’ resting-state functional connectivities between anatomically defined olfactory regions and assessed whether and how their olfactory functional network is affected by age, sex, and brain shape. Olfactory functional connectivity strength exhibited negative correlations with both age and brain shape: older dogs and those with rounder-shaped brains demonstrated lower functional connectivity, respectively, but no effect of sex was found. The results suggest that both aging and brain morphology can negatively impact a dog’s sense of smell, and older dogs and dogs with rounder-shaped brains may have diminished olfactory performance.
The abnormal audiovisual conflict in Parkinson’s disease patients is manifested in perception rather than response
Spatiotemporal evolution and influencing factors of carbon stock in the water receiving areas from the perspective of carbon neutrality
Investigation of dual memory behavior in RRAM: coexistence of resistive and capacitive switching phenomena
Energy-resolved neutron imaging and diffraction including grain orientation mapping using event camera technology
Abstract Time-of-flight neutron diffraction and energy-resolved imaging each provide unique perspectives into material properties. Neutron diffraction is useful for assessing microstructural parameters such as phase composition, texture, and dislocation densities, though it typically provides averaged data over the sampled volume. Energy-resolved imaging, on the other hand, offers both spatial and spectral information by detecting Bragg edges and neutron absorption resonances, which enables detailed mapping of microstructure and isotopic composition. When combined, these techniques have the potential to enrich our understanding of material behavior across different scales, enhancing our understanding of complex materials. Traditionally, these modalities are conducted on separate instruments, which is time-consuming and poses challenges for data integration. Here, we report the integration of the LumaCam, an event-mode energy-resolved neutron imaging camera with the HIPPO time-of-flight diffractometer at LANSCE. This integration enables simultaneous diffraction and imaging across the full spectrum, with analysis optimized for diffraction and Bragg-edge imaging in the thermal range (0.45–10 Å) and resonance imaging in the epithermal range (0.5–3000 eV), facilitating comprehensive multi-modal analysis. We demonstrate its capabilities through case studies, including spatial mapping of grain orientations in a steel sample and accurate thickness estimations for irregular samples including a depleted uranium cylinder and a natural silver-containing mineral specimen. The combined setup enhances real-time sample alignment and provides comprehensive data for crystal structure, texture, and isotopic composition analysis. This approach opens new possibilities for advanced applications in nuclear engineering, archaeology, and materials science.