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Gastrin-releasing peptide signaling in the nucleus accumbens medial shell regulates neuronal excitability and motivation
Abstract Neuropeptides are the largest class of neuromodulators. It has been shown that subpopulations of ventral tegmental area (VTA) dopamine neurons express mRNA for the neuropeptide Gastrin-releasing peptide (GRP); however, its functional relevance in mesolimbic circuits is unknown. Here we report that the GRP receptor (GRPR) is expressed in nucleus accumbens medial shell (NAc MSh) neurons, which are targeted by GRP-expressing inputs from the VTA, hippocampus, and amygdala. We show that NAc MSh GRPR-positive cells represent subpopulations of D2 receptor-expressing neurons, comprising both classical striatal projection neurons (SPNs) and eccentric SPNs. GRPR-expressing neurons have high intrinsic excitability and can be activated by GRP in vivo. NAc-specific deletion of Grpr in mice increases motivation in a progressive ratio test, demonstrating that GRPR regulates motivated behaviors. These experiments establish GRP/GRPR signaling as a potent modulator of mesolimbic circuits and advance our understanding of the diversity of cell types present in the NAc.
Enhancing brain tumor segmentation using attention based convolutional UNet on MRI images
Abstract Precise segmentation of brain tumors is essential for efficient diagnosis and therapy planning. While current automated methods frequently fail to capture complicated tumor shapes, traditional manual methods are laborious, subjective, and unpredictable. These issues are addressed by the suggested Attention-based Convolutional U-Net (ACU-Net) model, which incorporates attention processes into the U-Net architecture. The objective is to enhance the degree of precision and dependability of the tumor’s edge delineation by proposing and testing the ACU-Net model-based brain tumor segmentation on MRI data. The research framework consists of data acquisition from the BraTS 2018 MRI data set. The first processing steps carried out in this study were the normalization of acquired data, spatial resolution, and augmentation of the obtained data. ACU-Net is a model developed with the use of attention gates and has been trained with dice and cross-entropy losses. Precision, recall, dice similarity coefficient (DSC), and intersection over union (IoU) are the performance measures used in the proposed ACU-Net and compared with the basic benchmark models, including U-Nets and convolutional neural networks (CNNs). The model of ACU-Net was shown to be most effective in brain tumor segmentation, and the dice scores were 94.04% for Whole Tumor (WT), 98. 63% for Tumor Core (TC) and 98.77% for Enhancing Tumor (ET). The proposed ACU-Net performed better than baseline models, showing the high capacity of the current approach to segment various classes of tumors. The model ACU-Net enhances brain tumor segmentation, acting as a reliable tool for clinical applications. These findings confirm that attention mechanisms improve the accuracy and robustness of medical image segmentation.
Eosinophil CD48 interactions with Candida albicans Als6 is protective in vitro and in mouse systemic candidiasis
Randomized trial of the effect of esomeprazole on functional dyspepsia during Ramadan fasting
Real-time, high-throughput super-resolution microscopy via panoramic integration
Development and validation of a risk prediction model for postoperative pneumonia in elderly non-cardiac surgery patients: a retrospective cohort study
Abstract Postoperative pneumonia (POP) is a prevalent, severe complication in elderly noncardiac surgical patients, linked to extended hospital stays, increased healthcare costs, and higher mortality. Existing predictive models are often limited by single-center data, small cohorts, or restricted variables, highlighting the need for a comprehensive tool integrating multi-phase perioperative factors. This retrospective study analyzed 44,740 patients aged ≥ 65 years who underwent noncardiac surgery (November 2014–April 2022) at Henan Provincial People’s Hospital, with 3187 (7.1%) developing POP. Patients were stratified into development (n = 31,320) and validation (n = 13,420) cohorts via 70:30 random split. Key predictors were identified using LASSO logistic regression (from 44 candidates), followed by multivariate logistic regression with forward stepwise selection. Model performance was evaluated via AUC (discrimination), calibration (Hosmer–Lemeshow test, Brier score), clinical utility (decision curve analysis [DCA]), and interpretability (SHAP analysis). The final model included 9 predictors: anesthesia duration, anesthesia type, smoking status, pulmonary disease history, intraoperative colloid volume, preoperative anticoagulant/antihypertensive/steroid use, and intraoperative sufentanil dose. It demonstrated strong discrimination (validation AUC = 0.804, 95% CI 0.790–0.818) and good calibration (development: Hosmer–Lemeshow χ2 = 5.45, P = 0.79; validation: χ2 = 7.81, P = 0.55; Brier score = 0.058 for both). A derived nomogram (optimal cutoff = 190) showed high sensitivity (76.3%) and specificity (69.6%). DCA confirmed net benefit across 0–89% (development) and 0–88% (validation) thresholds. SHAP analysis identified prolonged anesthesia and pulmonary disease history as top predictors. This multifactorial model reliably predicts postoperative pneumonia in elderly noncardiac surgical patients using routinely collected perioperative data, with good discrimination and calibration. By integrating a wider range of variables than prior models, it enhances predictive accuracy and clinical applicability. External validation in multicenter prospective cohorts is needed to confirm its generalizability and support clinical integration.
Declining demand and circular transition possibilities of sand, gravel and crushed stone in China
Modelling cyclic compression of ballast aggregates using bounding surface model
Using in-situ small-angle scattering to reveal the structure and dynamics of supramolecular polymers
Abstract Small-angle scattering (SAS) is widely applied to nanoscale soft and hard material systems but has found limited use in the emerging field of supramolecular polymers (SPs). Key benefits to the field include in-situ measurement of SP assemblies in solution and the monitoring of triggered changes in real time. Here we summarise SAS basics and offer advice on the application of SAS to SP systems. To demonstrate applicability and show the capability of more advanced contrast-variation and time-resolved measurements, various successful SAS experiments on SP systems are highlighted. With a flexible sample environment allowing SAS measurement concurrent with other advanced techniques, plus ever-improving access to high quality data and analysis approaches, we conclude that SAS should be a more routine component in the toolbox of SP researchers.
Advancing image-based meta-analysis through systematic use of crowdsourced NeuroVault data
Citric acid modified red mud for valorization as a sustainable catalyst in bisulfite-activated congo red degradation
Abstract Bisulfite (BS)-based advanced oxidation processes (AOPs) are attractive for pollutant degradation, but often depend on costly transition metals with leaching risks. Herein, we report a citric acid-modified red mud catalyst (RMAC) for efficient Congo Red (CR) removal. Citric acid acted Simultaneously as an acid activator and carbon template, enlarging the surface area from 31.10 to 116.40 m2 g−1 (3.74-fold increase). Under optimal conditions (5 mM BS, pH = 5, 80 mg L−1 CR), RMAC3-800 achieved 98.8% CR removal with a pseudo-first-order rate constant of 0.1399 min−1 and retained > 80% efficiency after three reuse cycles. Radical scavenging and EPR analyses confirmed SO4 •− (53.7%) and •OH (46.3%) as the dominant species, whereas XPS identified Fe0 as the principal active site. GC-MS detected six intermediates, supporting the proposed oxidative cleavage and mineralization pathways of the degradation process. A preliminary bench-scale cost analysis estimated an operating cost of ~ 13.94 RMB m−3 (≈ 1.95 USD m−3), underscoring its economic feasibility. This study demonstrates a cost-effective, recyclable, and sustainable catalytic system for wastewater treatment and red mud valorization.