Anqing City Hospital

To compare the postoperative analgesic efficacy of oliceridine versus sufentanil in patients undergoing total laparoscopy hysterectomy. In this double-blind, randomized controlled trial, 80 patients scheduled for elective total laparoscopy hysterectomy were allocated in a 1:1 ratio to receive postoperative patient-controlled intravenous analgesia (PCIA) with either oliceridine (Group O) or sufentanil (Group S). The primary outcome was the cough Numeric Rating Scale (NRS) score at 6hours postoperatively. Secondary outcomes included resting and cough NRS scores at 0.5, 2, 6, 12, 24, and 48hours (excluding the 6-hour cough NRS), number of PCIA attempts, proportion requiring rescue analgesia, total rescue analgesic dose, time to first flatus and ambulation, and 24-hour postoperative recovery quality assessed by the 15-item Quality of Recovery (QoR-15) scale. Exploratory outcomes included hemodynamic parameters within 30minutes after the analgesic loading dose, anesthesia emergence time, and tracheal extubation time. Safety outcomes comprised the incidence of adverse events within 48hours postoperatively. No significant differences were found between groups in resting or cough NRS scores at any time point, PCIA attempts, rescue analgesia requirement, rescue tramadol dose, emergence time, or extubation time. However, Group O had a significantly shorter time to first flatus (18.63 ± 3.51 vs 24.70 ± 3.26hours, P < 0.001) and time to first ambulation (17.95 ± 3.30 vs 20.28 ± 3.76hours, P = 0.004), a lower overall incidence of adverse events (32.5% vs 62.5%, P = 0.007), and a higher QoR-15 score (120.85 ± 7.15 vs.115.63 ± 6.74, P = 0.001). Additionally, Group O demonstrated better hemodynamic stability than Group S. For patients undergoing total laparoscopy hysterectomy, oliceridine provides postoperative analgesic efficacy comparable to sufentanil but is associated with a lower incidence of adverse events and better recovery quality.

Glioma remains one of the most aggressive brain tumors, necessitating innovative therapeutic strategies. Quercetin (Que), a natural polyphenolic flavonoid, has shown potential in cancer treatment. It can form iron-based nanodots (Que-Fe) that exhibit peroxidase (POD)-mimicking activity. This study aimed to synthesize and characterize Que-Fe nanodots, evaluate their photothermal conversion capabilities, and assess their anti-tumor efficacy both in vitro and in vivo. Que-Fe was synthesized through the coordination of Que with iron ions, yielding nanodots approximately 10 nm in diameter, confirmed by transmission electron microscopy and dynamic light scattering (DLS). The nanodots demonstrated significant POD-like activity in the presence of hydrogen peroxide. Photothermal experiments revealed that Que-Fe effectively converted near-infrared light into heat, achieving temperatures above 62 °C, indicating its potential as a photothermal agent. In vitro studies showed that Que-Fe exhibited concentration-dependent cytotoxicity against C6 glioma cells, with enhanced effects when combined with NIR irradiation. Transcriptomic analysis revealed that Que-Fe treatment upregulated genes associated with ferroptosis and oxidative stress, suggesting a multifaceted mechanism of action. In vivo experiments using a BALB/c nude mouse model demonstrated that Que-Fe, particularly in combination with NIR, significantly inhibited tumor growth and induced apoptosis without notable toxicity. Overall, these findings suggest that Que-Fe is a promising candidate for glioma therapy, leveraging its photothermal properties and inducing ferroptosis to enhance anti-tumor efficacy.

To investigate the effects of Angelica dahurica extract (ADE) on radiosensitivity of non-small-cell lung cancer (NSCLC) cells and the JAK/STAT pathway. The viability and radiosensitivity of NSCLC cell lines (A549, Calu-1, and H460 cells) were determined using CCK-8 and colony formation assays. Apoptosis was measured using flow cytometry. The expression and phosphorylation levels of Janus kinase 1 (JAK1) and signal transducer and activator of transcription 3 (STAT3) were measured using Western blot. In vivo, a Calu-1 cell-derived xenograft tumor model in nude mice was constructed to determine the effect of ADE on radiosensitivity. ADE treatment significantly enhanced the radiosensitivity of NSCLC cell lines, as supported by increased apoptotic rate in irradiation (IR)-induced NSCLC cells. After ADE treatment, the phosphorylation level of STAT3 in IR-induced NSCLC cells was markedly decreased. Reactivation of STAT3 by co-administration of colivelin, a STAT3 activator, abolished the radio-sensitizing effect of ADE on A549, Calu-1, and H460 cells. In vivo, the combination of ADE and radiotherapy was well tolerated and significantly inhibited tumor growth. Compared with the control group and radiation group, the radiation + ADE treatment group showed lower STAT3 phosphorylation level in the transplanted tumor tissues. ADE exerts radio-sensitizing effects on NSCLC cells by blocking the JAK/STAT pathway.

The P53-mutated Hepatocellular Carcinoma (HCC) is an aggressive variant associated with vascular endothelial growth factor (VEGF) overexpression and increased microvascular density. This study aimed to develop an MRI-based deep learning model for predicting P53-mutated HCC. A total of 312 HCC patients who underwent gadolinium-enhanced MRI and were pathologically confirmed between January 2018 and December 2023 were retrospectively enrolled. Participants were randomly divided into training and test dataset at an 8:2 ratio. We developed an EfficientNetV2-based deep learning model, constructing arterial phase (AP) model, portal venous phase (VP), T2-weighted imaging (T2WI), hepatobiliary phase (HBP) single-sequence model, and combined models to predict P53 mutation status. Model performance was evaluated using the area under the curve (AUC), accuracy, sensitivity, specificity, precision, and F1 score as metrics. Differences in AUC values were compared using Delong's test. A total of 312 pathologically confirmed HCC patients (age: 56 ± 9 years; male = 240) were included, with a training dataset (n = 249) and test dataset (n = 63).Among single-sequence models, the HBP model demonstrated superior diagnostic performance (AUC = 0.715) compared to T2WI, AP, and VP models. The multiphase combined model (T2WI + AP + VP) significantly outperformed single-sequence models, achieving AUCs of 0.982 (95% CI: 0.959-1.000) in the training dataset and 0.914 (95% CI: 0.819-1.000) in the test dataset. However, incorporating the HBP sequence into the combined model (T2WI + AP + VP + HBP) did not further improve diagnostic performance (P > 0.05). The combined model incorporating AP, VP, T2WI, and HBP sequences demonstrated numerically highest performance in predicting P53-mutated HCC.

In this study, a machine learning-based quality control algorithm for heavy rainfall was developed by integrating automatic weather station observations with remote sensing data, minute-level data, and metadata. Based on heavy rainfall samples from 1 June 2022 to 31 December 2024, the performances of four gradient boosting models—eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), and Gradient Boosted Regression Trees (GBRT)—significantly outperformed precipitation-threshold-based conventional methods, including regional extreme value checks, temporal consistency checks, and others. Specifically, the XGBoost in particular achieves an increase in precision by 0.110 and recall by 0.162. This translates to a substantial reduction in both false alarms (higher precision) and missed detections (higher recall) of anomalous heavy rainfall events, thereby significantly enhancing the reliability of the quality-controlled data. The radar composite reflectivity, satellite cloud-top temperature, and minute-level precipitation were identified as dominant contributors to model predictions. The integration of multi-sensor observations effectively addressed limitations inherent in conventional threshold-based approaches. Through SHapley Additive exPlanations (SHAP)-based interpretability analysis, the model’s decision logic was shown to align with meteorological physical principles. Characteristic patterns such as combinations of low radar reflectivity and elevated cloud-top temperatures were flagged as anomalous rainfall events, typically corresponding to manual operational errors. Moreover, the model identified anomalous minute-level precipitation extremes to be critical signals for detecting instrument malfunctions, data encoding and transmission errors. The physical consistency of the model’s reasoning enhances its trustworthiness and supports its potential for operational implementation in heavy rainfall quality control.

The aim of this study is to investigate the effects of a peptide mixture composed of arginine (Arg), aspartic acid (Asp), and phenylalanine (Phe) on the proliferation and neural differentiation of human dermal mesenchymal stem cells (HDMSCs). HDMSCs were isolated from residual skin tissue following circumcision and purified using a combination of long-term trypsin stress and suspension culture. The impact of the peptide mixture on HDMSC proliferation was assessed at various concentrations using a Cell Counting Kit-8 (CCK-8) assay. Based on preliminary screening of proliferative activity, an optimal peptide mixture concentration for promoting proliferation was selected for further HDMSCs culture. During this culture, morphological changes were observed. Differentiation efficiency was evaluated using immunofluorescence staining for the neural markers Nestin and Neurofilament-L (NF-L) at weeks 3 and 6 of culture, respectively. Primary adherent HDMSCs exhibited a long spindle morphology and formed spherical cell clusters in suspension culture. A peptide mixture at concentrations of 5 ng/mL Arg, 20 ng/mL Asp, and 40 ng/mL Phe significantly promoted HDMSC proliferation (p < 0.05). During the induction period, the cells gradually acquired neuronal morphological characteristics, including cell body contraction and process extension. Immunofluorescence results showed that the cells expressed Nestin by week 3, shifting to NF-L positivity by week 6. This indicates that the peptide mixture induced the differentiation of HDMSCs into neural cells. A peptide mixture containing Arg, Asp, and Phe has been shown to effectively induce the differentiation of HDMSCs into neural cells. This provides a novel and safe strategy for the regeneration of neural tissue.

Efficient detection and localization of maize seedlings in complex field environments is essential for accurate plant segmentation and subsequent three-dimensional morphological reconstruction. To overcome the limited accuracy and high computational cost of existing models, we propose an enhanced architecture named MaizeStar-YOLO. The redesigned backbone integrates a novel C2F_StarsBlock to improve multi-scale feature fusion, while a PKIStage module is introduced to enhance feature representation under challenging field conditions. Evaluations on a diverse dataset of maize seedlings show that our model achieves a mean average precision (mAP) of 92.8%, surpassing the YOLOv8 baseline by 3.6 percentage points, while reducing computational complexity to 3.0 GFLOPs, representing a 63% decrease. This efficient and high-performing framework enables precise plant–background segmentation and robust three-dimensional feature extraction for morphological analysis. Additionally, it supports downstream applications such as pest and disease diagnosis and targeted agricultural interventions.