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Objective: The aim of this study was to evaluate the effect of dexmedetomidine on lung compliance in patients under general anesthesia with desflurane. Methods: This prospective, randomized, double-blind, controlled trial included 51 patients who received general anesthesia undergoing lower limb fracture surgery. Participants were assigned to either the experimental (loading dose of 0.25 μg/kg dexmedetomidine over 10 min, followed by a maintenance dose of 0.3 μg/kg/h until the end of the surgery) or control (0.9% saline) group. Anesthesia was maintained with desflurane, analgesics and muscle relaxants. The two groups were compared for hemodynamic parameters, dynamic lung compliance, oxygenation index, and postoperative complications. Results: While dynamic lung compliance showed no significant difference between the two groups at T1 (P = 0.321), it was significantly higher in the experimental group at all other time points (all P

Summary: Background: This study explores the potential of the deep learning-based convolutional neural network (CNN) to automatically recognize MMD using MRA images from atherosclerotic disease (ASD) and normal control (NC). Methods: In this retrospective study in China, 600 participants (200 MMD, 200 ASD and 200 NC) were collected from one institution as an internal dataset for training and 60 from another institution were collected as external testing set for validation. All participants were divided into training (N = 450) and validation sets (N = 90), internal testing set (N = 60), and external testing set (N = 60). The input to the CNN models comprised preprocessed MRA images, while the output was a tripartite classification label that identified the patient's diagnostic group. The performances of 3D CNN models were evaluated using a comprehensive set of metrics such as area under the curve (AUC) and accuracy. Gradient-weighted Class Activation Mapping (Grad-CAM) was used to visualize the CNN's decision-making process in MMD diagnosis by highlighting key areas. Finally, the diagnostic performances of the CNN models were compared with those of two experienced radiologists. Findings: DenseNet-121 exhibited superior discrimination capabilities, achieving a macro-average AUC of 0.977 (95% CI, 0.928–0.995) in the internal test sets and 0.880 (95% CI, 0.786–0.937) in the external validation sets, thus exhibiting comparable diagnostic capabilities to those of human radiologists. In the binary classification where ASD and NC were group together, with MMD as the separate group for targeted detection, DenseNet-121 achieved an accuracy of 0.967 (95% CI, 0.886–0.991). Additionally, the Grad-CAM results for the MMD, with areas of intense redness indicating critical areas identified by the model, reflected decision-making similar to human experts. Interpretation: This study highlights the efficacy of CNN model in the automated diagnosis of MMD on MRA images, easing the workload on radiologists and promising integration into clinical workflows. Funding: National Natural Science Foundation of China, Tianjin Science and Technology Project and Beijing Natural Science Foundation.

The massive accumulation of red mud (RM) and the abuse of antibiotics pose a threat to environment safety and human health. In this study, we synthesized RM-based Prussian blue (RM-PB) by acid solution-coprecipitation method to activate H2O2 to degrade norfloxacin, which reached about 90% degradation efficiency at pH 5 within 60 min and maintained excellent catalytic performance over a wide pH range (3−11). Due to better dispersion and unique pore properties, RM-PB exposed more active sites, thus the RM-PB/H2O2 system produced more reactive oxygen species. As a result, the removal rate of norfloxacin by RM-PB/H2O2 system was 8.58 times and 2.62 times of that by RM/H2O2 system and PB/H2O2 system, respectively. The reactive oxygen species (ROS) produced in the degradation process included ·OH, ·O2- and 1O2, with 1O2 playing a dominant role. The formation and transformation of these ROS was accompanied by the Fe(III)/Fe(II) cycle, which was conducive for the sustained production of ROS. The RM-PB/H2O2 system maintained a higher degradation efficiency after five cycles, and the material exhibited strong stability, with a low iron leaching concentration. Further research showed the degradation process was less affected by Cl-, SO42-, NO3-, and humic acids, but was inhibited by HCO3- and HPO42-. In addition, we also proposed the possible degradation pathway of norfloxacin. This work is expected to improve the resource utilization rate of RM and achieve treating waste with waste.

Summary: Background: Cervical squamous cell carcinoma (CSCC) and adenocarcinoma (CAde) are two major pathological types of cervical cancer (CC), but their high-resolution heterogeneity of tumor and immune microenvironment remains elusive. Methods: Here, we performed single-nucleus RNA sequencing (snRNA-seq) from five CSCC and three CAde samples, and systematically outlined their specific transcriptome atlas. Findings: We found CD8+ T cells in CSCC were more cytotoxic but lower exhausted compared to those in CAde, and phagocytic MRC1+ macrophages were specifically enriched in CSCC. Interestingly, we discovered that pro-tumoral cancer-associated myofibroblasts (myoCAFs) and cancer-associated vascular-fibroblasts (vCAFs) were more abundant in CSCC, and further verified their pro-metastatic roles in vitro. Furthermore, we also identified some specific chemotherapy drugs for CSCC (Dasatinib and Doramapimod) and CAde (Pyrimethamine and Lapatinib) by revealing their heterogeneity in transcriptomic profiles of malignant epithelial cells, and further verified their specific sensitivity in cell lines and constructed CC-derived organoids. Cell–cell communication networks revealed that the pathways of NRG1-ERBB2, and FN1-ITAG3 were specific for CAde and CSCC, respectively, which may partly explain the specificities of identified chemotherapy drugs. Interpretation: Our study described the immune heterogeneity and specific cellular interactions between CSCC and CAde, which could provide insights for uncovering pathogenesis and designing personalized treatment. Fundings: National Key R&D Program of China (2021YFC2701201), National Natural Science Foundation of China (82072895, 82141106, 82103134, 81903114).

Cuproptosis is a copper-dependent model of cell death involved in tumor genesis and progression. Its roles in uterine corpus endometrial carcinoma (UCEC) remains elusive. Here, we aimed to explore the expression and prognostic values of cuprotosis-related genes (CRGs) in UCEC. Expression profiles and clinical data of UCEC were downloaded from The Cancer Genome Atlas (TCGA), and randomly divided into testing or training cohort (1:1 ratio). The CRG signature was identified by LASSO regression analysis. The differentially expressed genes and their functional enrichment analysis were performed by the “limma” R package and Metascape, respectively. The immunocytes infiltration was measured by TIMER, and “GSVA” R package. In total, seven differentially expressed prognostic genes of CRGs in UCEC were identified, and four genes (GLS, CDKN2A, PC, and SUCLG1) were selected to construct a predictive model in training cohort. UCEC patients from training and testing cohorts were further divided into high- or low-risk groups according to the median risk score. High-risk group favored poor prognosis compared to low-risk group. Functional enrichment analysis revealed this CRG signature were got involved in the process of cell-cell adhesion and immune activities (e.g., IL-1 signaling pathway, cellular response to cytokine stimulus). Further analyses revealed there were significant differences between high- and low-risk patients regarding immunocytes infiltration, chemokines, and chemokine receptors. Finally, the expression and biological functions of identified CRGs were confirmed by UCEC samples and experimental methods in vitro. In summary, the CRG signature was significantly correlated with patients' overall survival, which could provide insights into the diagnosis and prognosis prediction for UCEC.

Background: Kidney renal clear cell carcinoma (KIRC) is the most common pathological subtype of renal cell cancer. APOBEC3 activity has been identified in a variety of human cancers. Although its involvement in cancer has been studied widely, its influence on the tumor immune microenvironment remains poorly understood. Therefore, this study aimed to focus on the effect of APOBEC3 on tumor immune microenvironment of KIRC. Methods: In this study, we comprehensively analyzed the expression and prognostic significance of the APOBEC3 family in pan-cancer using multiple databases. The functions of key APOBEC3 family members were further investigated in KIRC, with APOBEC3G determined to be a candidate biomarker for unfavorable prognosis. We subsequently explored the correlation of APOBEC3G with the tumor immune environment in KIRC by analyzing the Cancer Genome Atlas (TCGA) dataset, then validated the prognostic significance and PD-L1 correlation of APOBEC3G by using tissue microarrays which included 233 primary tumor samples from patients with renal clear cell carcinoma. Results: The APOBEC3 family was overexpressed in KIRC and high APOBEC3 expression predicted poor prognosis. In addition, APOBEC3G was positively correlated with the expression of immunoinhibitors such as TIGIT, LAG3, CD96, PD-1, and CTLA4. In addition, APOBEC3G had a positive correlation with immunosuppressive cells, including regulatory T cell and myeloid-derived suppressor cell. Finally, based on 233 clinical samples, we validated that high expression of APOBEC3G contributed to a poor prognosis for KIRC patients and the positive relationship between APOBEC3G and PD-L1 expression. High APOBEC3G expression was also found to be more common in patients with sarcomatoid histology (P = 0.0026). Conclusions: Our study showed that APOBEC3G was a prognostic biomarker correlated with the immune response in KIRC. In addition, APOBE3G had a positive correlation with PD-L1 expression and sarcomatoid histology, perhaps suggesting the potential impact of APOBEC3G on immunotherapy.