[Objective] To establish a new scoring system to predict the perioperative outcomes (operation time, intraoperative blood loss, and trifecta achievement) in patients undergoing robot-assisted partial nephrectomy (RAPN) by integrating the RENAL and Mayo adhesive probability (MAP) scores. [Methods] Clinical data of 178 patients with renal cell carcinoma who underwent RAPN performed by the same surgeon in our hospital during Jan.2015 and Jan.2022 were retrospectively analyzed.The RENAL and MAP scores of all patients were calculated.Linear regression and logistic regression were used to evaluate the associations between the components of the RENAL and MAP scores (a total of 6 variables) and perioperative outcomes.The factors with significant associations were then included into logistic regression analysis to identify independent predictors for constructing an assessment system for perioperative outcomes, and the receiver operating characteristic (ROC) curve was plotted to calculate the area under the curve (AUC) to predict its efficacy. [Results] Multivariate linear regression analysis showed that tumor size (β=6.14, 95%CI: 1.93—10.34, P=0.004), exophytic rate (β=10.60, 95%CI: 3.44—17.76, P=0.004), and perinephric fat thickness (β=16.48, 95%CI: 8.52—24.45, P<0.001) were significantly associated with operation time.Tumor size (β=10.55 95%CI: 5.60—15.49, P<0.001) was associated with both intraoperative blood loss and trifecta achievement (OR=1.73, 95%CI: 1.26—2.36, P=0.001). Multivariate logistic regression analysis of these 3 factors identified tumor size (OR=9.07, 95% CI: 1.18—69.45, P=0.03) and perinephric fat thickness (OR=2.28, 95%CI: 1.86—6.04, P=0.01) as independent predictors of perioperative outcomes.Based on these findings, the tumor size and perinephric fat thickness (RP) scoring was constructed, which demonstrated better predictive ability than RENAL score or MAP score alone (RP vs.RENAL vs.MAP: 0.766 vs.0.548 vs.0.684). [Conclusion] The RP score includes fewer variables than the RENAL and MAP scores but outperforms them.

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.

This study investigated the protective effect of arbutin(Arb) in yam on lipopolysaccharide(LPS)-induced acute lung injury(ALI) in a mouse model and revealed its possible mechanism of action by metabolomics technology, providing a theoretical basis for clinical treatment of ALI. SPF BALB/c mice were randomly divided into normal control group, model group, resveratrol(Rv)-positive control group, Arb low-dose(15 mg·kg~(-1)) group, and Arb high-dose(30 mg·kg~(-1)) group. The LPS-induced ALI model was established in all groups except the normal control group. Hematoxylin-eosin(HE) staining, TUNEL staining, and WBP whole-body non-invasive pulmonary function testing were used to evaluate the degree of lung tissue damage and lung function changes. Enzyme-linked immunosorbent assay(ELISA) was used to detect the level of inflammatory factors in lung tissue. Flow cytometry was used to analyze the M1/M2 polarization status of macrophages in lung tissue. Western blot was used to detect the expression levels of the TLR4 signaling pathway and related apoptotic proteins. Liquid chromatograph-mass spectrometer(LC-MS) metabolomics was used to analyze the changes in serum metabolic profile after Arb intervention. The results showed that Arb pretreatment significantly alleviated LPS-induced lung tissue injury, improved lung function, reduced the levels of pro-inflammatory factors(IL-6, TNF-α, IL-18, and IL-1β), and regulated the polarization status of M1/M2 macrophages. In addition, Arb inhibited the activation of the TLR4 signaling pathway, reduced the expression of pro-apoptotic proteins such as Bax, caspase-3, and caspase-9, up-regulated the level of Bcl-2 protein, and inhibited apoptosis of lung cells. Metabolomic analysis showed that Arb significantly improved LPS-induced metabolic abnormalities, mainly involving key pathways such as galactose metabolism, phenylalanine metabolism, and lipid metabolism. In summary, Arb can significantly reduce LPS-induced ALI by regulating the release of inflammatory factors, inhibiting the activation of the TLR4 signaling pathway, improving metabolic disorders, and regulating macrophage polarization, indicating that Arb has potential clinical application value.
Animals ; Acute Lung Injury/chemically induced* ; Mice ; Mice, Inbred BALB C ; Arbutin/administration & dosage* ; Male ; Toll-Like Receptor 4/immunology* ; Apoptosis/drug effects* ; Lung/metabolism* ; Signal Transduction/drug effects* ; Protective Agents/administration & dosage* ; Humans ; Macrophages/immunology* ; Drugs, Chinese Herbal/administration & dosage*

Alzheimer's disease (AD) is the major form of dementia in the elderly and is closely related to the toxic effects of microglia sustained activation. In AD, sustained microglial activation triggers impaired synaptic pruning, neuroinflammation, neurotoxicity, and cognitive deficits. Accumulating evidence has demonstrated that aberrant expression of deubiquitinating enzymes is associated with regulating microglia function. Here, we use RNA sequencing to identify a deubiquitinase YOD1 as a regulator of microglial function and AD pathology. Further study showed that YOD1 knockout significantly improved the migration, phagocytosis, and inflammatory response of microglia, thereby improving the cognitive impairment of AD model mice. Through LC-MS/MS analysis combined with Co-IP, we found that Myosin heavy chain 9 (MYH9), a key regulator maintaining microglia homeostasis, is an interacting protein of YOD1. Mechanistically, YOD1 binds to MYH9 and maintains its stability by removing the K48 ubiquitin chain from MYH9, thereby mediating the microglia polarization signaling pathway to mediate microglia homeostasis. Taken together, our study reveals a specific role of microglial YOD1 in mediating microglia homeostasis and AD pathology, which provides a potential strategy for targeting microglia to treat AD.

Extracellular vesicles (EVs) are crucial for facilitating intercellular communication, promoting cell migration, and orchestrating the immune response. Recently, EVs can diagnose and treat tumors. EVs can be measured as biomarkers to provide information about the type of disease and therapeutic efficacy. Furthermore, EVs with lower immunogenicity and better biocompatibility are natural carriers of chemicals and gene drugs. Herein, we review the molecular composition, biogenesis, and separation methods of EVs. We also highlight the important role of EVs from different origins as biomarkers and drug delivery systems in tumor therapy. Finally, we provide deep insights into how EVs play a role in reversing the immunosuppressive microenvironment.

Cardiac fibrosis is characterized by an elevated amount of extracellular matrix (ECM) within the heart. However, the persistence of cardiac fibrosis ultimately diminishes contractility and precipitates cardiac dysfunction. Circular RNAs (circRNAs) are emerging as important regulators of cardiac fibrosis. Here, we elucidate the functional role of a specific circular RNA CELF1 in cardiac fibrosis and delineate a novel feedback loop mechanism. Functionally, circ-CELF1 was involved in enhancing fibrosis-related markers' expression and promoting the proliferation of cardiac fibroblasts (CFs), thereby exacerbating cardiac fibrosis. Mechanistically, circ-CELF1 reduced the ubiquitination-degradation rate of BRPF3, leading to an elevation of BRPF3 protein levels. Additionally, BRPF3 acted as a modular scaffold for the recruitment of histone acetyltransferase KAT7 to facilitate the induction of H3K14 acetylation within the promoters of the Celf1 gene. Thus, the transcription of Celf1 was dramatically activated, thereby inhibiting the subsequent response of their downstream target gene Smad7 expression to promote cardiac fibrosis. Moreover, Celf1 further promoted Celf1 pre-mRNA transcription and back-splicing, thereby establishing a feedback loop for circ-CELF1 production. Consequently, a novel feedback loop involving CELF1/circ-CELF1/BRPF3/KAT7 was established, suggesting that circ-CELF1 may serve as a potential novel therapeutic target for cardiac fibrosis.

Objective:To study the epidemiological and genetic characteristics of coxsackievirus B3 (CVB3) circulating in Guangdong Province from 2008 to 2021.Methods:This study collected the specimens of hand, foot, and mouth disease (HFMD) cases from 2008 to 2021 that were positive for other enteroviruses except for enterovirus 71 (EV71), coxsackievirus A16 (CVA16), and CVA6, as well as the specimens of herpangina and neonatal infection cases from 2020 to 2021. Enteroviruses in these specimens were detected and their types were identified. CVB3 strains were isolated and the entire VP1 sequences of CVB3 strains were amplified and sequenced. The genetic features of CVB3 strains were analyzed using DNAStar 7.1 and MEGA 6.06 software packages.Results:Among 3 484 HFMD cases positive for other enteroviruses from 2008 to 2021, CVB3-positive cases accounted for 1.6% (57/3 484); among 560 cases of herpangina from 2020 to 2021, CVB3-positive cases accounted for 2.1% (12/560); one neonatal infection case in 2021 was positive for CVB3. CVB3-positive cases accounted for 67.1% (47/70) in 2021 and 18.6% (13/70) in 2020, while there were less than five cases in other years. Forty-eight CVB3 strains were isolated and the entire VP1 sequences of 26 CVB3 strains were obtained. Phylogenetic analysis indicated that the CVB3 strains could be divided into eight genotypes (A-H) and the strains of genotypes A, D and E were prevalent in the Chinese mainland. The 26 CVB3 strains isolated in Guangdong Province shared 80.2%-100.0% nucleotide homology, and belonged to two genotypes of D and E, with genotype D prevalent from 2008 to 2017 and genotype E prevalent from 2020 to 2021.Conclusions:CVB3 is prevalent sporadically in Guangdong Province from 2008 to 2017, but the epidemic intensity increased during 2020 and 2021. CVB3 strains of genotypes D and E are prevalent in Guangdong Province during 2008 to 2021, with genotype E being the prevalent genotype during 2020 and 2021.

The REDISCOVER international guidelines on the perioperative care of surgical patients with borderline-resectable and locally advanced pancreatic cancer were released in July 2024,and based on the existing clinical challenges,the guidelines provide important recommendations for the diagnosis,staging,and surgical treatment of borderline-resectable and locally advanced pancreatic cancer from an evidence-based perspective.This article gives an interpretation of the guidelines,in order to better guide clinical practice.

Objective:To investigate the post-traumatic stress response in women with unintended pregnancy termination and analyze its related factors.Methods:Totally 388 women with unintended pregnancy termination from two public hospitals in Fujian were selected as the research subjects.They were assessed with the Impact of E-vent Scale-Revised(IES-R),Hospital Anxiety and Depression Scale(HADS),Connor-Davidson Resilience Scale(CD-RISC)and Perinatal Grief Scale(PGS).The total score of IES-R ≥35 was positive for post-traumatic stress disorder screening.Results:The total score of IES-R was(20.6±12.4),and the positive rate of post-traumatic stress disorder screening was 16.5％.Multiple linear regression analysis showed that the IES-R total scores were higher in those who thought pregnancy timing was appropriate than those who thought pregnancy timing was wrong(β=0.13),and lower in women with moderate pain during termination than in those with severe pain(β=-0.09).The IES-R total scores were positively correlated with gestational weeks,depression scores of the HADS and grief scores of the PGS(β=0.18,0.27,0.16),and negatively correlated with optimism scores of the CD-RISC(β=-0.12).Conclusion:The post-traumatic stress response of unintended termination of pregnancy is at a moder-ate level.Post-traumatic stress symptoms are more likely to occur in women who thought pregnancy timing is ap-propriate,and those with severe pain during pregnancy termination,large gestational weeks,high level of depression during pregnancy,and high level of grief after pregnancy termination.