Machine Learning Model for Anesthetic Risk Stratification for Gynecologic and Obstetric Patients: Cross-Sectional Study Outlining a Novel Approach for Early Detection.

BACKGROUND: Preoperative evaluation is important, and this study explored the application of machine learning methods for anesthetic risk classification and the evaluation of the contributions of various factors. To minimize the effects of confounding variables during model training, we used a homogenous group with similar physiological states and ages undergoing similar pelvic organ-related procedures not involving malignancies. OBJECTIVE: Data on women of reproductive age (age 20-50 years) who underwent gestational or gynecological surgery between January 1, 2017, and December 31, 2021, were obtained from the National Taiwan University Hospital Integrated Medical Database. METHODS: We first performed an exploratory analysis and selected key features. We then performed data preprocessing to acquire relevant features related to preoperative examination. To further enhance predictive performance, we used the log-likelihood ratio algorithm to generate comorbidity patterns. Finally, we input the processed features into the light gradient boosting machine (LightGBM) model for training and subsequent prediction. RESULTS: A total of 10,892 patients were included. Within this data set, 9893 patients were classified as having low anesthetic risk (American Society of Anesthesiologists physical status score of 1-2), and 999 patients were classified as having high anesthetic risk (American Society of Anesthesiologists physical status score of >2). The area under the receiver operating characteristic curve of the proposed model was 0.6831. CONCLUSIONS: By combining comorbidity information and clinical laboratory data, our methodology based on the LightGBM model provides more accurate predictions for anesthetic risk classification. TRIAL REGISTRATION: Research Ethics Committee of the National Taiwan University Hospital 202204010RINB; https://www.ntuh.gov.tw/RECO/Index.action.

Uterine mesothelial cysts mimicking ovarian cysts in a primipara patient with a history of Cesarean section: A case report and review of the literature.

OBJECTIVE: We describe a rare case of uterine mesothelial cysts mimicking ovarian cysts in a primipara patient with a history of Cesarean section. CASE REPORT: A 39-year-old female patient with history of Cesarean section presented with dysmenorrhea. Sonography revealed that a hypoechoic and anechoic multicystic complex, which was located on the right side of the pelvic cavity, had infiltrated the adjacent posterior wall of the uterus, and it was preoperatively misdiagnosed as ovarian cysts with suspected endometrioma. Laparoscopic surgery revealed multiple cystic lesions filled with clear yellow fluid on the posterior uterine wall instead of the adnexa. Laparoscopic uterine cystectomy was performed, and the patient's recovery was uneventful. Pathohistological and immunohistochemical examinations confirmed the diagnosis of uterine mesothelial cysts. CONCLUSION: Uterine mesothelial cysts should be considered in the differential diagnosis of pelvic lesions. Increasing the awareness of this rare disease can contribute to improved evaluation, decision-making, and disease management.

Association of TRAIL receptor with phosphatase SHP-1 enables repressing T cell receptor signaling and T cell activation through inactivating Lck.

BACKGROUND: T cell receptor (TCR) signaling and T cell activation are tightly regulated by gatekeepers to maintain immune tolerance and avoid autoimmunity. The TRAIL receptor (TRAIL-R) is a TNF-family death receptor that transduces apoptotic signals to induce cell death. Recent studies have indicated that TRAIL-R regulates T cell-mediated immune responses by directly inhibiting T cell activation without inducing apoptosis; however, the distinct signaling pathway that regulates T cell activation remains unclear. In this study, we screened for intracellular TRAIL-R-binding proteins within T cells to explore the novel signaling pathway transduced by TRAIL-R that directly inhibits T cell activation. METHODS: Whole-transcriptome RNA sequencing was used to identify gene expression signatures associated with TRAIL-R signaling during T cell activation. High-throughput screening with mass spectrometry was used to identify the novel TRAIL-R binding proteins within T cells. Co-immunoprecipitation, lipid raft isolation, and confocal microscopic analyses were conducted to verify the association between TRAIL-R and the identified binding proteins within T cells. RESULTS: TRAIL engagement downregulated gene signatures in TCR signaling pathways and profoundly suppressed phosphorylation of TCR proximal tyrosine kinases without inducing cell death. The tyrosine phosphatase SHP-1 was identified as the major TRAIL-R binding protein within T cells, using high throughput mass spectrometry-based proteomics analysis. Furthermore, Lck was co-immunoprecipitated with the TRAIL-R/SHP-1 complex in the activated T cells. TRAIL engagement profoundly inhibited phosphorylation of Lck (Y394) and suppressed the recruitment of Lck into lipid rafts in the activated T cells, leading to the interruption of proximal TCR signaling and subsequent T cell activation. CONCLUSIONS: TRAIL-R associates with phosphatase SHP-1 and transduces a unique and distinct immune gatekeeper signal to repress TCR signaling and T cell activation via inactivating Lck. Thus, our results define TRAIL-R as a new class of immune checkpoint receptors for restraining T cell activation, and TRAIL-R/SHP-1 axis can serve as a potential therapeutic target for immune-mediated diseases.

Loss of LECT2 promotes ovarian cancer progression by inducing cancer invasiveness and facilitating an immunosuppressive environment.

Leukocyte cell-derived chemotaxin 2 (LECT2) is a multifunctional cytokine that can bind to several receptors and mediate distinct molecular pathways in various cell settings. Changing levels of LECT2 have been implicated in multiple human disease states, including cancers. Here, we have demonstrated reduced serum levels of LECT2 in patients with epithelial ovarian cancer (EOC) and down-regulated circulating Lect2 as the disease progresses in a syngeneic mouse ID8 EOC model. Using the murine EOC model, we discovered that loss of Lect2 promotes EOC progression by modulating both tumor cells and the tumor microenvironment. Lect2 inhibited EOC cells' invasive phenotype and suppressed EOC's transcoelomic metastasis by targeting c-Met signaling. In addition, Lect2 downregulation induced the accumulation and activation of myeloid-derived suppressor cells (MDSCs). This fostered an immunosuppressive microenvironment in EOC by inhibiting T-cell activation and skewing macrophages toward an M2 phenotype. The therapeutic efficacy of programmed cell death-1 (PD-1)/PD-L1 pathway blockade for the ID8 model was significantly hindered. Overall, our data highlight multiple functions of Lect2 during EOC progression and reveal a rationale for synergistic immunotherapeutic strategies by targeting Lect2.