This study aims to develop multiple machine learning models for predicting hypothermia risk in laparoscopic surgery and to perform interpretability analysis of the best-performing model. Our goal is to provide robust decision support for clinicians and ensure safe and effective patient care during surgical procedures. This study included 1,030 patients who underwent laparoscopic surgery at Shandong Provincial Cancer Hospital, affiliated with Shandong First Medical University, between July 2023 and June 2024. We employed the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm for feature selection. We explored the performance of five machine learning algorithms—logistic regression (LR), decision tree (DT), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost)—to predict hypothermia risk during laparoscopic surgery. Finally, we conducted an interpretability analysis of the top-performing model using Shapley Additive Explanations (SHAP). LASSO identified nine risk factors: BMI, ASA classification, total volume of intravenous fluids, irrigating fluids during the operation, volume of CO2, blood loss, ambient temperature, long-term alcohol consumption, and type of surgery. Performance comparison among the five models revealed that the XGBoost model performed the best, with an accuracy of 0.762 (95% CI: 0.717–0.807) and an area under the curve (AUC) of 0.835 (95% CI: 0.794–0.872). The model achieved a specificity of 0.749 (95% CI: 0.683–0.816) and a sensitivity of 0.773 (95% CI: 0.713–0.824). The F1 score was 0.778 (95% CI: 0.735–0.828). SHAP analysis revealed that the four most influential factors for hypothermia risk during laparoscopic surgery were operating room temperature, rinsing fluids during the operation, volume of CO2, and blood loss. This study developed an efficient and interpretable predictive model for the risk of hypothermia in patients undergoing laparoscopic surgery. This model supports clinical decision-making and contributes to the overall goal of providing personalized care in the surgical environment.

Objective: Whether women with breast cancer experience more severe menopause symptoms than comparison women without a history of breast cancer diagnosis remains unclear. We aimed to investigate whether women with breast cancer undergoing chemotherapy experience more severe menopause symptoms than comparison women and explore various factors influencing menopause symptoms in women with breast cancer undergoing chemotherapy. Methods: This cross-sectional observational study recruited 423 women with breast cancer undergoing chemotherapy and 1,829 community women without breast cancer. All participants completed a questionnaire assessing menopause symptoms using the Menopause Rating Scale and general characteristics (eg, sociodemographic and clinical data). Propensity score matching was used to reduce the confounders between the two groups. Student's t test or Mann-Whitney U test and chi-square tests were used to compare the differences in menopause symptoms between the two groups. Multivariate linear regression analysis was performed to explore various factors influencing menopause symptoms in women with breast cancer undergoing chemotherapy. Results: After propensity score matching, 808 participants were included. The mean ages of women with breast cancer undergoing chemotherapy and comparison women were 49.58 and 49.10 years, respectively. Women with breast cancer undergoing chemotherapy experienced significantly more severe vasomotor symptoms than comparison women. However, comparison women had higher Menopause Rating Scale scores and more severe menopause symptoms than women with breast cancer undergoing chemotherapy. Age, occupational status, chemotherapy-induced amenorrhea, family history of cancer, chemotherapy stage, mindfulness, resiliency, and illness perception were associated with menopause symptoms in women with breast cancer undergoing chemotherapy. Conclusions: Vasomotor symptoms are prominent among women with breast cancer undergoing chemotherapy. Understanding the factors contributing to menopause symptoms is crucial for healthcare practitioners to develop supportive guidelines for the well-being of women with breast cancer undergoing chemotherapy.

Background: Interventional embolization is a common treatment for hemoptysis, one of the complications of lung cancer. However, there are no official guidelines for the use of this method in antitumor therapy.Case Description: Herein, we describe a case of a patient who was pathologically diagnosed as central squamous cell lung cancer. The patient received chemotherapy, interventional embolization and radiotherapy successively. The tumor regressed rapidly within 48 hours of receipt of interventional embolization. Furthermore, the tumor decreased by more than 50% in size within 7 days during radiotherapy. Unfortunately, the patient has since developed lymph node metastases and remains under treatment.Conclusions: Thus, finding the suitable blood vessel embolized may be a suitable option to reduce the local tumor load and can be considered as antitumor therapy in combination with other treatments. The patient’s theoretical hypoxia state after interventional therapy still produced a good tumor regression after radiotherapy. However, so far, no related studies have reported the changes of tumor immune microenvironment in human body after intervention and radiotherapy.

Traditional risk assessment tools were commonly used for the prediction of falls in clinical practice. However, their tendency toward over-sensitivity addressed its limitations for hospitalized cancer patients. This study aimed to identify risk factors associated with falls in hospitalized patients and to develop a predictive risk model using machine learning algorithms specifically for cancer patients, to optimize the clinical assessment of fall risk. Using data from 298 cancer patients admitted to Shandong Cancer Hospital between January 2023 and December 2024 were included. We described the patient status through three aspects: clinical indicators, laboratory indicators and subjective evaluation indicators. The model was evaluated for performance using five-fold cross-validation. Least absolute shrinkage and selection operator (LASSO) regression was used for initiatory variable selection. We constructed predictive models using one baseline method and three machine learning methods, incorporating key clinical features, and selected the optimal model based on a review of categorical performance metrics. Additionally, Shapley Additive Explanations (SHAP) was used to interpret the predictive models and rank the importance of risk factors. Among 298 cancer patients assessed by the Morse Fall Scale, only 115 (39%) experienced falls during hospitalization. Based on LASSO regression and statistical analysis, 20 key predictors were identified from 30 candidate features. The Support Vector Machine model outperformed the other algorithms (Naive Bayes, Multivariate Logistic Regression, and Random Forest), achieving the best performance in both the low-risk and high-risk populations evaluated by the scale, with a composite F1-score of 0.87. SHAP interpretability analysis revealed that medication use, diagnostic findings, and treatment approaches were major risk factors, while the number of comorbidities further increased susceptibility to falls. The model’s precision in stratifying dynamic risks, particularly for high-risk subgroups (e.g., patients with brain metastases or neurotoxic regimens), highlights its clinical utility. This study establishes a clinically interpretable, machine learning-driven fall risk prediction model that dynamically integrates cancer-specific variables. Implementation of this model could enhance risk stratification accuracy, guide personalized intervention strategies, and optimize resource allocation in clinical oncology care settings. Not applicable.

Objective. To investigate the influence of long-term night shift nurses on the composition. Methods. The feces of 30 night shift nurses (test group) and 30 day shift nurses (control group) over 1 year were collected, and double-end sequencing based on bacterial 16S rDNA V3 + V4 and fungal 18S rDNA V3 + V4 was used to determine the differences by OTU clustering, diversity, flora abundance, and differential analysis. The results of 60 samples were sequenced for a total of 3, 052. There were 418 high-quality sequences, each sample produced an average of 50, 874 high-quality sequences. The OTU cluster analysis revealed that the number of OTUs was 365. The number of day-shift shift OTUs was 362. There was no significant difference ( ). There was no significant statistical difference in the Chao, Ace, Shan’non, and Simpson index between the 2 groups ( ). Differential analysis of gut microflora between 2 groups showed that at the phylum level, the relative abundance of firmicutes in long-term night shift nurses was higher than in day shift nurses. However, the relative abundance of bacteroidetes was lower than that of white shift nurses (all ). At the genus level, long-term night shift nurses’ Bacteroides, Faecacterium, and Bifidobacterial were lower than that of white shift nurses ( ). However, the relative abundance of Prevotella and Megomonas was higher than that of white shift nurses (all ). Conclusion. There are differences between night shift nurses, which lead to a decrease in intestinal probiotic flora and increased conditional pathogenic flora. Nursing managers should improve the intestinal flora change of night shift nurses through reasonable scheduling and dietary regulation.