OBJECTIVE To construct a predictive model for evaluating the efficacy of a triple antiemetic regimen （neurokinin- 1 receptor antagonist+5-hydroxytryptamine 3 receptor antagonist+dexamethasone） for preventing nausea and vomiting induced by highly emetogenic chemotherapy （HEC） based on interpretable deep learning algorithms. METHODS Clinical data of cancer patients who received HEC and were treated with the standard triple antiemetic regimen in the oncology department of Tianjin Medical University General Hospital from January 2018 to December 2022 were collected retrospectively. Demographic， clinical and metabolism-related variables were integrated. After data pre-processing， two deep learning algorithms （deep random forest and dense neural network） and four machine learning algorithms （support vector machine， categorical boosting， random forest and decision tree） were used to build predictive models. Subsequently， model performance evaluation and model interpretability analysis were conducted. RESULTS Among the six candidate models， the deep random forest model demonstrated the best predictive performance on the test set， with an area under the receiver operating characteristic curve of 0.850， an accuracy of 0.911， a precision of 0.805， a recall of 0.783， an F1 score of 0.793， and a Brier score of 0.075. Interpretability analysis revealed that creatinine clearance rate （Ccr） was the key predictive factor， and low Ccr levels， female gender， younger age， highly emetogenic drugs （particularly cisplatin-containing chemotherapy regimens）， and anticipatory nausea and vomiting were positively correlated with the risk of HEC-related nausea and vomiting. CONCLUSIONS The deep random forest model exhibits the best performance in predicting the efficacy of triple antiemetic regimen for preventing HEC-related nausea and vomiting. The key predictors in this model primarily include Ccr，anticipatory nausea and vomiting， gender， age， and highly emetogenic drugs.

Stress refers to the non-specific responses of a stimulated body to different stressors and the subsequent maintenance or restoration of internal environmental homeostasis.Adverse stress reactions lead to general balance disruption and may cause digestive,neurological,and endocrine disorders,and decreased immune capacity,which seriously impact host health.As the core compo-nent of intestinal micro-ecology,the intestinal flora can greatly alter its own composition,distribu-tion,function,metabolic product output,and other aspects during stress,which cause disorders and aggravate homeostatic imbalance in internal environments.While the intestinal flora is of great sig-nificance to animal medicine and agricultural production,little is known about stress and its impact on intestinal flora.Therefore,we briefly reviewed the impact of stress on animal intestinal flora in combination with the latest research and provided theoretical insights on intestinal health research.

Objective:To analyze the results of next generation sequencing (NGS) gene testing in liquid-based cytological specimens of lung adenocarcinoma cavity and evaluate the clinical efficacy of epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI) treatment.Methods:Liquid based cytological specimens of 222 cases of lung adenocarcinoma with cavity effusion and 201 cases of metastatic lymph node biopsy were collected. Specimens were obtained from the Cytology Laboratory of the Cancer Hospital of the Chinese Academy of Medical Sciences. The collection period was from January 2018 to December 2022. The results of NGS gene detection were compared. The clinical efficacy of 91 patients treated with EGFR-TKI was evaluated, and the survival curve was analyzed by Kaplan-Meier and other statistical methods.Results:The mutation rates of cancer-related genes detected by NGS were 82.0% (182/222) vs 79.1% (159/201), ( P=0.455) in liquid-based cytological specimens and histological specimens of metastatic lymph node biopsy, respectively. However, the mutation rate of EGFR T790M was significantly higher in cavity effusion than in lymph node biopsy specimens [12.2%(27/222)＞3.5%(7/201), P=0.001]. The results of gene mutation were identical in 10 of the 13 cases with cavity effusion and metastatic lymph node biopsy, and the agreement rate of EGFR was 84.6%(11/13). In 3 inconsistent cases, EGFR mutations were detected in 2 cavity effusion cases that were not detected by lymph node biopsy. Results of genetic analysis of fluid-based cytological samples of 91 patients with cavity effusion were evaluated after drug treatment with EGFR-TKI. The mean progression-free survival (PFS) of the patients was 11.4 months (95% CI: 9.9-12.9). The mean PFS of patients harboring EGFR mutation was 12.3 months (95% CI: 10.8-13.9), and the mean PFS of EGFR wild type was 4.1 months (95% CI: 2.1-6.2). Conclusions:The results of NGS gene detection in liquid-based cytological specimens of lung adenocarcinoma patients with cavity effusion show that the PFS time is similar to that of histological specimens after clinical treatment with EGFR-TKI, which proves the reliability of NGS gene detection results in liquid cytological specimens. NGS gene testing appears higher sensitivity in cavity liquid-based samples than in metastatic lymph node samples.

Protein arginine methyltransferase 5 (PRMT5) acts as an oncogene in liver cancer, yet its roles and in-depth molecular mechanisms within the liver cancer immune microenvironment remain mostly undefined. Here, we demonstrated that disruption of tumor-intrinsic PRMT5 enhances CD8⁺ T-cell-mediated antitumor immunity both in vivo and in vitro. Further experiments verified that this effect is achieved through downregulation of the inhibitory immune checkpoint molecule, fibrinogen-like protein 1 (FGL1). Mechanistically, PRMT5 catalyzed symmetric dimethylation of transcription factor 12 (TCF12) at arginine 554 (R554), prompting the binding of TCF12 to FGL1 promoter region, which transcriptionally activated FGL1 in tumor cells. Methylation deficiency at TCF12-R554 residue downregulated FGL1 expression, which promoted CD8⁺ T-cell-mediated antitumor immunity. Notably, combining the PRMT5 methyltransferase inhibitor GSK591 with PD-L1 blockade efficiently inhibited liver cancer growth and improved overall survival in mice. Collectively, our findings reveal the immunosuppressive role and mechanism of PRMT5 in liver cancer and highlight that targeting PRMT5 could boost checkpoint immunotherapy efficacy.

OBJECTIVE: Exposure to polycyclic aromatic hydrocarbons (PAHs) or metal(loid)s individually has been associated with neural tube defects (NTDs). However, the impacts of PAH and metal(loid) co-exposure and potential interaction effects on NTD risk remain unclear. We conducted a case-control study in China among population with a high prevalence of NTDs to investigate the combined effects of PAH and metal(loid) exposures on the risk of NTD. METHODS: Cases included 80 women who gave birth to offspring with NTDs, whereas controls were 50 women who delivered infants with no congenital malformations. We analyzed the levels of placental PAHs using gas chromatography and mass spectrometry, PAH-DNA adducts with ³²P-post-labeling method, and metal(loid)s with an inductively coupled plasma mass spectrometer. Unconditional logistic regression was employed to estimate the associations between individual exposures and NTDs. Least absolute shrinkage and selection operator (LASSO) penalized regression models were used to select a subset of exposures, while additive interaction models were used to identify interaction effects. RESULTS: In the single-exposure models, we found that eight PAHs, PAH-DNA adducts, and 28 metal(loid)s were associated with NTDs. Pyrene, selenium, molybdenum, cadmium, uranium, and rubidium were selected through LASSO regression and were statistically associated with NTDs in the multiple-exposure models. Women with high levels of pyrene and molybdenum or pyrene and selenium exhibited significantly increased risk of having offspring with NTDs, indicating that these combinations may have synergistic effects on the risk of NTDs. CONCLUSION Our findings suggest that individual PAHs and metal(loid)s, as well as their interactions, may be associated with the risk of NTDs, which warrants further investigation.
Humans ; Neural Tube Defects/chemically induced* ; Polycyclic Aromatic Hydrocarbons/adverse effects* ; Female ; Case-Control Studies ; China/epidemiology* ; Adult ; Pregnancy ; Environmental Pollutants ; Maternal Exposure/adverse effects* ; Metals/toxicity* ; Young Adult ; Risk Factors

Objective:To analyze the research trends and hotspots of bupivacaine liposomes from 1994 to 2023 using bibliometrics.Methods:A comprehensive search was conducted for the literature related to bupivacaine liposomes in the Web of Science Core Collection from 1994 to 2023. The CiteSpace software was used to conduct an in-depth analysis of the included literature data, including publication year, country, institution, author, journal, cited references, keywords, etc.Results:A total of 875 papers related to bupivacaine liposomes were included. The research and development of bupivacaine liposomes were divided into 3 stages: slow development (1994-2011), a sharp rise (2011-2021), and stabilization (2021-2023). The United States was in a leading position in terms of the number of publications, centrality, and author cooperation, and Harvard University had the largest number of publications. de Paula E had the most publications, Bramlett K had the highest number of citations, and Boogaerts J had the highest centrality of publications. Journals such as Anesthesia and Analgesia made significant contributions to this field. The most cited references focused on the infiltration of wounds and the periprosthetic injection of bupivacaine liposomes. The keyword analysis showed that local anesthetics, postoperative pain, etc. were commonly used keywords, and enhanced recovery after surgery was an emerging hotspot. Conclusions:Bupivacaine liposomes show good application prospects in the field of peripheral nerve block due to their unique pharmacological properties and safety characteristics and are expected to prolong the duration of postoperative analgesia. However, there is a difference between the actual effect and the expectation, and more clinical trials are needed to evaluate the curative effect, providing a more solid and reliable theoretical basis and practical guidance for clinical practice.

Objective:To investigate the effectiveness of predictive nursing based on root cause analysis in patients undergoing cesarean section.Methods:A convenience sampling method was used to select 180 women who underwent cesarean section under combined spinal-epidural anesthesia in the Affiliated Hospital of Jining Medical University from September 2021 to October 2022. According to the random number table method, they were divided into a control group ( n=90) and an observation group ( n=90). The control group received routine nursing care, while the observation group received predictive nursing based on root cause analysis. Compared the pain intensity at 24 hours after cesarean section and the incidence of postoperative complications between the two groups of parturients. Results:The Visual Analog Scale scores at 24 hours post-cesarean section and the overall incidence of postoperative complications were lower in the observation group than those in the control group, and the differences were statistically significant ( P<0.05) . Conclusions:Predictive nursing based on root cause analysis can effectively relieve postoperative pain and reduce the incidence of complications in patients undergoing cesarean section with combined spinal-epidural anesthesia.

Objective:To investigate the effectiveness of predictive nursing based on root cause analysis in patients undergoing cesarean section.Methods:A convenience sampling method was used to select 180 women who underwent cesarean section under combined spinal-epidural anesthesia in the Affiliated Hospital of Jining Medical University from September 2021 to October 2022. According to the random number table method, they were divided into a control group ( n=90) and an observation group ( n=90). The control group received routine nursing care, while the observation group received predictive nursing based on root cause analysis. Compared the pain intensity at 24 hours after cesarean section and the incidence of postoperative complications between the two groups of parturients. Results:The Visual Analog Scale scores at 24 hours post-cesarean section and the overall incidence of postoperative complications were lower in the observation group than those in the control group, and the differences were statistically significant ( P<0.05) . Conclusions:Predictive nursing based on root cause analysis can effectively relieve postoperative pain and reduce the incidence of complications in patients undergoing cesarean section with combined spinal-epidural anesthesia.

Objective The relationship between blood pressure variability(BPV)and short-term neurological prognosis in patients with aneurysmal subarachnoid hemorrhage(aSAH)was investigated.Methods The study conducted a retrospective analysis of clinical data from aSAH patients who underwent surgical treatment and were admitted to the ICU at Beijing Tiantan Hospital,Capital Medical University,from January 2023 to April 2024.BPV was quantitively assessed by calculating the standard deviation(SD),successive variation(SV),coefficient of variation(CV),and range of mean blood pressure(MBP).Patients were divided into two group based on discharge GOS scores:good prognosis[Glasgow Outcome Scale(GOS)4-5]and poor prognosis(GOS 1-3)groups.Comparative analyses were performed to evaluate differences in BPV metrics between the two groups,followed by multivariable logistic regression modeling to adjust for potential confounding factors and elucidate the association between BPV and clinical prognosis.Results A total of 150 patients were included,with 59 in the poor prognosis group and 91 in the good prognosis group.The poor prognosis group exhibited significantly elevated levels of MBP-SD[(9.85±3.20)mmHg vs.(8.04±2.31)mmHg,P<0.001],MBP-SV[(10.37±3.85)mmHg vs.(8.07±2.33)mmHg,P<0.001],MBP-CV(10.00±3.30%vs.8.19±2.33%,P<0.001),and MBP-range[(39.60±13.56)mmHg vs.(32.44±9.78)mmHg,P<0.05]compared to the good prognosis group.Cohen’s d values indicated moderate effect sizes for BPV differences(0.65,0.72,0.63,and 0.61,respectively).Multivariable logistic regression showed that MBP-SD(OR=1.22,95%CI:1.08-1.39,P=0.002)and MBP-SV(OR=1.19,95%CI:1.05-1.35,P=0.007)were independently associated with poor prognosis.Conclusion Elevated MBP-SD and MBP-SV within the first 24 hours postoperative period are independent predictors of unfavorable short-term neurological outcomes in aSAH patients.

Objective: To analyze the temporal variation patterns of sickness absenteeism among primary and secondary school students in Shanghai, so as to explore models suitable for predicting peaks and intensity of absenteeism rates. Methods: The seasonal and trend decomposition using loess (STL) method was used to analyze the seasonal and long term trend changes in sickness absenteeism among primary and secondary school students from September 1 in 2010 to June 30 in 2018, in Shanghai. A hierarchical clustering method based on Dynamic Time Warping (DTW) was employed to classify absenteeism symptoms with similar temporal patterns. Based on historical data, the study constructed and evaluated different time series algorithms and machine learning models to optimize the accuracy of predicting the trend of sickness absenteeism. Results: During the research period, the average new absenteeism rate due to illness was 16.86 per 10 000 person day for every academic year, and the trend of sickness absenteeism exhibited both seasonality and a long term upward trend, reaching its highest point in the 2017 academic year (22.47 per 10 000 person day). The symptoms of absenteeism were divided into three categories: high incidence in winter and spring (respiratory symptoms, fever and general discomfort, etc.), high incidence in summer (eye symptoms, nosebleeds, etc.) and those without obvious seasonality (skin symptoms, accidental injuries, etc.).The constructed time series models effectively predicted the trend of absenteeism due to illness, although the accuracy of predicting peak intensity was relatively low. Among them, the multi layer perceptron (MLP) model performed the best, with an root mean squared error (RMSE) of 8.96 and an mean absolute error (MAE) of 4.37, reducing 36.51% and 39.02% compared to the baseline model. Conclusion Time series models and machine learning algorithms could effectively predict the trend of sickness absenteeism, and corresponding prevention and control measures can be taken for absenteeism caused by different symptoms during peak periods.