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.

Background Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders in children and adolescents. Current research suggests that environmental factors, such as per- and polyfluoroalkyl substances (PFAS), may be associated with an increased risk of ADHD in offspring. However, most epidemiological evidence originates from non-Chinese populations, with limited research investigating the association between prenatal PFAS and ADHD-like symptoms in Chinese school-aged children. Objective To examine the association between prenatal PFAS exposure and ADHD-like symptoms in 7-year-old children. Methods Based on the Shanghai Birth Cohort, this study included 488 mother-child pairs. PFAS were measured in maternal serum during the second trimester using ultra-performance liquid chromatography-tandem mass spectrometry. The analysis focused on eight specific compounds from three PFAS categories [perfluorooctanoic acid (PFOA), perfluorooctanesulfonic acid (PFOS), and perfluorohexanesulfonic acid (PFHxS)]. The hyperactivity subscale of the Strengths and Difficulties Questionnaire (SDQ) was used to assess ADHD-like symptoms in children at age 7. Basic demographic information was collected via questionnaires and medical records. Negative binomial regression models were used to evaluate the relationship between individual exposures to the three typical PFAS categories (PFOA, PFOS, and PFHxS) and children's ADHD-like symptoms, while the bayesian kernel machine regression (BKMR) mixture model was employed to assess the overall effect of PFAS mixture exposure on ADHD-like symptoms. Results The detection rates of the eight target PFAS in maternal second-trimester serum were all above 85%, with PFOA exhibiting the highest median concentration (10.21 ng·mL⁻¹). Overall, 14.75% of the children exhibited ADHD-like symptoms. Results from the negative binomial regression models showed that second-trimester exposures to PFOA (IRR=1.15, 95%CI: 1.05, 1.26), n-PFOS (IRR=1.08, 95%CI: 1.01, 1.15), 6m-PFOS (IRR=1.13, 95%CI: 1.05, 1.21), and 1m-PFOS (IRR=1.11, 95%CI: 1.03, 1.18) were associated with an increased risk of ADHD-like symptoms in 7-year-old children. Stratified analyses suggested potentially statistically significant associations in girls [e.g., for PFOA, boys vs. girls: 1.09 (0.96, 1.23) vs. 1.25 (1.09, 1.44), P-int=0.31]. The BKMR mixture model indicated that the risk of ADHD-like symptoms trended upward with increasing PFAS mixture concentrations, although this association was statistically significant only among girls (P < 0.05). Conclusion Prenatal PFAS exposure may be associated with an increased risk of ADHD-like symptoms in 7-year-old children, and this adverse association appears to be more prominent in girls.

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:To review the diagnosis, treatment and quality of life of patients with primary immune thrombocytopenia (ITP) in seven medical centers in some areas of Hubei Province.Methods:A retrospective analysis was conducted on age, disease course, symptoms, diagnosis, and treatment status (including testing items, drug selection, and adverse reactions) of patients with ITP in seven medical centers in Hubei Province from January 2020 to December 2022. An online survey was conducted on the quality of life of patients using the ITP Patient Assessment Questionnaire (ITP-PAQ) .Results:Among the 1033 patients, those with newly diagnosed, persistent, and chronic ITP accounted for 39.8%, 19.1%, and 41.1%, respectively. Most patients exhibit varying degrees of bleeding. Regarding treatment, corticosteroids and thrombopoietin drugs are the most commonly chosen treatment drugs for ITP, and the adverse reactions to treatment mainly include diarrhea, liver dysfunction, and thrombosis. The ITP-PAQ survey of 125 patients revealed that ITP significantly impairs their life quality. Patients with ITP scored significantly lower in fatigue, sleep, fear, exercise, work, and social aspects.Conclusion:A relatively high proportion of patients with ITP progressed to the chronic phase. Corticosteroids and thrombopoietin drugs are the two main treatment drugs for ITP patients. The quality of life of patients with ITP is significantly reduced in multiple dimensions.

Objective:To analyze the current research hotspots and trends of traditional Chinese medicine (TCM) treatment for neurogenic bladder (NB).Methods:The Chinese and English articles on TCM treatment of neurogenic bladder were searched in CNKI, Wanfang Database, PubMed, and Web of Science from the inception to May 31, 2024, using the terms "neurogenic bladder" "intervention" "treatment" "clinical" "Chinese medicine" "electroacupuncture" "acupuncture", and "moxibustion". VOSviewer and Citespace bibliometric software were used to analyze the publication trend, authors, research institutions, source journals and keywords of these articles.Results:A total of 776 Chinese articles and 253 English articles on the diagnosis and treatment of NB by traditional Chinese medicine were retrieved, the number of publications was increasing every year. Most Chinese papers came from Shandong University of Traditional Clinese Medicine, and most English papers came from Sun Yat-sen University. Some authors and institutions had formed networks of cooperation. Most papers were published in the journal of Traditional Chineses Medicine Clinical Research (in Chinese) and Neural Regeneration Research (in English). This study generated 244 Chinese core key words with 14 clustering networks, and 233 English core key words with 10 clustering networks. The main symptoms of NB are uroschesis and urinary incontinence. NB are primarily caused by spinal cord injury, diabetes mellitus and stroke. The main treatment methods of TCM for NB are electroacupuncture, acupuncture and percutaneous acupoint electrical stimulation. The research on NB mechanisms focuses on the apoptosis, regeneration and plasticity of spinal neurons, the activation of the bladder autophagy signaling pathway, the expression of proteins related to the contractile function of the forced muscles. Conclusion:The research quantity and quality of traditional Chinese medicine in diagnosis and treatment NB have increased in recent years, and the mechanism and treatment of NB are the research hotspots; however, the extension and depth of researches are limited, and the institutional cooperations are insufficiente.

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.

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 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.

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 alterations in the topological properties of gray matter and white matter dynamic and static functional brain networks in patients with major depressive disorder (MDD) and bipolar depression (BDD) using graph theory analysis, and to evaluate the potential of their combination as biomarkers for differential diagnosis between unipolar and bipolar depression.Methods:From March 2021 to April 2024, inpatients were recruited from the Department of Psychosomatic Medicine, Zhongda Hospital, Southeast University, including 132 patients with MDD, 84 patients with BDD, and 91 healthy controls (HCs). Resting-state structural and functional MRI data were collected, and dynamic and static functional brain networks of gray matter and white matter were constructed. Graph theory analysis was applied to calculate global and nodal network properties, differences in topological attributes among the three groups were compared by One-way analysis of covariance, and Turkey′s post hoc test was used for further pairwise comparison. The network topology attribute indicators with statistically significant inter-group differences were selected using the Least Absolute Shrinkage and Selection Operator regression (LASSO) for feature classification. The diagnostic performance of combined gray and white matter network features for distinguishing MDD from BDD was assessed using receiver operating characteristic (ROC) curves and a random forest model.Results:In the analysis of the static gray matter functional network, both MDD and BDD patients showed abnormal local topological properties. Compared with HCs, the MDD group exhibited abnormal betweenness centrality (BC) in the left inferior frontal gyrus, left precuneus, left ventromedial occipital cortex, right ventromedial occipital cortex, and right anterior thalamus ( t=-3.95-3.62, all P<0.05). The degree centrality (DC) of the left and right anterior thalamus was also abnormal in the MDD group ( t=3.78,4.14, both P<0.001), as was the nodal efficiency (Ne) of the left precuneus and bilateral anterior thalamus ( t=2.37, 3.61, 3.82, all P<0.05). Compared with HCs, the BDD group showed abnormalities in DC and Ne of the left precuneus ( t=-2.76, P=0.014; t=-3.01, P=0.007). In the analysis of the dynamic white matter functional network, both MDD and BDD patients demonstrated abnormal temporal variability of local topological properties. Compared with HCs, the MDD and BDD groups showed reduced BC temporal variability in the left superior corona radiata ( t=-2.39, P=0.047; t=-4.28, P<0.001), and there were significant differences in DC temporal variability in the right posterior limb of the internal capsule and lentiform nucleus ( t=2.65, P=0.021; t=3.49, P=0.001) in MDD group compared with HCs and BBD. The differential diagnosis model combining gray and white matter dynamic and static network topological features achieved an area under the ROC curve of 0.80. Conclusion:Both MDD and BDD exhibit altered topological properties in static gray matter functional networks and dynamic white matter functional networks. The combination of these features may aid in the differential diagnosis of MDD and BDD.