OBJECTIVE: To observe the efficacy of "Biaoben acupoint compatibility" moxibustion for abdominal obesity and its effect on blood lipid, lipid accumulation product (LAP) and cardiometabolic index (CMI). METHODS: A total of 150 patients with abdominal obesity were randomly divided into an observation group (75 cases, 5 cases dropped out) and a control group (75 cases, 6 cases dropped out). The control group received lifestyle guidance. The observation group received "Biaoben acupoint compatibility" moxibustion at Zhongwan (CV12), Guanyuan (CV4) and bilateral Tianshu (ST25), Zusanli (ST36) on the basis of the control group, 20 min each time, once every other day, 3 times a week for 8 weeks. Before and after treatment, the waist circumference, hip circumference, weight, body mass index (BMI) were observed, the levels of total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) were measured, and the LAP and CMI were calculated in the two groups. RESULTS: After treatment, the waist circumference, weight and BMI were decreased compared with those before treatment in both groups (P<0.05), the changes of the above indexes in the observation group were larger than those in the control group (P<0.05). After treatment, the hip circumference, TC level, TG level, LAP and CMI in the observation group were decreased compared with those before treatment (P<0.05), the HDL-C level was increased compared with that before treatment (P<0.05)；the changes of the TC level, TG level, LAP, CMI and HDL-C level in the observation group were larger than those in the control group (P<0.05). CONCLUSION "Biaoben acupoint compatibility" moxibustion can reduce the degree of obesity in patients with abdominal obesity, and improve blood lipid and reduce lipid accumulation.
Humans ; Acupuncture Points ; Moxibustion ; Male ; Female ; Middle Aged ; Obesity, Abdominal/blood* ; Adult ; Lipids/blood* ; Lipid Metabolism ; Triglycerides/blood* ; Young Adult ; Treatment Outcome ; Aged

Objective To explore the nephrotoxic effects of exposure to perfluorobutanoic acid (PFBA) and its mechanism in mice, with a particular focus on analyzing the changes in kidney metabolism and their potential implications. Methods The specific pathogen free C57BL/6 mice were randomly divided into control group, low-dose group, and high-dose group, with 10 mice in each group. Mice in the three groups received intragastric administration of PFBA solution at doses of 0, 35 and 350 mg/kg body weight, once per day for seven consecutive days. The histopathological changes of kidneys of mice in these three groups were evaluated. Metabolomic profiling of mouse kidneys was performed using ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. Differentially accumulated metabolites (DAMs) were identified based on the Human Metabolome Database, and related metabolic pathways were analyzed through MetaboAnalyst 6.0 and Kyoto Encyclopedia of Genes and Genomes (KEGG). Results Histopathological analysis of kidneys showed that the renal pelvis mucosa of mice in the low-dose group presented focal mild inflammatory changes without marked structural damage, whereas mice in the high-dose group showed severe inflammation and partial destruction of renal structure. The kidney coefficient of mice in both low-dose group and the high-dose group decreased (both P<0.05), and the Paller scores of renal tissues increased (both P<0.05) compared with that in the control group. The Paller score of mouse renal tissue in the high-dose group was higher than that in the low-dose group (P<0.05). Metabolomic profiling identified 46 DAMs (26 upregulated, 20 downregulated) in the low-dose group and 104 DAMs (54 upregulated, 50 downregulated) in the high-dose group, with 26 shared DAMs between the two dose groups. KEGG pathway analysis revealed that DAMs were mainly involved in metabolic pathways such as glycerophospholipid metabolism, glycerolipid metabolism, sphingolipid and steroid hormone synthesis. Conclusion Acute exposure to PFBA can cause kidney injury in mice. Lipid metabolism pathways such as glycerophospholipid and sphingolipid metabolism is involved in the development of acute renal toxicity of PFBA.

Objective:Postoperative neurosurgical bacterial meningitis (PNBM) has been frequently reported, but fewer studies have focused on the contemporaneous comparison of clinical features of PNBM caused by different pathogenic bacteria. This study aimed to simultaneously investigate the clinical characteristics and outcomes of PNBM by Gram-positive bacterial(GPB) or Gram-negative bacterial (GNB) infection.Methods:Inpatients with PNBM at our institution were recruited between February 2013 and October 2023. These PNBM patients were categorized into two groups: GPB infection and GNB infection. Data from electronic medical records were collected and analyzed.Results:A total of 401 patients with PNBM were finally included, with 78 (19.5%) having GPB infections and 323 (80.5%)having GNB infection. The average age of the patients was 56 years, and 55.1% were male. Compared to the GPB group, PNBM patients with GNB infection had significantly higher SOFA and APACHE Ⅱ scores, higher proportions of hyperthermia (body temperature>39°C) and altered consciousness, increased ratios of postoperative cerebral hemorrhage or intracranial aneurysm, as well as greater needs for ICU treatment and mechanical ventilation (all P <0.05). The proportions of inflammatory indicators such as blood CRP and PCT≥2 ng/mL, alanine aminotransferase (ALT), aspartate aminotransferase (AST), and total bilirubin (TBIL) were significantly higher in the GNB group (all P<0.05). In contrast, The concentrations of hemoglobin and albumin were substantially lower in this group(both P <0.05). Additionally, the cerebrospinal fluid in the GNB group showed significantly higher nucleated cell counts, protein concentration, and adenosine deaminase concentration, and but lower glucose level (all P <0.001). A total of 426 bacterial strains were isolated, with 343 strains (80.5%) being GNB and 83 strains (19.5%) being GPB. Among these, 25 (6.2%) patients had 2 or more gram-positive or gram-negative bacterial infections. The proportions of multidrug-resistant (MDR) bacteria and intrathecal treatment were higher in the GNB group (80.5% vs. 68.7%, 36.5% vs. 2.6%, respectively), while the ratio of correct empirical antibiotic treatment was significantly lower (30.3% vs. 80.0%) (all P <0.05). In terms of outcomes, the length of stay in the ICU was significantly longer in the GNB group [(median (interquartile range, IQR): 11.5 (5.25,22.75) vs. 17.0 (9.0,30.0), P <0.01)], and the rate of septic shock (9.3% vs. 2.6%), poor prognosis (GCS≤8 at discharge) (65.9% vs. 32.1%), and 28-day hospital mortality rate (34.4% vs. 10.3%) were significantly higher compared to the GPB group (all P <0.05). However, there were no differences in 7-day hospital mortality and total hospitalization time. Conclusions:Gram-negative bacterial infections are more prevalent than Gram-positive bacterial infections in PNBM, and they are also associated with more severe symptoms, abnormal cerebrospinal fluid findings, higher severity, and more treatment difficulty. Despite comparable short-term (7-day) mortality rates between Gram-positive and Gram-negative bacterial infections, Gram-negative bacterial infections result in higher medium- to long-term (14-day and 28-day) case-fatality rates among patients with post-neurosurgical bacterial meningitis and are associated with overall poorer prognosis, warranting greater attention from clinicians.

Perfluorobutyric acid (PFBA) is a representative short-chain compound of per- and polyfluoroalkyl substances (PFAS), which is widely used in fluorochemical manufacturing, food packaging, and outdoor textile processing industries. PFBA primarily enters into the human body via oral intake, inhalation, and dermal exposure and can be efficiently metabolized. PFBA exhibits cytotoxicity by disrupting cell proliferation, inducing oxidative stress, and disturbing lipid metabolism, thereby impairing cellular homeostasis. In addition, PFBA can induce abnormal activation of peroxisome proliferator-activated receptor α-dependent and/or independent pathways, leading to lipid metabolism disorders and subsequent liver injury. Animal studies have demonstrated that PFBA exposure alters renal biochemical parameters and induces epidermal inflammation, abnormal keratinization, and even necrosis, suggesting potential nephrotoxicity and dermal toxicity. PFBA is capable of crossing the placental barrier, and PFBA levels in umbilical cord blood have been negatively correlated with insulin and insulin-like growth factor 1. Moreover, plasma PFBA levels in patients infected with coronavirus disease 2019 have been associated with infection severity, indicating potential reproductive, developmental, and immunotoxic effects. At present, systematic occupational and environmental exposure monitoring data for PFBA remain limited, the toxic mechanisms in certain target organs have not been fully elucidated, and the molecular regulatory networks underlying reproductive and immune toxicity remain unclear. Future research should focus on improving PFBA monitoring strategies, strengthening studies on PFBA occupational exposure detection methods, toxic effects and mechanisms, and refining occupational risk assessment systems, to provide a scientific basis for establishing occupational exposure limits, optimizing risk management strategies, and safeguarding public health.

Objective:To investigate the expression of serum miR-326 and miR-501 in patients with endometrial cancer (EC) and their correlation with postoperative recurrence.Methods:From Sep. 2020 to Sep. 2023, 108 patients diagnosed with EC who received initial treatment in Heping Hospital Affiliated to Changzhi Medical College were as the study group, and another 108 patients with benign endometrial hyperplasia were as the control group. All EC patients were followed up for 1 year after surgery, and were assigned into a recurrence group of 15 cases and a non recurrence group of 93 cases based on the follow-up results. Real-time fluorescence quantitative polymerase chain reaction (qRT-PCR) was applied to determine the expression levels of serum miR-326 and miR-501. Logistic regression was applied to analyze the factors affecting postoperative recurrence in EC patients. The predictive efficacy of serum miR-326 and miR-501 for postoperative recurrence in EC patients was analyzed by establishing receiver operating characteristic (ROC) curves.Results:Compared with the control group, the expression level of serum miR-326 in the study group was prominently lower ( P<0.05), while the expression level of miR-501 was obviously higher ( P<0.05). Compared with the non recurrence group, the expression level of serum miR-326 in the recurrence group was obviously lower ( P<0.05), while the expression level of miR-501 was obviously higher ( P<0.05). The combination of serum miR-326 and miR-501 had the highest AUC in predicting postoperative recurrence in EC patients, which was better than their individual predictions ( Zcombination - miR-326=2.456, P=0.014, Zcombination - miR-501=2.282, P=0.023). Logistic regression analysis revealed that serum miR-326 was a protective factor affecting postoperative recurrence in EC patients ( P<0.05), while miR-501 was a risk factor affecting postoperative recurrence in EC patients ( P<0.05) . Conclusions:The expression level of miR-326 in the serum of EC patients is prominently reduced, while the expression level of miR-501 is obviously increased. The combination of the two can be better used to predict postoperative recurrence in patients.

This paper aims to discuss the application value and progress of artificial intelligence (AI) in the diagnosis and treatment of gastrointestinal tumors and teaching practice of gastrointestinal oncology. Through a comprehensive analysis of the current clinical research status and literature, this paper summarizes the application practice and exploratory thinking of AI and deep learning technologies in gastrointestinal oncology. In diagnosis, AI technologies have improved the early detection and diagnosis efficiency for gastrointestinal tumors by optimizing medical image analysis, especially in the recognition of liver metastases. Applications of AI in pathological diagnosis include automatic recognition of tumor cells and tissue structure, as well as improving diagnostic sensitivity and specificity through feature extraction and pattern recognition. In treatment, the application scenarios of AI include rapid diagnosis, accurate staging, personalized treatment plan formulation, drug development, and surgical assistance. In surgical assistance, AI technology improves the safety and effectiveness of surgery through preoperative evaluation, surgical navigation, and postoperative evaluation. In teaching, AI technology facilitates knowledge acquisition and clinical skill enhancement of medical students by providing a multidisciplinary learning platform, simulating clinical environment and case details, and establishing a remote learning platform. The application of AI technology in teaching also includes deep learning and assessment feedback, providing personalized teaching and real-time assessment for students. This paper discusses the application prospects for AI technology in the teaching practice of gastrointestinal oncology. Although AI technology shows great potential in the diagnosis and treatment of gastrointestinal tumors and teaching gastrointestinal oncology, it also has limitations and needs to be combined with traditional teaching methods to achieve the best teaching results.

Objective:The influencing factors of intraoperative hypoxemia in patients with benign central airway stenosis were investigated by machine learning algorithm, and the prediction model of hypoxemia was constructed and verified.Methods:A case-control study was used in this study. The clinical data of 650 patients with benign central airway stenosis who who received surgical treatment in the First Affiliated Hospital of PLA Naval Medical University from June 2022 to April 2024 were retrospectively analyzed. And they were divided into a training set ( n=455) and a test set ( n=195) according to 7:3. The training set was used for establishing Logistic regression model and conducting internal verification, and the test set was used for external verification. The least absolute shrinkage and selection operator (LASSO) regression and Boruta algorithm were used to select the factors affecting intraoperative hypoxemia in patients with benign central airway stenosis. A Logistic regression prediction model was constructed, and the model was evaluated using area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA) and calibration curve. Shapley additive interpretation (SHAP) were used to analyze the importance of influencing factors. Results:Among 650 patients, 279 were males and 371 were females, aged (37.86 ± 8.82) years. Nine feature variables were screened by LASSO regression, while 7 feature variables were screened by Boruta algorithm, the intersection of the two was operation time, complications, degree of airway stenosis, thermal ablation therapy, balloon dilation, and airway stent, respectively, based on this, a logistic regression prediction model was constructed.The AUC values of the training set, validation set and test set of the model were 0.928 (95% CI 0.903-0.954), 0.922 (95% CI 0.843-0.995) and 0.919 (95% CI 0.872-0.965), respectively. The calibration curve showed that the predicted results of the model were in good agreement with the actual results, and the DCA curve showed that the model had clinical application value. SHAP analysis showed that the importance of variables affecting intraoperative hypoxemia in benign central airway stenosis patients was ranked as operation time, thermal ablation therapy, degree of airway stenosis, comorbidification, balloon dilation, and airway stent. Conclusions:The Logistic regression prediction model of intraoperative hypoxemia built based on machine learning algorithm has good prediction efficiency, which is helpful to early identification of risk groups and prevention of hypoxemia.

Objective:To investigate the expression of serum miR-326 and miR-501 in patients with endometrial cancer (EC) and their correlation with postoperative recurrence.Methods:From Sep. 2020 to Sep. 2023, 108 patients diagnosed with EC who received initial treatment in Heping Hospital Affiliated to Changzhi Medical College were as the study group, and another 108 patients with benign endometrial hyperplasia were as the control group. All EC patients were followed up for 1 year after surgery, and were assigned into a recurrence group of 15 cases and a non recurrence group of 93 cases based on the follow-up results. Real-time fluorescence quantitative polymerase chain reaction (qRT-PCR) was applied to determine the expression levels of serum miR-326 and miR-501. Logistic regression was applied to analyze the factors affecting postoperative recurrence in EC patients. The predictive efficacy of serum miR-326 and miR-501 for postoperative recurrence in EC patients was analyzed by establishing receiver operating characteristic (ROC) curves.Results:Compared with the control group, the expression level of serum miR-326 in the study group was prominently lower ( P<0.05), while the expression level of miR-501 was obviously higher ( P<0.05). Compared with the non recurrence group, the expression level of serum miR-326 in the recurrence group was obviously lower ( P<0.05), while the expression level of miR-501 was obviously higher ( P<0.05). The combination of serum miR-326 and miR-501 had the highest AUC in predicting postoperative recurrence in EC patients, which was better than their individual predictions ( Zcombination - miR-326=2.456, P=0.014, Zcombination - miR-501=2.282, P=0.023). Logistic regression analysis revealed that serum miR-326 was a protective factor affecting postoperative recurrence in EC patients ( P<0.05), while miR-501 was a risk factor affecting postoperative recurrence in EC patients ( P<0.05) . Conclusions:The expression level of miR-326 in the serum of EC patients is prominently reduced, while the expression level of miR-501 is obviously increased. The combination of the two can be better used to predict postoperative recurrence in patients.

This paper aims to discuss the application value and progress of artificial intelligence (AI) in the diagnosis and treatment of gastrointestinal tumors and teaching practice of gastrointestinal oncology. Through a comprehensive analysis of the current clinical research status and literature, this paper summarizes the application practice and exploratory thinking of AI and deep learning technologies in gastrointestinal oncology. In diagnosis, AI technologies have improved the early detection and diagnosis efficiency for gastrointestinal tumors by optimizing medical image analysis, especially in the recognition of liver metastases. Applications of AI in pathological diagnosis include automatic recognition of tumor cells and tissue structure, as well as improving diagnostic sensitivity and specificity through feature extraction and pattern recognition. In treatment, the application scenarios of AI include rapid diagnosis, accurate staging, personalized treatment plan formulation, drug development, and surgical assistance. In surgical assistance, AI technology improves the safety and effectiveness of surgery through preoperative evaluation, surgical navigation, and postoperative evaluation. In teaching, AI technology facilitates knowledge acquisition and clinical skill enhancement of medical students by providing a multidisciplinary learning platform, simulating clinical environment and case details, and establishing a remote learning platform. The application of AI technology in teaching also includes deep learning and assessment feedback, providing personalized teaching and real-time assessment for students. This paper discusses the application prospects for AI technology in the teaching practice of gastrointestinal oncology. Although AI technology shows great potential in the diagnosis and treatment of gastrointestinal tumors and teaching gastrointestinal oncology, it also has limitations and needs to be combined with traditional teaching methods to achieve the best teaching results.

Objective:The influencing factors of intraoperative hypoxemia in patients with benign central airway stenosis were investigated by machine learning algorithm, and the prediction model of hypoxemia was constructed and verified.Methods:A case-control study was used in this study. The clinical data of 650 patients with benign central airway stenosis who who received surgical treatment in the First Affiliated Hospital of PLA Naval Medical University from June 2022 to April 2024 were retrospectively analyzed. And they were divided into a training set ( n=455) and a test set ( n=195) according to 7:3. The training set was used for establishing Logistic regression model and conducting internal verification, and the test set was used for external verification. The least absolute shrinkage and selection operator (LASSO) regression and Boruta algorithm were used to select the factors affecting intraoperative hypoxemia in patients with benign central airway stenosis. A Logistic regression prediction model was constructed, and the model was evaluated using area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA) and calibration curve. Shapley additive interpretation (SHAP) were used to analyze the importance of influencing factors. Results:Among 650 patients, 279 were males and 371 were females, aged (37.86 ± 8.82) years. Nine feature variables were screened by LASSO regression, while 7 feature variables were screened by Boruta algorithm, the intersection of the two was operation time, complications, degree of airway stenosis, thermal ablation therapy, balloon dilation, and airway stent, respectively, based on this, a logistic regression prediction model was constructed.The AUC values of the training set, validation set and test set of the model were 0.928 (95% CI 0.903-0.954), 0.922 (95% CI 0.843-0.995) and 0.919 (95% CI 0.872-0.965), respectively. The calibration curve showed that the predicted results of the model were in good agreement with the actual results, and the DCA curve showed that the model had clinical application value. SHAP analysis showed that the importance of variables affecting intraoperative hypoxemia in benign central airway stenosis patients was ranked as operation time, thermal ablation therapy, degree of airway stenosis, comorbidification, balloon dilation, and airway stent. Conclusions:The Logistic regression prediction model of intraoperative hypoxemia built based on machine learning algorithm has good prediction efficiency, which is helpful to early identification of risk groups and prevention of hypoxemia.