Meditation aims to guide individuals into a state of deep calm and focused attention, and in recent years, it has shown promising potential in the field of medical treatment. Numerous studies have demonstrated that electroencephalogram (EEG) patterns change during meditation, suggesting the feasibility of using deep learning techniques to monitor meditation states. However, significant inter-subject differences in EEG signals poses challenges to the performance of such monitoring systems. To address this issue, this study proposed a novel model-calibrated multi-source adversarial adaptation network (CMAAN). The model first trained multiple domain-adversarial neural networks in a pairwise manner between various source-domain individuals and the target-domain individual. These networks were then integrated through a calibration process using a small amount of labeled data from the target domain to enhance performance. We evaluated the proposed model on an EEG dataset collected from 18 subjects undergoing methamphetamine rehabilitation. The model achieved a classification accuracy of 73.09%. Additionally, based on the learned model, we analyzed the key EEG frequency bands and brain regions involved in the meditation process. The proposed multi-source domain adaptation framework improves both the performance and robustness of EEG-based meditation monitoring and holds great promise for applications in biomedical informatics and clinical practice.
Humans ; Electroencephalography/methods* ; Meditation ; Calibration ; Neural Networks, Computer ; Brain/physiology* ; Rest/physiology* ; Deep Learning ; Signal Processing, Computer-Assisted

PURPOSE: The rate of complications among patients undergoing surgery has increased due to infection with SARS-CoV-2 and other variants of concern. However, Omicron has shown decreased pathogenicity, raising questions about the risk of postoperative complications among patients who are infected with this variant. This study aimed to investigate complications and related factors among patients with recent Omicron infection prior to undergoing orthopedic surgery. METHODS: A historical control study was conducted. Data were collected from all patients who underwent surgery during 2 distinct periods: (1) between Dec 12, 2022 and Jan 31, 2023 (COVID-19 positive group), (2) between Dec 12, 2021 and Jan 31, 2022 (COVID-19 negative control group). The patients were at least 18 years old. Patients who received conservative treatment after admission or had high-risk diseases or special circumstances (use of anticoagulants before surgery) were excluded from the study. The study outcomes were the total complication rate and related factors. Binary logistic regression analysis was used to identify related factors, and odds ratio (OR) and 95% confidence interval (CI) were calculated to assess the impact of COVID-19 infection on complications. RESULTS: In the analysis, a total of 847 patients who underwent surgery were included, with 275 of these patients testing positive for COVID-19 and 572 testing negative. The COVID-19-positive group had a significantly higher rate of total complications (11.27%) than the control group (4.90%, p < 0.001). After adjusting for relevant factors, the OR was 3.08 (95% CI: 1.45-6.53). Patients who were diagnosed with COVID-19 at 3-4 weeks (OR = 0.20 (95% CI: 0.06-0.59), p = 0.005), 5-6 weeks (OR = 0.16 (95% CI: 0.04-0.59), p = 0.010), or ≥7 weeks (OR = 0.26 (95% CI: 0.06-1.02), p = 0.069) prior to surgery had a lower risk of complications than those who were diagnosed at 0-2 weeks prior to surgery. Seven factors (age, indications for surgery, time of operation, time of COVID-19 diagnosis prior to surgery, C-reactive protein levels, alanine transaminase levels, and aspartate aminotransferase levels) were found to be associated with complications; thus, these factors were used to create a nomogram. CONCLUSION Omicron continues to be a significant factor in the incidence of postoperative complications among patients undergoing orthopedic surgery. By identifying the factors associated with these complications, we can determine the optimal surgical timing, provide more accurate prognostic information, and offer appropriate consultation for orthopedic surgery patients who have been infected with Omicron.
Humans ; COVID-19/complications* ; Male ; Female ; Middle Aged ; Postoperative Complications/epidemiology* ; SARS-CoV-2 ; Orthopedic Procedures/adverse effects* ; Aged ; Nomograms ; Adult ; Retrospective Studies ; Risk Factors

Objective:To construct an ensemble machine learning model for predicting the occurrence of clinically relevant postoperative pancreatic fistula (CR-POPF) after pancreaticoduodenectomy and evaluate its application value.Methods:This is a research on predictive model. Clinical data of 421 patients undergoing pancreaticoduodenectomy in the Department of Pancreatic Surgery,Union Hospital, Tongji Medical College,Huazhong University of Science and Technology from June 2020 to May 2023 were retrospectively collected. There were 241 males (57.2%) and 180 females (42.8%) with an age of (59.7±11.0)years (range: 12 to 85 years).The research objects were divided into training set (315 cases) and test set (106 cases) by stratified random sampling in the ratio of 3∶1. Recursive feature elimination is used to screen features,nine machine learning algorithms are used to model,three groups of models with better fitting ability are selected,and the ensemble model was constructed by Stacking algorithm for model fusion. The model performance was evaluated by various indexes,and the interpretability of the optimal model was analyzed by Shapley Additive Explanations(SHAP) method. The patients in the test set were divided into different risk groups according to the prediction probability (P) of the alternative pancreatic fistula risk score system (a-FRS). The a-FRS score was validated and the predictive efficacy of the model was compared.Results:Among 421 patients,CR-POPF occurred in 84 cases (20.0%). In the test set,the Stacking ensemble model performs best,with the area under the curve (AUC) of the subject′s work characteristic curve being 0.823,the accuracy being 0.83,the F1 score being 0.63,and the Brier score being 0.097. SHAP summary map showed that the top 9 factors affecting CR-POPF after pancreaticoduodenectomy were pancreatic duct diameter,CT value ratio,postoperative serum amylase,IL-6,body mass index,operative time,albumin difference before and after surgery,procalcitonin and IL-10. The effects of each feature on the occurrence of CR-POPF after pancreaticoduodenectomy showed a complex nonlinear relationship. The risk of CR-POPF increased when pancreatic duct diameter<3.5 mm,CT value ratio<0.95,postoperative serum amylase concentration>150 U/L,IL-6 level>280 ng/L,operative time>350 minutes,and albumin decreased by more than 10 g/L. The AUC of a-FRS in the test set was 0.668,and the prediction performance of a-FRS was lower than that of the Stacking ensemble machine learning model.Conclusion:The ensemble machine learning model constructed in this study can predict the occurrence of CR-POPF after pancreaticoduodenectomy,and has the potential to be a tool for personalized diagnosis and treatment after pancreaticoduodenectomy.

Objective:To construct an ensemble machine learning model for predicting the occurrence of clinically relevant postoperative pancreatic fistula (CR-POPF) after pancreaticoduodenectomy and evaluate its application value.Methods:This is a research on predictive model. Clinical data of 421 patients undergoing pancreaticoduodenectomy in the Department of Pancreatic Surgery,Union Hospital, Tongji Medical College,Huazhong University of Science and Technology from June 2020 to May 2023 were retrospectively collected. There were 241 males (57.2%) and 180 females (42.8%) with an age of (59.7±11.0)years (range: 12 to 85 years).The research objects were divided into training set (315 cases) and test set (106 cases) by stratified random sampling in the ratio of 3∶1. Recursive feature elimination is used to screen features,nine machine learning algorithms are used to model,three groups of models with better fitting ability are selected,and the ensemble model was constructed by Stacking algorithm for model fusion. The model performance was evaluated by various indexes,and the interpretability of the optimal model was analyzed by Shapley Additive Explanations(SHAP) method. The patients in the test set were divided into different risk groups according to the prediction probability (P) of the alternative pancreatic fistula risk score system (a-FRS). The a-FRS score was validated and the predictive efficacy of the model was compared.Results:Among 421 patients,CR-POPF occurred in 84 cases (20.0%). In the test set,the Stacking ensemble model performs best,with the area under the curve (AUC) of the subject′s work characteristic curve being 0.823,the accuracy being 0.83,the F1 score being 0.63,and the Brier score being 0.097. SHAP summary map showed that the top 9 factors affecting CR-POPF after pancreaticoduodenectomy were pancreatic duct diameter,CT value ratio,postoperative serum amylase,IL-6,body mass index,operative time,albumin difference before and after surgery,procalcitonin and IL-10. The effects of each feature on the occurrence of CR-POPF after pancreaticoduodenectomy showed a complex nonlinear relationship. The risk of CR-POPF increased when pancreatic duct diameter<3.5 mm,CT value ratio<0.95,postoperative serum amylase concentration>150 U/L,IL-6 level>280 ng/L,operative time>350 minutes,and albumin decreased by more than 10 g/L. The AUC of a-FRS in the test set was 0.668,and the prediction performance of a-FRS was lower than that of the Stacking ensemble machine learning model.Conclusion:The ensemble machine learning model constructed in this study can predict the occurrence of CR-POPF after pancreaticoduodenectomy,and has the potential to be a tool for personalized diagnosis and treatment after pancreaticoduodenectomy.

Intestinal dysbiosis and disrupted bile acid(BA)homeostasis are associated with obesity,but the precise mechanisms remain insufficiently explored.Hepatic protein phosphatase 1 regulatory subunit 3G(PPP1R3G)plays a pivotal role in regulating glycolipid metabolism;nevertheless,its obesity-combatting potency remains unclear.In this study,a substantial reduction was observed in serum PPP1R3G levels in high-body mass index(BMI)and high-fat diet(HFD)-exposed mice,establishing a positive correlation between PPP1R3G and non-12α-hydroxylated(non-12-OH)BA content.Additionally,hepatocyte-specific overexpression of Ppp1r3g(PPP1R3G HOE)mitigated HFD-induced obesity as evidenced by reduced weight,fat mass,and an improved serum lipid profile;hepatic steatosis alleviation was confirmed by normalized liver enzymes and histology.PPP1R3G HOE considerably impacted systemic BA homeostasis,which notably increased the non-12-OH BAs ratio,particularly lithocholic acid(LCA).16S ribosomal DNA(16S rDNA)sequencing assay indicated that PPP1R3G HOE reversed HFD-induced gut dysbiosis by reducing the Firmicutes/Bacteroidetes ratio and Lactobacillus population,and elevating the relative abundance of Blautia,which exhibited a positive correlation with serum LCA levels.A fecal microbiome transplantation test confirmed that the anti-obesity effect of hepatic PPP1R3G was gut microbiota-dependent.Mechanistically,PPP1R3G HOE markedly suppressed hepatic cholesterol 7α-hydroxylase(CYP7A1)and sterol-12α-hydroxylase(CYP8B1),and concurrently upregulated oxysterol 7-α hydroxylase and Takeda G protein-coupled BA receptor 5(TGR5)expression under HFD conditions.Furthermore,LCA administration significantly mitigated the HFD-induced obesity phenotype and elevated non-12-OH BA levels.These findings emphasize the significance of hepatic PPP1R3G in ameliorating diet-induced adiposity and hepatic steatosis through the gut microbiota-BA axis,which may serve as potential ther-apeutic targets for obesity-related disorders.

Objective:To understand the breastfeeding situation in the neonatal intensive care units (NICUs) in Qianxinan Prefecture, Guizhou Province, and to assess the knowledge, attitudes, and practices of doctors and nurses regarding breastfeeding, aiming to provide foundational data for improving breastfeeding quality.Methods:A questionnaire was developed to survey the knowledge, attitudes, and practices related to breastfeeding in NICUs. The questionnaire was divided into three dimensions: knowledge (seven items, total score of 7), attitudes (nine items, total score of 45), and practices (seven items, total score of 35). Lower scores indicated weaker recognition of breastfeeding. Additionally, five items were included to identify the most influential factors affecting breastfeeding. From November 25 to November 30, 2023, a survey was conducted among doctors and nurses with professional qualifications who had worked in the neonatal departments of nine hospitals in Qianxinan Prefecture for at least one year. Independent sample t-tests and Chi-square tests were used to compare the scores of doctors and nurses from different levels of hospitals and within the same level of hospitals across the three dimensions. Results:(1) Among the nine hospitals, three were tertiary grade A hospitals (referred to as "tertiary hospitals"), with 95.6% (43/45) of the doctors and 96.5% (110/114) of the nurses participating in the survey. Six were secondary grade A hospitals (referred to as "secondary hospitals"), with 95.0% (38/40) of the doctors and 97.6% (83/85) of the nurses participating. (2) All nine hospitals were baby-friendly hospitals and all had breastfeeding promotional materials. Six hospitals had NICUs that promoted breastfeeding, with an average NICU breastfeeding rate of 25.8% across the prefecture between year 2021 to 2023. (3) The proportion of doctors who had received breastfeeding training was higher than that of nurses within the same level of hospitals [tertiary hospitals: 69.8% (30/43) vs. 40.0% (44/110), χ 2=10.97, P=0.001; secondary hospitals: 47.4% (18/38) vs. 24.1% (20/83), χ 2=6.55, P=0.010], although the overall training rates were low. (4) In tertiary hospitals, doctors scored higher than nurses in the attitude dimension [(35.35±4.75) vs. (33.18±5.60) scores, t=－2.03, P=0.044] and also in the practice dimension [(26.98±3.00) vs. (25.60±3.75) scores, t=－2.17, P=0.032]. In secondary hospitals, the total knowledge dimension score of doctors was higher than that of nurses [(4.92±1.44) vs. (4.20±1.45) scores, t=－2.52, P=0.013]. In tertiary hospitals, the total scores for attitude and practice dimensions of doctors were higher than those of doctors in secondary hospitals, and the total scores for knowledge, attitude, and practice dimensions of nurses were higher than those of nurses in secondary hospitals (all P<0.05). (5) In the knowledge dimension, the lowest scoring item of doctors in the tertiary hospitals was "Breastfeeding is possible for maternal hepatitis B newborns after receiving vaccines and immunoglobulin"; the lowest scoring item of nurses in the tertiary hospital, and doctors and nurses in the secondary hospitals was "The duration of breastfeeding has a greater impact on neonatal outcomes". In the attitude dimension, the lowest scoring item for doctors and nurses in both tertiary and secondary hospitals was "You think the breastfeeding process is more troublesome than feeding preterm formula". In the practice dimension, the lowest scoring item of the doctors and nurses in the tertiary hospitals was "Your hospital had enough breastfeeding knowledge training", while for the doctors and nurses in the secondary hospitals were "You have more opportunities to participate in various breastfeeding-related training" and "Breast feeding should be started as soon as possible when the infant is stable after active treatment", respectively. (6) The most influential factors affecting breastfeeding were: lack of cooperation from parents (50.0%, 137/274), relative insufficient human resources for doctors and nurses (21.9%, 60/274), and the absence or poor implementation of breastfeeding management policies (18.3%, 50/274), etc. Conclusions:The breastfeeding rate in NICU of county-level hospitals is relatively low, and medical staff, especially nurses, have insufficient knowledge about breastfeeding. It is necessary to strengthen various breastfeeding training for medical staff to enhance their understanding of NICU breastfeeding.

Linezolid,a fully synthetic oxazolidinone antibiotic,is mainly used to treat severe infections caused by Gram-positive drug-resistant bacteria.In recent years,with the rise in drug-resistant bacteria,the clinical utilization rate of linezolid and the incidence of linezolid-related adverse reactions in the hematological system and metabolic system have increased.The main adverse reactions include thrombocytopenia,anemia and lactic acidosis.Studies have shown that the causes of adverse reactions in linezolid-induced hematological system and metabolic system are diverse,and the mechanisms are not fully elucidated.In this review,the pharmacokinetic characteristics,mechanism of adverse reactions,risk factors,as well as preventive measures and individualized drug administration strategies of linezolid in vivo were discussed based on literature reports at home and abroad,aiming to provide references for clinical prevention and treatment of linezolid-related adverse reactions of hematological system and metabolic system.

Objective This study aimed to investigate the potential relationship between urinary metals copper (Cu), arsenic (As), strontium (Sr), barium (Ba), iron (Fe), lead (Pb) and manganese (Mn) and grip strength. Methods We used linear regression models, quantile g-computation and Bayesian kernel machine regression (BKMR) to assess the relationship between metals and grip strength.Results In the multimetal linear regression, Cu (β=-2.119), As (β=-1.318), Sr (β=-2.480), Ba (β=0.781), Fe (β= 1.130) and Mn (β=-0.404) were significantly correlated with grip strength (P < 0.05). The results of the quantile g-computation showed that the risk of occurrence of grip strength reduction was -1.007 (95％ confidence interval:-1.362, -0.652; P < 0.001) when each quartile of the mixture of the seven metals was increased. Bayesian kernel function regression model analysis showed that mixtures of the seven metals had a negative overall effect on grip strength, with Cu, As and Sr being negatively associated with grip strength levels. In the total population, potential interactions were observed between As and Mn and between Cu and Mn (Pinteractions of 0.003 and 0.018, respectively).Conclusion In summary, this study suggests that combined exposure to metal mixtures is negatively associated with grip strength. Cu, Sr and As were negatively correlated with grip strength levels, and there were potential interactions between As and Mn and between Cu and Mn.

In the past 20 years,near infrared spectrum technology has been widely used in human body monitoring due to its non-invasive and real-time characteristics.Oxygen,as the main metabolic substance of the human body,is consumed the most in brain tissue.In order to prevent complications caused by a decrease in brain tissue oxygen during treatment,the patient's brain tissue blood oxygen saturation needs to be monitored in real time.Currently,most of the clinically used non-invasive cerebral blood oxygen detection equipments use dual wavelengths.Other substances on the detection path will cause errors in the measurement results.Therefore,this article proposes a three-wavelength method based on the basic principle of non-invasive monitoring of cerebral blood oxygen using near-infrared spectrum.The brain tissue oxygen saturation monitoring method of detecting light sources was initially verified through the built system,laying the foundation for subsequent system engineering.

Inflammatory bowel disease (IBD) is a formidable disease due to its complex pathogenesis. Macrophages, as a major immune cell population in IBD, are crucial for gut homeostasis. However, it is still unveiled how macrophages modulate IBD. Here, we found that LIM domain only 7 (LMO7) was downregulated in pro-inflammatory macrophages, and that LMO7 directly degraded 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3 (PFKFB3) through K48-mediated ubiquitination in macrophages. As an enzyme that regulates glycolysis, PFKFB3 degradation led to the glycolytic process inhibition in macrophages, which in turn inhibited macrophage activation and ultimately attenuated murine colitis. Moreover, we demonstrated that PFKFB3 was required for histone demethylase Jumonji domain-containing protein 3 (JMJD3) expression, thereby inhibiting the protein level of trimethylation of histone H3 on lysine 27 (H3K27me3). Overall, our results indicated the LMO7/PFKFB3/JMJD3 axis is essential for modulating macrophage function and IBD pathogenesis. Targeting LMO7 or macrophage metabolism could potentially be an effective strategy for treating inflammatory diseases.