Objective:To analyze the clinical characteristics of patients with pregnancy-related chronic pain visiting the pain clinic.Methods:The number of pregnant patients who completed a pregnancy registration at the Obstetrics and Gynecology Hospital Affiliated to Nanjing Medical University from 2022 to 2024 was collected. The medical records were reviewed to identify the patients who visited the department of pain of our hospital due to chronic pain related to pregnancy. The clinical characteristics such as the visiting situation, gestational weeks, age and types of pain were analyzed.Results:From 2022 to 2024, the total number of registered pregnant patients was 64, 818, of which, 2, 224 cases visited the pain clinic, and the annual proportions of pregnancy-related chronic pain visits were 2.540%, 3.836% and 3.889% respectively. Among the patients who attended the clinic, 77.97% were pregnant (5.82% in early pregnancy, 41.93% in mid-pregnancy, and 52.25% in late pregnancy), and 21.03% were postpartum patients. A total of 83.72% were aged 20-34 yr. The types of pain were pelvic girdle pain (40.96%), limb joint pain (28.82%), low back pain (14.16%), cervical spondylosis (3.64%), peripheral nerve entrapment syndrome (3.42%), headache (2.97%), chest and back pain (2.79%), pelvic and perineal pain (1.66%), neuralgia (0.94%) and other pains (0.63%).Conclusions:From 2022 to 2024, the proportion of registered pregnant women at our hospital who visited to the pain clinic due to pregnancy-related chronic pain increases year by year. The common types of pain are pelvic girdle pain, limb joint pain and low back pain.

Metabolites produced as intermediaries or end-products of microbial metabolism provide crucial signals for health and diseases, such as metabolic dysfunction-associated steatotic liver disease (MASLD). These metabolites include products of the bacterial metabolism of dietary substrates, modification of host molecules (such as bile acids [BAs], trimethylamine-N-oxide, and short-chain fatty acids), or products directly derived from bacteria. Recent studies have provided new insights into the association between MASLD and the risk of developing chronic kidney disease (CKD). Furthermore, alterations in microbiota composition and metabolite profiles, notably altered BAs, have been described in studies investigating the association between MASLD and the risk of CKD. This narrative review discusses alterations of specific classes of metabolites, BAs, fructose, vitamin D, and microbiota composition that may be implicated in the link between MASLD and CKD.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Metabolites produced as intermediaries or end-products of microbial metabolism provide crucial signals for health and diseases, such as metabolic dysfunction-associated steatotic liver disease (MASLD). These metabolites include products of the bacterial metabolism of dietary substrates, modification of host molecules (such as bile acids [BAs], trimethylamine-N-oxide, and short-chain fatty acids), or products directly derived from bacteria. Recent studies have provided new insights into the association between MASLD and the risk of developing chronic kidney disease (CKD). Furthermore, alterations in microbiota composition and metabolite profiles, notably altered BAs, have been described in studies investigating the association between MASLD and the risk of CKD. This narrative review discusses alterations of specific classes of metabolites, BAs, fructose, vitamin D, and microbiota composition that may be implicated in the link between MASLD and CKD.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Endosomes are characterized by the presence of various phosphoinositides that are essential for defining the membrane properties. However, the interplay between endosomal phosphoinositides metabolism and innate immunity is yet to be fully understood. Here, our findings highlight the evolutionary continuity of RAB-10/Rab10's involvement in regulating innate immunity. Upon infection of Caenorhabditis elegans with Pseudomonas aeruginosa, an increase in RAB-10 activity was observed in the intestine. Conversely, when RAB-10 was absent, the intestinal diacylglycerols (DAGs) decreased, and the animal's response to the pathogen was impaired. Further research revealed that UNC-16/JIP3 acts as an RAB-10 effector, facilitating the recruitment of phospholipase EGL-8 to endosomes. This leads to a decrease in endosomal phosphatidylinositol 4,5-bisphosphate (PI(4,5)P2) and an elevation of DAGs, as well as the activation of the PMK-1/p38 MAPK innate immune pathway. It is noteworthy that the dimerization of UNC-16 is a prerequisite for its interaction with RAB-10(GTP) and the recruitment of EGL-8. Moreover, we ascertained that the rise in RAB-10 activity, due to infection, was attributed to the augmented expression of LET-413/Erbin, and the nuclear receptor NHR-25/NR5A1/2 was determined to be indispensable for this increase. Hence, this study illuminates the significance of endosomal PI(4,5)P2 catabolism in boosting innate immunity and outlines an NHR-25-mediated mechanism for pathogen detection in intestinal epithelia.
Animals ; Caenorhabditis elegans/genetics* ; Endosomes/immunology* ; Caenorhabditis elegans Proteins/immunology* ; Phosphatidylinositol 4,5-Diphosphate/immunology* ; Immunity, Innate ; Pseudomonas aeruginosa/immunology* ; rab GTP-Binding Proteins/genetics* ; Diglycerides/metabolism*

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Metabolites produced as intermediaries or end-products of microbial metabolism provide crucial signals for health and diseases, such as metabolic dysfunction-associated steatotic liver disease (MASLD). These metabolites include products of the bacterial metabolism of dietary substrates, modification of host molecules (such as bile acids [BAs], trimethylamine-N-oxide, and short-chain fatty acids), or products directly derived from bacteria. Recent studies have provided new insights into the association between MASLD and the risk of developing chronic kidney disease (CKD). Furthermore, alterations in microbiota composition and metabolite profiles, notably altered BAs, have been described in studies investigating the association between MASLD and the risk of CKD. This narrative review discusses alterations of specific classes of metabolites, BAs, fructose, vitamin D, and microbiota composition that may be implicated in the link between MASLD and CKD.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.