AIM: To analyze the influencing factors of capsular constriction syndrome(CCS)in cataract patients after phacoemulsification(Phaco)combined with intraocular lens(IOL)implantation.METHODS: Retrospective study. The data of 2 900 cataract patients(2 900 eyes)in our hospital's information system from January 2021 to January 2024 were collected. All patients were treated with Phaco combined with IOL implantation, and the incidence of CCS within 30 wk after surgery was recorded. Patients were categorized into CCS(116 cases, 116 eyes)and N-CCS group(2 784 cases, 2 784 eyes)based on the occurrence of CCS. The basic data of the two groups were compared, and the influencing factors of CCS within 30 wk after Phaco combined with IOL implantation in cataract patients were analyzed by multivariate Logistic regression.RESULTS: Among 2 900 patients(2 900 eyes)included, 116 cataract patients(116 eyes)developed CCS within 30 wk after Phaco combined with IOL implantation, with an incidence rate of 4.00%. The single factor and multi-factor Logistic regression analysis showed that the complicated diabetes, high myopia, complicated glaucoma, and axial length(AL)>30 mm were the risk factors for the occurrence of CCS after Phaco IOL implantation in cataract patients(all P<0.05).CONCLUSION: Attention should be paid to cataract patients with diabetes, high myopia, glaucoma and AL>30 mm, which will increase the risk of CCS within 30 wk after Phaco combined with IOL implantation in cataract patients.

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.

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.

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.

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.

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.

ObjectiveThe study explores the influence of voluntary wheel running on the behavioral abnormalities and the activation state of the hypothalamic-pituitary-adrenal (HPA) axis in autism spectrum disorder (ASD) rats through gut microbiota. MethodsSD female rats were selected and administered either400 mg/kg of valproic acid (VPA) solution or an equivalent volume of saline via intraperitoneal injection on day 12.5 of pregnancy. The resulting offspring were divided into 2 groups: the ASD model group (PASD, n=35) and the normal control group (PCON, n=16). Behavioral assessments, including the three-chamber social test, open field test, and Morris water maze, were conducted on postnatal day 23. After behavioral testing, 8 rats from each group (PCON, PASD) were randomly selected for serum analysis using enzyme-linked immunosorbent assay (ELISA) to measure corticotropin-releasing hormone (CRH), adrenocorticotropic hormone (ACTH), and corticosterone (CORT) concentration, to evaluate the functional state of the HPA axis in rats. On postnatal day 28, the remaining 8 rats in the PCON group were designated as the control group (CON, n=8), and the remaining 27 rats in the PASD group were randomly divided into 4 groups: ASD non-intervention group (ASD, n=6), ASD exercise group (ASDE, n=8), ASD fecal microbiota transplantation group (FMT, n=8), and ASD sham fecal microbiota transplantation group (sFMT, n=5). The rats in the ASD group and the CON group were kept under standard conditions, while the rats in the ASDE group performed 6 weeks of voluntary wheel running intervention starting on postnatal day 28. The rats in the FMT group were gavaged daily from postnatal day 42 with 1 ml/100 g fresh fecal suspension from ASDE rats which had undergone exercise for 2 weeks, 5 d per week, continuing for 4 weeks. The sFMT group received an equivalent volume of saline. After the interventions were completed, behavioral assessments and HPA axis markers were measured for all groups. ResultsBefore the intervention, the ASD model group exhibited significantly reduced social ability, social novelty preference, spontaneous activity, and exploratory interest, as well as impaired spatial learning, memory, and navigation abilities compared to the normal control group (P<0.05). Serum concentration of corticotropin-releasing hormone (CRH), adrenocorticotropic hormone (ACTH), and corticosterone (CORT) in the PASD group were significantly higher than those in the PCON group (P<0.05). Following 6 weeks of voluntary wheel running, the ASDE group showed significant improvements in social ability, social novelty preference, spontaneous activity, exploratory interest, spatial learning, memory, and navigation skills compared to the ASD group (P<0.05), with a significant decrease in serum CORT concentration (P<0.05), and a downward trend in CRH and ACTH concentration. After 4 weeks of fecal microbiota transplantation in the exercise group, the FMT group showed marked improvements in social ability, social novelty preference, spontaneous activity, exploratory interest, as well as spatial learning, memory, and navigation abilities compared to both the ASD and sFMT groups (P<0.05). In addition, serum ACTH and CORT concentration were significantly reduced (P<0.05), and CRH concentration also showed a decreasing trend. ConclusionExercise may improve ASD-related behaviors by suppressing the activation of the HPA axis, with the gut microbiota likely playing a crucial role in this process.

This study aims to investigate the effects of renin inhibitor aliskiren on kidney injury in human angiotensinogen-renin (AGT-REN) double transgenic hypertensive (dTH) mice and explore its possible mechanism. The dTH mice were divided into hypertension group (HT group) and aliskiren intervention group (HT+Aliskiren group), while wild-type C57BL/6 mice were served as the control group (WT group). Blood pressure data of mice in HT+Aliskiren group were collected after 28 d of subcutaneous penetration of aliskiren (20 mg/kg), and the damage of renal tissue structure and collagen deposition were observed by HE, Masson and PAS staining. The ultrastructure of kidney was observed by transmission electron microscope. Coomassie bright blue staining and biochemical analyzer were used to detect renal function injury. The expression of renin-angiotensin system (RAS) was determined by ELISA and immunohistochemistry. The contents of superoxide dismutase (SOD) and malondialdehyde (MDA) in kidney were determined by chemiluminescence method. The content of nicotinamide adenine dinucleotide phosphate (NADPH) oxidase subunit p47^phox, inducible nitric oxide synthase (iNOS), 3-nitrotyrosine (3-NT), NADPH oxidase 2 (NOX2) and NADPH oxidase 4 (NOX4) were detected by Western blot analysis. The results showed that compared with WT group, the blood pressure of mice in HT group was significantly increased. The renal tissue structure in HT group showed glomerular sclerosis, severe interstitial tubular injury, and increased collagen deposition. In addition, 24 h urinary protein, serum creatinine and urea levels increased. Serum and renal tissue levels of angiotensin II (Ang II) were increased, serum angiotensin-(1-7) [Ang-(1-7)] expression was decreased, and renal Ang-(1-7) expression was elevated. The expressions of ACE, Ang II type 1 receptor (AT₁R) and MasR in renal tissue were increased, while the expression of ACE2 was decreased. MDA content increased, SOD content decreased, and the expressions of p47^phox, iNOS, 3-NT, NOX2 and NOX4 were increased. However, aliskiren reduced blood pressure in dTH mice, improved renal structure and renal function, reduced Ang II and Ang-(1-7) levels in serum and renal tissue, reduced the expression of ACE and AT₁R in renal tissue, increased the expression of ACE2 and MasR in renal tissue, and decreased the above levels of oxidative stress indexes in dTH mice. These results suggest that aliskiren may play a protective role in hypertensive renal injury by regulating the balance between ACE-Ang II-AT₁R and ACE2-Ang-(1-7)-MasR axes and inhibiting oxidative stress.
Animals ; Fumarates/therapeutic use* ; Mice ; Renin/antagonists & inhibitors* ; Amides/therapeutic use* ; Mice, Inbred C57BL ; Hypertension/physiopathology* ; Mice, Transgenic ; Kidney/pathology* ; Angiotensinogen/genetics* ; Renin-Angiotensin System/drug effects* ; NADPH Oxidases/metabolism* ; Male ; Antihypertensive Agents/pharmacology* ; Humans ; Superoxide Dismutase/metabolism* ; NADPH Oxidase 4

Selenoproteins, as the active form of selenium, play an important role in various physiological and pathological processes, such as anti-oxidation, anti-tumor, immune response, metabolic regulation, reproduction and aging. Although the expression level of selenoproteins in adipose tissue is significantly influenced by dietary selenium intake, it is closely related to the homeostasis of adipose tissue. In this review, we summarized the role of selenoproteins in the physiological function of adipose tissue and the pathogenesis of obesity in recent years, in order to provide a rationale for developing potential therapeutic agents for the treatment of obesity and related metabolic diseases.
Selenoproteins/metabolism* ; Adipose Tissue/physiology* ; Obesity/metabolism* ; Humans ; Animals ; Selenium

To explore the anti-depression effect of Suanzaoren Decoction(SZRD), the regulatory effects on endogenous metabolites in the liver of rats with depression induced by chronic unpredictable mild stress(CUMS) were analyzed by using LC-MS metabolomics. The rats were randomly divided into normal control group, model group, low-dose SZRD group, high-dose SZRD group, and positive drug group. The CUMS depression model was replicated by applying a variety of stimuli, such as fasting and water deprivation, ice water swimming, hot water swimming, day and night reversal, tail clamping, and restraint for rats. Modeling and treatment were conducted for 56 days. The behavioral indexes of rats in each group, including body weight, open field test, sucrose preference test, and tail suspension test, were observed. Plasma samples and liver tissue samples were collected, and the contents of 5-hydroxytryptamine(5-HT), dopamine(DA), and norepinephrine(NE) in plasma were measured using enzyme-linked immunosorbent assay(ELISA). Meanwhile, the regulatory effects of SZRD on the liver metabolic profile of CUMS model rats were analyzed by the LC-MS metabolomics method. The results show that SZRD can significantly improve the depression-like behavior of CUMS model rats and increase the neurotransmitter levels of 5-HT, DA, and NE in plasma. A total of 24 different metabolites in the rats' liver are identified using the LC-MS metabolomics method, and SZRD can reverse 13 of these metabolites. Metabolic pathway analysis indicates that nine metabolic pathways are found to be significantly associated with depression, and in the low-dose SZRD group, four pathways can be regulated, including pentose phosphate pathway, purine metabolism, inositol phosphate metabolism, and sphingolipid metabolism. In the high-dose SZRD group, two metabolic pathways can be regulated, including sphingolipid metabolism and glycerol glycerophospholipid metabolism. Sphingolipid metabolism is a metabolic pathway that can be regulated by SZRD at different doses, so it is speculated that it may be the primary pathway through which SZRD can alleviate metabolic disturbances in the liver of CUMS model rats.
Animals ; Rats ; Drugs, Chinese Herbal/administration & dosage* ; Metabolomics ; Depression/metabolism* ; Male ; Liver/drug effects* ; Rats, Sprague-Dawley ; Antidepressive Agents/administration & dosage* ; Serotonin/blood* ; Humans ; Disease Models, Animal ; Behavior, Animal/drug effects*