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.

OBJECTIVE To design pharmaceutical care pathway for the problems related to chemotherapy， and to evaluate whether it contributes to the detection and intervention of drug-related problems （DRPs） in chemotherapy patients. METHODS The pharmaceutical care pathway table and flow charts were constructed and implemented by pharmaceutical care practice experience. The patients who were admitted to our hospital for chemotherapy before and after the implementation of the pharmaceutical care pathway were divided into control group （before the implementation，60 cases） and observation group （after the implementation，64 cases）， respectively； the relevant medical records of patients in the control group were extracted to evaluate DRPs， and pharmaceutical care of chemotherapy-related problems was performed for patients in observation group to extract DRPs. The basic condition， chemotherapy condition， DRPs classification and intervention status， adverse reactions induced by chemotherapy， PCNE classification of DRPs， occurrence time of DRPs， and drug classes related to DRPs were compared between 2 groups. RESULTS There was no statistical significance in the basic situation， chemotherapy regimen and chemotherapy drug category between the two groups （P＞0.05）. DRPs occurred in 46 and 37 patients in control group and observation group， respectively. In both groups， DRPs mainly occurred during chemotherapy， and mainly in the early stage of chemotherapy. Using the new pathway， the detection of DRPs significantly increased from 52.17% in the control group to 91.89% in the observation group （P＜0.05）. The successful intervention rate of DRPs was significantly increased from 32.61% in the control group to 72.97% in the observation group （P＜ 0.05）. The incidence of adverse drug reactions significantly decreased from 28.33% in the control group to 12.50% in the observation group（P＜0.05）. The main problem type of DRPs in the control group was treatment effectiveness， which mainly involved adjuvant antitumor drugs， mainly due to the use of adjuvant anti-tumor drugs for off-label prescribing； that of the observation group was treatment effectiveness and treatment safety， which mainly involved vomiting drugs， mainly due to insufficient medication to prevent nausea and vomiting caused by chemotherapy. CONCLUSIONS The implementation of the pathway helps clinical pharmacists to detect and intervene in DRPs among chemotherapy patients， and reduces the occurrence of chemotherapy-induced adverse reactions.

Humans ; Adolescent ; Acne Vulgaris/therapy* ; Skin Care ; Surveys and Questionnaires ; Cognition ; China ; Skin

Objective To explore the effect of sevelamer carbonate on blood phosphorus,vascular calcification,and parathyroid function in maintenance hemodialysis(MHD)patients.Methods MHD patients were selected as the research subjects and divided into treatment group and control group according to the treatment plan using a cohort method.The control group was given calcium carbonate chewable tablets orally at a dose of 1.25 g,bid,while the treatment group was given sevelamer carbonate tablets orally at a dose of 0.8 g,tid;the duration of treatment for both groups was 6 months.The clinical efficacy,blood phosphorus,blood calcium,coronary artery calcification score,parathyroid function[intact parathyroid hormone(iPTH),parathyroid volume],and levels of inflammatory factors[C-reactive protein(CRP),interleukin-6(IL-6),tumor necrosis factor-α(TNF-α)]were compared between the two groups,and the occurrence of adverse drug reactions was recorded.Results The total effective rates of the treatment group and the control group were 92.45％(49 cases/53 cases)and 77.55％(38 cases/49 cases)respectively,showing statistically significant difference(P＜0.05).After 6 months of treatment,the blood phosphorus levels of the treatment group and the control group were(1.63±0.31)and(2.07±0.36)mmol·L-1,respectively;blood calcium levels were(2.31±0.17)and(2.47±0.12)mmol·L-1,respectively;coronary artery calcification scores were 71.81±15.45 and 86.03±17.49,respectively;iPTH expression levels were(223.59±64.85)and(304.25±74.61)pg·mL-1,respectively;parathyroid volumes were(1.03±0.26)and(1.17±0.21)cm3,respectively;CRP expression levels were(5.12±1.35)and(6.59±1.66)mg·L-1,respectively;IL-6 expression levels were(16.32±2.81)and(19.84±3.62)ng·L-1,respectively;TNF-αexpression levels were(18.54±3.88)and(22.69±4.89)ng·L-1,respectively.The differences in the above indicators between the treatment group and the control group were all statistically significant(all P＜0.05).The total incidence of adverse drug reactions in the treatment group and the control group were 9.43％(5 cases/53 cases)and 6.12％(3 cases/49 cases),respectively,with no statistically significant difference(P＞0.05).Conclusion Sevelamer carbonate can reduce blood phosphorus levels in MHD patients,has minimal effect on blood calcium concentration,improves parathyroid function in patients,delays vascular calcification,and has good clinical application effects.