Objective:To develop a post competence assessment scale for nurses in the health management (physical examination) center and assess its reliability and validity.Methods:This study adopted an empirical approach. A total of 801 nurses from the health management (physical examination) center were recruited to participate in this study. A research team was formed in August 2024. This team transformed the previously constructed core competence evaluation index system for health management specialist nurses in the health management (physical examination) center (comprising 6 first-level indicators and 70 third-level indicators) into a preliminary post competence assessment scale. Seven experts evaluated the content validity of the scale. In September 2024, a pilot survey was conducted among 27 nurses from the health management (physical examination) center of Sichuan Provincial People′s Hospital using convenience sampling. From October to November 2024, the first main survey was administered to 385 nurses of health management (physical examination) center across 54 cities in China using both convenience sampling and snowball sampling methods, followed by exploratory factor analysis (EFA). Subsequently, utilizing the refined scale obtained after eliminating certain items, a second main survey was conducted among 389 nurses in the health management (physical examination) center, followed by a confirmatory factor analysis (CFA). The reliability of the final scale was assessed using Cronbach′s α coefficient, split-half reliability, composite reliability, and test-retest reliability.Results:The finalized scale for nurses′ post competency in health management (physical examination) center comprises five dimensions—basic nursing service competency, health management practice competency, knowledge integration competency, professional development competency, and professional attitude—with a total of 57 items. The item level content validity index (I-CVI) of the items of the content validity display scale ranged from 0.857 to 1.000, and the content validity index of each dimension ranged from 0.984 to 1.000. The scale-level Content Validity index/average (S-CVI/Ave) was 0.995. The contribution rate of the 6 factors extracted by EFA was 74.07%. After group discussion and CFA, the scale of the 5-factor structural equation model was constructed. The total Cronbach′s α coefficient of the scale was 0.986, the split-half reliability was 0.865, the composite reliability was 0.960-0.980, the total table test-retest reliability was 0.762, and the test-retest reliability of each dimension was 0.681-0.731.Conclusion:The developed assessment scale for assessing the post competence of nurses in the health management (physical examination) center demonstrates excellent reliability and validity.

Objective:To establish machine learning models to predict visual improvement and anatomical response after anti-vascular endothelial growth factor (VEGF) treatment in patients with diabetic macular edema (DME).Methods:A multi-algorithm machine learning predictive modeling study based on retrospective clinical data was conducted.A total of 225 patients with DME who received their first intravitreal anti-VEGF injection at Fuzhou University Affiliated Provincial Hospital were enrolled between January 2023 and April 2025.According to data completeness, 204 cases were included in the visual recovery prediction model and 201 cases were included in the anatomical response prediction model.Baseline data included optical coherence tomography (OCT) features and blood biomarkers.The primary outcomes were defined as an improvement of ≥1 line in visual acuity and a reduction of ≥20% in central retinal thickness (CRT) after anti-VEGF treatment.Feature selection was performed using univariate logistic regression and Lasso regression.Four machine learning algorithms, logistic regression (LR), decision tree, multilayer perceptron, and random forest, were trained and validated.Model performance was evaluated using accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and decision curve analysis.The best-performing model was further interpreted using SHAP analysis, and a nomogram was constructed for clinical application.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Fuzhou University Affiliated Provincial Hospital (No.K2025-03-064).Written informed consent was obtained from each subject.Results:Among the 37 baseline variables, five key predictors were identified for the outcome of ≥20% CRT reduction: baseline CRT, baseline CRT ≥400 μm, presence of subretinal fluid (SRF), disorganization of the retinal inner layers (DRIL), and integrity of the ellipsoid zone (EZ).Among the four models, the LR model had the best performance, with an accuracy of 0.88, sensitivity of 0.94, specificity of 0.70, and an AUC of 0.94 (95% confidence interval [ CI]: 0.87-1.00).SHAP analysis showed that baseline CRT ≥400 μm, DRIL, SRF, and baseline CRT contributed positively to the outcome, while EZ integrity was a negative predictor for CRT reduction.For the outcome of ≥1-line visual improvement, two key predictors were identified: baseline best corrected visual acuity (BCVA) and EZ integrity.Both baseline BCVA and EZ integrity were negative predictors for ≥1-line visual improvement.The LR model also had the best performance in the internal validation cohort, with an accuracy of 0.71, sensitivity of 0.67, specificity of 0.75, and an AUC of 0.76 (95% confidence interval [ CI]: 0.61-0.91).A visual nomogram was developed based on the selected predictors and the best-performing model.By converting patient-specific clinical characteristics into scores, clinicians can calculate a total score and estimate the probability of achieving a reduction of ≥20% in CRT and a ≥1-line improvement in visual acuity after anti-VEGF therapy. Conclusions:Machine learning-based model building can effectively predict visual and anatomical response following anti-VEGF treatment in DME patients.Logistic regression shows robust predictive performance for both outcomes.Identification of key predictors, especially OCT features such as EZ integrity, SRF, and DRIL, may aid in guiding treatment expectation assessment and personalized intervention strategies.Nomogram constructed in this study shows good clinical applicability and may serve as a decision-support tool to improve the precision of DME management.

Objective:To establish machine learning models to predict visual improvement and anatomical response after anti-vascular endothelial growth factor (VEGF) treatment in patients with diabetic macular edema (DME).Methods:A multi-algorithm machine learning predictive modeling study based on retrospective clinical data was conducted.A total of 225 patients with DME who received their first intravitreal anti-VEGF injection at Fuzhou University Affiliated Provincial Hospital were enrolled between January 2023 and April 2025.According to data completeness, 204 cases were included in the visual recovery prediction model and 201 cases were included in the anatomical response prediction model.Baseline data included optical coherence tomography (OCT) features and blood biomarkers.The primary outcomes were defined as an improvement of ≥1 line in visual acuity and a reduction of ≥20% in central retinal thickness (CRT) after anti-VEGF treatment.Feature selection was performed using univariate logistic regression and Lasso regression.Four machine learning algorithms, logistic regression (LR), decision tree, multilayer perceptron, and random forest, were trained and validated.Model performance was evaluated using accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and decision curve analysis.The best-performing model was further interpreted using SHAP analysis, and a nomogram was constructed for clinical application.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Fuzhou University Affiliated Provincial Hospital (No.K2025-03-064).Written informed consent was obtained from each subject.Results:Among the 37 baseline variables, five key predictors were identified for the outcome of ≥20% CRT reduction: baseline CRT, baseline CRT ≥400 μm, presence of subretinal fluid (SRF), disorganization of the retinal inner layers (DRIL), and integrity of the ellipsoid zone (EZ).Among the four models, the LR model had the best performance, with an accuracy of 0.88, sensitivity of 0.94, specificity of 0.70, and an AUC of 0.94 (95% confidence interval [ CI]: 0.87-1.00).SHAP analysis showed that baseline CRT ≥400 μm, DRIL, SRF, and baseline CRT contributed positively to the outcome, while EZ integrity was a negative predictor for CRT reduction.For the outcome of ≥1-line visual improvement, two key predictors were identified: baseline best corrected visual acuity (BCVA) and EZ integrity.Both baseline BCVA and EZ integrity were negative predictors for ≥1-line visual improvement.The LR model also had the best performance in the internal validation cohort, with an accuracy of 0.71, sensitivity of 0.67, specificity of 0.75, and an AUC of 0.76 (95% confidence interval [ CI]: 0.61-0.91).A visual nomogram was developed based on the selected predictors and the best-performing model.By converting patient-specific clinical characteristics into scores, clinicians can calculate a total score and estimate the probability of achieving a reduction of ≥20% in CRT and a ≥1-line improvement in visual acuity after anti-VEGF therapy. Conclusions:Machine learning-based model building can effectively predict visual and anatomical response following anti-VEGF treatment in DME patients.Logistic regression shows robust predictive performance for both outcomes.Identification of key predictors, especially OCT features such as EZ integrity, SRF, and DRIL, may aid in guiding treatment expectation assessment and personalized intervention strategies.Nomogram constructed in this study shows good clinical applicability and may serve as a decision-support tool to improve the precision of DME management.

Objective:To develop a post competence assessment scale for nurses in the health management (physical examination) center and assess its reliability and validity.Methods:This study adopted an empirical approach. A total of 801 nurses from the health management (physical examination) center were recruited to participate in this study. A research team was formed in August 2024. This team transformed the previously constructed core competence evaluation index system for health management specialist nurses in the health management (physical examination) center (comprising 6 first-level indicators and 70 third-level indicators) into a preliminary post competence assessment scale. Seven experts evaluated the content validity of the scale. In September 2024, a pilot survey was conducted among 27 nurses from the health management (physical examination) center of Sichuan Provincial People′s Hospital using convenience sampling. From October to November 2024, the first main survey was administered to 385 nurses of health management (physical examination) center across 54 cities in China using both convenience sampling and snowball sampling methods, followed by exploratory factor analysis (EFA). Subsequently, utilizing the refined scale obtained after eliminating certain items, a second main survey was conducted among 389 nurses in the health management (physical examination) center, followed by a confirmatory factor analysis (CFA). The reliability of the final scale was assessed using Cronbach′s α coefficient, split-half reliability, composite reliability, and test-retest reliability.Results:The finalized scale for nurses′ post competency in health management (physical examination) center comprises five dimensions—basic nursing service competency, health management practice competency, knowledge integration competency, professional development competency, and professional attitude—with a total of 57 items. The item level content validity index (I-CVI) of the items of the content validity display scale ranged from 0.857 to 1.000, and the content validity index of each dimension ranged from 0.984 to 1.000. The scale-level Content Validity index/average (S-CVI/Ave) was 0.995. The contribution rate of the 6 factors extracted by EFA was 74.07%. After group discussion and CFA, the scale of the 5-factor structural equation model was constructed. The total Cronbach′s α coefficient of the scale was 0.986, the split-half reliability was 0.865, the composite reliability was 0.960-0.980, the total table test-retest reliability was 0.762, and the test-retest reliability of each dimension was 0.681-0.731.Conclusion:The developed assessment scale for assessing the post competence of nurses in the health management (physical examination) center demonstrates excellent reliability and validity.

Objective To establish an animal model of dampness-syndrome in mice (single model) and evaluate its characteristics of dampness-syndrome. The above-mentioned mice with dampness syndrome were used to construct mice model of ischemic stroke (double model) and observe the effect of dampness-pathogenic on the outcome of stroke. Methods Healthy C57BL/6J male mice were randomly divided into dampness-syndrome (including sham-surgery group and ischemic stroke group,with 10 mice in each group) and non dampness-syndrome groups (including sham-surgery group and ischemic stroke group,with 10 mice in each group). The dampness-syndrome group was fed with high-fat diet and the non dampness-syndrome group was fed with normal diet for 12 weeks. After the mice model of dampness-syndrome was successfully established,transient middle cerebral artery occlusion/reperfusion (tMCAO/R) surgery was used to replicate an ischemic stroke mice model. Evaluation indicators for dampness-syndrome mice model:the general status including body weight,morphology,posture,activity status,and physical characteristics,the histopathological observation of the aorta (oil red O staining,Masson-trichrome staining) and liver (HE staining,oil red O staining),electron microscopic observation of the tongue tissue (scanning electron microscopy,electron microscopy),blood lipid levels[total cholesterol(TC),triglycerides(TG)]and liver coefficient. Evaluation indicators for ischemic stroke mice model:neurological function score and the cerebral infarction volume ratio. Results Compared with the non dampness-syndrome group,the mice in the dampness-syndrome group showed an increased in body weight,poor hair color,sparse hair,fatigue and laziness,mental atrophy,anorexia and lethargy. It was observed that the aortic lumen was narrowed,the intima was significantly thickened,lipid plaque deposition was increased,and foam cells were visible. A large amount of red lipid droplets appeared in liver cells. There were obvious lipid infiltration and diffuse steatosis. Increased keratosis of the mucosal layer of tongue tissue,the thicker stratum corneum,lipofuscin,and bacteria on the tongue surface were found. Serum TG and TC levels significantly increased(P<0.01),and the liver coefficient significantly decreased (P<0.001). Compared with non dampness-syndrome group (sham-surgery group),neurological function score and the cerebral infarction volume ratio in dampness-syndrome ischemic stroke group obviously increased (P<0.001). Conclusion High-fat feeding for 12 weeks combined with tMCAO/R modeling can successfully establish a mice model with dampness-syndrome ischemic stroke,and the neurological function score and cerebral infarction volume in the dampness-syndrome ischemic stroke group was more severe than that in the non dampness-syndrome ischemic stroke group.

OBJECTIVE: To establish a deep-learning architecture based on faster region-based convolutional neural networks (Faster R-CNN) algorithm for detection and sorting of red ginseng (Ginseng Radix et Rhizoma Rubra) with internal defects automatically on an online X-ray machine vision system. METHODS: A Faster R-CNN based classifier was trained with around 20 000 samples with mean average precision value (mAP) of 0.95. A traditional image processing method based on feedforward neural network (FNN) obtained a bad performance with the accuracy, recall and specificity of 69.0%, 68.0%, and 70.0%, respectively. Therefore, the Faster R-CNN model was saved to evaluate the model performance on the defective red ginseng online sorting system. RESULTS: An independent set of 2 000 red ginsengs were used to validate the performance of the Faster R-CNN based online sorting system in three parallel tests, achieving accuracy of 95.8%, 95.2% and 96.2%, respectively. CONCLUSION The overall results indicated that the proposed Faster R-CNN based classification model has great potential for non-destructive detection of red ginseng with internal defects.

Objective To analyze the monitoring results of iodine deficiency disorders (IDD) of Dehong State in 2017,to find out the present situation of prevention and control of IDD,and to provide scientific basis for guiding the comprehensive prevention and control of IDD in our state in the future.Methods According to "Yunnan Iodine Deficiency Disorders Monitoring Program",a sampling survey was conducted in 5 counties (cities) of Dehong State,Yunnan Province.Urine and household salt samples were collected from children aged 8 to 10 years old and pregnant women in Mangshi,Lianghe County and Yingjiang County.Iodine content was detected.In addition,household salt samples of Ruili City and Longchuan County were collected to detect iodine content.Results There were 1 609 salt samples from local inhabitants,the coverage rate of iodized salt was 99.19％ (1 596/1 609),the edible rate of qualified iodized salt was 95.03％ (1 529/1 609) and the median of iodized salt was 23.76 mg/kg.The median of urinary iodine in 623 children aged 8 to 10 years old was 221.34 μg/L and the thyroid enlargement rate was 0.48％ (3/623).The median of urinary iodine in 346 pregnant women was 159.52 μg/L.Conclusion The iodine nutrition of children aged 8 to 10 years old and pregnant women of Dehong State is appropriate in 2017.

Objective: To investigate the relationship between personality characteristics and turnover intention of the medical staff in an infectious diseases hospital. Methods: Using the cluster sampling method, a total of 366 members of medical staff were selected from different departments in an infectious disease hospital from May to August, 2013. The general information, such as sex, age, education level, and professional title, were collected and they were subjected to a survey using Cattell’s 16 Personality Factor Questionnaire and Turnover Intention Scale. The data were subjected to logistic regression analysis. Results: Compared with the Chinese norm, the medical staff in the infectious disease hospital had significantly higher scores of intelligence, stability, bullying, excitability, perseverance, social boldness, fantasy, privateness, independence, and self-discipline and significantly lower scores of gregariousness, sensitivity, suspicion, anxiety, and tension (P<0.05). Of the 366 members of medical staff, 22 (6.01%) had a very low turnover intention, low in 152 (41.53%) , high in 61 (16.67%) , and very high in 131 (35.79%). The logistic regression analysis showed that sensitivity, suspicion, fantasy, privateness, anxiety, openness to change, and independence were the risk factors for turnover intention (P<0.05) . Conclusion Compared with the Chinese norm, the medical staff in the infectious disease hospital have a better mental quality and a higher turnover intention. The individuals with sensitivity, suspicion, fantasy, and anxiety are prone to having turnover intention.

Objective To investigate the differential expression of microRNA (miRNA) in schizophrenia (SZ) patients,and explore the comorbidity of SZ and depression disorder based upon miRNA expression.Methods Affymetrix array analysis was used to investigate the differentially expressed miRNA in SZ patients firstly,and then quantitative real-time polymerase chain reaction (qRT-PCR) was further carried out to confirm the selected miRNA in peripheral blood mononuclear cells of 40 SZ patients,whom were administered by Positive and Negative Syndrome Scale (PANSS) and the selected miRNAs in depression disorder patients has also been confirmed by Affymetrix array analysis and qRT-PCR in our previous studies.Results Affymetrix array analysis indicated that there existed 33 miRNAs which differentially expressed (32 up-regulated and 1 down-regulated) compared with normal controls.qRT-PCR results suggested that the expression of 8 miRNAs (miR-1273d,miR-1303,miR-3064-5p,miR3131,miR-3687,miR-4428,miR-4725-3p and miR-5096) were significantly up-regulated in SZ;the miRNA differentially expressed in depression disorder patients also had differential expression in SZ patients (P＜0.05).There were significant correlation between the miRNAs differentially expressed in depression disorder patients and in SZ patients (P＜0.01).MiR-1972 differentially expressed in depression disorder patients had significant positive correlation with the positive symptoms of PANSS (P＜0.05),and miR-26b was positively correlated with composite factor (P＜0.05).Conclusion Comorbidity of SZ and depression disorder is observed not only on the clinical symptoms,but on the molecular genetic basis.

Objective To investigate the effects of different doses of oxycodone on postoperative pain and stress response in patients undergoing gynecological laparoscopic surgery. Methods Sixty patients scheduled for gynecological laparoscopy,aging from 18 to 50 years old,of ASA Ⅰ or Ⅱ,were included and randomized into three groups:control group (group C),low dose of oxycodone group (group L),high dose of oxycodone group (group H),20 cases in each group.Pa-tients in group L,H received 0.05,0.1 mg/kg oxycodone respectively while paitents in group C re-ceived saline 5 ml 1 5 min before the end of the surgery.Visual analogue scale(VAS)pain score and RASS score were measured on 1,6,12 and 24 h postoperatively.Glucose and serum cortisol were also measured before the operation and on time points of 6,12 and 24 h after the operation.Adverse effects were recorded too.Results Compared with group C,VAS were significantly lower in group L and H within 1 hour postoperatively.(P <0.05).VAS was significantly lower in group H than that in group C and L at 6 h postoperatively (P <0.05 ).The RASS scores of group L and H were significantly lower than those in group C (P <0.05)at 1 h postoperatively.Blood glucose and serum cortisol of group L and H increased at 6,12 and 24 h after operation (P <0.05).Compared with group C,blood glucose and serum cortisol were significantly lower in group L and group H at 6,12 h after operation (P <0.05).There was no significant difference in the incidence of adverse reactions in each group. Conclusion Oxycodone 0.1 mg/kg injected before the end of gynecological laparoscopic surgery could effectively relieve postoperative pain with less adverse reactions,and decrease postoperation stress re-sponse.