ObjectiveTo explore the relationship between acne vulgaris and gut microbiota. MethodsA total of 29 clinical cases diagnosed with moderate-to-severe acne vulgaris and 26 healthy individuals as control subjects were recruited. Fecal specimens were collected from all participants， and further analysis of gut microbial communities was performed by leveraging high-throughput sequencing techniques that target the hypervariable regions of 16S rRNA genes. ResultsAssociations between acne vulgaris and alterations in gut microbiota were identified. At the phylum level， the relative abundance of Bacteroidota exhibited a statistically significant elevation in the acne vulgaris cohort when compared with the healthy control group （P<0.01）， while Cyanobacteria was significantly lower in the acne group （P<0.01）. At the genus level， the top five different bacterial taxa in both groups were Bacteroides， Escherichia⁃Shigella， Klebsiella， Roseburia， and Parabacteroides. Among them， Bacteroides， Roseburia， and Parabacteroides were more abundant in acne patients. Linear discriminant analysis identified five biomarkers all belonging to the Bacteroidota phylum in the acne and control groups. These biomarkers belong to the phylum Bacteroidetes. ConclusionThere are significant differences in the composition of intestinal microbiota between acne patients and healthy people. Changes in the richness of specific bacterial genera may become new targets for the diagnosis and treatment of acne.

ObjectiveTo investigate the mechanism of Xiezhuo Jiedu prescription in the treatment of ulcerative colitis （UC） by inhibiting ferroptosis and alleviating intestinal mucosal injury based on the nuclear factor E₂ related factor 2/solute carrier family 7 member/glutathione peroxidase 4 （Nrf2/SLC7A11/GPX4） signaling pathway. MethodsA total of 60 male SD rats were divided into a normal group， a model group， high- and low-dose Xiezhuo Jiedu prescription groups （26.64 and 13.32 g·kg^-1， respectively）， a ferroptosis inhibitor group （Ferrostatin-1， 0.005 g·kg^-1）， and a mesalazine group （0.27 g·kg^-1）， with 10 rats in each group. A UC rat model was established by intrarectal administration of trinitrobenzene sulfonic acid （TNBS）-ethanol. The normal group and the model group were intragastrically administered normal saline. The other groups were given intragastric administration according to the corresponding dosage for 7 d. The general condition， disease activity index （DAI） score， colon length， and mucosal injury index （CDMI） score were observed in each group. The pathological changes of colon tissue in each group were observed by hematoxylin-eosin （HE） staining. The intestinal mucosa and mitochondrial morphology in each group were observed by transmission electron microscopy. The expression levels of Occludin， Claudin-1， mucin 2 （MUC2）， and E-cadherin in intestinal tissue were detected by immunofluorescence （IF）. Enzyme-linked immunosorbent assay （ELISA） was used to detect the expression levels of serum tumor necrosis factor-α （TNF-α）， interleukin-6 （IL-6）， and interleukin-10 （IL-10） in each group， and a lactic acid assay kit or ELISA was employed to detect the expression levels of reactive oxygen species （ROS）， ferrous ions （Fe²⁺）， glutathione （GSH）， malondialdehyde （MDA）， 4-hydroxynonenal （4-HNE）， diamine oxidase （DAO）， and D-lactate （D-LA）. Real-time quantitative polymerase chain reaction （Real-time PCR） was applied to detect the mRNA expression levels of Nrf2， SLC7A11， GPX4， Occludin， Claudin-1， MUC2， and E-cadherin in each group， and Western blot was adopted to detect the protein expression levels of Nrf2， p-Nrf2， SLC7A11， and GPX4 in each group. ResultsCompared with the normal group， rats in the model group exhibited listlessness， sluggish response， and mucopurulent and bloody stools. The model group also showed significantly increased DAI score， colon length， CDMI score， and expression levels of TNF-α， IL-6， ROS， Fe²⁺， MDA， 4-HNE， DAO， and D-LA （P<0.01）. In addition， it presented significantly decreased IF values of Occludin， Claudin-1， MUC2， and E-cadherin and mRNA and protein expression levels of IL-10， GSH， Nrf2， p-Nrf2， SLC7A11， and GPX4 （P<0.01）. There were different degrees of improvement in each administration group after treatment， and the improvement was the most significant in the high-dose Xiezhuo Jiedu prescription group （P<0.01）. ConclusionXiezhuo Jiedu prescription may alleviate intestinal mucosal injury by inhibiting ferroptosis of intestinal epithelial cells via regulating the Nrf2/SLC7A11/GPX4 signaling pathway， thereby exhibiting efficacy in the treatment of UC.

ObjectiveThis paper aims to explore the risk factors for chronic atrophic gastritis （CAG） with turbidity toxin accumulation syndrome and establish a prediction model. MethodsClinical data of 180 patients with CAG who participated in the "clinical study of Xianglian Huazhuo Particles blocking CAG cancer transformation" of Hebei Sheng Zhong Yi Yuan from July 2021 to March 2022 were collected. After confounding factors were controlled by propensity score matching， patients were divided into a training set （namely dev） and a validation set （namely vad） in a seven to three ratio. The risk factors for CAG with turbidity toxin accumulation syndrome in the training set were investigated by using univariate Logistic regression analysis and least absolute shrinkage and selection operator （namely Lasso） regression algorithms. Subsequently， a model， named model 1se， was developed by using the training set data to predict the risk factors for CAG with turbidity toxin accumulation syndrome. The accuracy of the prediction model was assessed by using various methods， including the receiver operating characteristic （ROC） curve， Hosmer-Lemeshow test （H-L）， calibration plot， and decision curve analysis （DCA）. ResultsAge， body mass index （BMI）， family history of cancer， job and life satisfaction， yellow and greasy fur with slippery pulse， and heavy body sensation were independent risk factors of the model. The prediction model showed excellent predictive value for both the training and validation sets. ConclusionThe established prediction model for CAG with turbidity toxin accumulation syndrome has high discrimination and excellent calibration， which could provide an excellent clinical basis for disease diagnosis and individualized treatment of patients.

ObjectiveThis paper aims to explore the risk factors for chronic atrophic gastritis （CAG） with turbidity toxin accumulation syndrome and establish a prediction model. MethodsClinical data of 180 patients with CAG who participated in the "clinical study of Xianglian Huazhuo Particles blocking CAG cancer transformation" of Hebei Sheng Zhong Yi Yuan from July 2021 to March 2022 were collected. After confounding factors were controlled by propensity score matching， patients were divided into a training set （namely dev） and a validation set （namely vad） in a seven to three ratio. The risk factors for CAG with turbidity toxin accumulation syndrome in the training set were investigated by using univariate Logistic regression analysis and least absolute shrinkage and selection operator （namely Lasso） regression algorithms. Subsequently， a model， named model 1se， was developed by using the training set data to predict the risk factors for CAG with turbidity toxin accumulation syndrome. The accuracy of the prediction model was assessed by using various methods， including the receiver operating characteristic （ROC） curve， Hosmer-Lemeshow test （H-L）， calibration plot， and decision curve analysis （DCA）. ResultsAge， body mass index （BMI）， family history of cancer， job and life satisfaction， yellow and greasy fur with slippery pulse， and heavy body sensation were independent risk factors of the model. The prediction model showed excellent predictive value for both the training and validation sets. ConclusionThe established prediction model for CAG with turbidity toxin accumulation syndrome has high discrimination and excellent calibration， which could provide an excellent clinical basis for disease diagnosis and individualized treatment of patients.

Objective:To develop a 5-year mortality risk prediction model for patients with small cell neuroendocrine carcinoma of the cervix(SCNEC).Methods:Based on the Surveillance,Epidemiology,and End Results(SEER)database and R software version 4.3.3,variables were screened via Lasso regression,followed by multivariable logistic regression and stepwise regression to develop a 5-year mortality risk prediction model for SCNEC patients.The Akaike Information Criterion(AIC),C-index,receiver operating characteristic(ROC)curve,Hosmer-Lemeshow test,and calibration curve were employed to evaluate the model.Results:Age,M stage,surgical status,and lymph node metastasis were ultimately selected as variables to construct the 5-year mortality risk prediction model for SCNEC patients.The model demonstrated superior predictive performance compared to FIGO staging(P＜0.01).The Hosmer-Lemeshow test yielded a P-value＞0.05.The C-index values for the training and validation sets were 0.808 and 0.755,respectively,with the areas under the ROC curves of 0.826 and 0.744.The calibration curves of the model fluctuated near the diagonal line,indicating good agreement between predicted and observed outcomes.The decision curve analysis demonstrated significant clinical net benefit.Results showed that higher mortality risk was associated with advanced age,M1 status,lymph node metastasis,and lack of surgical opportunity.Conclusions:The model exhibits good discriminatory power and accuracy,providing significant benefits to patients.Enhanced management should be implemented for patients with advanced age,distant metastasis,lymph node metastasis,or ineligibility for surgery.Lymph node metastasis is an independent risk factor for 5-year mortality in patients with SCNEC.

Objective To develop a platform for reporting medication near miss events and evaluate its application effectiveness,aiming to enhance medication safety of patients.Methods Based on literature review,qualitative interviews,and expert group meetings,a medication near-miss event reporting platform was constructed,including 4 modules:event content filling,event risk grading,event handling,and statistical analysis.50 nurses were conveniently selected from the pediatric ward of a tertiary grade A hospital in Henan Province as the application subjects.The reporting situation and filling duration of medication near miss events,the score of the Medication Near Miss Reporting Disorder Scale,and the incidence of medication near miss events were compared after the application of the platform(from March to August 2023)and before the application(from September 2022 to February 2023).Results The reporting rate of medication near miss events after the application of the platform was higher than that before the application of the platform,and the comparison of the distribution of event nature and occurrence links showed statistically significant differences(P＜0.05).After the application of the platform,the reporting duration of medication near miss events was shorter than that before the application of the platform,and the score of the Medication Near Miss Reporting Disorder Scale was lower than that before the application of the platform.The differences were statistically significant(P＜0.001).There was no statistically significant difference in the incidence of medication near miss events before and after the application of the platform(P=0.241).Conclusion Using this platform can help improve the reporting rate of medication near miss events,reduce the time taken to fill out reports,and minimize reporting barriers for nurses.

Objective:To investigate the status quo of cognitive frailty in elderly patients with heart failure and analyze its influencing factors, so as to provide evidence for healthcare professionals to formulate effective intervention strategies.Methods:A total of 330 elderly patients with heart failure admitted to the First Affiliated Hospital of the University of South China and the Second Affiliated Hospital of the University of South China from October 2023 to January 2024 were selected as the study objects by convenience sampling method. General data questionnaire, Frailty Phenotype, Montreal Cognitive Assessment, Clinical Dementia Rating Scale, Geriatric Depression Scale-15 and Short Form Mini Nutritional Assessment were used for a sectional investigation. Random forest algorithm was used to rank the importance of variables and binary logistic regression was combined to explore the influencing factors of elderly patients with heart failure.Results:According to the evaluation criteria of cognitive frailty, 330 elderly patients with heart failure were divided into cognitive frailty group (124 cases) and non-cognitive frailty group (206 cases). The incidence of cognitive frailty was 37.6% (124/330). Among which, the median age of the cognitive frailty group was 73 years old, with 63 males and 61 females. The median age of the non-cognitive frailty group was 71 years old, with 117 males and 89 females. The random forest results showed that the top 7 variables in importance ranking were weekly intellectual activity, frequency of physical exercise, age, educational levels, depression status, cardiac function grade and risk of malnutrition. Binary logistic regression analysis showed that weekly intellectual activity ( OR=0.076, 95% CI 0.027-0.216), requency of physical exercise ( OR=0.184, 95% CI 0.079-0.430), age ( OR=1.173, 95% CI 1.077-1.277), educational levels ( OR=0.283, 95% CI 0.143-0.559), depression status ( OR=4.440, 95% CI 1.451-13.585), cardiac function grade ( OR=3.030, 95% CI 1.673-5.489) and risk of malnutrition ( OR=3.833, 95% CI 1.530-9.602) were the main influencing factors (all P<0.05). Conclusions:The incidence of cognitive frailty in elderly patients with heart failure is high. Healthcare professionals ought to focus on the screening and assessing of cognitive frailty in elderly patients with heart failure, and formulate effective intervention strategies by considering the above influencing factors to mitigate the occurrence of cognitive frailty.

Objective:To develop a 5-year mortality risk prediction model for patients with small cell neuroendocrine carcinoma of the cervix(SCNEC).Methods:Based on the Surveillance,Epidemiology,and End Results(SEER)database and R software version 4.3.3,variables were screened via Lasso regression,followed by multivariable logistic regression and stepwise regression to develop a 5-year mortality risk prediction model for SCNEC patients.The Akaike Information Criterion(AIC),C-index,receiver operating characteristic(ROC)curve,Hosmer-Lemeshow test,and calibration curve were employed to evaluate the model.Results:Age,M stage,surgical status,and lymph node metastasis were ultimately selected as variables to construct the 5-year mortality risk prediction model for SCNEC patients.The model demonstrated superior predictive performance compared to FIGO staging(P＜0.01).The Hosmer-Lemeshow test yielded a P-value＞0.05.The C-index values for the training and validation sets were 0.808 and 0.755,respectively,with the areas under the ROC curves of 0.826 and 0.744.The calibration curves of the model fluctuated near the diagonal line,indicating good agreement between predicted and observed outcomes.The decision curve analysis demonstrated significant clinical net benefit.Results showed that higher mortality risk was associated with advanced age,M1 status,lymph node metastasis,and lack of surgical opportunity.Conclusions:The model exhibits good discriminatory power and accuracy,providing significant benefits to patients.Enhanced management should be implemented for patients with advanced age,distant metastasis,lymph node metastasis,or ineligibility for surgery.Lymph node metastasis is an independent risk factor for 5-year mortality in patients with SCNEC.

Objective:To investigate the status quo of cognitive frailty in elderly patients with heart failure and analyze its influencing factors, so as to provide evidence for healthcare professionals to formulate effective intervention strategies.Methods:A total of 330 elderly patients with heart failure admitted to the First Affiliated Hospital of the University of South China and the Second Affiliated Hospital of the University of South China from October 2023 to January 2024 were selected as the study objects by convenience sampling method. General data questionnaire, Frailty Phenotype, Montreal Cognitive Assessment, Clinical Dementia Rating Scale, Geriatric Depression Scale-15 and Short Form Mini Nutritional Assessment were used for a sectional investigation. Random forest algorithm was used to rank the importance of variables and binary logistic regression was combined to explore the influencing factors of elderly patients with heart failure.Results:According to the evaluation criteria of cognitive frailty, 330 elderly patients with heart failure were divided into cognitive frailty group (124 cases) and non-cognitive frailty group (206 cases). The incidence of cognitive frailty was 37.6% (124/330). Among which, the median age of the cognitive frailty group was 73 years old, with 63 males and 61 females. The median age of the non-cognitive frailty group was 71 years old, with 117 males and 89 females. The random forest results showed that the top 7 variables in importance ranking were weekly intellectual activity, frequency of physical exercise, age, educational levels, depression status, cardiac function grade and risk of malnutrition. Binary logistic regression analysis showed that weekly intellectual activity ( OR=0.076, 95% CI 0.027-0.216), requency of physical exercise ( OR=0.184, 95% CI 0.079-0.430), age ( OR=1.173, 95% CI 1.077-1.277), educational levels ( OR=0.283, 95% CI 0.143-0.559), depression status ( OR=4.440, 95% CI 1.451-13.585), cardiac function grade ( OR=3.030, 95% CI 1.673-5.489) and risk of malnutrition ( OR=3.833, 95% CI 1.530-9.602) were the main influencing factors (all P<0.05). Conclusions:The incidence of cognitive frailty in elderly patients with heart failure is high. Healthcare professionals ought to focus on the screening and assessing of cognitive frailty in elderly patients with heart failure, and formulate effective intervention strategies by considering the above influencing factors to mitigate the occurrence of cognitive frailty.

Objective To develop a platform for reporting medication near miss events and evaluate its application effectiveness,aiming to enhance medication safety of patients.Methods Based on literature review,qualitative interviews,and expert group meetings,a medication near-miss event reporting platform was constructed,including 4 modules:event content filling,event risk grading,event handling,and statistical analysis.50 nurses were conveniently selected from the pediatric ward of a tertiary grade A hospital in Henan Province as the application subjects.The reporting situation and filling duration of medication near miss events,the score of the Medication Near Miss Reporting Disorder Scale,and the incidence of medication near miss events were compared after the application of the platform(from March to August 2023)and before the application(from September 2022 to February 2023).Results The reporting rate of medication near miss events after the application of the platform was higher than that before the application of the platform,and the comparison of the distribution of event nature and occurrence links showed statistically significant differences(P＜0.05).After the application of the platform,the reporting duration of medication near miss events was shorter than that before the application of the platform,and the score of the Medication Near Miss Reporting Disorder Scale was lower than that before the application of the platform.The differences were statistically significant(P＜0.001).There was no statistically significant difference in the incidence of medication near miss events before and after the application of the platform(P=0.241).Conclusion Using this platform can help improve the reporting rate of medication near miss events,reduce the time taken to fill out reports,and minimize reporting barriers for nurses.