Objective:To construct logistic regression, decision tree, and neural network models to predict pressure injury in neurocritically ill patients using machine learning algorithms, and compare the predictive performance of the three models.Methods:The clinical data of 341 neurocritically ill patients who received treatment in the Department of Neurosurgery at The First Affiliated Hospital of Wenzhou Medical University from May 2020 to February 2023 were collected retrospectively. The patients were randomly divided into a training set and a testing set in a 7:3 ratio. Univariate and multivariate analyses were conducted based on the clinical data from the training set. According to the results of the multivariate analysis, logistic regression, decision tree, and neural network models were constructed. The predictive performance of the three models was validated and compared using receiver operating characteristic curve analysis.Results:Among the 341 patients, 35 developed pressure injury (a total of 40 occurrences), with an incidence rate of 10.26%. Multivariate analysis indicated that incontinence ( OR = 47.32, 95% CI: 1.360-1 647.700), decreased albumin levels ( OR = 0.56, 95% CI: 0.360-0.870), increased sensory ability ( OR = 0.00, 95% CI: 0.000-0.190), and increased mobility ( OR = 0.03, 95% CI: 0.000-0.390) were independent risk factors for pressure injury in neurocritically ill patients (all P < 0.05). Based on these independent risk factors, logistic regression, decision tree, and neural network models were constructed. Receiver operating characteristic curve analysis revealed that the area under the curve for the three models was 0.987 (95% CI: 0.941-0.999), 0.945 (95% CI: 0.881-0.980), and 0.908 (95% CI: 0.834-0.956), respectively. These results suggest that all three models exhibited high predictive performance for pressure injury in neurocritically ill patients, with the logistic regression model showing a significantly greater area under the curve than the neural network model. Conclusions:The occurrence of pressure injury in neurocritically ill patients is closely related to incontinence, albumin levels, sensory ability, and mobility. Constructing predictive models using machine learning algorithms can provide valuable insights for the early prevention and management of pressure injury in neurocritically ill patients.

Objective To investigate the clinical effectiveness of a nursing safety program for laying moxibustion treatment in sub-health population based on the Delphi method.Methods 160 sub-health patients who sought treatment between April 2022 and August 2024 in Hangzhou Red Cross Hospital were selected and randomly divided into observation group(n=80)and control group(n=80).Both groups received laying moxibustion rehabilitation therapy.The control group received routine nursing care,while the observation group received nursing intervention based on the laying moxibustion nursing safety program developed through the Delphi method.Results Through Delphi method questionnaire survey of 15 experts,three main factors affecting laying moxibustion safety were identified and 11 specific nursing points were established.The incidence of adverse events in observation group was significantly lower than that in control group(P＜0.05),and nursing satisfaction was significantly higher than that in control group(P＜0.05).Conclusion The laying moxibustion nursing safety program constructed based on the Delphi method can effectively enhance the clinical safety of laying moxibustion treatment.

Objective:To construct logistic regression, decision tree, and neural network models to predict pressure injury in neurocritically ill patients using machine learning algorithms, and compare the predictive performance of the three models.Methods:The clinical data of 341 neurocritically ill patients who received treatment in the Department of Neurosurgery at The First Affiliated Hospital of Wenzhou Medical University from May 2020 to February 2023 were collected retrospectively. The patients were randomly divided into a training set and a testing set in a 7:3 ratio. Univariate and multivariate analyses were conducted based on the clinical data from the training set. According to the results of the multivariate analysis, logistic regression, decision tree, and neural network models were constructed. The predictive performance of the three models was validated and compared using receiver operating characteristic curve analysis.Results:Among the 341 patients, 35 developed pressure injury (a total of 40 occurrences), with an incidence rate of 10.26%. Multivariate analysis indicated that incontinence ( OR = 47.32, 95% CI: 1.360-1 647.700), decreased albumin levels ( OR = 0.56, 95% CI: 0.360-0.870), increased sensory ability ( OR = 0.00, 95% CI: 0.000-0.190), and increased mobility ( OR = 0.03, 95% CI: 0.000-0.390) were independent risk factors for pressure injury in neurocritically ill patients (all P < 0.05). Based on these independent risk factors, logistic regression, decision tree, and neural network models were constructed. Receiver operating characteristic curve analysis revealed that the area under the curve for the three models was 0.987 (95% CI: 0.941-0.999), 0.945 (95% CI: 0.881-0.980), and 0.908 (95% CI: 0.834-0.956), respectively. These results suggest that all three models exhibited high predictive performance for pressure injury in neurocritically ill patients, with the logistic regression model showing a significantly greater area under the curve than the neural network model. Conclusions:The occurrence of pressure injury in neurocritically ill patients is closely related to incontinence, albumin levels, sensory ability, and mobility. Constructing predictive models using machine learning algorithms can provide valuable insights for the early prevention and management of pressure injury in neurocritically ill patients.

ObjectiveTo explore the listed varieties and prescription characteristics of Chinese patent medicines for stroke in China， explore the medication rules of Chinese medicine for stroke， and provide guidance for further clinical research and development of Chinese patent medicines. MethodsExcel 2021 and the Ancient and Modern Medical Record Cloud Platform （V2.3.5） were used to systematically mine and analyze the varieties and prescriptions of Chinese patent medicines for stroke in China. ResultsA total of 244 Chinese patent medicines （two for different dosage forms of the same prescription）， 1 736 approval documents for Chinese patent medicines， 792 manufacturers， and 83 varieties of protected Chinese patent medicines were finally included in the database. The top three dosage forms were capsules （75）， pills （53）， and tablets （42）. There were 28 Chinese patent medicines for stroke in the National Essential Drug Catalogue （2018）， 129 in the National Essential Medical Insurance， Industrial Injury Insurance and Maternity Insurance Drug Catalogue （2023）， and 4 in the National Non-prescription Drug Catalogue. Among the 138 prescriptions screened out， Chinese patent medicines mainly treated stroke patients with the syndrome of Qi deficiency and blood stasis. The top three most frequent medicinal herbs were Chuanxiong Rhizoma （63）， Pheretima （47）， and Salviae Miltiorrhizae Radix et Rhizoma （47）. The medicinal herbs used were mainly warm， pungent， with the meridian tropism to the liver meridian. The correlation analysis showed that the herb pair with the highest support was Astragali Radix-Chuanxiong Rhizoma， and that with the highest confidence was Carthami Flos-Chuanxiong Rhizoma. Five herb combinations were identified based on the cluster analysis. ConclusionThe Chinese patent medicines for stroke mainly treat patients with the syndrome of Qi deficiency and blood stasis. The medicinal herbs used in the prescriptions mainly have the functions of activating blood and resolving stasis， extinguishing wind and stopping convulsions. Drug compatibility usually focuses on activating blood and resolving stasis， as well as expelling phlegm and opening orifices. This review of the varieties and prescription characteristics of Chinese patent medicines for stroke helps optimize clinical decision-making， guide drug research and development， promote medical research and scientific progress， and provide more effective support and guarantee for the treatment of stroke patients.

Objective:To explore the latent-classes of HIV high risk behaviors among college students, and the association between experiences of intimate partner violence (IPV) and HIV high risk behaviors, to provide evidence for reducing the HIV high risk behaviors among them.Methods:A cross-sectional study was conducted from October to December 2019 among university students from six higher education institutions in Zhuhai City, using a multi-stage cluster sampling method, with an estimated sample size of 1 318. The study included participants who self-reported being in a romantic relationship and having sexual experience within the past year. Data on sociodemographic characteristics, IPV experiences, and HIV high risk behaviors were collected. Latent-class analysis was performed on HIV high risk behaviors, and chi-squared tests and multivariable logistic regression were used to analyze the associations between IPV experiences and different latent classes of HIV high risk behaviors.Results:The effective response rate for the survey was 95.4% (12 235/12 821). 1 382 college students from Zhuhai were included as participants in the study, with 19.4% (268/1 382) self-reporting having experienced IPV. Latent-class analysis of HIV high risk behaviors classified the participants into three latent groups: low-risk group (78.1%, 1 079/1 382), multiple sexual partners/alcohol use before sex group (15.8%, 219/1 382), and high-risk group (6.1%, 84/1 382). Multivariable logistic regression analysis showed that students who had experienced psychological violence were more likely to be in the group that had multiple sexual partners/alcohol use before sex (a OR=2.51, 95% CI:1.48-4.27). Those who had experienced IPV (a OR=5.74, 95% CI:3.45-9.55), physical violence (a OR=9.26, 95% CI: 5.24-16.35), sexual violence (a OR=8.46, 95% CI:4.93-14.52), or psychological violence (a OR=15.99, 95% CI:8.64-29.57) were more likely to be in the high-risk group. Students who experienced two (a OR=9.37, 95% CI:3.55-24.71) or three types of IPV (a OR=50.09, 95% CI: 21.06-119.14) were more likely to be in the high-risk group compared to those with no IPV experiences. Conclusions:HIV high risk behaviors among college students in Zhuhai exhibited heterogeneity across different latent groups, and these groups have different associations with IPV experiences. Universities should tailor targeted HIV/AIDS education and prevention strategies based on the characteristics of each latent group to reduce HIV high risk behaviors among college students.

Objective To investigate the effect of the rs3765467 polymorphism of glucagon-like peptide-1 receptor(GLP-1R)gene on the efficacy of Liraglutide(Lir)in patients with type 2 diabetes mellitus(T2DM)and metabolic associated fatty liver disease(MAFLD).Methods A total of 281 patients with T2DM from May 2022 to May 2023 were selected,including 125 patients with simple T2DM(T2DM group)and 156 patients with T2DM combined with MAFLD(T2DM+MAFLD group).120 healthy individuals during the same period were selected as the control(NC)group.The related indexes of glucose and lipid metabolism were detected.The polymorphism of GLP-1R gene rs3765467 was detected.Results BMI,FPG,HbA1c,HOMA-IR and TG in each group increased in turn(P<0.05),while the distribution frequency of genotype GG and allele G decreased in turn(P<0.05).TC and LDL-C in T2DM and T2DM+MAFLD groups were higher than those in NC group(P<0.05).TC and TG levels in genotype GA/AA patients were significantly higher than those in genotype GG patients(P<0.05).Compared with before treatment,the levels of BMI,FPG,HbA1c,HOMA-IR,TC,TG and LDL-C in T2DM patients with MAFLD were significantly decreased after Lir treatment(P<0.05).There was no significant difference in BMI and related indexes of glucose and lipid metabolism in GG and GA/AA patients before and after Lir treatment(P>0.05).Conclusions The distribution frequency of GG and G allele at rs3765467 of GLP-1R gene is reduced in T2DM patients with MAFLD.The carrying of allele A was associated with increased TC and TG levels,but did not affect the efficacy of Lir in reducing weight and improving glycolipid metabolism.

Objective To investigate the clinical effectiveness of a nursing safety program for laying moxibustion treatment in sub-health population based on the Delphi method.Methods 160 sub-health patients who sought treatment between April 2022 and August 2024 in Hangzhou Red Cross Hospital were selected and randomly divided into observation group(n=80)and control group(n=80).Both groups received laying moxibustion rehabilitation therapy.The control group received routine nursing care,while the observation group received nursing intervention based on the laying moxibustion nursing safety program developed through the Delphi method.Results Through Delphi method questionnaire survey of 15 experts,three main factors affecting laying moxibustion safety were identified and 11 specific nursing points were established.The incidence of adverse events in observation group was significantly lower than that in control group(P＜0.05),and nursing satisfaction was significantly higher than that in control group(P＜0.05).Conclusion The laying moxibustion nursing safety program constructed based on the Delphi method can effectively enhance the clinical safety of laying moxibustion treatment.

Objective:To explore the latent-classes of HIV high risk behaviors among college students, and the association between experiences of intimate partner violence (IPV) and HIV high risk behaviors, to provide evidence for reducing the HIV high risk behaviors among them.Methods:A cross-sectional study was conducted from October to December 2019 among university students from six higher education institutions in Zhuhai City, using a multi-stage cluster sampling method, with an estimated sample size of 1 318. The study included participants who self-reported being in a romantic relationship and having sexual experience within the past year. Data on sociodemographic characteristics, IPV experiences, and HIV high risk behaviors were collected. Latent-class analysis was performed on HIV high risk behaviors, and chi-squared tests and multivariable logistic regression were used to analyze the associations between IPV experiences and different latent classes of HIV high risk behaviors.Results:The effective response rate for the survey was 95.4% (12 235/12 821). 1 382 college students from Zhuhai were included as participants in the study, with 19.4% (268/1 382) self-reporting having experienced IPV. Latent-class analysis of HIV high risk behaviors classified the participants into three latent groups: low-risk group (78.1%, 1 079/1 382), multiple sexual partners/alcohol use before sex group (15.8%, 219/1 382), and high-risk group (6.1%, 84/1 382). Multivariable logistic regression analysis showed that students who had experienced psychological violence were more likely to be in the group that had multiple sexual partners/alcohol use before sex (a OR=2.51, 95% CI:1.48-4.27). Those who had experienced IPV (a OR=5.74, 95% CI:3.45-9.55), physical violence (a OR=9.26, 95% CI: 5.24-16.35), sexual violence (a OR=8.46, 95% CI:4.93-14.52), or psychological violence (a OR=15.99, 95% CI:8.64-29.57) were more likely to be in the high-risk group. Students who experienced two (a OR=9.37, 95% CI:3.55-24.71) or three types of IPV (a OR=50.09, 95% CI: 21.06-119.14) were more likely to be in the high-risk group compared to those with no IPV experiences. Conclusions:HIV high risk behaviors among college students in Zhuhai exhibited heterogeneity across different latent groups, and these groups have different associations with IPV experiences. Universities should tailor targeted HIV/AIDS education and prevention strategies based on the characteristics of each latent group to reduce HIV high risk behaviors among college students.

Objective:To explore latent categories of decent work perception among nurses in tuberculosis wards and analyze their influencing factors.Methods:Convenience sampling was used to select tuberculosis ward nurses from 13 general or specialized hospitals in China in July 2024 for the study. General Information Questionnaire, Decent Work Perception Scale (DWPS), Work-Family Support Scale, and Connor-Davidson Resilience Scale were used to survey the tuberculosis ward nurses. Mplus 8.3 software was used for latent profile analysis of decent work perception of nurses in tuberculosis wards. Unordered multicategorical Logistic regression was used to explore the factors influencing the latent categories of decent work perception for nurses in tuberculosis wards.Results:A total of 920 questionnaires were distributed, and 833 valid questionnaires were recovered, with a valid recovery rate of 90.54%. The mean DWPS item score of 833 tuberculosis ward nurses was [3.44 (2.94, 3.88) ]. Decent work perceptions of tuberculosis ward nurses were classified into three latent categories, namely, low decent perception-low occupational recognition type (15.61%, 130/833), medium decent perception-moderate occupational recognition type (50.54%, 421/833), and high decent perception-high occupational recognition type (33.85%, 282/833). Unordered multicategorical Logistic regression analysis revealed that organizational support, psychological resilience, job title, monthly income, hospital class, and number of monthly night shifts were the influencing factors of latent categories of decent work perception among nurses in tuberculosis wards.Conclusions:The decent work perception of nurses in tuberculosis wards is at a medium level. Nursing managers should focus on nurses with low decent perception-low occupational recognition and manage and support nurses according to the characteristics and influencing factors of different categories.

Cell death is divided into uncontrolled accidental cell death (ACD) and controllable regulatory cell death (RCD). RCD is also known as programmed cell death (PCD) under physiological conditions. PANoptosis is a recently discovered inflammatory RCD pathway regulated by a cytoplasmic polymeric protein complex called PANoptosome. This complex has the key features of apoptosis, pyrodeath, and/or necrotic apoptosis, but cannot be explained by any of these 3 RCD pathways alone. However, the specific role of PANoptosis in human tumor cells remains to be clarified. At present, the understanding of the molecular mechanism of PANoptosis and the assembly of PANoptosome is very limited, but several important regulatory targets have been identified such as melanoma deficiency factor 2 (AIM2), caspase family members(including CASP3, CASP6, and CASP8), Z-DNA-binding protein 1 (ZBP1), receptor-interacting protein kinase 1/3 (RIPK1/3) and interferon regulatory factor 1 (IRF1). The discovery of these markers indicates a breakthrough for the optimization of cancer treatment strategies.