Objective To explore an artificial intelligence (AI)-based method for automated hand hygiene monitoring and to compare the effectiveness of three algorithms (UniFormerV2, TDN, C3D) in recognizing hand hygiene steps in surgical settings, thereby aiding hospital infection control. Methods From April to October 2024, we non-invasively collected 641 video recordings of healthcare staff performing hand hygiene at four-bay scrub sinks in two tertiary hospitals using overhead HD cameras. The dataset was annotated by five trained experts for model training and validation. Results Following training on 385 samples, internal validation (n=119) showed the C3D model achieved 81% accuracy, 87% recall, and an 83% F1-score. The TDN model achieved 93%, 91%, and 92% for the same metrics. The UniFormerV2 model outperformed both, with an accuracy, recall, and F1-score of 93%—an improvement of over 10 percentage points compared to traditional CNNs (TDN, C3D). It also achieved an 84% accuracy in external validation, demonstrating strong generalization. Conclusion The UniFormerV2 model is more accurate than CNN-based models for hand hygiene step recognition and shows robust performance in external validation. It presents a viable tool for healthcare facilities to enhance hand hygiene management, ultimately improving medical quality and patient safety.

Background Climate change poses a significant threat to public health. In China, relevant health intervention research is still in its early stages, and evidence for evaluating the effectiveness of regional climate change health adaptation strategies and measures is scarce. Objective To investigate the level of climate change-related health literacy among residents in Shenzhen, implement targeted health interventions, and assess the intervention effects as well as their influencing factors. Methods From July 2023 to January 2024, 4 communities were randomly selected in Shenzhen, and a total of 896 community residents were enrolled and divided into an intervention group (444 participants) and a control group (452 participants). Baseline and follow-up surveys on climate change-related health literacy were conducted among residents for both groups. During the period between the two surveys, the intervention group received targeted health interventions. Health literacy—comprising 3 dimensions: basic health knowledge and concepts, basic health skills, and healthy lifestyles—was defined as achieving ≥80% of the total score. A differences-in-differences model was adopted to analyze the impact of the intervention, and multiple linear regression was used to explore the factors influencing the intervention effect. Results The baseline survey showed that 240 out of the 896 surveyed residents (26.79%) possessed climate change health literacy. For the 3 dimensions, the number of residents and the proportions with corresponding literacy in descending order were: basic health skills (521, 58.15%), healthy lifestyles (345, 38.50%), and basic health knowledge and concepts (44, 4.91%). After the intervention, the intervention group showed a 3.19% increase in the total health literacy score, a 3.55% increase in basic health knowledge and concepts, and a 4.24% increase in basic health skills (t=2.79, 2.77, and 2.47 respectively) (P<0.05). No significant change was observed in healthy lifestyle scores (t=0.70, P>0.05). Further analysis showed that awareness of the “dual carbon goals” and occupation were significantly associated with the intervention effect on overall health literacy (P<0.05). For basic health knowledge and concepts, occupation, history of chronic diseases, and awareness of the “dual carbon goals” had statistically significant effects on the intervention outcomes (P<0.05). Regarding basic health skills, awareness of the “dual carbon goals” significantly influenced the intervention effect (P<0.001). In terms of healthy lifestyles, gender, educational level, occupation, and awareness of climate change were significantly associated with the intervention effect (P<0.05). Conclusion The climate change-related health literacy among community residents in Shenzhen is in urgent need of improvement. Health interventions can effectively enhance residents' basic health knowledge and concepts, basic health skills, and overall literacy level. In the future, it is necessary to strengthen the popularization of climate change health knowledge based on different population characteristics and further optimize intervention strategies, to comprehensively improve residents' health adaptation capacity to climate change.

Objective:To develop and validate a combined model integrating radiomics, dosiomics, and clinical parameters based on CT simulation and dosimetric images in order to predict the occurrence of radiation pneumonitis (RP) in patients with non-small cell lung cancer (NSCLC).Methods:A retrospective study was conducted on the clinic data of 143 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022. Patients were randomly stratified into a training group ( n = 100) and an internal validation group ( n = 43) at a 7∶3 ratio. Moreover, clinic data were collected from 34 NSCLC patients who received radiotherapy at the Jining Cancer Hospital between January 2019 and December 2022 as an external validation group. All three groups (the training group, internal validation, and external validation groups) were further categorized into two groups based on the RP severity (i.e., RP ≥ grade 2 and RP < grade 2). Their radiotherapy dose, CT simulation, and 3D dose distribution images were collected. Then, the total lung minus planning target volume (TL-PTV) was defined as the region of interest (ROI) for radiomics and dosiomic feature extraction, followed by feature dimensionality reduction. Consequently, key features associated with RP were determined. Four predictive models were developed using machine learning approaches (especially multilayer perceptron, MLP): a clinical model (CM), a radiomics model (RM), a dosiomics model (DM), and a radiomics and dosiomics nomogram (RDN), with a nomogram subsequently constructed. Ultimately, the performance and clinical feasibility of these models were assessed using receiver operating characteristic (ROC), area under the curve (AUC), and decision curve analysis (DCA). Results:A total of 1 834 radiomic features and 1 834 dosiomic features were extracted. Using the occurrence of RP ≥ grade 2 as the marker variable, 14 radiomic features, 15 dosiomic features, and three clinical features were selected from the training group to construct the prediction models (CM, RM, DM, and RDN). The performance and generalizability of these models were subsequently validated in both the internal validation and external validation groups. Specifically, the RDN exhibited AUCs of 0.915 (95% CI: 0.852-0.978), 0.879 (95% CI: 0.777-0.982), and 0.838 (95% CI: 0.701-0.975) in the three groups, respectively. A nomogram was established for RDN by integrating the radiomics score (R-score), dosiomics score (D-score), mean lung dose (MLD), V20, and V30. This nomogram allowed for individualized risk estimation of RP and facilitated personalized radiotherapy planning. Conclusions:The RDN model that is developed based on CT simulation and 3D dose distribution images and integrates radiomics, dosiomics, and clinical features can effectively predict the RP risk of NSCLC patients. The integration of multidimensional data contributes to the formation of the optimal predictive model, offering guidance for clinicians.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Objectives:To describe the use of antihypertensive, antidiabetic and lipid-lowering drugs, and evaluate the effects on blood pressure, blood glucose and blood lipids controls required by Chinese Guideline on the Primary Prevention of Cardiovascular Diseases (the guideline) in a community-based cohort of individuals at high risk for cardiovascular disease. To analyze the association of the uses of antihypertensive, antidiabetic and lipid-lowering drugs, and the comprehensive control of blood pressure, blood glucose and blood lipids with cardiovascular disease. Methods:From the CHinese Electronic health Records Research in Yinzhou (CHERRY), those who were at high risk for cardiovascular disease and aged 40-75 years as of January 1, 2013 in in Yinzhou District of Ningbo, Zhejiang Province were selected as study subjects. The information about their antihypertensive, antidiabetic, and lipid-lowering drug uses between 2013 and 2015 was collected, and blood pressure, blood glucose, and blood lipid measurements were conducted during the follow-up. The study constructed two kinds of comprehensive scores: the comprehensive medication score based on the guideline requirement for the treatment of hypertension, diabetes and hyperlipidemia, dividing the study participants into the compliancy group and non-compliancy group; and the comprehensive control score based on the guideline requirement for blood pressure, blood glucose, and blood lipids control, dividing the study participants into better control group, moderate control group, and poor control group. Cox proportional hazards regression model was used to analyze the association of the comprehensive medication score and comprehensive control score with cardiovascular disease. The incidence data of cardiovascular disease were collected from January 1, 2015 (baseline time) to August 31, 2020 (follow up end time).Results:A total of 79 734 participants at high risk for cardiovascular disease were included in the study, in whom 68.4%, 27.4%, and 4.2% had 1, 2, or 3 cardiometabolic conditions (hypertension, diabetes, or hyperlipidemia), respectively. In the participants with hypertension, diabetes, and hyperlipidemia from 2013 to 2015, the proportions of those who had two years of medication compliancy records were 66.0%, 67.4%, and 13.9%, respectively. In the hypertension patients, 59.2% had better blood pressure control, in the diabetes patients, 28.7% had better blood glucose control, and in the patients with hyperlipidemia, 27.4% had better blood lipid control. After a median follow-up of 5.66 years, 4 088 cardiovascular disease cases or deaths occurred. After multivariate adjustment, compared with the non-compliancy group, the compliancy group had lower risk for cardiovascular disease ( HR=0.91, 95% CI: 0.85-0.96). Compared with the better control group, the poor control group had an increased risk for cardiovascular disease ( HR=1.67, 95% CI: 1.53-1.81). In the moderate control group, the risk increased significantly in the diabetes patients ( HR=1.29, 95% CI: 1.07-1.56), while no additional risk for cardiovascular disease was observed in non-diabetes patients ( HR=1.06, 95% CI: 0.97-1.16). Conclusions:Compliancy to the medication required by the guideline is associated with lower risk for cardiovascular disease. However, it is still necessary to improve the medication compliancy in people at high risk in primary prevention, especially in the patients with hyperlipidemia, due to their low taking rate of lipid-lowering drugs. Additionally, as the requirement of the guideline becomes more stringent, the management of disease has met more challenges. Notably, diabetes patients who have not met the guideline requirement are at high risk for cardiovascular disease, to whom the disease management should be strengthened.

Objective To analyze the stimulation parameters and acupoint selection law of electroacupuncture in the treatment of cervical spondylotic radiculopathy(CSR)through data mining.Methods The clinical research literature about electroacupuncture in the treatment of CSR was retrieved from CNKI,Wanfang Data,VIP,PubMed and Web of Science from the establishment of the databases to September 2024.According to the inclusion and exclusion criteria,the literature was screened and the stimulation parameters and acupoint prescriptions were extracted.The association rule analysis was performed using SPSS Moderler 18.0 software,and clustering analysis was performed using SPSS Statistics 27.0 software.Results A total of 122 acupoint selection prescriptions for electroacupuncture treatment of CSR were extracted.The most commonly used stimulation parameters in electroacupuncture treatment of CSR were 2 Hz of continuous wave,2/100 Hz of dilatational wave,intensity tolerance,30 min/time,1 time/day,10 times/course,and a total of 20 treatments.The top 3 most commonly used acupoints were Jingjiaji,Houxi(SI3)and Fengchi(GB20);the meridians of acupoints were mainly the large intestine meridian,small intestine meridian and gallbladder meridian;the core prescription of association rule analysis was"Jingjiaji,Houxi,Quchi,Fengchi,Jianjing,Waiguan,Tianzhu,Hegu,Dazhui",and the acupoint combination with the highest support was"Jingjiaji-Houxi".5 clusters were obtained through clustering analysis.Conclusion In electroacupuncture treatment for CSR,the selection of acupoints is primarily aimed at unblocking meridians to relieve pain and nourishing the liver and kidney.The core acupoints group is Jingjiaji-Houxi;the stimulation parameters demonstrate a certain degree of regularity and clustering,primarily consisting of low-frequency continuous wave and alternating density wave of low and high frequencies.

Poly(lactic-co-glycolide)(PLGA)is a key excipient in long-acting sustained-release preparations,and its degradation properties directly affect the drug release behavior.In this study,PLGA microspheres were prepared by microfluidic techniques,and the morphology changes of the microspheres were observed by scanning electron microscopy(SEM).In alkaline environment,due to the accelerated hydrolysis of ester bonds,the surface of the microspheres was rapidly dissolved and eroded,and the degradation rate was significantly higher than that in acidic environment.High temperature accelerated the degradation of PLGA microspheres.Under neutral and alkaline conditions,the microspheres showed aggregation and adhesion.Under acidic conditions,the microspheres gradually decomposed into irregular fragments.The high ionic strength further promoted the surface corrosion of the microspheres,especially under extreme pH conditions.Simultaneously,PLGA microspheres encapsulating coumarin were prepared to simulate the microsphere formulation.The release rate of coumarin after degradation of the microspheres under different conditions was observed by measuring the absorbance with ultraviolet-visible spectrophotometry.The results were consistent with those of the blank microspheres.This study revealed that the degradation of PLGA microspheres was significantly pH-dependent,temperature sensitive and ion strength responsive.These findings not only helped to understand and optimize the long-term stability and controlled release performance of drug-carrying microspheres,but also provided a theoretical basis for further improvement of PLGA-based drug carrier design.

Objective To evaluate the effect of pneumatic logistics transport system(PTS)on the trans-portation efficiency of the transferred samples and the accuracy of the results.Methods The transportation speed,temperature and humidity change of PTS were analyzed by temperature and humidity transmitter.Anti-coagulant samples containing disodium ethylenediaminetetraacetate(EDTA-K2),sodium citrate,lithium hepa-rin and samples containing inert separation gel coagulant were selected.and used respectively for complete blood cell analysis,prothrombin time(PT),activated partial prothrombin time(APTT),troponin T(TnT)and other myocardial markers,as well as the detection of items such as glucose(Glu)and lactate dehydrogen-ase(LDH).According to the transfer mode,they were divided into the manual transfer group and the PTS transfer group,and according to the number of PTS transfers,they were divided into the one-time transfer group,the three-time transfer group and before transfer(control).The differences among each group were statistically analyzed,and 1/3 allowable total error(1/3TEa)was adopted as the criterion for determining the clinical application value.Results There was no statistically significant difference in the changes of tempera-ture and humidity during the transportation process of PTS compared with manual transportation(P＞0.05),but it was significantly faster than manual transportation in terms of transportation time(P＜0.05).Com-pared with before transfer,the differences between the PT,APTT,Glu and LDH items in the one-time trans-fer group and the three-time transfer group were statistically significant(P＜0.01),and their deviations were all much greater than 1/3TEa.However,in the plasma samples,compared with before transport,there were statistically significant differences in Glu and LDH between the one-time transfer group and the three-time transfer group(P＜0.05),but the deviations were all less than 1/3TEa.For the items of TnT,red blood cell count and hematocrit,compared with before transfer,there were statistically significant differences between some groups of the one-time transfer group and the three-time transfer group(P＜0.05),but the deviations were all less than 1/3TEa.Conclusion PTS can significantly improve the transportation efficiency of sam-ples,but it significantly affects the detection of Glu and LDH in plasma samples,which can be improved by u-sing serum sample transportation instead.In addition,PTS also affects the detection of PT and APTT,and it is not recommended to use PTS to transport coagulation specimen.

BACKGROUND:As a common disease in middle-aged and elderly,osteoarthritis is difficult to cure,and the pathogenesis is not clear.Long non-coding RNA participates in the pathogenesis of osteoarthritis through many ways,such as regulating translation,promoting or inhibiting mRNA,and adsorbing miRNAs. OBJECTIVE:To review the types of common long non-coding RNA in osteoarthritis,and the influence of multiple long non-coding RNAs on the pathological factors related to osteoarthritis,to analyze the future application of long non-coding RNAs in osteoarthritis. METHODS:Literature retrieval was conducted in CNKI,WanFang Data,VIP database,PubMed,Web of Science and Sciencedirect databases,using the search terms of"osteoarthritis,degenerative joint disease,degenerative arthritis,OA,LncRNA,long non-coding RNA,long noncoding RNA,long intergenic non-coding RNA"in Chinese and English.All relevant literature published from 1976 and May 2024 was retrieved.After literature screening,induction,analysis and summary,93 articles were finally included for review. RESULTS AND CONCLUSION:This review collected 25 long non-coding RNAs that are well studied with osteoarthritis.Long non-coding RNAs,as a molecular sponge for miRNA,are competing endogenous RNAs to competitively adsorb miRNAs and then affect downstream targets.Long non-coding RNAs can regulate physiopathological processes such as chondrocyte apoptosis and proliferation,cartilage extracellular matrix degradation,and inflammatory responses.Long non-coding RNAs are expected to become a biomarker and potential therapeutic target for the clinical diagnosis and therapeutic prognosis of osteoarthritis,and it may become a new strategy for the clinical treatment of osteoarthritis in the future.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.