Background Swimming is becoming increasingly popular for its combined leisure and fitness benefits. However, polluted swimming pool water may pose various health risks. Previous studies have indicated that health indicators of swimming venues have lower qualification rates compared to other public places, highlighting the urgent need to optimize hygiene management measures. Objective To assess the overall hygiene status and identify the key factors influencing water quality in Shanghai’s swimming venues from 2010 to 2024, and to provide a scientific basis for optimizing water quality management. Methods Water quality was assessed in three stages (2010—2019, 2020—2022, and 2023—2024) based on the monitoring data of Shanghai’s swimming venues (2010—2024). The influences of monitoring stage, region, season, scale, day of week, and per capita attendance on water quality were analyzed using chi-square tests and logistic regression. Results From 2010 to 2024, water quality was monitored in 1478 swimming venues. The compliance rates of turbidity, pH, urea, oxidation-reduction potential, free residual chlorine, combined residual chlorine, total bacteria, coliforms, and heat-resistant coliforms in swimming pool water, and free residual chlorine in the foot disinfection basin water samples were 99.56%, 94.73%, 73.00%, 47.50%, 56.55%, 54.12%, 97.74%, 99.49%, 99.90%, and 69.19%, respectively. The overall water quality compliance rate was 85.28%. The main non-compliant indicators for swimming pool water were urea, ORP, free residual chlorine, and combined residual chlorine, along with free chlorine residual in the foot disinfection basin water samples. Using the period before the implementation of the new national standard as reference, the results of logistic regression revealed that the qualification rates of urea, ORP, and free residual chlorine in the swimming pool water samples, as well as free residual chlorine in foot disinfection basin water samples, were significantly higher during the two phases after launching the new standard (2020–2022 and 2023–2024), and the associated odds ratios (95%CI) were 1.28 (1.00, 1.64) and 1.48 (1.12, 1.95), 13.35 (7.63, 23.37) and 10.78 (7.00, 16.60), 2.54 (1.81, 3.58) and 3.18 (2.41, 4.20), and 3.14 (2.14, 4.60) and 2.83 (1.89, 4.23), respectively. Compared with the reference group, those sampled in urban pools and in non-summer seasons showed higher qualification rates of urea and ORP; in contrast, the pools with high visitor flow showed lower qualification rates of free residual chlorine in both water samples of swimming pools and foot disinfection basins (P<0.05). Conclusion The implementation of the new national standard has effectively enhanced the management standards and significantly improved overall water quality of swimming venues in Shanghai. However, the compliance rates of free residual chlorine and urea in swimming pool water still need further improvement. The compliance of some indicators is influenced by region, season, scale, and average passenger flow, particularly in small suburban swimming venues. Future efforts should focus on refining water quality management strategies and enhancing public health management.

AIM To establish a UPLC-MS/MS method for the simultaneous content determination of Asp,Guad,Adeno,Arg,Ade,Cyti,Phe,Leu,Ile,Glu,Ser,Gln,Gly,Ala,Hyp,Thr,Pro and Lys in Asini Corii Colla.METHODS The analysis was performed on a 45℃ thermostatic Waters BEH C18column(2.1 mm×50 mm,1.7 μm),with the mobile phase comprising of acetonitrile(containing 0.1% formic acid)-water flowing at 0.35 mL/min in a gradient elution manner,and electron spray ionization source was adopted in positive ion scanning with multiple reaction monitoring mode.Subsequently,chemical pattern recognition was performed by hierarchical clustering analysis,principal component analysis and orthogonal partial least squares-discriminant analysis.RESULTS Eighteen nucleosides and free amino acids showed good linear relationships within their own ranges(r≥0.999 0),whose average recoveries were 98.0%-104.9% with the RSDs of 1.6%-4.9% .Seventeen batches of samples were clustered into two categories,two principal components demonstrated the accumulative variance contribution rate of 60.75%,Leu,Phe,Ade and Guad were potential index constituents.CONCLUSION This stable and reliable method can be used for the quality control of Asini Corii Colla.

Objective To clarify the understanding of types of laboratories and manufacturers for GB 9706.1-2020 Medical electrical equipment-Part 1:General requirements for basic safety and essential performance by laboratory proficiency testing for creepage distance and electrical clearance test.Methods An operation guide was formed according to the testing program in GB 9706.1-2020,and the homogeneity and stability of the samples were evaluated according to CNAS-GL003:2018 Guidance on Evaluating the Homogenneity and Stability of Samples Used for Proficiency Testing.Robust statistic methods were used to assess the quantitative parameters of the test results of the participating laboratories according to the requirements in GB/T 28043-2019 Statistical methods for use in proficiency testing by interlaboratory comparison;the results reported by the expert laboratories were used as the specified values of the qualitative parameters.SPSS 25.0 statistical software was used for data analysis.Results All the results of the crreepage distance and electrical clearance tests met the requirements for homogeneity and stability.Of the 46 laboratories involved in,37 ones did have comprehensive satisfactory determinations while the remained 9 ones not.Conclusion Some laboratories don't behave well in understanding the standard,which have to be reformed accordingly to enhance their proficiencies.[Chinese Medical Equipment Journal,2024,45(10):54-59]

Objective To analyze the literature on artificial intelligence in forensic research from 2012 to 2022 in the Web of Science Core Collection Database,to explore research hotspots and developmen-tal trends.Methods A total of 736 articles on artificial intelligence in forensic medicine in the Web of Science Core Collection Database from 2012 to 2022 were visualized and analyzed through the litera-ture measuring tool CiteSpace.The authors,institution,country(region),title,journal,keywords,cited references and other information of relevant literatures were analyzed.Results A total of 736 articles published in 220 journals by 355 authors from 289 institutions in 69 countries(regions)were identi-fied,with the number of articles published showing an increasing trend year by year.Among them,the United States had the highest number of publications and China ranked the second.Academy of Forensic Science had the highest number of publications among the institutions.Forensic Science Inter-national,Journal of Forensic Sciences,International Journal of Legal Medicine ranked high in publica-tion and citation frequency.Through the analysis of keywords,it was found that the research hotspots of artificial intelligence in the forensic field mainly focused on the use of artificial intelligence technol-ogy for sex and age estimation,cause of death analysis,postmortem interval estimation,individual identification and so on.Conclusion It is necessary to pay attention to international and institutional cooperation and to strengthen the cross-disciplinary research.Exploring the combination of advanced ar-tificial intelligence technologies with forensic research will be a hotspot and direction for future re-search.

To establish and optimize a method for the detection of recombinant human midkine (rhMK) activity and verify its methodology, cell counting kit-8 (cck-8) method was used to measure the proliferation activity of rat knee chondrocytes. The specificity, accuracy, precision, linearity and robustness of the method were also verified in this study. The established method was proven to have good specificity because the buffer of rhMK and recombinant human interleukin-1 receptor antagonist have no obvious active effect; the recoveries of the samples with relative activities of 50%, 75%, 100%, 125%, 150% were in the range of 80.0% to 124.0% by statistical analysis, the relative standard deviations (RSD) of relative potency were all within 20%, the linear correlation coefficient, R² ≥ 0.98, suggesting that the accuracy, precision and linearity of the method were good; the robustness correlation coefficient, R² ≥ 0.92 and the ratio of maximum to minimum of sigmoidal dose-response were no less than 1.5, indicating that robustness of the methods was good. In conclusion, a bioactivity measurement method for rhMK was established and fully validated in this study and it provides a reliable method for the bioactivity analysis of rhMK routine samples during the development. This study was approved by the Animal Care and Use Committee of Shanghai Model Organisms Center, Inc. (approval number: 2019-0008-06).

AIM To investigate the variation rules of main secondary metabolites in Hedysari Radix before and after rubbing strip.METHODS UPLC-MS/MS was adopted in the content determination of formononetin,ononin,calycosin,calycosin-7-glucoside,medicarpin,genistein,luteolin,liquiritigenin,isoliquiritigenin,vanillic acid,ferulic acid,γ-aminobutyric acid,adenosine and betaine,after which cluster analysis,principal component analysis and orthogonal partial least squares discriminant analysis were used for chemical pattern recognition to explore differential components.RESULTS After rubbing strip,formononetin,calycosin,liquiritigenin and γ-aminobutynic acid demonstrated increased contents,along with decreased contents of ononin,calycosin-7-glucoside and vanillic acid.The samples with and without rubbing strip were clustered into two types,calycosin-7-glucoside,formononetin,γ-aminobutynic acid,vanillic acid,calycosin-7-glucoside and formononetin were differential components.CONCLUSION This experiment clarifies the differences of chemical constituents in Hedysari Radix before and after rubbing strip,which can provide a reference for the research on rubbing strip mechanism of other medicinal materials.

Objective: To construct a prognostic model for unsuccessful removal of nasogastric tube (NGT) was the aim of our study. Methods: This study examined patients with swallowing disorders receiving NGT feeding due to stroke or traumatic brain injury in a regional hospital. Clinical data was collected, such as age, sex, body mass index (BMI), level of activities of daily living (ADLs) dependence. Additionally, gather information regarding the enhancement in Functional Oral Intake Scale (FOIS) levels and the increase in food types according to the International Dysphagia Diet Standardization Initiative (IDDSI) after one month of swallowing training. A stepwise logistic regression analysis model was employed to predict NGT removal failure using these parameters. Results: Out of 203 patients, 53 patients (26.1%) had experienced a failed removal of NGT after six months of follow-up. The strongest predictors for failed removal were age over 60 years, underweight BMI, total dependence in ADLs, and ischemic stroke. The admission prediction model categorized patients into high, moderate, and low-risk groups for removal failure. The failure rate of NGT removal was high not only in the high-risk group but also in the moderate-risk groups when there was no improvement in FOIS levels and IDDSI food types. Conclusion Our predictive model categorizes patients with brain insults into risk groups for swallowing disorders, enabling advanced interventions such as percutaneous endoscopic gastrostomy for high-risk patients struggling with NGT removal, while follow-up assessments using FOIS and IDDSI aid in guiding rehabilitation decisions for those at moderate risk.

Metabolic dysfunction-associated fatty liver disease (MAFLD) is an increasingly common liver disease worldwide. MAFLD is diagnosed based on the presence of steatosis on images, histological findings, or serum marker levels as well as the presence of at least one of the three metabolic features: overweight/obesity, type 2 diabetes mellitus, and metabolic risk factors. MAFLD is not only a liver disease but also a factor contributing to or related to cardiovascular diseases (CVD), which is the major etiology responsible for morbidity and mortality in patients with MAFLD. Hence, understanding the association between MAFLD and CVD, surveillance and risk stratification of MAFLD in patients with CVD, and assessment of the current status of MAFLD management are urgent requirements for both hepatologists and cardiologists. This Taiwan position statement reviews the literature and provides suggestions regarding the epidemiology, etiology, risk factors, risk stratification, nonpharmacological interventions, and potential drug treatments of MAFLD, focusing on its association with CVD.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

A gas chromatography-triple quadrupole mass spectrometry(GC-MS) method was established for the simultaneous determination of eleven volatile components in Cinnamomi Oleum and the chemical pattern recognition was utilized to evaluate the quality of essential oil obtained from Cinnamomi Fructus medicinal materials in various habitats. The Cinnamomi Fructus medicinal materials were treated by water distillation, analyzed using GC-MS, and detected by selective ion monitoring(SIM), and the internal standards were used for quantification. The content results of Cinnamomi Oleum from various batches were analyzed by hierarchical clustering analysis(HCA), principal component analysis(PCA), and orthogonal partial least squares-discriminant analysis(OPLS-DA) for the statistic analysis. Eleven components showed good linear relationships within their respective concentration ranges(R~2>0.999 7), with average recoveries of 92.41%-102.1% and RSD of 1.2%-3.2%(n=6). The samples were classified into three categories by HCA and PCA, and 2-nonanone was screened as a marker of variability between batches in combination with OPLS-DA. This method is specific, sensitive, simple, and accurate, and the screened components can be utilized as a basis for the quality control of Cinnamomi Oleum.
Gas Chromatography-Mass Spectrometry ; Plant Oils ; Oils, Volatile ; Drugs, Chinese Herbal/analysis* ; Cluster Analysis