Objective: To investigate the status of antibiotic exposure in third grade primary school students in Shenzhen,so as to provide evidence for the scientific management of antibiotic use and reduction of population health risks. Methods: From 1 September to 30 October 2021, 200 third grade students from 8 primary schools in Luohu District of Shenzhen were selected by cluster random sampling as research subjects. The body composition was measured, urine samples were collected, and the contents of 35 antibiotics in the samples were detected by mass spectrometry. Relevant dietary habit information of the subjects was collected via questionnaires. The Chi square test was used to compare the detection rate of antibiotics among different genders and weight grades. The Logistic regression model was adopted to evaluate the correlation between the target antibiotic detection rate and dietary habits. Results: At least one type of antibiotic was detected in 198 of the subjects with an overall detection rate of 99.0% . Among the 35 target antibiotics, 23 were detected with detection rates ranging from 0.5%-69.5%. Quinolones had the highest detection rate of 86.5% , followed by macrolides and sulfonamides with detection rates of 77.5% and 76.5%, respectively. The detection rate of antibiotics was 98.3% in boys and 100.0% in girls with no statistically significant difference ( χ 2=1.35, P >0.05). The detection rates of quinolones, macrolides, and sulfonamides varied significantly among children with different BMI categories ( χ 2=38.18, 12.45, 9.76 , all P <0.05). The multivariate Logistic regression model analysis showed that the macrolide detection rate was affected by genders( OR =0.42) and the sulfonamide detection rate was significantly correlated with the frequency of dairy product consumption and being overweight( OR =2.01)(both P <0.05). Enrofloxacin was associated with the weekly consumption frequency of livestock meat such as pork, beef and mutton, as well as the weekly consumption frequency of poultry meat such as chicken, duck and goose ( OR = 2.81,2.17,both P <0.05). Trimethoprim was associated with the weekly frequency of drinking pure milk ( OR =5.49, P < 0.05 ). Conclusions Third grade primary school students in Shenzhen are generally exposed to low dose antibiotics. Macrolides, quinolones, and sulfonamides may be associated with the risk of obesity in primary school students.

Gender recognition based on the analysis of fingerprint residue can assist investigators in narrowing down the scope of investigation and play an important role in the field of criminal investigation.This study established a quantitative analysis method for lipid substances in fingerprints based on gas chromatography-mass spectrometry(GC-MS).Fatty acids in fingerprints were methylated using sulfuric acid methanol derivatization reagent(7%,V/V),the extraction reagent was dichloromethane-methanol(1∶1,V/V)solution,the reaction temperature was 70℃and the heating time was 45 min.Quantitative analysis of the relative content of 23 kinds of fatty acids and squalene in fingerprints residue by different genders was conducted,and orthogonal partial least squares-discriminant analysis(OPLS-DA)was used to reduce the dimensionality of the quantitative results.A total of 13 kinds of components in the fingerprints were selected to maximize the difference in relative content between male and female fingerprints.Three machine learning models,including binary logistic regression(BLR),support vector machine(SVM)and random forest(RF),were further used as feature variables to classify the gender of fingerprints.The classification performance of each model was compared through five indicators,and it was found that the most suitable model for binary classification of fingerprint gender was SVM model.The results showed that the SVM fingerprint residual gender binary classification model established based on the relative content data of 13 kinds of lipid substances in fingerprints achieved a classification accuracy of 90%and an area under the receiver operating characteristic curve(AUC)value of 0.98.This study provided a new research method for detecting lipid components in fingerprints and a methodological basis for gender recognition of fingerprints.

Forensic medicine has been designated as a first-level discipline,presenting new opportunities and challenges for the development of forensic medicine.Since the 1980s,the establishment of foren-sic medicine discipline and the cultivation of high-level forensic talents have become hot topics in the development of forensic medicine in China.Since the 13th Five-Year Plan,the forensic team of Shanxi Medical University has been aiming at the forefront,proposing the development goals of"Five First-class"and the discipline development path"Six Major Achievements".It has selected benchmark disci-plines,identified gaps in disciplinary development,unified thoughts,formulated completion timelines,concentrated superior resources,assigned tasks to individuals,and created an"Organized Fill-in-the-Blank Format"forensic medicine discipline construction model with the characteristics of the new era.The construction model of forensic medicine has achieved good results in the goals,discipline frame-work,scientific research,talent cultivation,discipline team and platform construction,forming a rela-tively complete discipline construction and management system,and accumulating valuable experience for the construction of first-level discipline and high-level talent cultivation of forensic medicine.

Objective To explore the correlation between the actual age and the pulp chamber volume(PCV)and pulp dentinal index(PDI)of the right first molars based on cone beam computed tomog-raphy(CBCT)technology,and to construct an accurate and convenient model for age estimation.Methods CBCT image data of 1 857 Han adults(883 males and 974 females)from the Department of Stomatology,Affiliated Hospital of North Sichuan Medical College were collected.The data were di-vided into training and validation sets at a ratio of 8∶2.A total of 1 485 training samples were used to construct the age estimation model,and 372 samples were used to validate the accuracy of the model.The Mimics 21.0 software was used to measure the PCV and calculate the PDI of the right first molars.Their correlations with age and the differences between different sexes and tooth positions were analyzed.Results Both the PCV and the PDI of the first molars showed strong negative correla-tions with the actual age(r values ranged from 0.82 to 0.89).The differences in PCV and PDI be-tween different sexes and tooth positions were statistically significant(P<0.05).The age estimation model based on PDI was superior to that based on PCV.The model based on the PDI values of the two right first molars(y=73.72-44.15 x3-28.27 x4,where x3 and x4 are the PDI values of the right maxil-lary and mandibular first molars,respectively)was the best,with the R2 of 0.79 and the mean abso-lute error of 4.90 years.Conclusion Both PCV and PDI of the first molars are effective indicators for age estimation.The age estimation model based on the PDI is more convenient and accurate than that based on the PCV,providing a more effective method for age estimation in forensic practice.

Traditional Chinese medicine(TCM) decoction pieces are a core carrier for the inheritance and innovation of TCM, and their quality and safety are critical to public health and the sustainable development of the industry. Conventional quality control models, while having established a well-developed system through long-term practice, still face challenges such as relatively long inspection cycles, insufficient objectivity in characterizing complex traits, and urgent needs for improving the efficiency of integrating multidimensional quality information when confronted with the dual demands of large-scale production and precision quality control. With the rapid development of artificial intelligence, machine learning can deeply analyze multidimensional data of the morphology, spectroscopy, and chemical fingerprints of decoction pieces by constructing high-dimensional feature space analysis models, significantly improving the standardization level and decision-making efficiency of quality evaluation. This article reviews the research progress in the application of machine learning in the processing, production, and rapid quality evaluation of TCM decoction pieces. It further analyzes current challenges in technological implementation and proposes potential solutions, offering theoretical and technical references to advance the digital and intelligent transformation of the industry.
Machine Learning ; Drugs, Chinese Herbal/standards* ; Quality Control ; Medicine, Chinese Traditional/standards* ; Humans

Objective: To investigate the changing trends for associations of anxiety and depressive symptoms with smartphone addiction among middle school students, so as to provide a scientific basis for preventing smartphone addiction in middle school students. Methods: From 2022 to 2023, a method of combining convenient sampling with cluster sampling was used to select 8 923 middle school students from 27 junior high schools and 3 senior high schools in a district of Shenzhen City between September 2022 (baseline, T1) and September 2023 (follow up, T2). The Smartphone Addiction Scale-Short Version (SAS-SV), Patients Health Questionnaire-9 Item (PHQ-9), and Generalized Anxiety Disorder 7-item Scale (GAD-7) were administered to assess smartphone addiction, anxiety and depressive symptoms. Mixed effects models were used to analyze the association of anxiety and depressive symptoms with smartphone addiction among middle school students. Results: From September 2022 to September 2023, the reported prevalence of smartphone addiction increased from 24.22% to 25.25% ( χ 2=45.71); and smartphone addiction scores [ 24.00 (16.00, 32.00),25.00(16.00, 33.00)], anxiety symptom scores [2.00(0.00, 7.00),3.00(0.00, 7.00)] and depressive symptom scores[3.00(0.00, 8.00),5.00(0.00, 9.00)] all significantly increased ( Z =-17.43, -42.38, -41.57) (all P <0.05). There were statistically significant difference in the distribution of anxiety and depression symptom levels among middle school students in 2022 and 2023 ( χ 2=85.15, 106.85, both P <0.05). After adjusting for covariates such as age, gender and family background, mixed effects models revealed dose response associations of anxiety and depressive symptoms with smartphone addiction among middle school students:mild anxiety symptom( OR =3.22), moderate to severe anxiety symptom ( OR =5.36), mild depressive symptom ( OR =3.32) and moderate to severe depressive symptom ( OR =6.13) were significantly associated with higher risks of smartphone addiction (all P <0.05). Interaction effect analysis found that co existing anxiety and depressive symptoms synergistically increased addiction risk by 5.60 times ( OR =5.60) compared to the asymptomatic group, with 32% of the combined risk attributable to their interaction ( S=1.64, AP =0.32)(both P < 0.05 ). Conclusions Anxiety and depressive symptoms are significantly associated with smartphone addiction, exhibiting a synergistic effect. Attention should be paid to emotional issues and smartphone addiction among middle school students.

Objectives:The aim is to analyze the cost structure and coefficient of variation for hospitalized patients with malnutrition based on Diagnosis-Related Groups(DRG),providing a reference for the further application and promotion of DRG.Method:Data were collected from patients admitted to Ningxia Hui Autonomous Region People's Hospital between March 2023 and August 2023.A diagnostic system based on artificial intelligence was used to identify malnourished patients.The composition of hospitalization costs for these individuals was described and analyzed,as was the coefficient of variation for various costs within DRG groupings.A multivariate regression analysis was conducted to identify the factors that influence patient hospitalization costs.Results:The average age of hospitalized patients with malnutrition was(68.12±16.43)years,with an average length of stay of(14.55±8.47)days,with an average hospitalization cost of(32 128.89±35 345.61)yuan.Among patients within the same DRG group,the coefficient of variation for various costs was found to be lower in the malnutrition group than in the normal group.This suggests that when assessed individually,the malnutrition group exhibited a higher degree of homogeneity in their cost structures.The factors influencing total hospitalization costs were found to be:length of hospital stay(P=0.001),nutritional monitoring fees(P=0.020),number of chronic diseases(P=0.003),and Karnofsky Performance Status(KPS)score(P=0.038).Hospitalization costs were positively correlated with both length of stay and nutritional assessment fees,but negatively correlated with the number of chronic diseases and KPS scores.Conclusions:Malnutrition has a profound impact on health outcomes,medical expenses,length of hospital stay,and disease severity.The implementation of the DRG system aims to standardize and improve the nutritional diagnosis and treatment process by categorizing different stages of malnutrition.This approach can minimize variations within DRG groups,making it easier to allocate medical resources more precisely and efficiently.Furthermore,it is a valuable reference tool for promoting DRG payment reform in different regions.

The accumulation of uremic toxins such as urea nitrogen, blood creatinine, and uric acid of patients with renal failure in vivo would lead to aggravated kidney damage. In this study, coated aldehyde oxy-starch (CAO) was used as an adsorbent to investigate its in vitro adsorption performance on renal failure indexes for urea, indoxyl sulfate (INS), monomethylamine (MMA), dimethylamine (DMA), uric acid (UA), and creatinine (Cr). The effects of variables such as pH, temperature, concentration, dosage, and time on the adsorption capacity of CAO were systematically investigated, employing analytical techniques of high-performance liquid chromatography (HPLC) and gas chromatography (GC). The results revealed that CAO exhibited a strong adsorption capacity for urea, INS, and MMA, alongside a moderate adsorption capacity for DMA, UA, and Cr. The adsorption kinetics and thermodynamic studies indicated that the adsorption of urea and UA by CAO were fitted in pseudo-first-order kinetics, and the adsorption isotherm aligned with the Freundlich adsorption model. The enthalpy change ΔH of urea in adsorption was in the range of 40 to 60 kJ·mol^-1, which demonstrated the presence of strong adsorption force due to the interactions of coordinating groups. The ΔH of UA was greater than 80 kJ·mol^-1, indicating the generation of chemical bonds during the adsorption. Both of them, Gibbs free energy ΔG was less than 0, within the range of -20 to 0 kJ·mol^-1, suggested that the adsorption of urea and UA by CAO occurred spontaneously as physical adsorption process. The adsorption entropy ΔS of urea and UA was > 0, which indicated an increase in entropy throughout the adsorption. The infrared spectroscopygram showed the formation of a chemical bond, specifically the imine bond, following the adsorption of urea by CAO, thereby indicating a chemical reaction during the adsorption. This study elucidates the adsorption mechanism of CAO on various indexes of renal failure, providing a scientific basis for its clinical usage.