Objective: To analyze the school tuberculosis (TB) outbreaks and preventive treatment in Hefei from 2022 to 2024, so as to provide reference for TB prevention and control in schools. Methods: Data were collected on all school based TB outbreaks occurring during 2022-2024 in Hefei, defined as ≥2 epidemiologically linked TB cases within the same school during a single semester. Statistical analyses were performed using the Chi square test. Results: Close contacts exhibited significantly higher TB incidence (2.88%) and latent mycobacterium tuberculosis infection (LTBI) rates (13.80%) in the school TB outbreaks, compared to non close contacts (0.12% and 2.63%, respectively). Among close contacts, secondary school students showed lower TB incidence (0.48%) and LTBI prevalence (3.42%) than both primary school or younger children (0.68%, 6.95%) and college students ( 0.78% , 6.50%), with statistically significant differences ( χ 2=360.91, 6.37; 791.71, 102.03, all P <0.05). The proportion of LTBI individuals recommended for preventive therapy was higher in primary school or younger groups (98.59%) than in secondary (95.25%) or college students (86.34%) ( χ 2=25.86, P <0.01). However, among those recommended, close contacts had higher uptake (85.82%) and completion rates (87.25%) of preventive therapy than non close contacts (69.63% and 70.57%); similarly, secondary school students demonstrated higher uptake (91.21%) and completion rates (86.45%) compared to primary school or younger (88.57%, 83.87%) and college students (57.28%, 64.08%) ( χ 2=30.52, 26.72; 125.17, 38.84, all P <0.01). Subsequent TB incidence among LTBI close contacts (13.30%) and among those who did not complete preventive therapy (22.73%) were significantly higher than among non close contacts (2.80%, 2.41%), respectively ( χ 2=32.19, 13.87, both P <0.05). Conclusions In school TB outbreaks, close contacts face higher LTBI prevalence and subsequent TB risk than non close contacts. College students show notably low adherence to preventive therapy. It is necessary to take targeted measures to improve the compliance of preventive measures among students.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

Tumor drug resistance is an important problem in the failure of chemotherapy and targeted drug therapy, which is a complex process involving chromatin remodeling. SWI/SNF is one of the most studied ATP-dependent chromatin remodeling complexes in tumorigenesis, which plays an important role in the coordination of chromatin structural stability, gene expression, and post-translation modification. However, its mechanism in tumor drug resistance has not been systematically combed. SWI/SNF can be divided into 3 types according to its subunit composition: BAF, PBAF, and ncBAF. These 3 subtypes all contain two mutually exclusive ATPase catalytic subunits (SMARCA2 or SMARCA4), core subunits (SMARCC1 and SMARCD1), and regulatory subunits (ARID1A, PBRM1, and ACTB, etc.), which can control gene expression by regulating chromatin structure. The change of SWI/SNF complex subunits is one of the important factors of tumor drug resistance and progress. SMARCA4 and ARID1A are the most widely studied subunits in tumor drug resistance. Low expression of SMARCA4 can lead to the deletion of the transcription inhibitor of the BCL2L1 gene in mantle cell lymphoma, which will result in transcription up-regulation and significant resistance to the combination therapy of ibrutinib and venetoclax. Low expression of SMARCA4 and high expression of SMARCA2 can activate the FGFR1-pERK1/2 signaling pathway in ovarian high-grade serous carcinoma cells, which induces the overexpression of anti-apoptosis gene BCL2 and results in carboplatin resistance. SMARCA4 deletion can up-regulate epithelial-mesenchymal transition (EMT) by activating YAP1 gene expression in triple-negative breast cancer. It can also reduce the expression of Ca²⁺ channel IP3R3 in ovarian and lung cancer, resulting in the transfer of Ca²⁺ needed to induce apoptosis from endoplasmic reticulum to mitochondria damage. Thus, these two tumors are resistant to cisplatin. It has been found that verteporfin can overcome the drug resistance induced by SMARCA4 deletion. However, this inhibitor has not been applied in clinical practice. Therefore, it is a promising research direction to develop SWI/SNF ATPase targeted drugs with high oral bioavailability to treat patients with tumor resistance induced by low expression or deletion of SMARCA4. ARID1A deletion can activate the expression of ANXA1 protein in HER2+ breast cancer cells or down-regulate the expression of progesterone receptor B protein in endometrial cancer cells. The drug resistance of these two tumor cells to trastuzumab or progesterone is induced by activating AKT pathway. ARID1A deletion in ovarian cancer can increase the expression of MRP2 protein and make it resistant to carboplatin and paclitaxel. ARID1A deletion also can up-regulate the phosphorylation levels of EGFR, ErbB2, and RAF1 oncogene proteins.The ErbB and VEGF pathway are activated and EMT is increased. As a result, lung adenocarcinoma is resistant to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs). Although great progress has been made in the research on the mechanism of SWI/SNF complex inducing tumor drug resistance, most of the research is still at the protein level. It is necessary to comprehensively and deeply explore the detailed mechanism of drug resistance from gene, transcription, protein, and metabolite levels by using multi-omics techniques, which can provide sufficient theoretical basis for the diagnosis and treatment of poor tumor prognosis caused by mutation or abnormal expression of SWI/SNF subunits in clinical practice.

This consensus will introduce the characteristics of fillers used in the surgical cavities of domestic nasal surgery patients based on relevant literature and expert opinions. It will also provide recommendations for the selection of cavity fillers for different nasal diseases, with chronic sinusitis as a representative example.
Humans ; Nasal Cavity/surgery* ; Nasal Surgical Procedures ; China ; Consensus ; Sinusitis/surgery* ; Dermal Fillers

The development status of the key technologies of first-aid robotics was introduced in autonomous system,embo-died intelligence,digital twins,large artificial intelligence model and autonomous unmanned medical treatment.The present situation in first-aid robotics equipment was reviewed for first-aid diagnosis,treatment,assistance and transportation.The development trends of the key technology and equipment of first-aid robotics were analyzed.It was pointed out the involve-ment of big model-based embodied intelligence technology and digital twins technology in first aid might provide new pers-pectives for the application and advancement of specialized first-aid robotics.[Chinese Medical Equipment Journal,2025,46(3):96-114]

Objective:To explore the impact of the stimulator of interferon genes(STING)-interferon regulatory factor 3(IRF3)pathway on the senescence of rapid pacing HL-1 myocytes.Methods:HL-1 cells were divided into five groups: the control group(HL-1 cells without any treatment), pacing group(HL-1 cells paced for 48 hours), STING siRNA group(HL-1 cells paced for 48 hours and transfected with STING siRNA), NC siRNA group(HL-1 cells paced for 48 hours and transfected with NC siRNA), and H151 inhibitor group(HL-1 cells paced for 48 hours with the addition of 1 μmol/L STING inhibitor H151). Mitochondrial membrane potential was assessed in control and pacing group cells, and mitochondrial MitoTracker and TFAM co-localization staining was performed on these cells.Cellular senescence was evaluated using β-galactosidase staining in each group, and the positive rate of cellular senescence was observed and calculated.Western blotting was employed to detect the expression levels of STING, IRF3, P-IRF3, P16, P21, and P53 proteins in all groups.Immunofluorescence was utilized to examine the expression distribution of STING and P21 across the various groups.ELISA was performed to measure the concentrations of interleukin(IL)-1β, IL-6, and IL-8 in the cell supernatants from each group as part of the senescence-associated secretory phenotype(SASP).Results:Compared with the control group, the ratio of mitochondrial JC-1 multimer to monomer was significantly decreased in the pacing group( t=16.42, P<0.05), the co-localization of mitochondrial MitoTracker and TFAM in the cells was significantly weakened, the proportion of cells with positive cellular senescence-associated β-galactosidase staining significantly increased in the pacing group, the expression levels of STING, P-IRF3/IRF3, P16, P21, and P53 proteins were significantly elevated in the pacing group, and the concentrations of IL-1β, IL-6, and IL-8 in the cell supernatants were markedly increased.Compared with the pacing group, the proportion of cells with positive cellular senescence-associated β-galactosidase staining decreased in the STING siRNA group and H151 inhibitor group ( F= 18.13, P<0.05), the expression levels of STING, P-IRF3/IRF3, P16, P21, and P53 were reduced in the STING siRNA group and H151 inhibitor group ( F=16.84, 26.56, 74.70, 31.80, 31.23, all P<0.05), and the concentrations of IL-1β, IL-6, and IL-8 in the cell supernatants decreased( F=197.80、1 339.00、1 308.00, all P<0.001). Conclusions:Rapid pacing of HL-1 cells can promote mtDNA release into the cytoplasm, activate the STING-IRF3 pathway, accelerate cellular senescence, and enhance the secretion of SASP.Inhibiting the expression of STING can delay the senescence induced by the rapid pacing of HL-1 cells and reduce SASP secretion.

Objective:To compare the efficacy and safety of crisaborole ointment 2% versus pimecrolimus cream 1% in the treatment of mild to moderate atopic dermatitis in children aged 2 years or older.Methods:A multicenter, randomized, open-label, controlled clinical trial was conducted. A total of 120 pediatric patients aged 2 - 17 years with mild to moderate atopic dermatitis were enrolled from departments of dermatology of 8 hospitals in China between March 2022 and February 2023. The participants were randomly assigned in a 1∶1 ratio to the crisaborole group and the pimecrolimus group, and received the treatment with crisaborole ointment 2% and pimecrolimus cream 1% respectively, twice a day for 4 weeks. Visits were scheduled at baseline/on day 1, as well as on days 8, 15, and 29. The primary efficacy outcome was the percentage of patients achieving the Investigator's Static Global Assessment (ISGA) success (defined as clear [0] or almost clear [1] on the ISGA scale, combined with ≥ 2‐grade improvement from baseline) on day 29. The secondary efficacy outcomes included changes in the Eczema Area and Severity Index (EASI) total scores from baseline to day 29, percentages of patients achieving ISGA improvement (defined as clear [0] or almost clear [1] on the ISGA scale), as well as changes in the Peak Pruritus Numerical Rating Scale (NRS) scores, Dermatology Life Quality Index (DLQI) /Infants' Dermatology Life Quality Index (IDLQI) /Children's Dermatology Life Quality Index (CDLQI) scores, and in the Dermatitis Family Impact (DFI) scores. Drug safety was evaluated according to the incidence of adverse events. Categorical data were compared using the chi-square test. Since measurement data did not follow a normal distribution, the rank sum test was used for comparisons of measurement data between groups.Results:A total of 106 children with mild to moderate atopic dermatitis were included in the per-protocol analysis set, with 52 in the crisaborole group (26 males and 26 females) and 54 in the pimecrolimus group (27 males and 27 females). There were no significant differences in age, disease duration, ISGA and EASI scores at baseline between the two groups (all P > 0.05). On day 29, 22 patients (42.31%) in the crisaborole group and 25 (46.30%) in the pimecrolimus group achieved ISGA success, with no significant difference between the two groups ( χ2 = 0.17, P = 0.68) ; 35 patients (67.31%) in the crisaborole group and 45 (83.33%) in the pimecrolimus group achieved ISGA improvement, also with no significant difference between the two groups ( χ2 = 3.68, P = 0.06) ; additionally, there were no significant differences in the EASI, pruritus NRS, DLQI/IDLQI/CDLQI, or DFI scores between the two groups (all P > 0.05). Adverse reactions to the two topical agents were mainly local reactions such as mild to moderate pain, itching, or worsening of itching, and no obvious systemic adverse reactions occurred. The incidence of drug-related adverse reactions was 46.15% (24 cases) in the crisaborole group and 37.04% (20 cases) in the pimecrolimus group, with no significant difference between the two groups ( χ2 = 0.91, P = 0.34) . Conclusion:The efficacy of crisaborole ointment 2% was comparable to that of pimecrolimus cream 1% in the treatment of mild to moderate atopic dermatitis in children aged ≥ 2 years, and it yielded early and rapid improvement in the quality of life of patients and their families, with good safety and tolerability profiles.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Microbial detection and control of traditional Chinese medicine(TCM) decoction pieces are crucial for the quality control of TCM preparations. It is also a key area of research in the measurement technology and equipment development for TCM manufacturing. Guided by TCM manufacturing measurement methodologies, this study presented a design of a novel portable microbial detection device, using Atractylodis Macrocephalae Rhizoma decoction pieces as a demonstration. Immunomagnetic separation technology was employed for specific isolation and labeling of target microorganisms. Enzymatic signal amplification was utilized to convert weak biological signals into colorimetric signals, constructing an optical biosensor. A self-developed smartphone APP was further applied to analyze the colorimetric signals and quantify target concentrations. A portable and automated detection system based on Arduino microcontroller was developed to automatically perform target microbial separation/extraction, as well as mimetic enzyme labeling and catalytic reactions. The developed equipment specifically focuses on the rapid and quantitative microbial analysis of TCM active pharmaceutical ingredients, intermediates in TCM manufacturing, and final TCM products. Experimental results demonstrate that the equipment could detect Salmonella in samples within 2 h, with a detection limit as low as 5.1 × 10~3 CFU·mL~(-1). The equipment enables the rapid detection of microorganisms in TCM decoction pieces, providing a potential technical solution for on-site rapid screening of microbial contamination indicators in TCM. It has broad application prospects in measurement technology for TCM manufacturing and offers strong technical support for the modernization, industrialization, and intelligent development of TCM.
Drugs, Chinese Herbal/analysis* ; Atractylodes/microbiology* ; Rhizome/microbiology* ; Biosensing Techniques/methods* ; Medicine, Chinese Traditional ; Colorimetry/instrumentation* ; Quality Control