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.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

ObjectiveColorectal cancer (CRC) is a highly heterogeneous malignancy, and traditional Chinese medicine (TCM) syndrome differentiation is widely used in its clinical management. However, the biological basis underlying different TCM syndromes remains insufficiently understood. This study aimed to investigate the proteomic differences between qi-blood deficiency syndrome (QBDS) and damp-heat syndrome (DHS) in CRC patients and to establish a “syndrome-differential protein-herbal component” network for exploring potential molecular mechanisms and therapeutic targets associated with syndrome-specific treatment strategies. MethodsTumor tissue samples were collected from 10 patients with pathologically confirmed CRC, including 5 patients diagnosed with QBDS and 5 with DHS according to standardized TCM syndrome differentiation criteria. Tandem mass tag (TMT)-based quantitative proteomics was employed to identify differentially expressed proteins (DEPs) between the two syndrome groups. Functional enrichment analyses, including Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, were performed to characterize the biological functions and signaling pathways associated with the identified DEPs. Least absolute shrinkage and selection operator (LASSO) regression analysis was further applied to screen key syndrome-related proteins and construct a core protein prediction model. Subsequently, molecular docking was conducted to evaluate the binding affinities between representative herbal compounds, astragaloside IV and berberine, and the identified core proteins. Finally, cell counting kit-8 (CCK-8) assays were performed in HCT 116 and HT-29 colorectal cancer cell lines to validate the anti-proliferative activities of these compounds in vitro. ResultsA total of 44 DEPs were identified between QBDS and DHS tissues, including 21 upregulated and 23 downregulated proteins in the QBDS group relative to the DHS group. Functional enrichment analyses revealed that these proteins were primarily associated with antiviral immune responses, regulation of inflammatory signaling, NF‑κB signaling pathways, cholesterol metabolism, and other biological processes relevant to tumor progression and host immune regulation. LASSO regression analysis identified eight core proteins with potential syndrome-specific significance. Among them, ST7, GPCPD1, and PODXL were closely associated with QBDS, whereas FAT1, GRB7, KRT7, MAFF, and MMP8 were associated with DHS. Molecular docking demonstrated favorable interactions between astragaloside IV and QBDS-related proteins, with binding energies lower than -5 kcal/mol, indicating stable binding activity. Berberine exhibited even lower binding energies and stronger binding specificity toward DHS-related core proteins. In vitro experiments further confirmed that both compounds inhibited CRC cell proliferation, while berberine displayed significantly greater anti-proliferative efficacy. The lowest 48 h IC₅₀ value of berberine reached 15.21 mg/L in CRC cells, indicating a stronger growth-inhibitory effect than astragaloside IV. ConclusionCRC patients with QBDS and DHS exhibit distinct proteomic characteristics, suggesting that different TCM syndromes possess unique molecular signatures. The identified core proteins may serve as potential biomarkers for syndrome differentiation, while astragaloside IV and berberine may exert therapeutic effects through targeting syndrome-specific molecular networks. These findings provide preliminary proteomic evidence supporting syndrome-guided precision integrative medicine in CRC and offer new insights into the biological basis of TCM syndrome differentiation and individualized therapeutic strategies.

Objective: To explore the therapeutic effects of different surgical methods for temporomandibular joint disc reduction and anchoring surgery, providing reference for optimizing this surgical procedure. Method: The study was approved by the hospital ethics committee. 173 patients (195 joints) who underwent temporomandibular joint disc repositioning and anchoring surgery were selected for retrospective analysis. Patients were categorized into groups A (traditional preauricular incision-scalpel/tissue scissors anterior attachment release), 35 patients (40 joints), B (traditional preauricular incision-plasma bipolar radiofrequency electrode anterior attachment release), 42 patients (46 joints), C (revised tragus incision - scalpel/tissue scissors anterior attachment release), 50 patients (58 joints), and D (revised tragus incision-plasma bipolar radiofrequency electrode anterior attachment release), 46 patients (51 joints). After a 6-month postoperative follow-up, the differences in maximum mouth opening (MMO), visual analogue scale (VAS), effective rate of joint disc reduction, incidence of preauricular numbness, obvious scars among patients in each group at 1, 3, and 6 months were compared postoperatively. Results: After surgery, the MMO of all four groups of patients initially shrunk and then gradually increased compared to before surgery. At the 1-month follow-up after surgery, the plasma bipolar radiofrequency release (B+D) group had a smaller impact on the patient’s MMO compared to the surgical knife/tissue scissors release (A+C) group (P < 0.05). Postoperative VAS scores for all four groups showed a gradual decrease from pre-operative levels, with the (B+D) group scoring significantly lower in the first month post-surgery compared to the (A+C) group (P < 0.05). Six months post-surgery, the rate of joint disc reduction of the four groups were higher than 95%, with no significant differences observed between the groups (P > 0.05). Patients in the revised tragus incision (C+D) group experienced a lower rate of preauricular numbness compared to those in the traditional preauricular incision (A+B) group (4.59% vs. 12.79%, P < 0.05), The incidence of obvious scars in the (C+D) group was significantly lower than that in the (A+B) group (3.67% vs. 23.26%, P < 0.05). Conclusion The revised tragus incision is superior to traditional preauricular incision in terms of protecting the auriculotemporal nerve and the scars were more inconspicuous. Further, the plasma bipolar radiofrequency electrode is superior to the scalpel/tissue scissors in terms of mouth opening recovery and pain control. For temporomandibular joint disc reduction and anchoring surgery, a modified tragus incision combined with plasma bipolar radiofrequency electrode to release the anterior attachment of the joint disc can be recommended as a surgical option.

Objective To develop a machine learning-based risk prediction model for postoperative acute kidney injury(AKI)and a model for mortality in obese patients admitted to intensive care unit(ICU)in order to improve early warning and prognostic evaluation to support clinical decision-making.Methods Data of obese postoperative ICU patients were retrospectively retrieved from the MIMIC-Ⅳ and eICU databases for statistical analysis.Ultimately,2 520 patients(670 from MIMIC-Ⅳ and 1 850 from eICU databases)were included to build the risk prediction models for AKI and mortality.The data included demographic information,vital signs,laboratory findings,surgical types,comorbidities,and medication use.After data cleaning and preprocessing,Boruta feature selection was applied,followed by the construction of prediction models using 7 machine learning algorithms,that is,Gradient Boosting Machine(GBM),Generalized Linear Model(GLM),k-Nearest Neighbors(KNN),Na?ve Bayes(NB),Neural Network(NNET),Support Vector Machine(SVM),and XGBoost.Model performance was evaluated through cross-validation and external validation.Results In the risk prediction models of AKI,the SVM model achieved the highest AUC value of 0.80 in the testing set and 0.71 in the external validation test.For the risk prediction models of mortality,the GBM model outperformed others in the prediction,attaining an AUC value of 0.91 in the testing set.Conclusion Risk predictive models for postoperative AKI and mortality in obese ICU patients are successfully constructed,and are valuable tools for clinicians to optimize early intervention and improve clinical outcomes for the patients.

The major components of Olaflur raw material were characterized using ultra performance liquid chromatography-quadrupole time-of-flight-mass spectrometry(UPLC-Q-TOF/MS).The results revealed that cetyl amine fluoride(C16-AmF),octadecene amine fluoride(C18:1-AmF),and octadecyl amine fluoride(Olaflur)were the main components.The contents of C16-AmF,C18:1-AmF,and Olaflur in oral care products were determined via ultra performance liquid chromatography-charged aerosol detector coupled with solid-phase extraction(SPE-UPLC-CAD).The oral care sample was dispersed evenly with a 50%ethanol aqueous solution,and then vortexed with ethanol.The supernatant was collected by centrifugation,concentrated to near dryness,and redissolved with ultrapure water.The re-dissolved sample was loaded onto a Poly-Sery HLB Pro SPE column for purification and elution.The acetonitrile eluate was collected and concentrated to 1.0 mL.Finally,a prepared test solution was separated on a Thermo Acclaim Surfactant Plus chromatographic column(2.1 mm×150 mm,3 μm).Acetonitrile and 100 mmol/L acetic acid-ammonium acetate aqueous solution(pH=4.8)were used as the mobile phases for gradient elution.The flow rate was 0.3 mL/min and cloumn temperature was maintained at 40℃.The sample was detected using a charged aerosol detector,and quantified using an external standard method.The experimental results indicated that the three organic fluorinated amines showed good linear relationship in their respective concentration ranges.The correlation coefficients(r)were greater than 0.99.The limit of detection(LOD)and the limit of quantification(LOQ)of C16-AmF were 2.0 and 8.0 μg/mL,respectively.The LOD and LOQ of C18:1-AmF were 2.0 and 8.0 μg/mL,respectively.The LOD and LOQ of Olaflur were 3.0 μg/mL and 10.0 μg/mL,respectively.The spiked recoveries of the three organic fluorinated amines were 84.3%-104.2%,with relative standard deviations(RSDs)of 4.93%-5.82%.The 28 batches of commercial oral care samples were detected by this method and the results indicated that three organic fluorinated amines were detected in 18 samples and the total content were 22.2-11477.8 μg/g.This method had high sensitivity and good reproducibility.It was suitable for verifying the authenticity of the claims of oral care products promoted with Olaflur as the main efficacy ingredient and selling point,and provided a valuable reference for establishing and improving the standard analytical method for Olaflur.

Due to the poor ionization efficiency and the weak mass spectrometry(MS)intensity of weakly polar substances,direct analysis using the traditional electrospray ionization mass spectrometry(ESI-MS)is a big challenge.In this study,a novel rapid on-site detection method of four prohibited sex hormones in cosmetics was proposed using online derivatization strategy coupled with a miniature mass spectrometer.The target substances in the samples were extracted by a custom-made polyaniline/multi-walled carbon nanotube solid-phase microextraction(SPME)probe.The stirring speed was 200 r/min,the extraction temperature was 40℃,and the extraction time was 2 min.A pulled dual-channel θ borosilicate glass capillary emitter was used as the nano-ESI ion source.The SPME probe was inserted into the channel containing methanol in theθborosilicate glass capillary.When the spray voltage was applied,the four sex hormones were desorbed and formed spray microdroplets,which then collided with the hydroxylamine microdroplets generated from the other channel.The microdroplets of reaction product entered into the miniature mass spectrometer for direct analysis.The limits of detection(LOD)and limits of quantification(LOQ)for the four sex hormones were 10-20 ng/mL and 20-50 ng/mL,respectively.The recoveries were from 84.6%to 107.8%with the relative standard deviations(RSD)from 4.1%to 11.6%.Compared to detection without derivatization,the MS signals of the four target substances were increased by 3 to 15 times.This method was simple,rapid,highly efficient and sensitive,and suitable for on-site rapid analysis of weakly polar sex hormones in cosmetics.

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.

Aim To investigate the effect of chrysoeriol on pulmonary vascular remodeling in pulmonary hyper-tension by animal experiments combined with cell ex-periments,and to explore its potential therapeutic tar-gets by network pharmacology.Methods The target of chrysoeriol was collected in Targetnet,SEA and SwissTargetPrediction database.Pulmonary arterial hy-pertension(PAH)targets were collected in the Dis-GeNET and GeneCards databases,and PPI network map was drawn in the STRING database,and key tar-gets were screened.The GO and KEGG pathway en-richment analysis was carried out through DAVID data-base and Weishengxing platform.AutoDock software was used for molecular docking of key core targets.The PAH model of rats was constructed,and the pulmo-nary hemodynamics and vascular remodeling were de-tected by echocardiography,HE and Masson staining.Primary pulmonary smooth muscle cells were extracted,and the effects of drugs on pathway proteins were de-tected in vitro.Results The results of network phar-macology showed that chrysoeriol exerted therapeutic effects on pulmonary hypertension by affecting key tar-gets such as AKT1,SRC,EGFR,MMP9 and gsk3 β,and signaling pathways such as EGFR and PI3K-AKT.Molecular docking showed that chrysoeriol had good binding ability with 5 key target genes.Animal experi-ments showed that the pulmonary hemodynamic func-tion of PAH rats was significantly improved after ad-ministration of chrysoeriol.The remodeling of small pulmonary arteries was significantly reduced.Cell ex-periments showed that chrysoeriol could inhibit the ex-pression of proliferation,migration and phenotypic transformation genes.Conclusion Chrysoeriol may play a role in the treatment of pulmonary hypertension through multiple targets.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.