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.

Liver transplantation is the standard treatment for end-stage liver disease， but it has long been limited by the shortage of donor livers. In clinical practice， three types of donor liver （domino donor livers， small-for-size grafts， and steatotic donor livers） are limited due to issues such as metabolic defects， insufficient volume， and poor quality. This article systematically reviews the definition， clinical application， and bottlenecks of the above three types of donor livers and elaborates on the unique value and clinical practice advances of auxiliary liver transplantation （a special surgical procedure that retains part or all of the recipient’s own liver and superimposes donor liver function support） in avoiding small-for-size syndrome， compensating for metabolic defects， and expanding the application of marginal livers， in order to provide a targeted clinical practice reference for optimizing liver transplantation strategies for end-stage liver disease and improving donor liver utilization rate.

Objective To examine the impact of climate, land use and their interaction effect on the population density of Oncomelania hupensis. Methods O. hupensis snail surveillance data in Yunnan Province from 2013 to 2022 were obtained from the National Information System for Parasitic Diseases Prevention and Control of Chinese Information System for Disease Control and Prevention, and the density of living snails was calculated in each county-level administrative district. Climate data were obtained from the China Meteorological Forcing Dataset version 2.0, and land use data were captured from the China Land Cover Dataset. The raster climate data and land use data were clipped to the administrative border of Yunnan Province in the R package version 4.5, and the climate data were resampled to a spatial resolution of 30 m × 30 m consistent with the land use data using bilinear interpolation. Annual meteorological indicators were extracted from each county-level schistosomiasis surveillance administrative area in Yunnan Province from 2013 to 2022, including average temperature, maximum temperature, minimum temperature, temperature difference, average precipitation rate, maximum precipitation rate, minimum precipitation rate, downward shortwave radiation flux, downward longwave radiation flux, air pressure, wind speed, specific humidity and relative humidity. Annual land use structure and landscape pattern index in each county-level schistosomiasis surveillance administrative area in Yunnan Province from 2013 to 2022 were extracted and calculated, including the proportion of areas occupied by each type of land use, patch density, largest patch index, landscape separation index, landscape fragmentation index, landscape segmentation index, contagion index, patch cohesion index, edge density, and shape index. Land use entropy index was introduced to represent the degree of habitat fragmentation and ecological niche breadth. The density of living snails and environmental factors were subjected to rank correlation analysis, and variables with no statistically significant correlation (P>0.1) were excluded. Then, a correlation coefficient matrix was constructed, and |r_s| of 0.85 and greater was defined as the threshold of high collinearity. For highly correlated variables in each group, indicators with a more biological explanatory power or a better statistical efficiency were retained. A generalized additive model (GAM) was constructed with O. hupensis snail density as a response variable and screened environmental variables as independent variables, and the model performance was evaluated using the variance explained. Results The area occupied by O. hupensis snail habitats was 1 056 to 1 680 hm² in Yunnan Province from 2013 to 2022, appearing an overall tendency towards a decline; however, the density of living snails appeared a tendency towards a slight rise. Based on the correlation coefficient matrix, 14 core explanatory variables were retained, including farmland, water bodies, bare land, impervious surface, landscape separation index, landscape fragmentation index, edge density, shape index, land use entropy index, downward longwave radiation flux, downward shortwave radiation flux, maximum temperature, minimum precipitation rate and specific humidity. Two types of GAM were constructed, including the main-effects model without interaction terms and the interaction model with interaction terms. The corrected Akaike information criterion (AICc) of the optimal main-effects model was −285.223, and the core variables included bare land, impervious surface, landscape separation index, edge density, shape index, land use entropy index and downward shortwave radiation flux. Following introduction of second-order interaction terms, the AICc value of the optimal interaction model was −345.526, and the significant interaction combinations included: farmland × impervious surface, farmland × landscape separation index, farmland × shape index, farmland × land use entropy index, impervious surface × landscape separation index, impervious surface × edge density, and shape index × downward shortwave radiation flux. The proportion of the main-effects model explaining the variation of O. hupensis snail density was 39.8%, and the explanatory power of GAM increased to 79.0% following introduction of interaction terms. Conclusion Landscape separation index, edge density, shape index and other landscape pattern indicators pose significant impacts on the intensity and direction of changes in O. hupensis snail density in Yunnan Province.

Objective To develop and validate a model to predict the risk of radiation-induced lung injury (RILI) and assess its clinical feasibility. Methods Clinical data from 125 patients with non-small cell lung cancer (NSCLC) were included in the study. The patients were divided into training group (88 cases) and validation group (38 cases). Key predictive factors were identified using univariate and multivariate logistic regression analyses combined with least absolute shrinkage and selection operator (LASSO) regression. A predictive model was constructed and evaluated using a nomogram, receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis. Results The key variables identified by the model were tumor volume (P = 0.017), Eastern Cooperative Oncology Group performance status score (P = 0.035), 95% of the minimum dose to the target volume (P = 0.028), percentage of bilateral lung volume receiving 20 Gy of radiation (P < 0.001), and neutrophil-to-lymphocyte ratio (P = 0.021). The ROC curve showed that the areas under the curve (AUC) for the model in the training and validation groups were 0.987 and 0.992, respectively, indicating good predictive ability. The calibration curve and decision curve further confirmed the accuracy and clinical practicability of the model. Conclusion The predictive model proposed in this study can accurately assess the risk of developing RILI in patients with NSCLC who have undergone radiotherapy, demonstrating its potential value in clinical practice.

BACKGROUND:Multiple studies have confirmed that mitogen-activated protein kinase(MAPK)signaling pathway is involved in cell proliferation,and microRNA(miR)is involved in the occurrence and development of hypertrophic scars.Therefore,the role of miR-27a-3p and MAPK signaling pathways in pathological scar formation has been further explored. OBJECTIVE:To explore the effect of miR-27a-3p on the proliferation of human hypertrophic scar fibroblasts through the MAPK signaling pathway. METHODS:The primary fibroblasts were isolated and collected from the skin samples.The primary fibroblasts were observed by inverted microscope and verified by immunofluorescence.The relative expression level of miR-27a-3p in tissues was detected by qRT-PCR.The target genes of hsa-miR-27a-3p were predicted using the database,and then the predicted target genes were enriched by gene ontology function analysis and biological pathway enrichment analysis of the Kyoto Encyclopedia of Genes and Genomes.There were seven groups:blank control,negative control,miR-27a-3p mimic,miR-27a-3p inhibitor,miR-27a-3p mimic+p38 MAPK inhibitor,miR-27a-3p mimic+extracellular regulated protein kinase inhibitor,miR-27a-3p mimic+c-Jun N-terminal kinase inhibitor.Western blot was used to detect the levels of extracellular regulated protein kinase,c-Jun N-terminal kinase inhibitor.and p38 kinase and their phosphorylation levels.Cell counting kit-8 and EdU were used to detect cell proliferation. RESULTS AND CONCLUSION:Compared with normal skin fibroblasts,hypertrophic scar fibroblasts had stronger proliferative activity(P<0.05)and faster proliferation level(P<0.001).Compared with normal skin,miR-27a-3p was highly expressed in hypertrophic scars(P<0.001).Compared with the negative control group,overexpression of miR-27a-3p could promote cell proliferation activity(P<0.001)and proliferation levels(P<0.001).Compared with the negative control group,knockdown of miR-27a-3p could inhibit the proliferation activity(P<0.05)and proliferation levels(P<0.001).Compared with the negative control group,overexpression of miR-27a-3p promoted the phosphorylated levels of extracellular regulated protein kinase,c-Jun N-terminal kinase,and p38 mitogen-activated protein kinase(P<0.05).Compared with the negative control group,knockdown of miR-27a-3p inhibited the phosphorylated levels of extracellular regulated protein kinase,c-Jun N-terminal kinase,and p38 MAPK(P<0.05).Compared with the miR-27a-3p mimic group,specific inhibitors of extracellular regulated protein kinase,c-Jun N-terminal kinase,and p38 MAPK reversed the effects of miR-27a-3p on the proliferative activity(P<0.01)and proliferation level(P<0.001)of fibroblasts.To conclude,these results suggest that miR-27a-3p promotes the proliferation of human hypertrophic scar fibroblasts by activating the MAPK signaling pathway.

Objective To analyze the influencing factors of malaria infection of labor service exported to overseas in Langfang City, in order to establish a visualization tool to assist clinicians in predicting the risk of malaria. Methods A total of 4 774 expatriate employees of the Nibei Pipeline Project of the Pipeline Bureau from October 2021 to August 2023 were taken as the subjects, and the gender, age, overseas residence area and Knowledge of malaria controlscores of the study subjects were investigated by questionnaire survey, and the possible risk factors of malaria were screened by logistic regression model. At the same time, the nomogram prediction model was established, and the subjects were divided into the training group and the validation group at a ratio of 2:1, and the area under the curve (ROC) and the decision curve were plotted to evaluate the prediction ability and practicability of the prediction model in this study. Results Among the 4 774 study subjects, 96 cases of malaria occurred, and the detection rate was 2.01%. Junior school （OR=1.723，95% CI:1.361-2.173）， and residence in rural areas（OR=2.091，95%CI:1.760 -3.100）were risk factors (OR>1), while protective measures（OR=0.826，95% CI : 0.781 - 0.901） and high malaria education scores （OR=0.872，95% CI : 0.621 - 0.899）were protective factors.The nomogram prediction model results showed that the area under the curve of the nomogram prediction model in the training group was 0.94 (95% CI : 0.85 - 1.00), while the validation group was 0.93 (95% CI : 0.80 - 1.00). The results of the decision curve showed that when the threshold probability of the population was 0-0.9, the nomogram model was used to predict the risk of malaria occurrence with the highest net income. Conclusion The nomogram prediction model (including gender, education, region, protection and malaria education score) established and validated in this study is of great value for clinicians to screen high-risk patients with malaria.

Objective:To construct and validate a predictive model for postoperative recurrence in early non-small cell lung cancer patients.Methods:The clinical data of 252 patients with early non-small cell lung cancer admitted to the 926th Hospital of Joint Logistic Support Force of PLA from January 2016 to January 2018were retrospectively collected. All of the patients underwent surgical treatment and they were followed up for 5 years after surgery, according the recurrence after surgery, they were divided into the recurrence group (103 cases) and non- recurrence group (149 cases). The risk factors for postoperative recurrence in early non-small cell lung cancer patients were analyzed. A predictive model for postoperative recurrence in early non-small cell lung cancer patients was constructed and validated.Results:The results of Logistic regression analysis showed that tumor long diameter≥ 3 cm, lymph node metastasis, low differentiation, spicules and pleural traction were independent risk factors for postoperative recurrence in early non-small cell lung cancer patients ( P<0.05). Using R4.0.3 statistical software, the dataset was randomly divided into a training set and a validation set, with a sample size of 176 cases in the training set and 76 cases in the validation set. A prediction model was constructed, with thearea under the curve (AUC) of the receiver operating characteristic (ROC) curve of 0.754 (95% CI 0.679 - 0.828) in the training set and AUC of 0.749 (95% CI 0.634 - 0.864) in the validation set. The model was subjected to a Hosmer-Lemeshow Goodness-of-Fit Test in the validation set, χ2 = 11.31, P = 0.185. Conclusions:The predictive model base on tumor long diameter ≥ 3 cm, lymph node metastasis, low differentiation, spicules and pleural traction can identify patients at high risk of postoperative recurrence in early non-small cell lung cancer effectively.

Objective The mechanism of Huazhuo xingxue decoction(HZXXD)in the treatment of ischemic stroke was explored through network pharmacology,molecular docking and cell validation.Methods TCMSP,TCMID,BATMAN-TCM database and literature search were used to get the chemical components and related target proteins of Huazhuo Xingxue Decoction,and the targets of dementia,stroke and amnesia were obtained from Genecards database and OMIM database.The traditional Chinese medicine-active components-target-network and protein interaction map were constructed by using Cytoscape,and the target was enriched by KEGG pathway by David database.Western blot was used to investigate the effect of HZXXD on inflammation-related core targets expression using oxygen and glucose deprivation/reoxygenation cell model.Finally,Autodock was used for molecular docking of key active ingredients and important targets to evaluate their binding activity.Results 76 active molecules and 33 common targets of herb-disease were screened out.KEGG bioaccumulation results involve multiple inflammatory signal pathways such as TNF,chemical carcinogenesis-reactive oxygen species and HIF-1.TNF-α was found to be the core target of HZXXD by oxygen glucose deprivation/reoxygenation cell experiments.Five compounds with the strongest binding ability to TNF-α,kaempferol,apigenin,aloe-emodin,baicalein and stigasterol,were screened by traditional Chinese medicine-active ingredient-target network map and molecular docking.Conclusion Huazhuo Xingxue Decoction may down regulate the expression of core target TNF-α,kaempferol,apigenin,aloe emodin,baicalein and stigasterol may be the main active substances for TNF-α binding.

BACKGROUND: The clinical impact of post-percutaneous coronary intervention (PCI) quantitative flow ratio (QFR) in patients treated with PCI for chronic total occlusion (CTO) was still undetermined. METHODS: All CTO vessels treated with successful anatomical PCI in patients from PANDA III trial were retrospectively measured for post-PCI QFR. The primary outcome was 2-year vessel-oriented composite endpoints (VOCEs, composite of target vessel-related cardiac death, target vessel-related myocardial infarction, and ischemia-driven target vessel revascularization). Receiver operator characteristic curve analysis was conducted to identify optimal cutoff value of post-PCI QFR for predicting the 2-year VOCEs, and all vessels were stratified by this optimal cutoff value. Cox proportional hazards models were employed to calculate the hazard ratio (HR) with 95% CI. RESULTS: Among 428 CTO vessels treated with PCI, 353 vessels (82.5%) were analyzable for post-PCI QFR. 31 VOCEs (8.7%) occurred at 2 years. Mean value of post-PCI QFR was 0.92 ± 0.13. Receiver operator characteristic curve analysis shown the optimal cutoff value of post-PCI QFR for predicting 2-year VOCEs was 0.91. The incidence of 2-year VOCEs in the vessel with post-PCI QFR < 0.91 (n = 91) was significantly higher compared with the vessels with post-PCI QFR ≥ 0.91 (n = 262) (22.0% vs. 4.2%, HR = 4.98, 95% CI: 2.32-10.70). CONCLUSIONS Higher post-PCI QFR values were associated with improved prognosis in the PCI practice for coronary CTO. Achieving functionally optimal PCI results (post-PCI QFR value ≥ 0.91) tends to get better prognosis for patients with CTO lesions.