Objective To comprehensively analyze the current epidemic characteristics and disease burden of brucellosis in Tongliao City, and to provide a basis for the prevention and control strategy of brucellosis in Tongliao City. Methods The report data of brucellosis in Tongliao City from 2018 to 2023 were collected. Descriptive methods were used for data analysis, and the disability-adjusted life years and indirect economic losses were calculated. Results From 2018 to 2023, a total of 22 034 cases were reported in Tongliao City, with an average annual incidence of 136.17/100 000. The incidence was statistically different between men and women ( χ²=12.23, P=0.032). The majority of cases were farmers (94.25%), followed by herdsmen (1.67%). The age group was concentrated between 30-60 years old (79.30%), among which the majority of cases were in the 40-50 years group (6 883/22 034). The onset time had seasonal characteristics, and the peak period was from March to August (the seasonal index was between 115.40%-151.29%). In terms of regional distribution, cases were reported in all counties (banners). The average annual incidence was highest in Kulun Banner (233.85/100 000) and Zalut Banner (210.13/100 000), and lowest in Keerqin District (42.28/100 000) and Holingol City (31.87/100 000). The analysis of disease burden showed that a total of 677.55 person-years (YLD) were lost from 2018 to 2023, with an average annual loss of 112.92 person-years. The total indirect economic loss was 59.3576 million yuan, with an average annual loss of 9.892 9 million yuan, and the people over 60 years old had the lowest annual loss. Conclusion The overall brucellosis epidemic in Tongliao City has shown a fluctuating downward trend. The epidemic prevention and control should be strengthened in farmers, people aged 40-50 years old, and areas such as Zalut Banner and Kulun Banner to further control the epidemic of brucellosis.

Objective To analyze the spatial distribution pattern of the potential suitable habitats for the Tibetan medicinal plant Sinopodophyllum hexandrum under current climatic conditions and the factors influencing the spatial differentiation of the habitats.Methods Based on the maximum entropy model,a species distribution model was established using selected species distribution data and environmental variable data.The geographic detector and the interaction detector were applied to quantify the factors affecting the spatial differentiation of the suitable area.By overlaying the suitable area with land cover types,the distribution characteristics of potential arable land and forest land within the potential suitable area were quantified.Results Under the current climatic conditions,altitude,precipitation in July,precipitation during the warmest season,water vapor pressure in June,precipitation in December,and the highest temperature in February are the key environmental factors affecting the distribution of Sinopodophyllum hexandrum.Under the current climatic conditions,the potential geographical distribution range of Sinopodophyllum hexandrum covers an area of 1.30×106 km2.Considering land cover types,the actual suitable area for Sinopodophyllum hexandrum is 6.13×105 km2,including 4.25×105 km2 of forest land and 1.88×105 km2 of arable land.The highly suitable forest areas are mainly distributed in the Aba Tibetan and Qiang Autonomous Prefecture,Ganzi Tibetan Autonomous Prefecture of Sichuan Province,Diqing Tibetan Autonomous Prefecture,Nujiang Lisu Autonomous Prefecture of Yunnan Province and Linzhi City within the Tibet Autonomous Region.The highly suitable arable land areas are mainly distributed in the Linxia Hui Autonomous Prefecture,Dingxi City,Tianshui City,and Longnan City of Gansu Province,with sporadic belt-like distributions in Sichuan Province,Yunnan Province and the Tibet Autonomous Region.Conclusion The study can provide evidence for the protection of wild Sinopodophyllum hexandrum resources and the selection of optimal planting areas.

Objective To analyze the spatial distribution patterns of the potential suitable habitats for Cremastrae Pseudobulbus Pleiones Pseudobulbus,including Cremastra appendiculata(D.Don)Makino,Pleione bulbocodioides(Franch.)Rolfe and Pleione yunnanensis Rolfe under current climatic conditions,and the factors influencing the spatial differentiation of their habitats.Methods Based on the maximum entropy(MaxEnt)model,a species distribution model was established using the screened species distribution data and environmental variable data.The factor detection and interaction detection of geographical detector were applied to quantify the factors influencing the spatial differentiation of the suitable areas.By overlaying the suitable areas with land cover types,the distribution characteristics of forest land and arable land in the potential suitable areas were quantified.Results Under the current climatic scenario,environmental variables such as solar radiation intensity,precipitation and temperature were closely related to the spatial distribution of Cremastrae Pseudobulbus Pleiones Pseudobulbus.The potential suitable areas for Cremastra appendiculata(D.Don)Makino,Pleione bulbocodioides(Franch.)Rolfe and Pleione yunnanensis Rolfe were 2.20×106 km2,2.75×106 km2 and 7.22×105 km2,respectively.Considering land cover types,the actual possible suitable areas for Cremastra appendiculata(D.Don)Makino,Pleione bulbocodioides(Franch.)Rolfe and Pleione yunnanensis Rolfe were 1.86×106 km2,2.20×106 km2 and 5.77×105 km2,respectively.Among these,the area of forest land was 1.17×106 km2,1.34×106 km2 and 3.67×105 km2,respectively,and the area of arable land was 6.95×105 km2,8.64×105 km2 and 2.10×105 km2,respectively.Conclusion This study can provide a basis for the protection and sustainable utilization of wild resources of the original plant materials of Cremastrae Pseudobulbus Pleiones Pseudobulbus.

Objective To analyze the spatial distribution pattern of potential suitable habitats and the influencing factors of habitat spatial differentiation of Gastrodiae Rhizoma under current climate conditions.Methods Based on 137 distribution records of Gastrodiae Rhizoma sourced from the Global Biodiversity Information Facility and 104 environmental variables acquired from the WorldClim dataset,a predictive model for the potential suitable habitat of Gastrodiae Rhizoma was developed using the MaxEnt model.The geodetector was applied to quantitatively analyze the environmental factors influencing the spatial heterogeneity of the suitable habitats.Subsequently,an overlay analysis with land cover types was conducted to obtain the distribution characteristics of forest land and cultivated land in the potential suitable habitats.Results Under current climate conditions,key environmental factors affecting the distribution of Gastrodiae Rhizoma included precipitation in July,average temperature in the coldest quarter,precipitation in the warmest quarter,water vapor pressure in September,altitude,and solar radiation in December.The potential geographical distribution range of Gastrodiae Rhizoma was 1.64×106 km2.Considering land cover types,the actual potential suitable area for Gastrodiae Rhizoma was 1.33×106 km2,of which the forest land area was 8.56×105 km2 and the cultivated land area was 4.74×105 km2.The highly suitable forest areas were mainly located within the provinces of Guizhou,Sichuan,Shaanxi,Hubei,Hunan and Gansu.The cultivated land suitable areas are mainly located within the provinces of Guizhou,Sichuan,Yunnan,Shaanxi and Hubei.Conclusion The highly suitable habitats of Gastrodiae Rhizoma are mainly located in provinces such as Guizhou,Sichuan,Shaanxi and Hubei,and are affected by factors such as solar radiation,water vapor pressure,temperature and precipitation.The research results can provide reference for the protection of wild Gastrodiae Rhizoma resources,artificial nurturing and optimal selection of ecological planting areas.

Objective To analyze the spatial distribution pattern of the potential suitable habitats for the Tibetan medicinal plant Sinopodophyllum hexandrum under current climatic conditions and the factors influencing the spatial differentiation of the habitats.Methods Based on the maximum entropy model,a species distribution model was established using selected species distribution data and environmental variable data.The geographic detector and the interaction detector were applied to quantify the factors affecting the spatial differentiation of the suitable area.By overlaying the suitable area with land cover types,the distribution characteristics of potential arable land and forest land within the potential suitable area were quantified.Results Under the current climatic conditions,altitude,precipitation in July,precipitation during the warmest season,water vapor pressure in June,precipitation in December,and the highest temperature in February are the key environmental factors affecting the distribution of Sinopodophyllum hexandrum.Under the current climatic conditions,the potential geographical distribution range of Sinopodophyllum hexandrum covers an area of 1.30×106 km2.Considering land cover types,the actual suitable area for Sinopodophyllum hexandrum is 6.13×105 km2,including 4.25×105 km2 of forest land and 1.88×105 km2 of arable land.The highly suitable forest areas are mainly distributed in the Aba Tibetan and Qiang Autonomous Prefecture,Ganzi Tibetan Autonomous Prefecture of Sichuan Province,Diqing Tibetan Autonomous Prefecture,Nujiang Lisu Autonomous Prefecture of Yunnan Province and Linzhi City within the Tibet Autonomous Region.The highly suitable arable land areas are mainly distributed in the Linxia Hui Autonomous Prefecture,Dingxi City,Tianshui City,and Longnan City of Gansu Province,with sporadic belt-like distributions in Sichuan Province,Yunnan Province and the Tibet Autonomous Region.Conclusion The study can provide evidence for the protection of wild Sinopodophyllum hexandrum resources and the selection of optimal planting areas.

Objective To investigate the geographical distribution patterns and influencing factors of suitable habitats for the desert medicinal plant Cynomorium songaricum Rupr under current climatic conditions;To provide a basis for its resource conservation and sustainable utilization.Methods The MaxEnt model was used to analyze potential suitable habitats for Cynomorium songaricum Rupr.Geographical Detector model was used to identify key environmental factors affecting habitat suitability.Surface cover data were overlaid to assess the distribution of sandy and Gobi regions within suitable habitats,enabling a quantitative evaluation of actual potential suitable areas.Results Model predictions indicated a total suitable habitat area of approximately 2.98×106 km2,representing 30.99%of China's mainland area.Highly suitable habitats are concentrated in desert and Gobi regions of Gansu,Xinjiang,Inner Mongolia,Qinghai and Ningxia.Among climatic factors,precipitation of the coldest quarter(bio19),solar radiation in August(srad8),and mean temperature of the coldest quarter(bio11)significantly influence Cynomorium songaricum Rupr distribution.The interaction between temperature and solar radiation intensity exhibited the highest explanatory power for habitat distribution patterns(q=0.82).Overlay analysis with surface cover data estimated the actual potential suitable area at approximately 9.70×105 km2,with sandy regions comprising 5.73×105 km2 and Gobi regions 3.98×105 km2.Conclusion By integrating multi-source data and modeling approaches,this study delineates the potential suitable habitats for Cynomorium songaricum Rupr across China and evaluates the spatial distribution characteristics and influencing factors of suitable habitats in Cynomorium songaricum Rupr.These findings offer a foundation for conserving wild Cynomorium songaricum Rupr resources,optimizing ecological planting regions,and promoting sustainable industry development.

Objective:To explore the application effectiveness of the large language model DeepSeek in the cultivation of clinical thinking skills for medical laboratory technology students.Methods:A non-randomized controlled study was conducted. In the 2024-2025 academic year, two classes of second-year medical laboratory technology students from Hubei University of Chinese Medicine were selected and divided into a DeepSeek-assisted teaching group (Class A, n=53) and a traditional teaching control group (Class B, n=53), totaling 106 students. Both groups followed a 20-week problem-based learning (PBL) framework with identical teaching content, instructors, and class hours. Class A utilized DeepSeek via the"Learning Pass AI"platform for case diagnosis reasoning, prompt construction training, test plan formulation, and result analysis, while Class B received traditional PBL instruction. Paired t-tests were used to compare pre-and post-teaching scores in clinical thinking skills, AI interaction literacy, and prompt construction in Class A. Independent samples t-tests and chi-square ( χ2) tests were used to evaluate differences in case reasoning scores, etiology analysis accuracy, and teaching satisfaction between groups. Structured questionnaires supplemented the evaluation of model-assisted teaching processes. Results:The comparison of pre-and post-teaching scores in Class A showed that post-teaching scores significantly improved in clinical thinking skills[(4.02±0.45) points vs. (3.09±0.50) points, t=2.23)] and AI interaction literacy [(4.62±0.41) points vs. (3.27±0.54) points, t=2.18]. Compared to Class B, Class A demonstrated superior performance in case reasoning scores [(81.1±3.8) points vs.(74.3±4.2) points, t=8.97], etiology analysis accuracy [94.3% (50/53) vs. 81.1% (43/53), χ2=4.29], and teaching satisfaction [(95.6±3.2)points vs. (82.6±4.8) points, t=11.86] ( P<0.05). The results of questionnaires indicated that during model application, the prompt construction improved in logic [(2.85±0.58) points to (4.25±0.50) points, t=14.23, P<0.01] and innovation [(2.60±0.53) points to (4.05±0.46) points, t=11.57, P<0.05], but question clarity (77.4%, 41/53) and medical terminology accuracy (43.4%, 23/53) remained primary shortcomings. Conclusion:Integrating large language models into AI-teacher collaborative learning pathways can effectively promote students′ autonomous inquiry and clinical reasoning skills, thereby enhancing medical laboratory technology students′ clinical thinking skills.

BACKGROUND: Treatment with chimeric antigen receptor-T (CAR-T) cells has shown promising effectiveness in patients with relapsed/refractory B-cell acute lymphoblastic leukemia (R/R B-ALL), although the process of preparing for this therapy usually takes a long time. We have recently created CD19 Fast-CAR-T (F-CAR-T) cells, which can be produced within a single day. The objective of this study was to evaluate and contrast the effectiveness and safety of CD19 F-CAR-T cells with those of CD19 conventional CAR-T cells in the management of R/R B-ALL. METHODS: A multicenter, retrospective analysis of the clinical data of 44 patients with R/R B-ALL was conducted. Overall, 23 patients were administered with innovative CD19 F-CAR-T cells (F-CAR-T group), whereas 21 patients were given CD19 conventional CAR-T cells (C-CAR-T group). We compared the rates of complete remission (CR), minimal residual disease (MRD)-negative CR, leukemia-free survival (LFS), overall survival (OS), and the incidence of cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS) between the two groups. RESULTS: Compared with the C-CAR-T group, the F-CAR-T group had significantly higher CR and MRD-negative rates (95.7% and 91.3%, respectively; 71.4% and 66.7%, respectively; P = 0.036 and P = 0.044). No significant differences were observed in the 1-year or 2-year LFS or OS rates between the two groups: the 1-year and 2-year LFS for the F-CAR-T group vs.C-CAR-T group were 47.8% and 43.5% vs. 38.1% and 23.8% (P = 0.384 and P = 0.216), while the 1-year and 2-year OS rates were 65.2% and 56.5% vs. 52.4% and 47.6% (P = 0.395 and P = 0.540). Additionally, among CR patients who underwent allogeneic hematopoietic stem cell transplantation (allo-HSCT) following CAR-T-cell therapy, there were no significant differences in the 1-year or 2-year LFS or OS rates: 57.1% and 50.0% vs. 47.8% and 34.8% (P = 0.506 and P = 0.356), 64.3% and 57.1% vs. 65.2% and 56.5% (P = 0.985 and P = 0.883), respectively. The incidence of CRS was greater in the F-CAR-T group (91.3%) than in the C-CAR-T group (66.7%) (P = 0.044). The incidence of ICANS was also greater in the F-CAR-T group (30.4%) than in the C-CAR-T group (9.5%) (P = 0.085), but no treatment-related deaths occurred in the two groups. CONCLUSION Compared with C-CAR-T-cell therapy, F-CAR-T-cell therapy has a superior remission rate but also leads to a tolerably increased incidence of CRS/ICANS. Further research is needed to explore the function of allo-HSCT as an intermediary therapy after CAR-T-cell therapy.

Climate and land use changes may significantly impact the habitat distribution of Gastrodia elata, an endangered traditional medicinal plant. Accurately predicting its future potential suitable habitats is crucial for its conservation and sustainable development. This study integrates current distribution data of G. elata with 56 environmental variables and uses the MaxEnt model to predict changes in its suitable habitats under current climate conditions and four future climate scenarios(SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). The results show that October precipitation and December minimum temperature are key environmental factors influencing its distribution. Under the current climate, optimal habitats for G. elata are concentrated in montane forest areas in Sichuan, Yunnan, Guizhou, and Hubei, which meet the species' requirements for understory growth. Across all future scenarios, the suitable habitat of G. elata consistently shows a stable northward shift, with a steady increase in suitable areas, extending to the middle and lower reaches of the Yangtze River and the Huang-Huai region, and even expanding into Liaoning, Jilin, and southern Heilongjiang. Land use analysis, taking into account the protection of arable land and the utilization of forest resources, indicates that by 2100, under future climate conditions, arable land in medium-to high-suitability areas is expected to increase by 30%-124%. While the conversion of non-suitable forest land into suitable habitats is projected to increase by 5%-52%, the growth of medium-to high-suitability areas within forests is relatively modest, ranging from 1% to 24%. These findings highlight the need to balance agricultural expansion with forest resource conservation to ensure the long-term sustainability of G. elata and provide scientific guidance for future suitable habitat management.
Ecosystem ; China ; Climate Change ; Gastrodia/growth & development* ; Conservation of Natural Resources ; Plants, Medicinal/growth & development*

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