Objective To investigate the current distribution and predict the future suitable habitats of Haemaphysalis longicornis in Nanjing City, so as to provide insights into control and early warning of ticks and management of tick-borne diseases in Nanjing City. Methods The electronic map of Nanjing City was obtained from the National Platform for Common GeoSpatial Information Services. The distribution of H. longicornis and the longitude and latitude of distribution points from 2022 to 2024 were obtained from centers for disease control and prevention across each district in Nanjing City. Climatic and environmental variable data in Nanjing City were captured from the Worldclim database. Initially, 19 bioclimatic variables in this database were selected, including annual mean temperature, mean diurnal range, isothermality, temperature seasonality, maximum temperature of the warmest month, minimum temperature of the warmest month, temperature annual range, mean temperature of the wettest quarter, mean temperature of the driest quarter, mean temperature of the warmest quarter, mean temperature of the coldest quarter, annual precipitation, precipitation of the wettest month, precipitation of the driest month, precipitation seasonality, precipitation of the wettest quarter, precipitation of the driest quarter, precipitation of the warmest quarter, and precipitation of the coldest quarter. The elevation and normalized difference vegetation index were obtained from Data Sharing Platform of the Center for Resources and Environmental Sciences, Chinese Academy of Sciences. Then, the distribution points of H. longicornis, elevation, vegetation index and 19 bioclimatic variables were loaded into the software MaxEnt 3.4.4 to evaluate and screen out the variables with a contribution rate of 1% and higher. ArcGIS 10.8.1 software was used to extract the elevation, vegetation index and 19 bioclimatic variables of the distribution points of H. longicornis for a correlation analysis. If the absolute value of the correlation coefficient was 0.8 and higher, the variable with the higher contribution was retained. The 2050 dataset of the BCCCSM2-MR atmospheric circulation model in the coupled model intercomparison project phase 6 (CMIP6) were obtained from the Worldclim database as climate data for 2050. Screened H. longicornis species data and environmental and climate data were loaded into the maximum entropy (MaxEnt) model with the software MaxEnt 3.4.4 for training and validation, and then, all data generated from the model were imported into the software ArcGIS 10.8.1 to generate raster data and yield the map pertaining to the distribution of H. longicornis risk in Nanjing City. The accuracy of the model was evaluated with a receiver operating characteristic (ROC) curve, and the predictive effect of the model was assessed with area under the ROC curve (AUC). The suitable habitats of H. longicornis were classified in Nanjing City with the software ArcGIS 10.8.1, and the areas of distribution of suitable habitats in various categories were recorded to create the map of current H. longicornis suitable habitats classification in Nanjing City. The climatic and geographic information data in 2050 were employed as future environmental and climatic factors, and current environmental and climatic factors and current H. longicornis distribution data were additionally used to predict the future suitable habitats of H. longicornis in Nanjing City. In addition, the contributions of environmental and climatic factors to distribution of suitable habitats of H. longicornis was evaluated with the Jackknife method in Nanjing City. Results A total of 10 environmental and climatic variables were screened for analysis of the suitability of H. longicornis in Nanjing City based on correlation analyses and contributions of the MaxEnt model, including annual mean temperature, precipitation of the warmest quarter, vegetation index, precipitation of the wettest month, temperature annual range, annual precipitation, mean temperature of the warmest quarter, elevation, mean temperature of the wettest quarter, and maximum temperature of the warmest month, and annual mean temperature (34.8%), precipitation of the warmest quarter (17.3%), vegetation index (13.1%), and precipitation of the wettest month (10.8%) contributed relatively highly to the distribution of suitable habitats of H. longicornis in Nanjing City. The mean AUC of the ROC curve was 0.810 ± 0.055 for 10 repeated modeling results of the MaxEnt model, indicating high predictive performance of the model. The potential distribution areas of H. longicornis were predicted to be mainly located in Luhe District, Pukou District, Jiangning District, Lishui District, and Gaochun District in Nanjing City with the MaxEnt model. Under current climatic conditions, the area of potential suitable habitats of H. longicornis was 4 182.42 km² in Nanjing City, including 1 252.94 km² highly suitable habitats, which accounted for 19.00% of the total area of Nanjing City. Under the climate scenario in 2050, the area of potential suitable habitats of H. longicornis was projected to increase to 5 467.58 km² in Nanjing City, accounting for 82.95% of the total area of the city, and these habitats were mainly concentrated in Luhe District, Pukou District, Jiangning District, Lishui District, and Gaochun District. The areas of suitable habitats of H. longicornis at various categories were predicted to vary greatly in 2050, and the area of highly suitable habitats of H. longicornis was projected to increase to 2 378.82 km², accounting for 36.08% of the total area of Nanjing City. Based on jackknife tests and contributions of environmental and climatic variables, 6 dominant environmental and climatic factors were screened, including annual mean temperature (34.8% contribution), precipitation of the warmest quarter (17.3% contribution), vegetation index (13.1% contribution), precipitation of the wettest month (10.8% contribution), temperature annual range (5.4% contribution), and mean temperature of the warmest quarter (5.0% contribution), with cumulative contributions of 86.4%. Conclusion The distribution of H. longicornis is strongly associated with vegetation, temperature and precipitation in Nanjing City. Future climate change may lead to an expansion of the distribution area of H. longicornis in Nanjing City.

ObjectiveTo delineate imaging findings using an imaging platform and investigate the correlation between MRI characteristics of spinal cord atrophy and clinical diagnosis in children with spinal cord injury (SCI). MethodsImaging data of 150 children with SCI admitted to Beijing Bo'ai Hospital, China Rehabilitation Research Center, from January, 2002 to March, 2024 were collected and imported into the imaging platform. The anteroposterior and transverse diameters of the middle part of the spinal cord at the cross-section with the most severe atrophy were measured, and the relevant indicators of the previous normal spinal cord segment were measured as controls; the radiomic features were extracted. Clinical data of the children including gender, age, cause of injury, sensory level, motor level, spinal cord injury level, injury severity and disease course were collected. ResultsSpinal cord atrophy was identified in 81 cases (54%), among which 78 cases (96%) were American Spinal Injury Association Impairment Scale (AIS) grade A and 3 cases (4%) were AIS grade C. The upper boundary of the spinal cord atrophy site strongly correlated with the injury level, motor level and sensory level (r > 0.8, P < 0.001). ConclusionMore than half of children with SCI may develop secondary spinal cord atrophy, the vast majority of whom suffer from complete spinal cord injury; the upper boundary of spinal cord atrophy is correlated with the injury level.

ObjectiveTo explore the application of virtual reality biofeedback training combined with oral localization therapy in dysphagia after oral cancer surgery. MethodsFrom May, 2023 to July, 2024, 86 patients with dysphagia after oral cancer surgery in Zhejiang Provincial People's Hospital were randomly divided into control group (n = 43) and experimental group (n = 43). The control group received conventional swallowing function training, while the experimental group added virtual reality biofeedback training combined with oral positioning therapy, for four weeks. The Standardized Swallowing Function Assessment Scale (SSA), Functional Oral Intake Scale (FOIS) and M.D.Anderson Dysphagia Inventory (MDADI) were used for evaluation before intervention, and two weeks, four weeks and eight weeks after intervention. ResultsFor scores of SSA , the main effects of group (F = 150.190, P < 0.001, η²_p = 0.641) and time (F = 230.870, P < 0.001, η²_p = 0.733), as well as the interaction effect (F = 16.910, P < 0.001, η²_p = 0.168) were all significant. For scores of FOIS, the main effects of group (F = 59.601, P < 0.001, η²_p = 0.415) and time (F = 89.464, P < 0.001, η²_p = 0.516), as well as the interaction effect (F = 7.990, P < 0.001, η²_p = 0.087) were all significant. For scores of MDADI, the main effects of group (F = 33.133, P < 0.001, η²_p = 0.283) and time (F = 49.650, P < 0.001, η²_p = 0.371), as well as the interaction effect (F = 3.224, P = 0.023, η²_p = 0.037) were all significant. ConclusionVirtual reality biofeedback training combined with oral localization therapy could improve the swallowing function, oral feeding ability and overall quality of life of patients with dysphagia after oral cancer surgery.

ObjectiveTo develop a full-process data platform of renal rehabilitation, diagnosis and quality control information. MethodsA hierarchical architectural design was proposed, adhering to clinical pathway models and standardized data protocols. The platform comprehensively covered assessment, intervention, follow-up and quality control for maintenance hemodialysis (MHD) patients. By integrating multidisciplinary resources and standardizing rehabilitation workflows, it delivered standardized and intelligent rehabilitation services. ResultsThe platform achieved standardized and intelligent management of rehabilitation services, effectively improved the physiological function, psychological state and quality of life convenience for MHD patients, while significantly reduced the economic and care burden on patients' families and society. ConclusionThe rehabilitation service model based on a full-process data platform may provide scientific and systematic support for MHD patients.

ObjectiveThis study aims to explore the potential of different orders of magnitude single-nucleotide polymorphism (SNP) locus combinations for predicting distant kinship relationships. A high-density SNP locus set was constructed, and a comprehensive assessment of its inference capability was conducted. MethodsFirstly, we selected three commercial chip panels, CGA (Chinese genotyping array, Illumina), GSA (Global screening array, Illumina), Affy (23MF_V2 high-density SNP array, Affymetrix) and merged them after quality control, forming a high-density SNP locus panel(1 180 k). Secondly, we selected 161 samples and collected their peripheral blood samples by using whole-genome sequencing technology. Within this sample population, the levels of kinship relationships fully covered the range from level 1 to level 9, and the number of kinship pairs at each level was consistently maintained at over 50 pairs. From 161 samples data of whole-genome sequencing, the 1 180 k locus set was extracted, which is referred to as the high-density SNP locus set in the following text. The kinship inference was conducted using the identity-by-descent (IBD) algorithm with the selected optimal parameters. To comprehensively evaluate the performance of the high-density SNP locus set in kinship inference, we compared it with the three commercial chip panels, the intersection of these three chip loci, and the control sets constructed by randomly reducing the number of the high-density SNP locus set. Based on the changes in the IBD lengths, as well as the dynamic trends in prediction accuracy, we conducted a scientific assessment of the kinship inference capability of the high-density SNP locus set. ResultsAfter screening, a set of 1 184 334 autosomal SNPs was obtained. During the process of screening the optimal IBD length threshold, the result revealed that 0 cM, 1 cM, and 2 cM all demonstrated good applicability. However, to avoid the issue of a large amount of redundant information caused by setting a too low IBD length threshold, this study ultimately selected 2 cM as the optimal threshold. Compared with the average results of three chip panels, the high-density SNP locus set increased the total IBD length and the average IBD length across levels 1-9; the accuracy of the confidence interval for level 8 was 70.97%, which represented a 3.50% improvement; the average confidence interval accuracy for levels 1-8 was 91.39%, representing a 1.00% increase; and the false negative rates at levels 8 and 9 were reduced by 2.42% and 6.76%, respectively. The system efficacy of the high-density SNP locus set for kinship inference of first to eighth degree relationships reached 98.91%. Through random reduction of the high-density SNP locus set results, it is found that increasing the number of SNPs with the panel, the detection efficiency of IBD length showed a significant upward trend. At the same time, the overall trend in the accuracy of kinship relationship prediction as well as the confidence interval accuracy also indicated that both metrics steadily increased with the addition of more loci. ConclusionThe results show that the high-density SNPs panel significantly enhances the efficacy of distant kinship inference, accurately covering kinship degrees, with the average confidence interval accuracy for first to eighth degree relationships stably above 90%. The study finds that increasing the number of SNPs panel can improve the ability to predict distant kinship.

ObjectiveBiochemistry and Molecular Biology, a discipline that elucidates life phenomena at the molecular level, serves as a core foundational course in medical education. It provides the theoretical basis for studying other basic and clinical medical subjects, as well as for understanding pathogenesis, disease diagnosis, and treatment. However, its complex content and highly abstract concepts have posed a dual challenge to traditional teaching models: “inefficient instruction” and “inadequate learning outcomes”. Within limited classroom hours, how to engage students and stimulate their intrinsic motivation, and how to help them recognize, understand, and develop a passion for biochemistry from the perspective of the discipline’s essence, have long been key focuses of curriculum research. MethodsUsing the lipid metabolism chapter as an example, this study employs “Rain Classroom”, a generative artificial intelligence (AI)-assisted platform, to support education in four dimensions: teaching, learning, evaluation, and research. In teaching, it assists instructors through virtual experiments, lesson preparation support, knowledge mapping, and assignment design. For learning, it serves as an intelligent study assistant for students, providing automated assignment review, enabling educational resource sharing, and facilitating personalized learning pathways. In evaluation, the platform automates assignment grading, analyzes student performance data, and offers diagnostic feedback and teaching recommendations. In research, it aids educators in collecting and analyzing teaching data, as well as searching for and summarizing relevant literature. ResultsThe results indicate that an educational model integrating teacher-led instruction, student-centered learning, and generative AI assistance significantly enhances teaching quality, students’ self-directed learning abilities, and knowledge mastery. Furthermore, with the support of generative AI, curriculum-based ideological education—focusing on cutting-edge disciplinary advances and topical medical issues—helps cultivate students’ medical spirit of “honoring life and healing the wounded”, thereby fostering the establishment of appropriate professional values. Finally, while generative AI presents both opportunities and challenges for higher education, this study also analyzes potential risks in its teaching applications, emphasizing the need for both instructors and students to avoid over-reliance and to ensure that technological tools consistently serve the fundamental goals of education. ConclusionThis study demonstrates that integrating generative AI, specifically via the “Rain Classroom” platform, can effectively enhance biochemistry education. By supporting teaching, learning, evaluation, and research, this approach improves both educational effectiveness and student outcomes. It also facilitates the incorporation of cutting-edge knowledge and professional ethics, nurturing a patient-centered mindset. Additionally, the study addresses potential implementation risks to ensure that such technological tools remain aligned with the core purpose of education.

Traditional Chinese medicine （TCM） clinical trials face challenges such as low participant compliance， insufficient geographical coverage， and cost-effectiveness imbalances. Decentralized clinical trials （DCT）， enabled by digital technology for remote data collection and monitoring， offer a new direction for TCM clinical trial research. This article systematically reviews three novel clinical trial design models. Combining the holistic concept and indivi-dualized treatment characteristics of TCM， it analyzes the challenges currently faced in TCM DCT practice， including the digitization and standardization of TCM theory， data security， privacy protection and patient engagement difficu-lties， insufficient ethical review and regulatory system adaptation， inadequate personnel training， and a shortage of interdisciplinary talent. Addressing these challenges， the article proposes methodological recommendations for DCT implementation that align with the principles of TCM diagnosis and treatment. These recommendations include promoting the intelligentization and standardization of TCM practices， constructing a full-chain data security and privacy protection system， improving the ethical framework and clarifying regulatory responsibilities， and cultivating and building interdisciplinary talent and capabilities， which provide theoretical and technical references for establishing standardized DCT practices in TCM.

Objective To investigate the role and potential mechanism of secreted phosphoprotein 1 (SPP1)⁺ macrophages in the formation of chronic renal allograft fibrosis. Methods The expression features of SPP1⁺ macrophages in renal allografts of chronic allograft dysfunction (CAD) patients were analyzed based on single-cell transcriptome data of renal tissues from patients with CAD. Transcription factor VIPER analysis and DoRothEA transcription factor activity analysis were performed on the single-cell transcriptome data. Renal tissue samples were collected from kidney transplant recipients, including the CAD group (n=5) and the non-renal allograft fibrosis group (CTL group, n=5). A mouse model of chronic allograft rejection was established and divided into the allogeneic kidney transplantation group (CAD group, n=3) and the syngeneic kidney transplantation group (SYN group, n=3). Hematoxylin-eosin staining was used to detect renal tissue injury in mice, and Masson staining was used to detect renal tissue fibrosis. Immunofluorescence staining was performed to detect SPP1 expression in renal tissues of transplant recipients and mouse renal allografts. Bone marrow-derived macrophages (BMDMs) were extracted from mice and subjected to hypoxia stimulation. The expression of hypoxia-inducible factor (HIF)-1α and SPP1 was detected by Western blot, and SPP1 expression was detected by flow cytometry. BMDMs were transfected with HIF-1α overexpression plasmid and HIF-1α small interfering RNA (siRNA) followed by hypoxia intervention, and the expression of HIF-1α and SPP1 was detected by Western blot. Mouse aortic endothelial cells (MAECs) were co-cultured with the supernatant of BMDMs, and the expression of endothelial-mesenchymal transition (EndMT)-related markers was detected by Western blot and immunofluorescence. Results Single-cell transcriptome analysis showed that the proportion of SPP1⁺ macrophages in renal allograft tissues was significantly higher in the CAD group than in the CTL group (P<0.05). The renal injury score and the percentage of interstitial fibrotic area in the CAD group were significantly higher than those in the SYN group (both P<0.05). Immunofluorescence staining showed that the proportion of SPP1⁺ macrophages was increased in the CAD group compared with the CTL group, and also increased in the CAD group compared with the SYN group (both P<0.05). VIPER analysis and DoRothEA transcription factor activity analysis revealed activation of the hypoxia pathway and upregulated expression of transcription factors such as HIF-1α in SPP1⁺ macrophages. SPP1 expression was elevated in BMDMs under hypoxic conditions. Knockdown of HIF-1α inhibited hypoxia-induced SPP1 protein expression, whereas overexpression of HIF-1α upregulated SPP1 protein levels. After co-culture of hypoxia-induced BMDMs with MAECs, the expression levels of EndMT-related markers were increased. Conclusions SPP1⁺ macrophages differentiated under hypoxia are significantly infiltrated in the formation of chronic renal allograft fibrosis, and may promote renal allograft fibrosis by inducing EndMT in renal vascular endothelial cells.

Objective To investigate the value of 3.0 T MRI in-phase/opposed-phase (IP-OP) imaging features in the quantitative assessment of hepatic steatosis after liver transplantation. Methods Clinical data of 115 patients who underwent liver transplantation and completed MRI IP-OP examination and liver biopsy within 3 months were retrospectively collected. According to the gold standard of pathological results, patients were divided into the steatosis group (n=18) and the non-steatosis group (n=97). The relative signal intensity of hepatic parenchyma was measured on MRI IP-OP sequences, and the hepatic fat fraction (HFF) was calculated. Differences in clinical data and imaging features between the two groups were compared, and the efficacy of HFF in the diagnosis of graft hepatic steatosis was evaluated using the receiver operating characteristic (ROC) curve. Results Among the 115 recipients, 18 cases were diagnosed with hepatic steatosis by liver biopsy, including 13 cases of mild steatosis, 3 cases of moderate steatosis and 2 cases of severe steatosis. Compared with the non-steatosis group, the steatosis group had a lower proportion of hepatitis B virus (HBV)-related hepatocellular carcinoma, higher proportions of HBV-related liver failure and alcoholic liver cirrhosis, and higher platelet levels (all P<0.05). There were statistically significant differences in hepatic imaging features between the two groups (all P<0.05). The ROC curve showed that the area under the curve (AUC) of the quantitative value calculated by IP-OP in the diagnosis of graft hepatic steatosis was 0.925 (95% confidence interval 0.870-0.980). Conclusions 3.0 T MRI IP-OP sequence may accurately and non-invasively quantitatively assess the severity of hepatic steatosis after liver transplantation, which is of great value for the early detection of graft hepatic steatosis and the guidance of clinical intervention.

Traditional Chinese medicine （TCM） clinical trials face challenges such as low participant compliance， insufficient geographical coverage， and cost-effectiveness imbalances. Decentralized clinical trials （DCT）， enabled by digital technology for remote data collection and monitoring， offer a new direction for TCM clinical trial research. This article systematically reviews three novel clinical trial design models. Combining the holistic concept and indivi-dualized treatment characteristics of TCM， it analyzes the challenges currently faced in TCM DCT practice， including the digitization and standardization of TCM theory， data security， privacy protection and patient engagement difficu-lties， insufficient ethical review and regulatory system adaptation， inadequate personnel training， and a shortage of interdisciplinary talent. Addressing these challenges， the article proposes methodological recommendations for DCT implementation that align with the principles of TCM diagnosis and treatment. These recommendations include promoting the intelligentization and standardization of TCM practices， constructing a full-chain data security and privacy protection system， improving the ethical framework and clarifying regulatory responsibilities， and cultivating and building interdisciplinary talent and capabilities， which provide theoretical and technical references for establishing standardized DCT practices in TCM.