Objective To explore the number of peripheral blood regulatory T cells (Treg) in patients with chronic kidney disease (CKD) and its correlation with vascular calcification. Methods This was a single-center, cross-sectional, and observational study. Non-dialysis patients with CKD treated at Zhongshan Hospital, Fudan University from March 2021 to March 2022 were enrolled. Abdominal aortic calcification (AAC) was assessed using lateral abdominal X-ray. Number of Treg and cytokine levels were measured by flow cytometry. Logistic regression analysis was performed to evaluate the related factors for AAC in CKD patients. Results A total of 83 patients were included, aged 17–86 years, with 57 males (68.7%). The distribution of CKD stages was as follows: stage G1 in 7 patients (8.4%), stage G2 in 17 patients (20.5%), stage G3 in 21 patients (25.3%), stage G4 in 19 patients (22.9%), and stage G5 in 19 patients (22.9%). No AAC was observed in patients with stages G1 and G2, while the prevalence of AAC in patients with stages G3, G4, and G5 was 23.8%, 21.1%, and 26.3%, respectively. Compared with stage G1 patients, those with stages G3–5 showed decreased number of peripheral blood Treg and elevated levels of interleukin (IL)-6 and IL-17F (P＜0.05). The area under the receiver operating characteristic curve for number of peripheral blood Treg in predicting AAC in CKD patients was 0.766 (95%CI 0.652–0.879, P＝0.002). Logistic regression analysis showed that decreased number of Treg was related factor for AAC in CKD patients (OR＝0.957, 95%CI 0.922–0.992, P＝0.018). Conclusion As CKD progresses, number of peripheral blood Treg significantly decreases, which is correlated with AAC in CKD patients.

Background: Influenza A virus (IAV) is a negative-sense RNA virus of the Orthomyxoviridae family and is the etiological agent of a highly contagious acute respiratory disease that can lead to acute lung injury. Objective: To elucidate the molecular mechanisms of IAV infection, an integrative research approach combining gene expression profiling, multinetwork analysis, and in vivo experimental validations was employed. Methods: First, a series of network-based analyses were performed, including protein-protein interaction network construction, weighted gene co-expression network analysis, and subsequent gene set enrichment analysis, to identify the major underlying mechanisms of IAV infection. Following gene expression analysis, core targets, both direct and indirect regulators, were screened. An IAV (H1N1) strain A/PR/8/34-induced acute lung injury mouse model was constructed for in vivo validations. Batch one included two groups to evaluate findings from the multi-network analysis: Mock (n = 10; 5 males and 5 females) and IAV (n = 10; 5 males and 5 females). Batch two included three groups to assess the role of toll-like receptor 3 (TLR3) in IAV infection: Mock (n = 6; 3 males and 3 females), IAV (n = 6; 3 males and 3 females), and TLR3 inhibitor (n = 6; 3 males and 3 females). Body weight was measured on days 0, 3, and 5 after infection. On day 5, lung tissues were collected to assess viral load and histopathological changes. Key targets were examined using enzyme-linked immunosorbent assay, Western blotting, and immunofluorescence staining, both in sera and lung tissues. Results: IAV infection was significantly associated with dysregulation of the immune-inflammation system, such as the LTR, nucle-otide-binding oligomerization domain-(NOD) like receptor, retinoic acid-inducible gene I-like receptor, and nuclear factor kappa-B signaling pathways. Gene set enrichment analysis further indicated that the TLR and necroptosis signaling pathways played crucial roles in the progression of IAV infection (TLR signaling pathway normalized enrichment score = 2.3941, P = 1.00 × 10 ⁻¹⁰; necroptosis normalized enrichment score = 1.9421, P = 6.21 × 10 ⁻⁷). Among the core targets, TLR3 and mixed lineage kinase domain-like protein (MLKL) may regulate gene expression at the transcriptional level (all P < 0.05). In vivo validation using an IAV (PR8) infected acute lung injury mouse model demonstrated increased viral load and lung index, alveolar structural damage, and inflammatory cell infiltration. Immunofluorescence staining exhibited large gaps in Lamin B1 staining and breaches in Emerin signals following IAV-PR8 infection. Expression levels of TLR3, p-receptor-interacting serine/threonine-protein kinase 3 (RIPK3)/RIPK3, and p-mixed lineage kinase domain-like protein (MLKL)/MLKL proteins in lung tissues, as well as proinflammatory factors and mediators in sera, were significantly elevated after IAV infection. Moreover, enhanced neutrophil infiltration (myeloperoxidase) and citrullinated histone H3 (a neutrophil extracellular trap-specific marker), both established indicators of neutrophil extracellular trap formation, were observed. Notably, treatment with a TLR3 inhibitor significantly ameliorated IAV-induced acute lung injury by regulating necroptosis-related targets. Conclusion: Our study provides network-based in vivo evidence that TLR3-receptor-interacting serine/threonine-protein kinase 3-MLKL-mediated necroptosis may underlie IAV-induced acute lung injury and could serve as a potential therapeutic target in severe influenza cases.

Objective:To assess the application value of one-hour post-load glucose (1hPG) for detecting prediabetes among individuals with high risk of type 2 diabetes mellitus (T2DM).Methods:The study was conducted between August 2023 and January 2024, and individuals with a high risk of T2DM were invited to receive an oral glucose tolerance test (OGTT), structural questionnaires, physical measurements, and other biochemical examinations. The fasting, one-, and two-hour glucose and insulin were tested. According to the 1hPG cut point on hyperglycemia suggested by International Diabetes Federation (IDF), normal glucose tolerance (NGT) and prediabetes were further divided into two subgroups, respectively, i.e., NGT with 1hPG<8.6 mmol/L (NGT-1hPG-normal), NGT with 1hPG≥8.6 mmol/L (NGT-1hPG-high), prediabetes with 1hPG<8.6 mmol/L (PDM-1hPG-normal), and prediabetes with 1hPG≥8.6 mmol/L (PDM-1hPG-high). The insulin release curve was drawn by the groups as above. Insulin resistance was evaluated by homeostasis model assessment for insulin resistance (HOMA-IR), and β-cell secretory function was evaluated by homeostasis model assessment for β cell function (HOMA-β)/HOMA-IR. Spearman rank correlation analysis was used to calculate the correlation coefficients among 1hPG, 2hPG and HOMA indices, and Steiger′s Z test was used to compare the difference between two correlation coefficients. Receiver operating characteristics (ROC) curves and area under the curve (AUC) were used to assess the accuracy of 1hPG for detecting prediabetes. Results:A total of 2 469 subjects consisting of 1 485 men (60.1%) and 984 (39.9%) women, with a mean age of (45.76±6.20) years, of which 1 844 (74.7%) had 1hPG≥8.6 mmol/L. The prevalence of 1hPG≥8.6 mmol/L was 46.8%, 93.0% and 99.8% in individuals with NGT, prediabetes and newly diagnosed T2DM, respectively ( χ 2=763.78, P<0.001). The insulin release curve showed that insulin secretion increased rapidly in subjects with NGT-1hPG-high, and peaked at one hour, then decreased rapidly, with a significantly higher level of one- and two-hour insulin than those with NGT-1hPG-normal ( P<0.001). Compared to individuals with NGT-1hPG-normal, the counterparts with NGT-1hPG-high exhibited higher HOMA-IR and lower adjusted HOMA-β ( P<0.001). Spearman rank correlation analysis showed that the correlation coefficient of 1hPG with HOMA-IR was similar to the correlation coefficient of 2hPG with HOMA-IR (0.493 vs. 0.480, P=0.550), while the correlation of 1hPG with adjusted HOMA-β was significantly stronger than that of 2hPG (-0.692 vs. -0.587, P<0.001). Excluding patients with T2DM, according to the cut point recommended by IDF, the AUC of 1hPG≥8.6 mmol/L for detecting prediabetes was 0.731 (95% CI: 0.714-0.748), and the sensitivity and specificity were 0.930 and 0.532, respectively, with the kappa value of 0.45. Conclusion:1hPG is closely related to insulin resistance and islet function, and there′s substantial value for individuals with a high risk of T2DM to detect prediabetes by using the 1hPG cut points recommended by IDF.

In recent years, generative artificial intelligence (AI) technologies have achieved remarkable progress in the early screening, risk prediction, disease progression assessment, and clinical trial design of glaucoma.Using advanced algorithms, such as generative adversarial networks, variational autoencoders, and diffusion models, researchers have synthesized high-quality structural images of the optic disc, macular region, and retinal nerve fiber layer, which effectively alleviates the limitations of scarce clinical imaging data and label imbalance.These methods have substantially improved the accuracy and generalization of deep learning models in visual field defect prediction, structure-function mapping, and longitudinal disease progression simulation.Meanwhile, multimodal generative approaches that integrate imaging data, visual field tests, and clinical features have facilitated individualized prediction of glaucoma progression.In addition, large language models have shown preliminary potential in ophthalmic image interpretation, clinical text information extraction, and decision support, providing new insights into intelligent ophthalmic diagnosis and treatment.However, the clinical implementation of generative AI in glaucoma faces challenges.The pathological authenticity and cross-device consistency of generated images require further validation, which may affect the reliability of early glaucoma detection.The heterogeneous characteristics of different glaucoma subtypes, such as open-angle and angle-closure glaucoma, also limit the generalization of synthetic data.Moreover, issues related to model interpretability (" black-box" nature), artifact generation, data privacy, and ethical governance remain key barriers to clinical translation.In the future, it is expected that establishing large-scale training frameworks that incorporate multicenter, multimodal, and multiethnic datasets will enhance model robustness and clinical applicability.Furthermore, generative AI may contribute to remote ophthalmic care and personalized precision therapy by enhancing low-quality image, reconstructing missing data, and simulating dynamic disease courses.This article reviews the current applications, core technologies, and challenges of generative AI in glaucoma diagnosis and management, and discusses its future directions and translational potential in clinical ophthalmology.

In recent years, generative artificial intelligence (AI) technologies have achieved remarkable progress in the early screening, risk prediction, disease progression assessment, and clinical trial design of glaucoma.Using advanced algorithms, such as generative adversarial networks, variational autoencoders, and diffusion models, researchers have synthesized high-quality structural images of the optic disc, macular region, and retinal nerve fiber layer, which effectively alleviates the limitations of scarce clinical imaging data and label imbalance.These methods have substantially improved the accuracy and generalization of deep learning models in visual field defect prediction, structure-function mapping, and longitudinal disease progression simulation.Meanwhile, multimodal generative approaches that integrate imaging data, visual field tests, and clinical features have facilitated individualized prediction of glaucoma progression.In addition, large language models have shown preliminary potential in ophthalmic image interpretation, clinical text information extraction, and decision support, providing new insights into intelligent ophthalmic diagnosis and treatment.However, the clinical implementation of generative AI in glaucoma faces challenges.The pathological authenticity and cross-device consistency of generated images require further validation, which may affect the reliability of early glaucoma detection.The heterogeneous characteristics of different glaucoma subtypes, such as open-angle and angle-closure glaucoma, also limit the generalization of synthetic data.Moreover, issues related to model interpretability (" black-box" nature), artifact generation, data privacy, and ethical governance remain key barriers to clinical translation.In the future, it is expected that establishing large-scale training frameworks that incorporate multicenter, multimodal, and multiethnic datasets will enhance model robustness and clinical applicability.Furthermore, generative AI may contribute to remote ophthalmic care and personalized precision therapy by enhancing low-quality image, reconstructing missing data, and simulating dynamic disease courses.This article reviews the current applications, core technologies, and challenges of generative AI in glaucoma diagnosis and management, and discusses its future directions and translational potential in clinical ophthalmology.

Mutations in the IARS1 gene are rare in clinical practice， and up to now， only ten cases with detailed clinical and genetic data have been recorded in the literature. This article reports a case of growth retardation， intellectual developmental disorder， hypotonia， and hepatopathy （GRIDHH） associated with single heterozygous mutations in the IARS1 gene and summarizes the clinical and genetic features of GRIDHH， thereby expanding the genetic spectrum of GRIDHH.

OBJECTIVE To explore the current status of surveillance data of nosocomial infections in a three-A hos-pital of Jiangsu Province and find out the problems in prevention and control of the nosocomial infection so as to provide scientific bases for formulating targeted prevention strategies.METHODS The related data of all infection cases from Changzhou First People's Hospital between 2014 and 2023 were exported from information real-time surveillance system and were analyzed by using Excel 2021 and R 4.3.2 software.RESULTS The average incidence rate of hospital-associated infection was 1.32％from 2014 to 2023,declining from 2.11％in 2014 to 0.97％in 2023,and it showed a downward trend(Z=-30.010,P＜0.001).The incidence rate of hospital-associated infec-tion was 19.86％in neurosurgery department(Z=-3.041,P=0.002),10.27％in hematology department(Z=5990,P＜0.001),8.85％in neurology department(Z=-1.437,P=0.151),7.00％in critical care medicine de-partment(Z=5.907,P＜0.001),6.28％in gastrointestinal surgery department(Z=-4.435,P＜0.001),thorac-ic surgery department(Z=1.696,P=0.090),4.52％in cardiothoracic surgery department(Z=13.218,P＜0.001),2.95％in oncology department(Z=9.064,P＜0.001).Among the patients with hospital-associated infec-tions,the patients with lower respiratory tract infection accounted for 46.71％(the highest),increasing from 34.94％to 51.67％(Z=12.532,P＜0.001),the patients with upper respiratory tract infection 10.37％(Z=-10.229,P＜0.001),the patients with urinary tract infection 6.92％(Z=1.489,P=0.137),the patients with urinary catheter-related infection 3.71％(Z=13.218,P=0.317).Totally 13,593 strains of pathogens were isola-ted,65.26％of which were gram-negative bacteria and did not show a significant changing trend,24.62％were gram-positive bacteria,and 10.12％were fungi;Klebsiella pneumoniae(15.76％)and Acinetobacter baumannii(15.34％)were the predominant species of the gram-negative bacteria;Staphylococcus aureus(8.37％)and Corynebacterium striatum(3.60％)were dominant among the gram-positive bacteria;Candida albicans(5.13％)and Candida tropicalis(0.96％)were the major species of the fungi.The percentage of central venous catheteriza-tion was the highest(27.04％)among the risk factors for the hospital-associated infection,increasing from 14.87％to 21.75％(Z=17.482,P＜0.001).CONCLUSIONS The control of hospital-associated infection has been made remarkable achievement.It is necessary to strengthen the construction of infection control team,push for-ward the whole process and delicacy management,normalize the use of antibiotics,reduce the unnecessary inva-sive procedures and shorten the duration of invasive operations so as to further improve the medical quality.

OBJECTIVE: To identify the underlying molecular mechanism of Modified Hu-Lu-Ba-Wan (MHW) in alleviating renal lesions in mice with diabetic kidney disease (DKD). METHODS: The db/db mice were divided into model group and MHW group according to a random number table, while db/m mice were settled as the control group (n=8 per group). The control and model groups were gavaged daily with distilled water [10 mL/(kg·d)], and the MHW group was treated with MHW [17.8 g/(kg·d)] for 6 weeks. After MHW administration for 6 weeks, indicators associated with glucolipid metabolism and urinary albumin were tested. Podocytes were observed by transmission electron microscopy. Kidney transcriptomics was performed after confirming therapeutic effects of MHW on DKD mice. The relevant target of MHW' effect in DKD was further determined by enzyme-linked immunosorbent assay, Western blot analysis, immunohistochemistry, and immunofluorescence staining. RESULTS: Compared with the model group, MHW improved glucose and lipid metabolism (P<0.05), and reduced lipid deposition in the kidney. Meanwhile, MHW reduced the excretion of urinary albumin (P<0.05) and ameliorated renal damage. Transcriptomic analysis revealed that the inflammation response, particularly the interleukin-17 (IL-17) signaling pathway, may be responsible for the effect of MHW on DKD. Furtherly, our results found that MHW inhibited IL-17A and alleviated early fibrosis in the diabetic kidney. CONCLUSION MHW ameliorated renal damage in DKD via inhibiting IL-17A, suggesting a potential strategy for DKD therapy.
Animals ; Diabetic Nephropathies/genetics* ; Interleukin-17/antagonists & inhibitors* ; Drugs, Chinese Herbal/therapeutic use* ; Male ; Kidney/ultrastructure* ; Podocytes/metabolism* ; Mice ; Albuminuria ; Lipid Metabolism/drug effects* ; Mice, Inbred C57BL

Objective:To assess the application value of one-hour post-load glucose (1hPG) for detecting prediabetes among individuals with high risk of type 2 diabetes mellitus (T2DM).Methods:The study was conducted between August 2023 and January 2024, and individuals with a high risk of T2DM were invited to receive an oral glucose tolerance test (OGTT), structural questionnaires, physical measurements, and other biochemical examinations. The fasting, one-, and two-hour glucose and insulin were tested. According to the 1hPG cut point on hyperglycemia suggested by International Diabetes Federation (IDF), normal glucose tolerance (NGT) and prediabetes were further divided into two subgroups, respectively, i.e., NGT with 1hPG<8.6 mmol/L (NGT-1hPG-normal), NGT with 1hPG≥8.6 mmol/L (NGT-1hPG-high), prediabetes with 1hPG<8.6 mmol/L (PDM-1hPG-normal), and prediabetes with 1hPG≥8.6 mmol/L (PDM-1hPG-high). The insulin release curve was drawn by the groups as above. Insulin resistance was evaluated by homeostasis model assessment for insulin resistance (HOMA-IR), and β-cell secretory function was evaluated by homeostasis model assessment for β cell function (HOMA-β)/HOMA-IR. Spearman rank correlation analysis was used to calculate the correlation coefficients among 1hPG, 2hPG and HOMA indices, and Steiger′s Z test was used to compare the difference between two correlation coefficients. Receiver operating characteristics (ROC) curves and area under the curve (AUC) were used to assess the accuracy of 1hPG for detecting prediabetes. Results:A total of 2 469 subjects consisting of 1 485 men (60.1%) and 984 (39.9%) women, with a mean age of (45.76±6.20) years, of which 1 844 (74.7%) had 1hPG≥8.6 mmol/L. The prevalence of 1hPG≥8.6 mmol/L was 46.8%, 93.0% and 99.8% in individuals with NGT, prediabetes and newly diagnosed T2DM, respectively ( χ 2=763.78, P<0.001). The insulin release curve showed that insulin secretion increased rapidly in subjects with NGT-1hPG-high, and peaked at one hour, then decreased rapidly, with a significantly higher level of one- and two-hour insulin than those with NGT-1hPG-normal ( P<0.001). Compared to individuals with NGT-1hPG-normal, the counterparts with NGT-1hPG-high exhibited higher HOMA-IR and lower adjusted HOMA-β ( P<0.001). Spearman rank correlation analysis showed that the correlation coefficient of 1hPG with HOMA-IR was similar to the correlation coefficient of 2hPG with HOMA-IR (0.493 vs. 0.480, P=0.550), while the correlation of 1hPG with adjusted HOMA-β was significantly stronger than that of 2hPG (-0.692 vs. -0.587, P<0.001). Excluding patients with T2DM, according to the cut point recommended by IDF, the AUC of 1hPG≥8.6 mmol/L for detecting prediabetes was 0.731 (95% CI: 0.714-0.748), and the sensitivity and specificity were 0.930 and 0.532, respectively, with the kappa value of 0.45. Conclusion:1hPG is closely related to insulin resistance and islet function, and there′s substantial value for individuals with a high risk of T2DM to detect prediabetes by using the 1hPG cut points recommended by IDF.

" Mutual recognition and sharing of medical examination and test results in Zhejiang Province" (MRZJ) holds significant value in rationally utilizing healthcare resources, reducing patient costs, streamlining treatment procedures, and enhancing patient satisfaction. This study reviewed the development of medical test result mutual recognition in China and summarized the implementation of MRZJ at Zhejiang Provincial People′s Hospital. Since its launched in October 2021, the hospital had achieved interoperability through enhanced organizational leadership, standardized coding, process optimization, capacity building, quality control reinforcement, and public awareness campaigns. By March 2024, MRZJ covered all outpatient departments of the pilot hospital, with 936 items recognized daily, improving healthcare efficiency and generating notable social and economic benefits. These outcomes provided references for advancing nationwide medical test mutual recognition. Future efforts should focused on strengthening standardized training, refining performance evaluation mechanisms, developing MRZJ early warning functions, and safeguarding patients′ informed consent and choice rights.