Objective: To analyze the spatio-temporal distribution of pulmonary tuberculosis (PTB) among students in Suzhou City, Jiangsu Province from 2015 to 2023, so as to provide the evidence for the prevention and control of PTB in schools. Methods: Data of PTB cases among students in Suzhou City from 2015 to 2023 were collected from Chinese Disease Prevention and Control Information System and Suzhou Report of Investigation and Disposal of Tuberculosis in Schools. The seasonal incidence of PTB among students was analyzed using seasonal index (SI). The spatio-temporal clustering characteristics of PTB among students were analyzed using spatial autocorrelation and retrospective spatio-temporal permutation scanning. Results: Totally 1 374 PTB cases among students were reported in Suzhou City from 2015 to 2023. PTB cases were reported in each month, and the SIs were 100.69%, 124.38%, 108.98%, 135.04%, 106.61% and 106.61% in April, May, July, September, October and November, respectively, indicating the prevalence of PTB among students. Spatial autocorrelation analysis showed there was a positive spatial correlation of PTB among students in 2019 and 2020 (Moran's I=0.053 and 0.089, both P<0.05). From 2015 to 2023, there were high-high clustering sites mainly in Hengtang Street and Shishan Street. Retrospective spatio-temporal permutation scanning showed a primary cluster in Hengtang Street, with aggregation time in 2017, and 6 secondary clusters covering 25 towns (streets). Conclusion From 2015 to 2023, the PTB cases among students in Suzhou City were mainly concentrated in summer and autumn, and were predominantly clustered in Hengtang Street and Shishan Street.

Obstructive sleep apnea (OSA) is an increasingly widespread sleep-breathing disordered disease, and is an independent risk factor for many high-risk chronic diseases such as hypertension, coronary heart disease, stroke, arrhythmias and diabetes, which is potentially fatal. The key to the prevention and treatment of OSA is early diagnosis and treatment, so the assessment and diagnostic technologies of OSA have become a research hotspot. This paper reviews the research progresses of severity assessment parameters and diagnostic technologies of OSA, and discusses their future development trends. In terms of severity assessment parameters of OSA, apnea hypopnea index (AHI), as the gold standard, together with the percentage of duration of apnea hypopnea (AH%), lowest oxygen saturation (LSpO₂), heart rate variability (HRV), oxygen desaturation index (ODI) and the emerging biomarkers, constitute a multi-dimensional evaluation system. Specifically, the AHI, which measures the frequency of sleep respiratory events per hour, does not fully reflect the patients’ overall sleep quality or the extent of their daytime functional impairments. To address this limitation, the AH%, which measures the proportion of the entire sleep cycle affected by apneas and hypopneas, deepens our understanding of the impact on sleep quality. The LSpO₂ plays a critical role in highlighting the potential severe hypoxic episodes during sleep, while the HRV offers a different perspective by analyzing the fluctuations in heart rate thereby revealing the activity of the autonomic nervous system. The ODI provides a direct and objective measure of patients’ nocturnal oxygenation stability by calculating the number of desaturation events per hour, and the biomarkers offers novel insights into the diagnosis and management of OSA, and fosters the development of more precise and tailored OSA therapeutic strategies. In terms of diagnostic techniques of OSA, the standardized questionnaire and Epworth sleepiness scale (ESS) is a simple and effective method for preliminary screening of OSA, and the polysomnography (PSG) which is based on recording multiple physiological signals stands for gold standard, but it has limitations of complex operations, high costs and inconvenience. As a convenient alternative, the home sleep apnea testing (HSAT) allows patients to monitor their sleep with simplified equipment in the comfort of their own homes, and the cardiopulmonary coupling (CPC) offers a minimal version that simply analyzes the electrocardiogram (ECG) signals. As an emerging diagnostic technology of OSA, machine learning (ML) and artificial intelligence (AI) adeptly pinpoint respiratory incidents and expose delicate physiological changes, thus casting new light on the diagnostic approach to OSA. In addition, imaging examination utilizes detailed visual representations of the airway’s structure and assists in recognizing structural abnormalities that may result in obstructed airways, while sound monitoring technology records and analyzes snoring and breathing sounds to detect the condition subtly, and thus further expands our medical diagnostic toolkit. As for the future development directions, it can be predicted that interdisciplinary integrated researches, the construction of personalized diagnosis and treatment models, and the popularization of high-tech in clinical applications will become the development trends in the field of OSA evaluation and diagnosis.

Isosteviol is a tetracyclic diterpenoid compound obtained by hydrolysis of natural stevia glycoside under acidic conditions. It has many pharmacological activities, such as anti-tumor, hypoglycemic, anti-inflammatory and antibacterial. Due to its low water solubility, low activity and low bioavailability, isosteviol has poor performance. In order to overcome these shortcomings, scholars have obtained a large number of isosteviol derivatives with novel structures and excellent activity. In this paper, we review the recent progress in the research on the structure modification, biological activity, structure-activity relationship and microbial transformation of isosteviol, in order to provide a reference for the development of new drugs of isosteviol and its derivatives.

ObjectiveTo investigate the therapeutic effects and mechanism of Shenqi Dihuang decoction （SQDHD） on diabetic kidney disease （DKD）， with a focus on its impact on arachidonic acid-related ferroptosis. MethodsSixty C57BL/6 mice were allocated into a normal group （n=10） and a modeling group （n=50）， with 43 mice successfully modeled. The successfully modeled mice were further allocated into model， low-， medium-， and high-dose （4.68， 9.36， and 18.72 g·kg^-1， respectively） SQDHD， and dapagliflozin （0.13 mg·kg^-1） groups. The drug treatment groups were administrated with corresponding agents by gavage， and the normal and model groups were administrated with equal volumes of normal saline by gavage. An electronic balance and a glucometer were used to monitor the body weight and fasting blood glucose level from the tail tip， respectively. Serum creatinine （Scr） and blood urea nitrogen （BUN） levels were measured by enzyme-linked immunosorbent assay （ELISA）. Histopathological changes in the renal tissue were assessed by hematoxylin-eosin staining， Masson staining， and periodic acid-Schiff （PAS） staining. The fluorescence intensity of reactive oxygen species （ROS） in frozen sections was observed by an inverted fluorescence microscope to evaluate the levels of ferrous ions （Fe²⁺） and lipid peroxidation in the renal tissue. Immunofluorescence staining of glutathione peroxidase 4 （GPX4） and acyl-CoA synthetase long-chain family member 4 （ACSL4） in the renal tissue was performed to detect their localization and expression. Western blot was employed to assess the expression levels of key ferroptosis proteins such as GPX4 and cystine/glutamate antiporter （xCT）， as well as the arachidonic acid metabolic pathway-related proteins， including ACSL4， lysophosphatidylcholine acyltransferase 3 （LPCAT3）， and arachidonate 15-lipoxygenase （ALOX15）. Real-time PCR was employed to measure the mRNA levels of key ferroptosis proteins， including solute carrier family 7 member 11 （SLC7A11） and GPX4， as well as arachidonic acid metabolism-related factors （ACSL4， LPCAT3， and ALOX15） in the renal tissue. ResultsCompared with the normal group， DKD model mice exhibited a decrease in body weight （P<0.01）， increases in levels of blood glucose （P<0.01）， 24-hour urinary protein， Scr， and BUN （P<0.01）， along with severe pathological changes， such as mesangial cell proliferation， basement membrane thickening， tubular atrophy， and interstitial inflammatory cell infiltration. In addition， the modeling elevated the levels of Fe²⁺， MDA， LPO， and ROS （P<0.01）， lowered the GPX4 and xCT levels （P<0.01）， raised the ACSL4， LPCAT3， and ALOX15 levels （P<0.01）， down-regulated the mRNA levels of GPX4 and SLC7A11 （P<0.01）， and up-regulated the mRNA levels of ACSL4， LPCAT3， and ALOX15 （P<0.01） in the renal tissue. Compared with the model group， low-， medium-， and high-dose SQDHD groups and the dapagliflozin group showed an increase in body weight （P<0.01）， decreases in levels of blood glucose （P<0.01）， 24-hour urinary protein， and Scr （P<0.01）， alleviated pathological changes in glomeruli and tubules， and reduced degree of glomerular and tubular fibrosis. The high-dose SQDHD group and the dapagliflozin group showed reductions in Fe²⁺， MDA， LPO， and ROS levels （P<0.01）. The medium- and high-dose SQDHD groups and the dapagliflozin group exhibited increased levels of GPX4 and xCT （P<0.01）， decreased levels of ACSL4， LPCAT3， and ALOX15 （P<0.05， P<0.01）， and down-regulated mRNA levels of ACSL4， LPCAT3， and ALOX15 （P<0.01）. ConclusionSQDHD ameliorates DKD by inhibiting ferroptosis potentially by reducing iron ion levels， inhibiting lipid peroxidation， up-regulating GPX4 expression， and down-regulating ACSL4 expression. This study provides new insights and a theoretical basis for the treatment of DKD with traditional Chinese medicine and identifies potential targets for developing novel therapeutics for DKD.

ObjectiveTo investigate the therapeutic effects and mechanism of Shenqi Dihuang decoction （SQDHD） on diabetic kidney disease （DKD）， with a focus on its impact on arachidonic acid-related ferroptosis. MethodsSixty C57BL/6 mice were allocated into a normal group （n=10） and a modeling group （n=50）， with 43 mice successfully modeled. The successfully modeled mice were further allocated into model， low-， medium-， and high-dose （4.68， 9.36， and 18.72 g·kg^-1， respectively） SQDHD， and dapagliflozin （0.13 mg·kg^-1） groups. The drug treatment groups were administrated with corresponding agents by gavage， and the normal and model groups were administrated with equal volumes of normal saline by gavage. An electronic balance and a glucometer were used to monitor the body weight and fasting blood glucose level from the tail tip， respectively. Serum creatinine （Scr） and blood urea nitrogen （BUN） levels were measured by enzyme-linked immunosorbent assay （ELISA）. Histopathological changes in the renal tissue were assessed by hematoxylin-eosin staining， Masson staining， and periodic acid-Schiff （PAS） staining. The fluorescence intensity of reactive oxygen species （ROS） in frozen sections was observed by an inverted fluorescence microscope to evaluate the levels of ferrous ions （Fe²⁺） and lipid peroxidation in the renal tissue. Immunofluorescence staining of glutathione peroxidase 4 （GPX4） and acyl-CoA synthetase long-chain family member 4 （ACSL4） in the renal tissue was performed to detect their localization and expression. Western blot was employed to assess the expression levels of key ferroptosis proteins such as GPX4 and cystine/glutamate antiporter （xCT）， as well as the arachidonic acid metabolic pathway-related proteins， including ACSL4， lysophosphatidylcholine acyltransferase 3 （LPCAT3）， and arachidonate 15-lipoxygenase （ALOX15）. Real-time PCR was employed to measure the mRNA levels of key ferroptosis proteins， including solute carrier family 7 member 11 （SLC7A11） and GPX4， as well as arachidonic acid metabolism-related factors （ACSL4， LPCAT3， and ALOX15） in the renal tissue. ResultsCompared with the normal group， DKD model mice exhibited a decrease in body weight （P<0.01）， increases in levels of blood glucose （P<0.01）， 24-hour urinary protein， Scr， and BUN （P<0.01）， along with severe pathological changes， such as mesangial cell proliferation， basement membrane thickening， tubular atrophy， and interstitial inflammatory cell infiltration. In addition， the modeling elevated the levels of Fe²⁺， MDA， LPO， and ROS （P<0.01）， lowered the GPX4 and xCT levels （P<0.01）， raised the ACSL4， LPCAT3， and ALOX15 levels （P<0.01）， down-regulated the mRNA levels of GPX4 and SLC7A11 （P<0.01）， and up-regulated the mRNA levels of ACSL4， LPCAT3， and ALOX15 （P<0.01） in the renal tissue. Compared with the model group， low-， medium-， and high-dose SQDHD groups and the dapagliflozin group showed an increase in body weight （P<0.01）， decreases in levels of blood glucose （P<0.01）， 24-hour urinary protein， and Scr （P<0.01）， alleviated pathological changes in glomeruli and tubules， and reduced degree of glomerular and tubular fibrosis. The high-dose SQDHD group and the dapagliflozin group showed reductions in Fe²⁺， MDA， LPO， and ROS levels （P<0.01）. The medium- and high-dose SQDHD groups and the dapagliflozin group exhibited increased levels of GPX4 and xCT （P<0.01）， decreased levels of ACSL4， LPCAT3， and ALOX15 （P<0.05， P<0.01）， and down-regulated mRNA levels of ACSL4， LPCAT3， and ALOX15 （P<0.01）. ConclusionSQDHD ameliorates DKD by inhibiting ferroptosis potentially by reducing iron ion levels， inhibiting lipid peroxidation， up-regulating GPX4 expression， and down-regulating ACSL4 expression. This study provides new insights and a theoretical basis for the treatment of DKD with traditional Chinese medicine and identifies potential targets for developing novel therapeutics for DKD.

Objective To predict the lymph node metastasis status of patients with invasive pulmonary adenocarcinoma by constructing machine learning models based on primary tumor radiomics, peritumoral radiomics, and habitat radiomics, and to evaluate the predictive performance and generalization ability of different imaging features. Methods A retrospective analysis was performed on the clinical data of 1 263 patients with invasive pulmonary adenocarcinoma who underwent surgery at the Department of Thoracic Surgery, Jiangsu Province Hospital, from 2016 to 2019. Habitat regions were delineated by applying K-means clustering (average cluster number of 2) to the grayscale values of CT images. The peritumoral region was defined as a uniformly expanded area of 3 mm around the primary tumor. The primary tumor region was automatically segmented using V-net combined with manual correction and annotation. Subsequently, radiomics features were extracted based on these regions, and stacked machine learning models were constructed. Model performance was evaluated on the training, testing, and internal validation sets using the area under the receiver operating characteristic curve (AUC), F1 score, recall, and precision. Results After excluding patients who did not meet the screening criteria, a total of 651 patients were included. The training set consisted of 468 patients (181 males, 287 females) with an average age of (58.39±11.23) years, ranging from 29 to 78 years, the testing set included 140 patients (56 males, 84 females) with an average age of (58.81±10.70) years, ranging from 34 to 82 years, and the internal validation set comprised 43 patients (14 males, 29 females) with an average age of (60.16±10.68) years, ranging from 29 to 78 years. Although the habitat radiomics model did not show the optimal performance in the training set, it exhibited superior performance in the internal validation set, with an AUC of 0.952 [95%CI (0.87, 1.00)], an F1 score of 84.62%, and a precision-recall AUC of 0.892, outperforming the models based on the primary tumor and peritumoral regions. Conclusion The model constructed based on habitat radiomics demonstrated superior performance in the internal validation set, suggesting its potential for better generalization ability and clinical application in predicting lymph node metastasis status in pulmonary adenocarcinoma.

Cardiovascular disease (CVD), chronic kidney disease (CKD), and metabolic disorders are the 3 major chronic diseases threatening human health, which are closely related and often coexist, significantly increasing the difficulty of disease management. In response, the American Heart Association (AHA) proposed a novel disease concept of “cardiovascular-kidney-metabolic (CKM) syndrome” in October 2023, which has triggered widespread concern about the co-treatment of heart and kidney diseases and the prevention and treatment of metabolic disorders around the world. This review posits that effectively managing CKM syndrome requires a new and multidimensional paradigm for diagnosis and risk prediction that integrates biological insights, advanced technology and social determinants of health (SDoH). We argue that the core pathological driver is a “metabolic toxic environment”, fueled by adipose tissue dysfunction and characterized by a vicious cycle of systemic inflammation and oxidative stress, which forms a common pathway to multi-organ injury. The at-risk population is defined not only by biological characteristics but also significantly impacted by adverse SDoH, which can elevate the risk of advanced CKM by a factor of 1.18 to 3.50, underscoring the critical need for equity in screening and care strategies. This review systematically charts the progression of diagnostic technologies. In diagnostics, we highlight a crucial shift from single-marker assessments to comprehensive multi-marker panels. The synergistic application of traditional biomarkers like NT-proBNP (reflecting cardiac stress) and UACR (indicating kidney damage) with emerging indicators such as systemic immune-inflammation index (SII) and Klotho protein facilitates a holistic evaluation of multi-organ health. Furthermore, this paper explores the pivotal role of non-invasive monitoring technologies in detecting subclinical disease. Techniques like multi-wavelength photoplethysmography (PPG) and impedance cardiography (ICG) provide a real-time window into microcirculatory and hemodynamic status, enabling the identification of early, often asymptomatic, functional abnormalities that precede overt organ failure. In imaging, progress is marked by a move towards precise, quantitative evaluation, exemplified by artificial intelligence-powered quantitative computed tomography (AI-QCT). By integrating AI-QCT with clinical risk factors, the predictive accuracy for cardiovascular events within 6 months significantly improves, with the area under the curve (AUC) increasing from 0.637 to 0.688, demonstrating its potential for reclassifying risk in CKM stage 3. In the domain of risk prediction, we trace the evolution from traditional statistical tools to next-generation models. The new PREVENT equation represents a major advancement by incorporating key kidney function markers (eGFR, UACR), which can enhance the detection rate of CKD in primary care by 20%-30%. However, we contend that the future lies in dynamic, machine learning-based models. Algorithms such as XGBoost have achieved an AUC of 0.82 for predicting 365-day cardiovascular events, while deep learning models like KFDeep have demonstrated exceptional performance in predicting kidney failure risk with an AUC of 0.946. Unlike static calculators, these AI-driven tools can process complex, multimodal data and continuously update risk profiles, paving the way for truly personalized and proactive medicine. In conclusion, this review advocates for a paradigm shift toward a holistic and technologically advanced framework for CKM management. Future efforts must focus on the deep integration of multimodal data, the development of novel AI-driven biomarkers, the implementation of refined SDoH-informed interventions, and the promotion of interdisciplinary collaboration to construct an efficient, equitable, and effective system for CKM screening and intervention.

OBJECTIVE To analyze the current status and trend in the application of artificial intelligence in pharmaceutical service in China and globally. METHODS The research literature on the application of artificial intelligence technology in the field of hospital pharmaceutical service from database establishment to June 16， 2023， was searched in Web of Science and CNKI. The authors， countries/regions， institutions and the co-occurrence， clustering， and emergence of keywords were visually processed and analyzed using tools including Endnote， CiteSpace， and Python. RESULTS & CONCLUSIONS Overall， 1 190 global literature and 178 Chinese literature were included. The number of publications issued in China and globally is increasing year by year， yet a gap remains in the quantity and quality of Chinese research compared with global research. Europe and the United States have built a close cooperation network in this field， while China’s regional development in this field remains imbalanced. Global research hotspots mainly focus on the development and application of high-end technologies such as machine learning， natural language processing, and deep learning； Chinese research concentrates more on actual medical services and medical policies， especially in promoting rational drug use， prescription review， and the development of traditional Chinese medicine.

Objective: To analyze the relationship between the risk of tuberculosis outbreaks in schools and the visit interval of index cases, so as to provide a scientific reference for predicting the risks of tuberculosis outbreak and making preventive measures. Methods: A total of 630 index cases from school tuberculosis outbreaks were studied during January, 2015 to December, 2022. Data on demographics, consultation history, etiological diagnosis, and methods of detection were collected. Restricted Cubic Splines (RCS), unconditional Logistic regression, and the receiver operating characteristic curve (ROC curve) were used for analysis. Results: The RCS fitted curve showed that the risk of a tuberculosis outbreak linearly increased when the consultation interval for etiologically negative patients exceeded 5.79 days, or for etiologically positive patients exceeded 8.37 days. After multi factor adjustment, for every additional day in the visit interval of the index case, the odds ratio ( OR ) value for a high risk outbreak was 1.10 (95% CI =1.07-1.13)( P <0.05). When analyzed by tertiles of visit intervals, compared to an interval of <14 days, the OR values (95% CI ) for high risk outbreaks in schools with intervals of 14-<28 days and ≥28 days were 10.32(3.04-35.10) and 82.58( 28.42 -239.95), respectively( P <0.01), indicating a trend of increasing outbreak risk with longer visit intervals. Based on the ROC curve analysis, the optimal threshold for predicting a high risk school tuberculosis outbreak was 23.5 days, with an area under the curve ( AUC ) of 0.93 (95% CI =0.89-0.98). Conclusion An extended visit interval of index cases is a good early warning indicator for high risk tuberculosis outbreaks in schools and could be considered a key factor in early intervention and risk control strategies.

Female ; Humans ; Anti-Mullerian Hormone ; Sexual Development ; Peptide Hormones