Digital and intelligent technologies serve as the core engine driving the inheritance of the essence and the innovation while upholding the fundamentals of traditional Chinese medicine（TCM）. Currently， the digital and intelligent transformation of TCM has undergone four developmental stages， exhibiting inherent characteristics such as long-term inevitability， objective standardization， and ecological evolution. By introducing quantitative metrics， digital and intelligent technologies have achieved breakthroughs in TCM knowledge inheritance and innovation， clinical diagnosis and treatment， and herbal medicine supply. The practical applicability of methodological innovations has been empirically validated， though significant disparities exist in technological adaptability and application depth across different fields. Overall， the digital and intelligent transformation of TCM remains in its nascent stage， grappling with multiple structural challenges：weak data foundations， inadequate technological adaptability， incomplete institutional frameworks， shortages of multidisciplinary talent， lagging policies and regulations， and urban-rural digital divide. In order to foster sustainable development and modernization of TCM， this paper establishes a six-dimensional collaborative governance framework of encompassing data， technology， organization， institutions， environment and ethics， which is rooted in data governance and digital governance theories. Future efforts should center on standardization， integration， and ecosystem development to build a data and technology foundation. Focus should be placed on deepening innovation and application of key TCM-specific technologies， while simultaneously strengthening interdisciplinary talent cultivation， improving institutional mechanisms and policy frameworks， and increasing support for rural areas. By adopting a people-centered and technology-empowered approach， we can overcome developmental constraints and unleash the powerful driving force of digital and intelligent technologies for the inheritance of TCM.

Objective To analyze the differences in distribution of traditional Chinese medicine (TCM) syndrome elements and salivary microbiota between the individuals with pulmonary nodules and those without, and to explore the potential correlation between the distribution of TCM syndrome elements and salivary microbiota in patients with pulmonary nodules. Methods We retrospectively recruited 173 patients with pulmonary nodules (PN) and 40 healthy controls (HC). The four diagnostic information was collected from all participants, and syndrome differentiation method was used to analyze the distribution of TCM syndrome elements in both groups. Saliva samples were obtained from the subjects for 16S rRNA high-throughput sequencing to obtain differential microbiota and to explore the correlation between TCM syndrome elements and salivary microbiota in the evolution of the pulmonary nodule disease. Results The study found that in the PN group, the primary TCM syndrome elements related to disease location were the lung and liver, and the primary TCM syndrome elements related to disease nature were yin deficiency and phlegm. In the HC group, the primary TCM syndrome elements related to disease location were the lung and spleen, and the primary TCM syndrome elements related to disease nature were dampness and qi deficiency. There were differences between the two groups in the distribution of TCM syndrome elements related to disease location (lung, liver, kidney, exterior, heart) and disease nature (yin deficiency, phlegm, qi stagnation, qi deficiency, dampness, blood deficiency, heat, blood stasis) (P<0.05). The species abundance of the salivary microbiota was higher in the PN group than that in the HC group (P<0.05), and there was significant difference in community composition between the two groups (P<0.05). Correlation analysis using multiple methods, including Mantel test network heatmap analysis and Spearman correlation analysis and so on, the results showed that in the PN group, Prevotella and Porphyromonas were positively correlated with disease location in the lung, and Porphyromonas and Granulicatella were positively correlated with disease nature in yin deficiency (P<0.05). Conclusion The study concludes that there are notable differences in the distribution of TCM syndrome elements and the species abundance and composition of salivary microbiota between the patients with pulmonary nodules and the healthy individuals. The distinct external syndrome manifestations in patients with pulmonary nodules, compared to healthy individuals, may be a cascade event triggered by changes in the salivary microbiota. The dual correlation of Porphyromonas with both disease location and nature suggests that changes in its abundance may serve as an objective indicator for the improvement of symptoms in patients with yin deficiency-type pulmonary nodules.

ObjectiveTo explore the effect of Yishen Tongluo prescription on sperm DNA fragmentation index （DFI） and sperm mitochondrial membrane potential （MMP） in patients with asymptomatic idiopathic asthenospermia infertility. MethodsA total of 128 patients with asymptomatic idiopathic asthenospermia were randomly assigned to an experimental group （64 cases） and a control group （64 cases）. The experimental group received Yishen Tongluo prescription， while the control group was treated with Wuzi Yanzongwan combined with L-carnitine oral solution. One treatment course lasted 12 weeks. Spouse pregnancy rate， sperm progressive motility （PR）， total sperm motility （PR+NP）， sperm function （sperm tail hypotonic swelling rate， sperm acrosin activity）， sperm DFI， and sperm MMP were compared between the two groups before and after treatment. Adverse reactions were observed and recorded during the study， and clinical efficacy and safety were systematically evaluated. ResultsA total of 121 patients completed the study， including 61 in the experimental group and 60 in the control group. The spouse pregnancy rate in the experimental group was 14.75% （9/61）， higher than that in the control group at 6.67% （4/60）， though the difference was not statistically significant. Clinical efficacy in the experimental group was superior to that in the control group （P<0.05）. Compared with the results before treatment， sperm PR， PR + NP， sperm tail hypotonic swelling rate， sperm acrosin activity， sperm DFI， and sperm MMP were significantly improved in both groups after treatment （P<0.05）， with greater improvements in the experimental group （P<0.05）. However， there was no significant change in sperm concentration in either group after treatment. During the study， no abnormal safety indicators or significant adverse reactions occurred in either group. ConclusionThe kidney-tonifying and collateral-dredging method shows good clinical efficacy in the treatment of asymptomatic idiopathic asthenospermia infertility. Yishen Tongluo prescription can improve sperm motility， increase spouse pregnancy rate， enhance sperm function， and demonstrates good safety. Its mechanism may be related to reducing sperm DFI and increasing sperm MMP.

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 surveillance data of schistosomiasis and snail status in Jiaxing City, Zhejiang Province from 2001 to 2024, so as to provide the reference for prevention and control of schistosomiasis in jiaxing City. Methods: Data on schistosomiasis and snail surveillance in Jiaxing City from 2001 to 2024 were collected through schistosomiasis control work reports and the Zhejiang Provincial Schistosomiasis (Parasitic Diseases) Control Information Management System. These data included serological testing results, stool etiological examination (stool examination) results, area surveyed for snails, snail-infested areas, number of snail-positive frames, and number of live snails. Indicators, including the positive rate of serological testing, the positive rate of stool examinations, the rate of snail-positive frames, and the density of live snails were analyzed. The Prophet time series model was employed to forecast the schistosomiasis epidemic in Jiaxing City from 2025 to 2029. Results: A total of 636 493 serological testing were conducted in Jiaxing City from 2001 to 2024, with a positive rate of 1.532%, showing a decreasing trend (P<0.05). Among 7 582 stool examinations, positive cases were all imported, resulting in a positivity rate of 0.066%. During the same period, snail surveys covered a cumulative area of 30 545.999 hm2, with snail-infested areas totaling 69.355 hm2; no significant trend was observed (P>0.05). All snail habitats were identified as recurrent foci within hydrographic network regions, primarily distributed across Xiuzhou District, Nanhu District, Pinghu City, Jiashan County, and Tongxiang City, with snail-infested areas of 39.588, 12.538, 10.728, 4.315, and 2.186 hm2, respectively. From 2009 to 2024, a total of 35 692 134 frames of snails were surveyed, of which 16 543 were snail-positive, yielding a snail-positive frame rate of 0.046%. A total of 33 175 live snails were collected, with a mean density of 0.000 98 snails per frame. No infected Oncomelania snails were detected. The projection results indicated that from 2025 to 2029, the positive rate of serological testing rate in Jiaxing City would range between 0.253% to 0.389%, the snail-infested areas would range from 0.025 to 1.818 hm2, and the density of live snails would vary from 0.001 56 to 0.001 66 snails per frame. None of these indicators showed a significant trend (all P>0.05). Conclusions From 2001 to 2024, the positive rate of serological testing rate of schistosomiasis in Jiaxing City showed a declining trend, with no new autochthonous cases or infected Oncomelania snails detected. However, imported cases were still reported. All identified snail habitats were recurrent foci within hydrographic network regions. It is recommended to enhance schistosomiasis and snail status surveillance in high-risk areas.

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.

Objective To explore the recognition capabilities of electronic nose combined with machine learning in identifying the breath odor map of benign and malignant pulmonary nodules and Traditional Chinese Medicine (TCM) syndrome elements. Methods The study design was a single-center observational study. General data and four diagnostic information were collected from 108 patients with pulmonary nodules admitted to the Department of Cardiothoracic Surgery of Hospital of Chengdu University of TCM from April 2023 to March 2024. The patients' TCM disease location and nature distribution characteristics were analyzed using the syndrome differentiation method. The Cyranose 320 electronic nose was used to collect the odor profiles of oral exhalation, and five machine learning algorithms including random forest (RF), K-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM), and eXtreme gradient boosting (XGBoost) were employed to identify the exhaled breath profiles of benign and malignant pulmonary nodules and different TCM syndromes. Results (1) The common disease locations in pulmonary nodules were ranked in descending order as liver, lung, and kidney; the common disease natures were ranked in descending order as Yin deficiency, phlegm, dampness, Qi stagnation, and blood deficiency. (2) The electronic nose combined with the RF algorithm had the best efficacy in identifying the exhaled breath profiles of benign and malignant pulmonary nodules, with an AUC of 0.91, accuracy of 86.36%, specificity of 75.00%, and sensitivity of 92.85%. (3) The electronic nose combined with RF, LR, or XGBoost algorithms could effectively identify the different TCM disease locations and natures of pulmonary nodules, with classification accuracy, specificity, and sensitivity generally exceeding 80.00%.Conclusion Electronic nose combined with machine learning not only has the potential capabilities to differentiate the benign and malignant pulmonary nodules, but also provides new technologies and methods for the objective diagnosis of TCM syndromes in pulmonary nodules.

The accumulation of uremic toxins such as urea nitrogen, blood creatinine, and uric acid of patients with renal failure in vivo would lead to aggravated kidney damage. In this study, coated aldehyde oxy-starch (CAO) was used as an adsorbent to investigate its in vitro adsorption performance on renal failure indexes for urea, indoxyl sulfate (INS), monomethylamine (MMA), dimethylamine (DMA), uric acid (UA), and creatinine (Cr). The effects of variables such as pH, temperature, concentration, dosage, and time on the adsorption capacity of CAO were systematically investigated, employing analytical techniques of high-performance liquid chromatography (HPLC) and gas chromatography (GC). The results revealed that CAO exhibited a strong adsorption capacity for urea, INS, and MMA, alongside a moderate adsorption capacity for DMA, UA, and Cr. The adsorption kinetics and thermodynamic studies indicated that the adsorption of urea and UA by CAO were fitted in pseudo-first-order kinetics, and the adsorption isotherm aligned with the Freundlich adsorption model. The enthalpy change ΔH of urea in adsorption was in the range of 40 to 60 kJ·mol^-1, which demonstrated the presence of strong adsorption force due to the interactions of coordinating groups. The ΔH of UA was greater than 80 kJ·mol^-1, indicating the generation of chemical bonds during the adsorption. Both of them, Gibbs free energy ΔG was less than 0, within the range of -20 to 0 kJ·mol^-1, suggested that the adsorption of urea and UA by CAO occurred spontaneously as physical adsorption process. The adsorption entropy ΔS of urea and UA was > 0, which indicated an increase in entropy throughout the adsorption. The infrared spectroscopygram showed the formation of a chemical bond, specifically the imine bond, following the adsorption of urea by CAO, thereby indicating a chemical reaction during the adsorption. This study elucidates the adsorption mechanism of CAO on various indexes of renal failure, providing a scientific basis for its clinical usage.

Objective To establish a method for rapid detection of tramadol in urine by liquid-liquid extraction（LLE）-surface-enhanced Raman spectroscopy （SERS）. Methods Tramadol was extracted from urine with chloroform∶isopropyl alcohol （9∶1） extractant and detected in urine samples by enhanced Raman spectroscopy （wavelength 785 nm）. Results The quantitative curve of tramadol was Y=204.35 X−465.62, r=0.9952, and the linear range was 1-100 μg/ml. The detection limit of tramadol by this method （S/N=3） was 0.53 μg/ml. The sensitivity of SERS was higher than that of conventional methods, and it had reasonable reproducibility. Conclusion This method is simple, efficient and economical, and can be used for qualitative and quantitative analysis of tramadol personalized medicine.

Objective To investigate the seroprevalence of antibody against Toxoplasma gondii among patients with hematological malignancies, and compare it with that among health individuals, so as to provide insights into unraveling the pathogenesis of hematological malignancies. Methods A total of 225 patients with hematological malignancies in Department of Hematology, Xuzhou Central Hospital and 300 healthy individuals in the same hospital were enrolled from 2017 to 2024. Blood samples were collected from all subjects, and the serum IgG and IgM antibodies against T. gondii were detected using chemiluminescent immunoassay. Demographic and clinical features were collected from patients with hematological malignancies, including gender, age, contact with cats, consumption of raw or undercooked meat, type of malignancy, clinical symptoms, blood transfusion and treatment, and the seroprevalence of anti-T. gondii antibody was compared among patients with different characteristics. Results The age (t = 0.72, P > 0.05) and gender (χ² = 0.93, P > 0.05) were compared between patients with hematological malignancies and healthy individuals. The seroprevalence of T. gondii infection was 20.89% among patients with hematological malignancies and 4.33% among healthy individuals (χ² = 34.81, P < 0.01), and the seroprevalence of anti-T. gondii IgG antibody was 20.89% among patients with hematological malignancies and 4.33% among healthy individuals (χ² = 34.81, P < 0.01), while there was no significant difference in the seroprevalence of anti-T. gondii IgM antibody between patients with hematological malignancies and healthy individuals (1.33% vs. 0; corrected χ² = 2.02, P > 0.05). The seroprevalence of T. gondii infection was 23.08% among patients with leukemia, 16.67% among patients with lymphoma, 19.23% among patients with multiple myeloma, 24.00% among patients with myeloproliferative neoplasm, and 26.09% among patients with myelodysplastic syndrome (χ² = 1.44, P > 0.05), and was all higher than among healthy individuals (corrected χ² = 23.92, 10.74, 13.76, 12.84 and 14.54; all P values < 0.01). In addition, there were no significant differences in the detection of anti-T. gondii antibody among patients with hematological malignancies in terms of gender, age, contact with cats, consumption of raw or undercooked meat, chemotherapy or blood transfusion (χ² = 0.76, 1.97, 0, 2.81, 2.38 and 0.66; all P values > 0.05). Conclusions There is a high risk of T. gondii infection among patients with hematological malignancies, and intensified surveillance of T. gondii infection is recommended among patients with hematological malignancies.