This review has introduced the Coronary Artery Tree description and Lesion EvaluaTion(CatLet or Hexu)angiographic scoring system,which,based solely on coronary angiography results,has characterized 6 types of right coronary artery,3 types of left descending artery,and 3 types of diagonal size,together resulting in 54 types of coronary circulation pattern.This novel angiographic scoring system can be utilized to account for coronary anatomy in its diversity,severity and complexity of diseased coronary arteries,and their subtended myocardial territories in jeopardy.The CatLet angiographic scoring system is unique in that the importance of a coronary artery is weighted according to its subtended myocardial segments,by which the variability of coronary artery has been accounted for and measured.Researchers at home and abroad have increasingly paid attention to its clinical utilities,which warrant further validation in the context of large sample size,prospective,and randomized controlled trials.The CatLet angiographic scoring system is accessible at www.catletscore.com.

Objective:To explore the potential profiles of health literacy in patients with chronic atrophic gastritis (CAG) and analyze their influencing factors.Methods:A cross-sectional study was conducted to select CAG patients admitted to Nanjing Hospital of Traditional Chinese Medicine or attending outpatient clinics from January 2023 to June 2024 by convenience sampling method, and all patients participated in this study two months after treatment. The General Information Questionnaire, the General Self-Efficacy Scale, and the All-around Health Literacy Measurement Scale (AAHLS) were used to conduct the questionnaire survey. The three dimensions of AAHLS were used as exogenous indicators for potential profile analysis, to compare the differences in general information and self-efficacy of patients with different potential profiles, and to analyze the influencing factors of different potential profiles of health literacy in CAG patients by multi classification Logistic regression.Results:The recovery rate of valid questionnaires was 97.71%(256/262). Among the 256 patients, there were 164 males and 92 females, with 86 patients aged 18-44 years old, 110 patients aged 45-59 years old, and 60 patients aged ≥ 60 years old. The health literacy of CAG patients was categorized into 3 potential profiles, including the high health literacy-appraisal and application competence group (14.84%, 38/256), the medium health literacy-appraisal and application competence group (32.81%, 84/256), and the low health literacy-appraisal and application competence group (52.34%, 134/256). The results of multi classification Logistic regression analysis showed that when the low health literacy-appraisal and application competence group was used as a reference, aged 18-44 years old ( OR=8.954, 95% CI 2.049-39.127), poor self-efficacy ( OR=0.089, 95% CI 0.027-0.293), duration of disease <5 years ( OR=0.049, 95% CI 0.014-0.169), per capita monthly household income<3 000 yuan ( OR=0.072, 95% CI 0.018-0.299), and primary school education or below ( OR=0.016, 95% CI 0.003-0.088) were independent influences in the the high health literacy-appraisal and application competence group (all P<0.05); poorer self-efficacy ( OR=0.458, 95% CI 0.251-0.834) and per capita monthly household income<3 000 yuan ( OR=0.212, 95% CI 0.100-0.453) were independent influences in the medium health literacy-appraisal and application competence group (both P<0.05). Conclusions:The health literacy of CAG patients is obviously heterogeneous, and healthcare professionals in clinical work should adopt targeted assessment and intervention according to the different characteristics of each category in order to improve the health literacy of CAG patients.

This review has introduced the Coronary Artery Tree description and Lesion EvaluaTion(CatLet or Hexu)angiographic scoring system,which,based solely on coronary angiography results,has characterized 6 types of right coronary artery,3 types of left descending artery,and 3 types of diagonal size,together resulting in 54 types of coronary circulation pattern.This novel angiographic scoring system can be utilized to account for coronary anatomy in its diversity,severity and complexity of diseased coronary arteries,and their subtended myocardial territories in jeopardy.The CatLet angiographic scoring system is unique in that the importance of a coronary artery is weighted according to its subtended myocardial segments,by which the variability of coronary artery has been accounted for and measured.Researchers at home and abroad have increasingly paid attention to its clinical utilities,which warrant further validation in the context of large sample size,prospective,and randomized controlled trials.The CatLet angiographic scoring system is accessible at www.catletscore.com.

Objective:To investigate the MRI characteristics of ductal lesions with microcalcifications detected by mammography,and to evaluate the value of MRI in the differential diagnosis of benign and malignant ductal lesions.Methods:The clinical data of 112 patients with pathologically confirmed ductal lesions with microcalcifications detected by mammography in our hospital from January 2022 to June 2024 were retrospectively analyzed.All patients underwent mammography and MRI examinations.MRI morphological features,dynamic enhancement characteristics,diffusion-weighted imaging(DWI)findings,and apparent diffusion coefficient(ADC)values were analyzed and compared with pathological results.The diagnostic performance of various MRI features was analyzed using ROC curves.Results:Among the 112 patients,there were 47 benign lesions and 65 malignant lesions.Malignant lesions were more likely to present with irregular morphology,heterogeneous T2 signal,lobulation,and spiculation(P＜0.05),while benign lesions were more likely to present with round or oval shapes and homogeneous T2 signal(P＜0.05).Malignant lesions predominantly showed fast rise-fast washout(Washout type)enhancement curves(58.5％),while benign lesions predominantly showed persistent rise(Progressive type)enhancement curves(55.3％).The ADC values of malignant lesions(0.92±0.21× 10-3 mm2/s)were significantly lower than those of benign lesions(1.45±0.28× 10-3 mm2/s)(P＜0.001).The combined application of morphological features,dynamic enhancement curves,and ADC values had the highest diagnostic performance,with a sensitivity of 92.3％,specificity of 89.4％,and accuracy of 91.1％.Conclusion:Multi-parameter MRI can comprehensively evaluate ductal lesions with microcalcifications detected by mammography.The combined application of morphological features,dynamic enhancement curve types,and ADC values can effectively improve the accuracy of differential diagnosis between benign and malignant lesions,providing important basis for clinical treatment decisions.

Objective To investigate the effects of ischemia time and storage periods on RNA quality in fresh-frozen breast cancer(BC)and esophageal cancer(EC)tissue samples in order to establish evidence-based protocols for biobank sample management.Methods The tumor(T)and paired normal(N)tissue samples from 6 cases of BC and 6 cases of EC were collected and cryopreserved in Biobank,Shantou Central Hospital.Mirror paraffin-embedded tissues were simultaneously prepared into sections for morphological analysis.The samples were divided into two groups of<15 min and 15-30 min according to ischemia time,and RNA quality was analyzed at 4 storage periods of 8-10 months(T1),14-16 months(T2),26-28 months(T3)and 38-40 months(T4).Results In 96 analyzed samples,93.8%(90/96)exhibited high quality(RIN≥6),with 89.6%(43/48)in BC and 97.9%(47/48)in EC.Significant differences in RIN were observed between BC group and EC group(8.050 vs.8.600,P=0.009).In EC group,RIN value was significantly negatively correlated with RNA yield(P<0.001).Moreover,RIN values of tumor-normal pairs exhibited markedly significant differences(7.550 vs.9.000,P<0.001).In contrast,no significant difference was detected in BC group(8.200 vs.7.700,P=0.348).Statistical analysis showed that RIN value was positively correlated with 28S/18S(P<0.001),but had no correlation with tumor content(P=0.676)and necrotic content(P=0.055).Neither ischemia time(<15 min vs.15-30 min:8.200 vs.8.300,P=0.932)nor storage periods(T1-T4:8.400,7.700,8.450,8.600,P=0.163)compromised RNA quality.Conclusion Organ origin and tissue type could influence RNA quality of fresh-frozen tissue samples.However,limited ischemia time(≤30 min)and long-term storage period(38-40 months)do not adversely affect RNA quality in fresh-frozen breast cancer and esophageal cancer tissue samples.

Objective To optimize the design of the existing water conductivity control system for pharmaceutical water equipment of full membrane process so as to solve its problems in precision and long cycle time due to water source,ambient temperature and intermittent working mode.Methods The optimized water conductivity control system was composed of an alkali metering pump,a conductivity sensor and a programmable logic controller(PLC),which used a fuzzy proportional-integral-derivative(PID)controller to regulate the water conductivity of pharmaceutical water equipment of full membrane process,and the particle swarm optimization(PSO)algorithm to optimize the parameters of the fuzzy PID controller.A simulation model was established with MATLAB software to verify the performance of the optimized control system.Results Simulation results showed the optimized control system had reductions in overshoot(by 19％)and adjustment time(by 29％)when compared with the fuzzy PID control system,and enhanced control efficiency effectively.Conclusion The optimized control system optimized by the PSO algorithm improves the quality of produced water,and can meet the demands for rapid and safe production of pharmaceutical water by pharmaceutical water equipment of full membrane process in different conditions.[Chinese Medical Equipment Journal,2025,46(6):14-19]

Objective To investigate the effects of ischemia time and storage periods on RNA quality in fresh-frozen breast cancer(BC)and esophageal cancer(EC)tissue samples in order to establish evidence-based protocols for biobank sample management.Methods The tumor(T)and paired normal(N)tissue samples from 6 cases of BC and 6 cases of EC were collected and cryopreserved in Biobank,Shantou Central Hospital.Mirror paraffin-embedded tissues were simultaneously prepared into sections for morphological analysis.The samples were divided into two groups of<15 min and 15-30 min according to ischemia time,and RNA quality was analyzed at 4 storage periods of 8-10 months(T1),14-16 months(T2),26-28 months(T3)and 38-40 months(T4).Results In 96 analyzed samples,93.8%(90/96)exhibited high quality(RIN≥6),with 89.6%(43/48)in BC and 97.9%(47/48)in EC.Significant differences in RIN were observed between BC group and EC group(8.050 vs.8.600,P=0.009).In EC group,RIN value was significantly negatively correlated with RNA yield(P<0.001).Moreover,RIN values of tumor-normal pairs exhibited markedly significant differences(7.550 vs.9.000,P<0.001).In contrast,no significant difference was detected in BC group(8.200 vs.7.700,P=0.348).Statistical analysis showed that RIN value was positively correlated with 28S/18S(P<0.001),but had no correlation with tumor content(P=0.676)and necrotic content(P=0.055).Neither ischemia time(<15 min vs.15-30 min:8.200 vs.8.300,P=0.932)nor storage periods(T1-T4:8.400,7.700,8.450,8.600,P=0.163)compromised RNA quality.Conclusion Organ origin and tissue type could influence RNA quality of fresh-frozen tissue samples.However,limited ischemia time(≤30 min)and long-term storage period(38-40 months)do not adversely affect RNA quality in fresh-frozen breast cancer and esophageal cancer tissue samples.

This study selected 6 529 plant-based Chinese materia medica(PCMM) from Chinese Materia Medica as research subjects and applied a random permutation test to explore the overall correlation characteristics between nature and flavor, as well as the correlation characteristics after distinguishing different medicinal parts and harvest seasons. The results showed that the overall correlation characteristics between nature and flavor in PCMM were significantly associated in the following pairs: cold and bitter, cool and bitter, cool and astringent, cool and light, neutral and sweet, neutral and astringent, neutral and light, neutral and sour, hot and pungent, and warm and pungent. When analyzing the data by distinguishing medicinal parts and/or harvest seasons, new correlation patterns emerged, characterized by the disappearance of some significant correlations and the emergence of new ones. When analyzing by medicinal parts alone, significant correlations were found in the following cases: cold and light in leaves, cold and salty in barks, cool and sweet in fruits and seeds, neutral and pungent in whole herbs, neutral and salty in stems, and warm and salty in flowers. However, no significant correlations were found between cool and bitter in stems and other types of herbs, cool and astringent in fruits, seeds, flowers, and other types of herbs, cool and light in leaves, fruits, seeds, barks, flowers and other types of herbs, neutral and sweet in barks, neutral and astringent in whole herbs and stems, neutral and light in leaves, fruits, seeds, and flowers, neutral and sour in whole herbs, stems, barks, flowers, and other types of herbs, and hot and pungent in whole herbs, stems, flowers, and other types of herbs. When analyzing by harvest season alone, significant correlations were found in the following cases: cold and salty, and cool and sour in herbs harvested in winter, and neutral and salty in herbs harvested year-round. However, no significant correlation was found between cool and light in herbs harvested in winter. When considering both medicinal parts and harvest seasons, compared to the independent influence of medicinal parts, 14 new significant correlations emerged(e.g., the correlation between cool and bitter in stems harvested in spring), while 53 previously significant correlations disappeared(e.g., the correlation between cool and bitter in barks harvested in summer). Compared to the independent influence of harvest seasons, 11 new significant correlations appeared(e.g., the correlation between cold and light in barks harvested in autumn), while 50 previously significant correlations disappeared(e.g., the correlation between hot and pungent in leaves harvested in winter). This study is the first to reveal the influence of medicinal parts and harvest seasons on the correlation between nature and flavor in PCMM, which highlights that these two factors can interact and jointly affect nature-flavor correlations. Further research is needed to explore the underlying mechanisms. This study provides a deeper understanding of the inherent scientific connotations of herbal properties and offers a theoretical foundation for the cultivation and harvesting of PCMM.
Seasons ; Plants, Medicinal/growth & development* ; Drugs, Chinese Herbal/chemistry* ; Taste

Objective To analyze the relationship between obesity indicators,muscle mass indices and hypertension in Dongxiang adults,and establish and evaluate a Nomogram model based on these indicators used to predict the risk of hypertension in this population.Methods A total of 1209 Dongxiang adults from Linxia Prefecture,Gansu Province were selected,11 obesity indicators and 5 muscle mass indicators,including neck circumference(NC),waist-to-hip ratio(WHR),BMI,ponderal index(PI),conicity index(CI),a body shape index(ABSI),body roundness index(BRI),abdominal volume index(AVI),hip index(HI),body adiposity index(BAI),and the ratio of limb fat mass to body weight(LFWR),appendicular skeletal muscle mass(ASM),appendicular skeletal muscle mass index(ASMI),appendicular skeletal muscle mass to BMI ratio(ASMBMI),skeletal muscle index(SMI),and trunk muscle mass to body weight ratio(TMWR)were measured.Logistic regression analysis was used to explore the relationship between each indicator and hypertension.Nomogram prediction model was constructed and validated by using R language.Results Among Dongxiang males,the NC,BMI,PI,WHR,CI,AVI,BRI,BAI,ASM,ASMI,and LFWR were lower in the normal and high-normal blood pressure groups compared to the hypertensive group,while SMI,ASMBMI,and TMWR were higher.Among females,similar trends were observed,with lower NC,BMI,PI,WHR,CI,AVI,BRI,BAI,and LFWR in the normal blood pressure and high-normal blood pressure groups compared to the hypertensive group,but SMI,ASMBMI,and TMWR higher than the hypertensive group(P＜0.05).Results of multivariate Logistic regression analysis indicated that age,NC,and WHR were risk factors for hypertension in Dongxiang adults,while was protective factor(P＜0.05).The area under curve(AUC)of the Nomogram model constructed based on these factors was 0.796,and the Bootstrap internal validation C-index was 0.7957,indicating good calibration of the model.Conclusion The Nomogram model constructed based on obesity and muscle mass indicators has good predictive efficiency for predicting the risk of hypertension in Dongxiang adults.

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.