OBJECTIVE To develop a mobile intelligent pharmacy management assistant system （abbreviated as “assistant system”）， thus providing convenient support for the daily operational management of pharmacies. METHODS The system was developed using the WeChat developer tools with WXML， WXSS， and JavaScript based on the platform as a service （PaaS）. Test users were recruited， and in five different scenarios， namely pharmaceutical knowledge inquiry， drug damage registration， medication error data analysis， drug expiration date registration， and duty handover， the task execution time was recorded for both the new （assistant system） and old （paper-based records and fixed computer records） modes. Additionally， users’ opinions on the advantages of the new mode were collected. RESULTS The assistant system comprised six modules: shift handover， pharmaceutical knowledge database， medication error registration， drug expiry management， drug damage registration， and medication consultation records. Among 10 test users， in all scenarios except for the shift handover record， the task execution time under the new mode was shorter than that under the old mode （P＜0.05）. Most test pharmacists highlighted that the primary advantages of the new mode lie in its iterative flexibility， and ease of use among other benefits. CONCLUSIONS The assistant system based on PaaS cloud model WeChat mini program developed in this study can significantly improve the daily management efficiency of the pharmacy and optimize the task execution experience for pharmacists.

Electromagnetic fields can regulate the fundamental biological processes involved in bone remodeling. As a non-invasive physical therapy, electromagnetic fields with specific parameters have demonstrated therapeutic effects on bone remodeling diseases, such as fractures and osteoporosis. Electromagnetic fields can be generated by the movement of charged particles or induced by varying currents. Based on whether the strength and direction of the electric field change over time, electromagnetic fields can be classified into static and time-varying fields. The treatment of bone remodeling diseases with static magnetic fields primarily focuses on fractures, often using magnetic splints to immobilize the fracture site while studying the effects of static magnetic fields on bone healing. However, there has been relatively little research on the prevention and treatment of osteoporosis using static magnetic fields. Pulsed electromagnetic fields, a type of time-varying field, have been widely used in clinical studies for treating fractures, osteoporosis, and non-union. However, current clinical applications are limited to low-frequency, and research on the relationship between frequency and biological effects remains insufficient. We believe that different types of electromagnetic fields acting on bone can induce various “secondary physical quantities”, such as magnetism, force, electricity, acoustics, and thermal energy, which can stimulate bone cells either individually or simultaneously. Bone cells possess specific electromagnetic properties, and in a static magnetic field, the presence of a magnetic field gradient can exert a certain magnetism on the bone tissue, leading to observable effects. In a time-varying magnetic field, the charged particles within the bone experience varying Lorentz forces, causing vibrations and generating acoustic effects. Additionally, as the frequency of the time-varying field increases, induced currents or potentials can be generated within the bone, leading to electrical effects. When the frequency and power exceed a certain threshold, electromagnetic energy can be converted into thermal energy, producing thermal effects. In summary, external electromagnetic fields with different characteristics can generate multiple physical quantities within biological tissues, such as magnetic, electric, mechanical, acoustic, and thermal effects. These physical quantities may also interact and couple with each other, stimulating the biological tissues in a combined or composite manner, thereby producing biological effects. This understanding is key to elucidating the electromagnetic mechanisms of how electromagnetic fields influence biological tissues. In the study of electromagnetic fields for bone remodeling diseases, attention should be paid to the biological effects of bone remodeling under different electromagnetic wave characteristics. This includes exploring innovative electromagnetic source technologies applicable to bone remodeling, identifying safe and effective electromagnetic field parameters, and combining basic research with technological invention to develop scientifically grounded, advanced key technologies for innovative electromagnetic treatment devices targeting bone remodeling diseases. In conclusion, electromagnetic fields and multiple physical factors have the potential to prevent and treat bone remodeling diseases, and have significant application prospects.

Objective: To analyze the status and trends of the disease burden of periodontal disease among women of reproductive age (15-49 years) in China from 1990 to 2021, and to provide a reference for the development of periodontal disease prevention and control strategies for women of reproductive age. Methods: Using the global burden of disease (GBD) data from 1990 to 2021, this study investigated the periodontal disease burden among women of reproductive age, including prevalence, incidence, disability-adjusted life years (DALYs), DALY rates, and their corresponding standardized indicators. Joinpoint 5.2.0.0 software was used for time trend analysis of DALYs, age-specific DALY rates, and annual average percentage change (AAPC) values. A log-linear regression model was used to test trends for DALYs and DALY rates. Results: Compared with 1990, the prevalence and incidence of periodontal disease among Chinese women in 2021 increased by 45.67% (per 100,000 people) and 29.29% (per 100,000 people), respectively. The distribution of periodontal disease among women (15-49 years) showed a continuous and rapid upward trend, with the growth rate increasing rapidly with age. The number of cases increased the fastest in the 45-49 age group, and the prevalence increased the fastest in the 35-44 age group. The incidence of periodontal disease continued to rise with age, with the fastest increase in the 35-44 age group among women of reproductive age. The Joinpoint regression model results showed that periodontal disease led to an expanding trend in the disease burden among women of reproductive age in China, with an AAPC of DALYs = 1.20% and an AAPC of DALY rate = 1.25% (P<0.001). Conclusion The periodontal disease burden among Chinese women aged 15-49 years showed a gradually increasing trend from 1990 to 2021.

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.

Aim To study the neuroprotective effects of Herba siegesbeckiae extract on cerebral ischemia/ reperfusion rats and its mechanism. Methods Sixty SD rats were randomly divided into model group, low, middle and high dose groups of Herba siegesbeckiae, and Sham operation group, and the drug was given continuously for seven days. The degree of neurologic impairment was evaluated by mNSS, and the infarct volume was measured by MRI. The number of Nissl-posi- tive cells was detected by Nissl staining, and the apop- tosis was accessed by Tunel staining. Furthermore, the expression of Bax, Bcl-2 and NeuN was observed by Western blot, and the expression of NeuN was detected by immunofluorescence staining. The expression of IL- 1β, TNF-α and IL-6 mRNA was performed by RT- qPCR. Results The mNSS score and the volume of ischemic cerebral infarction in the model group were significantly increased, and Herba siegesbeckiae extract treatment significantly decreased the mNSS score and infarct volume (P<0.05, P<0.01). Herba siegesbeckiae extract could increase the number of Nissl-pos- itive cells and the expression of NeuN (P<0.01), and reduce the number of Tunel-positive cells (P<0.01). Western blot showed that Herba siegesbeckiae extract inhibited the expression of Bax, increased Bcl-2 and NeuN in ischemic brain tissue (P<0.01). RT-qPCR showed that Herba siegesbeckiae extract inhibited the expression of IL-1 β, TNF-α and IL-6 mRNA in the is-chemic brain tissue (P<0.01). Conclusions Herba siegesbeckiae extract can reduce the cerebral infarction volume, improve the neurological function damage, inhibit the apoptosis of nerve cells and the expression of inflammatory factors and promote the expression of NeuN, there by exerting protective effects on MCAO rats.

Pyroptosis is the programmed death of cells accompanied by an inflammatory response and is widely involved in the development of a variety of diseases, such as infectious diseases, cardiovascular diseases, and neurodegeneration. It has been shown that cellular scorching is involved in the pathogenesis of pulmonary arterial hypertension ( PAH) in cardiovascular diseases. Patients with PAH have perivascular inflammatory infiltrates in lungs, pulmonary vasculopathy exists in an extremely inflam-matory microenvironment, and pro-inflammatory factors in cellular scorching drive pulmonary vascular remodelling in PAH patients. This article reviews the role of cellular scorch in the pathogenesis of PAH and the related research on drugs for the treatment of PAH, with the aim of providing new ideas for clinical treatment of PAH.

Objective To investigate the association between four single nucleotide polymorphisms(SNP)(rs9340 in MAPK1,rs14804 in NRAS,rs712 and rs7973450 in KRAS)in the 3'UTR of ERK1/2 signaling pathway-related genes and non-small cell lung cancer(NSCLC).Methods A total of 478 NSCLC patients and 480 healthy controls were enrolled in this study.Four SNPs were genotyped by using TaqMan assays.The association between the four SNPs and NSCLC was analyzed.Results The distribution frequency difference of the allele of rs9340 was statistically significant between the control group and the non-small cell squamous cell carcinoma(SCC)group(P = 0.009),suggesting that the G allele of rs9340 may be a protective factor for non-small cell lung squamous cell carcinoma(OR = 0.67,95%CI:0.50～0.91).In addition,in the<50 years age group,the distribution frequency difference of the allele of rs9340 was statistically significant between the control group and the NSCLC group(P = 5.07×10-4),indicating that the G allele of rs9340 may be a protective factor for NSCLC(OR = 0.46,95%CI:0.29～0.72).Conclusion The SNP rs9340 in MAPK1 may be associated with the risk of NSCLC.

Objective To establish a mouse model of type Ⅰ tricho-hepato-enteric syndrome(THES)induced by Ttc37 deficiency.Methods Ttc37 flox strain was established by site-specifically inserted loxP sites into Ttc37 gene via CRISPR/CAS9 technology.Ubiquitously expressed CAG-Cre was introduced for all-tissue removal of Ttc37 in Ttc37flox/flox;CAG-Cre mice.The knock-out effect was confirmed by fluorescence quantitative PCR and Western blot.Phenotypic evaluations were conducted in 8-week-old mice including hematoxylin-eosin staining of skin,spleen,liver,bladder,and gastrointestinal tract(GI),serum enzyme activity assay of aspartate aminotransferase(AST)and alanine aminotransferase(ALT),measurement of serum hemoglobin level,and ELISA for IgG and IgM level upon antigen immunization.Results Similar to type Ⅰ THES patients,Ttc37flox/flox;CAG-Cre mice exhibited impaired development of hair shaft,epidermis,B cell and eyes,while liver,GI,bladder and serum hemoglobin level seemed normal under unstressed condition.Conclusion A novel mouse model of typeⅠ THES was constructed successfully,which was applicable for pathological study.

Objective:To evaluate the diagnostic performance of radiomics model based on contrast-enhanced ultrasound(CEUS) in predicting pathological complete response(pCR) after neoadjuvant chemoradiotherapy(nCRT) in patients with locally advanced rectal cancer(LARC).Methods:One hundred and six patients with LARC who underwent total mesorectal excision after nCRT between April 2018 and April 2023 in the First Affiliated Hospital of Guangxi Medical University were retrospectively included, the patients were randomly divided into a training set of 63(14 pCR patients) and a validation set of 43(12 pCR patients) in a 6∶4 ratios. Radiomics features were extracted from the tumors′ region of interest of CEUS images based on PyRadiomics. Intra-class correlation coefficient(ICC), Mann-Whitney U test, and least absolute shrinkage and selection operator(LASSO) algorithms were used to reduce features dimension. Finally, 7 radiomics features relevanted to pCR were selected to construct an ultrasomics model using elastic network regression, based on the R language. A combined model was constructed by jointing clinical feature. The performance of the models was assessed with the area under the ROC curve(AUC). Results:The AUC of the ultrasomics model and the combined model was 0.695(95% CI=0.532-0.859) and 0.726(95% CI=0.584-0.868) respectively in the training set. The AUC of the ultrasomics model and the combined model was 0.763(95% CI=0.625-0.902) and 0.790(95% CI=0.653-0.928) respectively in the validation set. Both univariate and multivariate Logistic regression analyses showed that CA199( P<0.05) and ultrasomics score( P<0.001) could be an independent predictor of pCR after nCRT in patients with LARC. Conclusions:The CEUS-based radiomics scores has certain predictive value for whether LARC patients achieve pCR after nCRT, and may provide a non-invasive imaging biomarker for predicting LARC patients achieve pCR after nCRT.

Objective:To investigate the impact of daily step count on glycemic outcomes in community residents with impaired glucose tolerance (IGT).Methods:This was a prospective cohort study, in October 2018, 204 residents who met the criteria of IGT were recruited in the Shijingshan District in Beijing. The subjects were tested for fasting blood glucose, oral glucose tolerance test 2-hour blood glucose (2hBG), glycated hemoglobin A 1c (HbA 1c), lipid profile, liver and kidney function, as well as measurements of height, weight and waist circumference. A dedicated mobile application was used to deliver prediabetes health knowledge monthly. Online guidance was provided to answer questions and daily step count was collected using the application. Three years later, a follow-up was conducted to assess the participants′ glycemic outcomes and other indexes, and a total of 142 participants completed the follow-up review. According to daily step count, the subjects were categorized into high step count group (42 cases,>7 000 steps daily), moderate step count group (54 cases, 5 000-7 000 steps daily), and low step count group (46 cases,<5 000 steps daily). Subjects were categorized into diabetes group (30 cases), prediabetes group (77 cases) and normal glucose tolerance group (35 cases) with glycemic outcomes. Independent sample t test was used to compare the differences in blood glucose, blood lipids, and step counts between the two groups. Kruskal-Wallis H test or one-way ANOVA was used to compare the differences in blood glucose, blood lipids, and step counts between multiple groups. The χ2 test was used to compare the differences in glycemic outcomes between multiple groups. Multivariate logistic regression analysis was used to assess the impact of daily step counts and body mass index on glycemic outcomes. Linear regression analysis was used to evaluate the relationship between daily step counts and 2 h BG. Results:A total of 142 participants completed the 3-year follow-up, including 43 males and 99 females, with a mean age of (60.15±5.67) years. At baseline, males had significantly higher body mass index, waist circumference, and fasting blood glucose when compared to those in females [(26.97±2.43) vs (24.89±2.93) kg/m 2, (92.68±7.75) vs (83.83±8.60) cm, (5.83±0.61) vs (5.62±0.52) mmol/L], the total cholesterol and HDL-C were also significantly lower in males than those in females [(5.10±1.16) vs (5.55±0.95) mmol/L, (1.35±0.34) vs (1.56±0.35) mmol/L] (all P<0.05). After 3-year follow-up, 21.1% (30/142) of IGT participants progressed to diabetes, with an annual conversion rate of approximately 7%. The normal glucose tolerance group showed significantly higher daily step counts when compared with the prediabetes and diabetes groups [(7 886±2 867) vs (5 981±2 655) vs (4 117±2 674) steps] ( H=31.778, P<0.001). Individuals with higher daily step counts exhibited lower body mass index, 2 h BG, and HbA 1c level when compared with those in the ones with moderate and low step counts [(24.26±3.09) vs (25.44±3.38) vs (26.26±3.59) kg/m 2, (7.50±1.71) vs (9.15±3.30) vs (11.19±3.84) mmol/L, 5.97%±0.46% vs 6.14%±0.99% vs 6.40%±0.96%] (all P<0.05). Higher step count was positively correlated with the reversal of prediabetes to normal blood glucose levels (moderate step count, OR=0.297, 95% CI: 0.109-0.804; low step count, OR=0.055, 95% CI: 0.010-0.287), lower daily step count correlated positively with prediabetes progressing to diabetes ( OR=4.857, 95% CI: 1.140-20.689) (all P<0.05). For every additional 1 000 steps per day, the 2 h BG decreased by 0.5 mmol/L. Conclusion:As daily step count increases, the glucose metabolism improves in IGT community residents. Higher daily step count is associated with reversal of IGT to normal glucose tolerance, while lower daily step count may be associated with the progression of IGT to diabetes.