OBJECTIVE To establish a Chinese drug-related problem （DRP） classification system applicable to pharmacist-led pharmaceutical care in China， providing pharmacists with an effective and practical tool for pharmaceutical care. METHODS A multi-stage process was employed to construct the DRP classification system， including literature review and analysis， comparison of existing classification systems， refinement of classification items and framework development， two rounds of standard case validation， expert discussion， and system revision. The Fleiss′ kappa test was used to calculate the consistency coefficient κ， assessing the reliability of pharmacists participating in evaluating the classification system. An electronic questionnaire comprising six items was employed to evaluate the system’s applicability. RESULTS The constructed Chinese DRP classification system comprised six sections [problem（including potential problems）， DRP evaluation， cause （including possible causes of potential problems）， intervention， acceptance of intervention and DRP status]， with 24 primary codes and 96 secondary codes. In the first round of case validation， κ values exceeded 0.4 for all sections except “intervention” and “DRP status”. In the second round， κ values exceeded 0.4 for all sections. In the applicability evaluation of the classification system， positive ratings （“strongly agree” or “agree”） exceeded 85% for all items. Specifically， positive ratings for“the classification system can provide appropriate category selection”，“ the classification system is comprehensive”，“ the classification system is convenient to use” and “the classification system is highly satisfactory” exceeded 92%. CONCLUSIONS The Chinese DRP classification system developed demonstrates both high reliability and applicability， providing an effective and practical classification tool for pharmacists in China to conduct pharmaceutical care.

Endoscopic full-thickness resection (EFTR) is a minimally invasive technique that is increasingly used for gastrointestinal stromal tumors (GISTs) originating from the muscularis propria. Despite its advantages over conventional surgery, such as complete tumor resection and faster recovery, EFTR faces challenges related to its efficacy, safety, and feasibility, particularly in gastric GISTs. By summarizing the literature published over the past decade, this review provides a comprehensive overview of the clinical outcomes of EFTR and the evolution of defect closure devices.

ObjectiveTo explore the vascular endothelial injury in male mice caused by exposure to polystyrene microplastics （PS-MPs） and the intervention effect of luteolin on vascular remodeling. Additionally， to investigate the mechanism through the oxidative system and metabolomics. MethodsThirty-two C57BL/6 mice （6-8 weeks old） were randomly divided into the saline group （saline group）， the 0.1 mg/kg PS-MPs exposure group （0.1PS-MPs group）， the 1 mg/kg PS-MPs exposure group （1PS-MPs group）， and the 1 mg/kg PS-MPs + luteolin treatment group （1PS-MPs + Lut group）， with 8 mice in each group. After 8 weeks of intervention， the body weight， blood pressure， aortic organ coefficient， and aortic histopathological changes of mice in each group were detected； the total cholesterol （TC）， triglyceride （TG）， and high-density lipoprotein cholesterol （HDL-C） lipid metabolism-related indicators in the aorta of mice were detected； the reactive oxygen species （ROS）， glutathione （GSH）， and malondialdehyde （MDA） oxidative stress-related indicators were detected； the endothelin （ET-1）， nitric oxide （NO）， vascular endothelial growth factor A （VEGF-A）， vascular cell adhesion molecule-1 （VCAM-1/CD106）， and intercellular adhesion molecule-1 （ICAM-1/CD54） endothelial function-related indicators and serum metabolomics were detected. ResultsCompared to the saline group， exposure to PS-MPs resulted in pathological thickening of the mouse aorta， increased aortic organ coefficient， and elevated blood pressure. Lipid metabolism-related indicators， including TC and TG， were elevated， while HDL-C was reduced， indicating lipid metabolism disorder in mice. Oxidative stress markers such as ROS and MDA increased， whereas GSH decreased， demonstrating oxidative damage. Vascular endothelial inflammation and injury markers， including ET-1， VEGF-A， VCAM-1， and ICAM-1， were upregulated， while the vasodilatory substance NO was downregulated， confirming endothelial injury. Furthermore， serum metabolomics results revealed that PS-MPs exposure induced endothelial damage by disrupting metabolic pathways such as the citrate cycle. Compared to the PS-MPs group， luteolin significantly reversed these effects， attenuating oxidative stress and lipid metabolism disorders， and effectively repairing endothelial injury. ConclusionPS-MPs induce vascular toxicity through oxidative stress and lipid metabolism. Luteolin effectively alleviates endothelial damage and vascular remodeling.

Electroencephalography (EEG) is a non-invasive neurophysiological monitoring technique. It records the electrical activity of the cerebral cortex using electrodes placed on the scalp surface. Owing to its high safety, portability, and millisecond-level temporal resolution, EEG has been widely utilized in a variety of fields, including clinical diagnosis, brain-computer interfaces (BCIs), and cognitive neuroscience research. However, due to its microvolt-level amplitude, EEG is highly susceptible to various artifacts, including electrooculographic (EOG), electrocardiographic (ECG), electromyographic (EMG), and power line interference (PLI). These artifacts can obscure genuine neural activity and introduce spurious electrophysiological features. Consequently, they may compromise EEG signal quality, thereby reducing the reliability of downstream analyses. To address this issue, numerous EEG artifact removal methods have been developed, including both traditional denoising techniques and deep learning-based approaches. Traditional EEG denoising methods have long served as the primary solutions for artifact removal. Representative approaches include filtering, regression, and blind source separation. Although these methods have demonstrated effectiveness in specific scenarios, they suffer from several inherent limitations. Filtering assumes that artifacts and EEG signals can be separated in the frequency domain, but many artifacts, such as EOG and EMG, overlap with EEG spectra, which may lead to the loss of valuable neural information. Regression methods require high-quality artifact references to estimate and subtract contaminations, limiting their effectiveness in reference-free scenarios. Blind source separation can remove artifacts without external references, but it typically requires the number of EEG channels to exceed the number of sources, restricting its application in single- or low-channel EEG recordings. Deep learning-based EEG denoising methods address these limitations effectively. First, they learn the nonlinear mapping between contaminated and clean EEG directly from data in an end-to-end manner. This approach does not rely on assumptions about spectral separability, thereby preserving neural activity more completely. Second, the reference information is incorporated during the training phase, allowing the trained model to perform artifact removal independently without external references. Third, deep learning models can be flexibly designed to accommodate various recording setups, achieving robust denoising for both high-density and single-channel EEG. Collectively, these advantages enable deep learning-based methods to overcome the main challenges of traditional approaches, providing more accurate and reliable EEG signal recovery. The superior denoising performance of deep learning-based EEG denoising methods has attracted increasing attention in EEG artifact removal research. As a result, many deep learning-based denoising methods have been developed and successfully applied in neural engineering areas. However, a systematic review of the techniques and applications in this field is still lacking. To address this gap, this paper reviews recent advances in deep learning-based EEG denoising from four perspectives: technical principle, benchmark dataset, denoising model, and evaluation method. Representative applications in neural signal analysis and BCI decoding are also summarized. Furthermore, the advantage, existing challenge, and future research direction of deep learning-based EEG denoising are discussed. This review aims to provide valuable theoretical insights and technical guidance for researchers. It is also expected to promote further advances and broader applications of deep learning-based EEG denoising techniques.

ObjectiveTo construct a risk prediction model for chronic atrophic gastritis （CAG） that combines disease and syndrome by integrating spleen deficiency and blood stasis syndrome scores， gastric stem cell marker analysis， and network pharmacology and to elucidate the mechanism link between spleen deficiency， blood stasis， and gastric stem cell dysregulation. MethodsA total of 60 patients were stratified into high-risk CAG， low-risk CAG， and non-atrophic groups based on the operative link for gastritis assessment （OLGA）/operative link for gastric intestinal metaplasia assessment （OLGIM） stages. General data and syndrome scores of the patients were collected. Immunohistochemistry was employed to measure the protein levels of leucine-rich repeat-containing G protein-coupled receptor 5 （LGR5）， trefoil factor 2 （TFF2）， and β-catenin in gastric mucosa across groups. Transcriptomic data （GSE130823） from the Gene Expression Omnibus （GEO） database were analyzed to validate the expression of these molecular markers at the gene level. A Firth penalized maximum likelihood logistic regression model was employed to integrate clinical， molecular， and TCM syndrome data to develop a CAG risk prediction model. Finally， network pharmacology analysis was conducted on spleen-invigorating herbs （Astragali Radix， Atractylodis Macrocephalae Rhizoma， and Dioscorea Rhizoma） and blood-activating herbs （Hedyotis diffusa， Salviae Miltiorrhizae Radix et Rhizoma， and Curcumae Rhizoma） to identify overlapping targets between these herbs and differentially expressed genes between low-grade intraepithelial neoplasia （LGIN） and gastritis， thereby clarifying the role of spleen deficiency and blood stasis in CAG progression from a pharmacological perspective. ResultsHigh-risk CAG patients exhibited higher blood stasis scores （4.60±1.50，2.62±1.32， P<0.01）， progressive nuclear accumulation of β-catenin （A 0.28-0.53， P<0.01）， and an increased LGR5⁺/TFF2⁺ ratio （A 1.02-1.92， P<0.01）. Blood stasis scores were correlated with LGR5/TFF2 imbalance （P=0.03） and activation of the Wnt/β-catenin signaling pathway （CTNNB1 increase， log₂=0.78）. The integrated prediction model （blood stasis score≥5 + β-catenin>0.38 + LGR5/TFF2>1.5） outperformed a pure biomarker approach ［Area Under the Curve （AUC） 0.92，0.89， ΔAUC=0.03， P=0.02］. Moreover， network pharmacology revealed 66 overlapping targets between spleen-invigorating/blood-activating herbs and LGIN， which were functionally enriched in mucosal repair and anti-oxidative stress， and negatively correlated with the Wnt/β-catenin signaling pathway （P<0.01）. ConclusionThis study developed an integrated risk prediction model for CAG by combining TCM spleen deficiency and blood stasis syndromes with gastric stem cell molecular markers. Furthermore， it proposed for the first time the "blood stasis-Wnt/β-catenin-stem cell axis" hypothesis， preliminarily elucidating that spleen deficiency and blood stasis may exacerbate gastric stem cell dysregulation and promote malignant transformation of CAG through hypoxia-induced Wnt activation. This provides a theoretical foundation and a translational tool for risk stratification and multi-target TCM intervention in CAG.

ObjectiveTo construct a risk prediction model for chronic atrophic gastritis （CAG） that combines disease and syndrome by integrating spleen deficiency and blood stasis syndrome scores， gastric stem cell marker analysis， and network pharmacology and to elucidate the mechanism link between spleen deficiency， blood stasis， and gastric stem cell dysregulation. MethodsA total of 60 patients were stratified into high-risk CAG， low-risk CAG， and non-atrophic groups based on the operative link for gastritis assessment （OLGA）/operative link for gastric intestinal metaplasia assessment （OLGIM） stages. General data and syndrome scores of the patients were collected. Immunohistochemistry was employed to measure the protein levels of leucine-rich repeat-containing G protein-coupled receptor 5 （LGR5）， trefoil factor 2 （TFF2）， and β-catenin in gastric mucosa across groups. Transcriptomic data （GSE130823） from the Gene Expression Omnibus （GEO） database were analyzed to validate the expression of these molecular markers at the gene level. A Firth penalized maximum likelihood logistic regression model was employed to integrate clinical， molecular， and TCM syndrome data to develop a CAG risk prediction model. Finally， network pharmacology analysis was conducted on spleen-invigorating herbs （Astragali Radix， Atractylodis Macrocephalae Rhizoma， and Dioscorea Rhizoma） and blood-activating herbs （Hedyotis diffusa， Salviae Miltiorrhizae Radix et Rhizoma， and Curcumae Rhizoma） to identify overlapping targets between these herbs and differentially expressed genes between low-grade intraepithelial neoplasia （LGIN） and gastritis， thereby clarifying the role of spleen deficiency and blood stasis in CAG progression from a pharmacological perspective. ResultsHigh-risk CAG patients exhibited higher blood stasis scores （4.60±1.50，2.62±1.32， P<0.01）， progressive nuclear accumulation of β-catenin （A 0.28-0.53， P<0.01）， and an increased LGR5⁺/TFF2⁺ ratio （A 1.02-1.92， P<0.01）. Blood stasis scores were correlated with LGR5/TFF2 imbalance （P=0.03） and activation of the Wnt/β-catenin signaling pathway （CTNNB1 increase， log₂=0.78）. The integrated prediction model （blood stasis score≥5 + β-catenin>0.38 + LGR5/TFF2>1.5） outperformed a pure biomarker approach ［Area Under the Curve （AUC） 0.92，0.89， ΔAUC=0.03， P=0.02］. Moreover， network pharmacology revealed 66 overlapping targets between spleen-invigorating/blood-activating herbs and LGIN， which were functionally enriched in mucosal repair and anti-oxidative stress， and negatively correlated with the Wnt/β-catenin signaling pathway （P<0.01）. ConclusionThis study developed an integrated risk prediction model for CAG by combining TCM spleen deficiency and blood stasis syndromes with gastric stem cell molecular markers. Furthermore， it proposed for the first time the "blood stasis-Wnt/β-catenin-stem cell axis" hypothesis， preliminarily elucidating that spleen deficiency and blood stasis may exacerbate gastric stem cell dysregulation and promote malignant transformation of CAG through hypoxia-induced Wnt activation. This provides a theoretical foundation and a translational tool for risk stratification and multi-target TCM intervention in CAG.

Recent studies have shown that an imbalance in iron metabolism can affect the composition and microstructural changes of bone, disrupting bone homeostasis and leading to osteoporosis(OP). The imbalance in iron metabolism, along with its induced local abnormal microenvironment and cellular iron death, has become a new focal point in OP research, drawing increasing attention from the academic community regarding the regulation of iron metabolism to prevent and manage OP. From the perspective of traditional Chinese medicine(TCM), iron metabolism imbalance has potential connections to TCM theories regarding internal organs, as well as treatments aimed at tonifying the kidney, strengthening the spleen, and activating blood circulation. Evidence is continually emerging that TCMs and effective components that tonify the kidney, strengthen the spleen, and activate blood circulation can prevent and manage OP by regulating iron metabolism. This article analyzes the relationship between iron and bone, as well as the effects of TCM formulations on improving iron metabolism and influencing bone metabolism, from the perspectives of iron metabolism mechanisms and TCM interventions, aiming to broaden existing clinical strategies for prevention and treatment and inject new momentum into the field of OP as it moves into a new era.
Osteoporosis/drug therapy* ; Humans ; Iron/metabolism* ; Drugs, Chinese Herbal/pharmacology* ; Animals ; Medicine, Chinese Traditional ; Bone and Bones/drug effects*

Based on the high-fat diet-induced hyperlipidemia rat model, this study aimed to evaluate the lipid-lowering effect of Arisaema Cum Bile and explore its mechanisms, providing experimental evidence for its clinical application. Biochemical analysis was used to detect serum levels of alanine aminotransferase(ALT), aspartate aminotransferase(AST), high-density lipoprotein cholesterol(HDL-C), low-density lipoprotein cholesterol(LDL-C), triglycerides(TG), and total cholesterol(TC) to assess the lipid-lowering activity of Arisaema Cum Bile. Additionally, 16S rDNA sequencing and metabolomics techniques were employed to jointly elucidate the lipid-lowering mechanisms of Arisaema Cum Bile. The experimental results showed that high-dose Arisaema Cum Bile(PBA-H) significantly reduced serum ALT, AST, LDL-C, TG, and TC levels(P<0.01), and significantly increased HDL-C levels(P<0.01). The effect was similar to that of fenofibrate, with no significant difference. Furthermore, Arisaema Cum Bile significantly alleviated hepatocyte ballooning and mitigated fatty degeneration in liver tissues. As indicated by 16S rDNA sequencing results, PBA-H significantly enhanced both alpha and beta diversity of the gut microbiota in the model rats, notably increasing the relative abundance of Akkermansia and Subdoligranulum species(P<0.01). Liver metabolomics analysis revealed that PBA-H primarily regulated pathways involved in arachidonic acid metabolism, vitamin B_6 metabolism, and steroid biosynthesis. In summary, Arisaema Cum Bile significantly improved abnormal blood lipid levels and liver pathology induced by a high-fat diet, regulated hepatic metabolic disorders, and improved the abundance and structural composition of gut microbiota, thereby exerting its lipid-lowering effect. The findings of this study provide experimental evidence for the clinical application of Arisaema Cum Bile and the treatment of hyperlipidemia.
Animals ; Gastrointestinal Microbiome/drug effects* ; Rats ; Male ; Metabolomics ; Hyperlipidemias/microbiology* ; Drugs, Chinese Herbal/administration & dosage* ; Rats, Sprague-Dawley ; Hypolipidemic Agents/pharmacology* ; Liver/metabolism* ; Humans ; Alanine Transaminase/metabolism* ; Triglycerides/metabolism* ; Aspartate Aminotransferases/metabolism*

Medical institutions, with their clinical practice foundation and abundant human use experience data, have become important carriers for the inheritance and innovation of traditional Chinese medicine(TCM) and the "cradles" of the preparation of new TCM. To effectively promote the transformation of new TCM originating from the TCM clinical practice in medical institutions and establish an effective evaluation index system for the transformation of new TCM conforming to the characteristics of TCM, consensus experts adopted the literature research, questionnaire survey, Delphi method, etc. By focusing on the policy and technical evaluation of new TCM originating from the TCM clinical practice in medical institutions, a comprehensive evaluation from the dimensions of drug safety, efficacy, feasibility, and characteristic advantages was conducted, thus forming a comprehensive evaluation system with four primary indicators and 37 secondary indicators. The expert consensus reached aims to encourage medical institutions at all levels to continuously improve the high-quality research and development and transformation of new TCM originating from the TCM clinical practice in medical institutions and targeted at clinical needs, so as to provide a decision-making basis for the preparation, selection, cultivation, and transformation of new TCM for medical institutions, improve the development efficiency of new TCM, and precisely respond to the public medication needs.
Medicine, Chinese Traditional/standards* ; Humans ; Consensus ; Drugs, Chinese Herbal/therapeutic use* ; Surveys and Questionnaires

OBJECTIVES: To report the development, validation, and findings of the Multi-dimensional Attention Rating Scale (MARS), a self-report tool crafted to evaluate six-dimension attention levels. METHODS: The MARS was developed based on Classical Test Theory (CTT). Totally 202 highly educated healthy adult participants were recruited for reliability and validity tests. Reliability was measured using Cronbach's alpha and test-retest reliability. Structural validity was explored using principal component analysis. Criterion validity was analyzed by correlating MARS scores with the Toronto Hospital Alertness Test (THAT), the Attentional Control Scale (ACS), and the Attention Network Test (ANT). RESULTS: The MARS comprises 12 items spanning six distinct dimensions of attention: focused attention, sustained attention, shifting attention, selective attention, divided attention, and response inhibition.As assessed by six experts, the content validation index (CVI) was 0.95, the Cronbach's alpha for the MARS was 0.78, and the test-retest reliability was 0.81. Four factors were identified (cumulative variance contribution rate 68.79%). The total score of MARS was correlated positively with THAT (r = 0.60, P < 0.01) and ACS (r = 0.78, P < 0.01) and negatively with ANT's reaction time for alerting (r = -0.31, P = 0.049). CONCLUSIONS The MARS can reliably and validly assess six-dimension attention levels in real-world settings and is expected to be a new tool for assessing multi-dimensional attention impairments in different mental disorders.
Humans ; Adult ; Male ; Attention/physiology* ; Female ; Middle Aged ; Reproducibility of Results ; Young Adult ; Psychometrics