Objective To externally validate a prediction model based on clinical and CT imaging features for the preoperative identification of high-grade patterns (HGP), such as micropapillary and solid subtypes, in early-stage lung adenocarcinoma, in order to guide clinical treatment decisions. Methods This study conducted an external validation of a previously developed prediction model using a cohort of patients with clinical stage ⅠA lung adenocarcinoma from the Fourth Hospital of Hebei Medical University. The model, which incorporated factors including tumor size, density, and lobulation, was assessed for its discrimination, calibration performance, and clinical impact. Results A total of 650 patients (293 males, 357 females; age range: 30-82 years) were included. The validation showed that the model demonstrated good performance in discriminating HGP (area under the curve>0.7). After recalibration, the model's calibration performance was improved. Decision curve analysis (DCA) indicated that at a threshold probability>0.6, the number of HGP patients predicted by the model closely approximated the actual number of cases. Conclusion This study confirms the effectiveness of a clinical and imaging feature-based prediction model for identifying HGP in stage ⅠA lung adenocarcinoma in a clinical setting. Successful application of this model may be significant for determining surgical strategies and improving patients' prognosis. Despite certain limitations, these findings provide new directions for future research.

Atractylodes lancea is a perennial herbaceous plant of Asteraceae, with rhizomes for medical use. However, A. lancea plants from different habitats have great variability, and the germplasm resources of A. lancea are unclear and mixed during production. Therefore, it is urgent to protect new varieties of A. lancea. The distinctness, uniformity, and stability(DUS) testing of new plant varieties is the foundation of plant variety protection, and the DUS testing guidelines are the technical basis for variety approval agencies to conduct DUS testing. In this study, the phenotypic traits of 94 germplasm accessions of A. lancea were investigated considering the breeding and variety characteristics of A. lancea in China. The traits were classified and described, and 24 traits were preliminarily determined, including 20 basic traits that must be tested and four traits selected to be tested. The 20 basic traits included 3 quality traits, 5 false quality traits, and 12 quantitative traits, corresponding to 1 plant traits, 2 stem traits, 8 leaf traits, 6 flower traits, and 3 seed traits. The measurement ranges and coefficients of variation of eight quantitative traits were determined, on the basis of which the grading criteria and codes of the traits were determined and assigned. The guidelines has guiding significance for the trait evaluation, utilization, and breeding of new varieties of A. lancea.
Atractylodes/growth & development* ; China ; Phenotype ; Guidelines as Topic ; Plant Breeding

By employing the analytic hierarchy process(AHP), the CRITIC method(a weight determination method based on indicator correlations), and the AHP-CRITIC hybrid weighting method, the weight coefficients of evaluation indicators were determined, followed by a comprehensive score comparison. The grey correlation analysis was then performed to analyze the results calculated using the hybrid weighting method. Subsequently, a backpropagation-artificial neural network(BP-ANN) model was constructed to predict the extraction process parameters and optimize the extraction process for Shenxiong Huanglian Jiedu Granules(SHJG). In the extraction process, an L_9(3~4) orthogonal experiment was designed to optimize three factors at three levels, including extraction frequency, water addition amount, and extraction time. The evaluation indicators included geniposide, berberine, ginsenoside Rg_1 + Re, ginsenoside Rb_1, ferulic acid, and extract yield. Finally, the optimal extraction results obtained by the orthogonal experiment, grey correlation analysis, and BP-ANN method were compared, and validation experiments were conducted. The results showed that the optimal extraction process involved two rounds of aqueous extraction, each lasting one hour; the first extraction used ten times the amount of added water, while the second extraction used eight times the amount. In the validation experiments, the average content of each indicator component was higher than the average content obtained in the orthogonal experiment, with a higher comprehensive score. The optimized extraction process parameters were reliable and stable, making them suitable for subsequent preparation process research.
Drugs, Chinese Herbal/analysis* ; Neural Networks, Computer

Moringa oleifera, widely utilized in Ayurvedic medicine, is recognized for its leaves, seeds, and velamen possessing traditional effects such as vātahara(wind alleviation), sirovirecaka(brain clearing), and hridya(mental nourishment). This study aims to identify the medicinal part of ■ in the Sārasvata ghee formulation as described in the Bower Manuscript, while investigating the ameliorative effects of different medicinal parts of M. oleifera on learning and memory deficits in mice and elucidating the underlying molecular mechanisms. A total of 144 male ICR mice were randomly assigned to the following groups: control, model(scopolamine hydrobromide, Sco, 2 mg·kg~(-1)), donepezil(donepezil hydrochloride, Don, 3 mg·kg~(-1)), M. oleifera leaf low-, medium-, and high-dose groups(0.5, 1, 2 g·kg~(-1)), M. oleifera seeds low-, medium-, and high-dose groups(0.25, 0.5, 1 g·kg~(-1)), and M. oleifera velamen low-, medium-, and high-dose groups(0.31, 0.62, 1.24 g·kg~(-1)). Learning and memory abilities were assessed using the passive avoidance test and Morris water maze. Nissl and HE staining were employed to examine histopathological changes in the hippocampus. Transcriptomics and targeted metabolomics were used to screen differential genes and metabolites, with MetaboAnalyst 6.0 and O2PLS methods applied to identify key disease-related targets and pathways. RESULTS:: demonstrated that M. oleifera leaf(1 g·kg~(-1)) significantly ameliorated Sco-induced learning and memory deficits, outperforming M. oleifera seeds(0.25 g·kg~(-1)) and M. oleifera velamen(1.24 g·kg~(-1)). This was evidenced by improved behavioral performance, reversal of neuronal damage, and reduced acetylcholinesterase(AChE) activity. Multi-omics analysis revealed that M. oleifera leaf upregulated Tuba1c gene expression through the synaptic vesicle cycle, enhancing glutamate(Glu), dopamine(DA), and acetylcholine(ACh) release via Tuba1c-Glu associations for neuroprotection. M. oleifera seeds targeted the dopaminergic synapse pathway, promoting memory consolidation through Drd2-ACh associations. M. oleifera velamen was associated with the cocaine addiction pathway, modulating dopamine metabolism via Adora2a-DOPAC, with limited relevance to learning and memory. In conclusion, M. oleifera leaf exhibits superior efficacy and mechanistic advantages over M. oleifera seeds and velamen, suggesting that the ■ in the Sārasvata ghee formulation is likely M. oleifera leaf, providing scientific evidence for its identification in ancient texts.
Animals ; Moringa oleifera/chemistry* ; Male ; Mice ; Seeds/chemistry* ; Plant Leaves/chemistry* ; Mice, Inbred ICR ; Memory Disorders/psychology* ; Transcriptome/drug effects* ; Memory/drug effects* ; Learning/drug effects* ; Metabolomics ; Humans ; Drugs, Chinese Herbal/administration & dosage* ; Maze Learning/drug effects*

Specific learning disorder (SLD) is a common neurodevelopmental disorder in children that significantly affects academic performance and quality of life. At present, diagnosis mainly relies on standardized tests and professional evaluations, a process that is complex and time-consuming. Multiple studies have shown that machine learning can analyze diverse data, including test scores, handwriting samples, eye movement data, neuroimaging data, and genetic data, to automatically learn the relationships between input features and output labels and achieve efficient prediction. It shows great potential for early screening, auxiliary diagnosis, and research on underlying mechanisms in SLD. This article reviews the applications of machine learning in the auxiliary diagnosis of SLD and discusses its performance when handling different data types.
Humans ; Machine Learning ; Specific Learning Disorder/diagnosis* ; Child

Objective To comprehensively excavate and analyze the research status,research hotspots and future trends of the literature related to the field of acupoint catgut embedding therapy for pain treatment in the CNKI database.Methods We searched the CNKI database from its establishment to June 2022,and scientifically analyzed the authors,keywords,and institutions of the included literature of acupoint catgut embedding therapy for pain treatment through specific algorithms of Citespace to generate a visual knowledge map.Results A total of 319 documents were included for statistical analysis,the number of publications in the field of acupoint catgut embedding therapy for the treatment of pain was generally on the rise,the number of publications by various authors was on the low side,and there was a lack of co-operation between the research teams,with the main institutions being the Guang'anmen Hospital,Chinese Academy of Chinese Medical Sciences,Affiliated Hospital of Youjiang Medical Universities of Nationalities and the Guangzhou University of Chinese Medicine,forming a 10-keyword clustering,and the hotspots of diseases under study were mainly mixed haemorrhoids,postoperative pain,low back and leg pain and dysmenorrhoea,etc..The main interventions were pure acupoint catgut embedding therapy and the combination of acupoint catgut embedding therapy and other acupuncture therapies,and the main research method was clinical research.Conclusion Acupoint catgut embedding therapy for the treatment of pain has a good development prospect,the future needs to deepen the clinical research,strengthen the mechanism research,pay attention to the joint use of acupoint catgut embedding therapy and other traditional Chinese medicine methods,and pay attention to the research of different thread materials.

Objective To explore the effects of different acupoints,different target organs,and different interventions on acupoint efficacy based on ACU&MOX-DATA platform;To illustrate and visualize whether the above factors have the characteristics of"specific effect"or"common effect"of acupoint efficacy.Methods The multi-source heterogeneous data were integrated from the original omics data and public omics data.After standardization,differential gene analysis,disease pathology network analysis,and enrichment analysis were performed using Batch Search and Stimulation Mode modules in ACU&MOX-DATA platform under the conditions of different acupoints,different target organs,and different interventions.Results Under the same disease state and the same intervention,there were differences in effects among different acupoints;under the same disease state,the same acupoint and intervention,the responses produced by different target organs were not completely consistent;under the same disease state and acupoint,there were differences in effects among different intervention measures.Conclusion Based on the analysis of ACU&MOX-DATA platform,it is preliminary clear that acupoints,target organs,and interventions are the key factors affecting acupoint efficacy.Meanwhile,the above results have indicated that there are specific or common regulatory characteristics of acupoint efficacy.Applying ACU&MOX-DATA platform to analyze and visualize the critical scientific problems in the field of acupuncture and moxibustion can provide references for deepening acupoint cognition,guiding clinical acupoint selection,and improving clinical efficacy.

Exploring the action targets (groups) of traditional Chinese medicine (TCM) is an important proposition to promote the innovation and development of TCM, but it has attracted a lot of attention as to whether it is related to the efficacy or the disease. Our team found that the metabolomic signature molecules in the development of diabetes mellitus (DM) were significantly associated with the clinical efficacy of Yuquan Pill through a large clinical sample study. Taking this as a clue, our team intends to expand the information on the omics features of DM development, and discover the key targets (groups) and their lead compounds for the hypoglycemic effect of Yuquan Pill. The project includes: ① Based on the retrospective clinical trials, using omics technology integrated with generative artificial intelligence, mining the characteristic information of proteome and microbiome, forming driving factors together with metabolome characteristic molecules, and characterizing the molecular trajectories of diabetes evolution and their interference by Yuquan Pill; ② Taking the evolving molecular trajectories as a link and pointer, using anthropomorphic modeling and molecular biology techniques such as chemical proteomics to discover the key targets (groups) of Yuquan Pill's hypoglycemic effect, with the prospective clinical samples for validation; ③ Evaluate the overall response of key targets (groups) using graph neural network technology, and search for drug-derived/endogenous lead compounds with proven clinical pathologies and clear mechanisms of action, so as to provide a new paradigm and technology for the discovery of complex active ingredient targets (groups) of TCM that are related to their clinical efficacy, as well as for the discovery of innovative medicines.

Objective To investigate the status quo of drug resistance and influencing factors of tuberculosis in In-ner Mongolia,and to provide reference for accurate prevention and control of drug-resistant tuberculosis.Methods Random sampling was used in this study.TB patients from Tuberculosis designated hospital in Inner Mongolia were included,according to the rules and drug-resistant strains were identified and tested according to relevant norms.Composition ratio or rate was calculated for statistical description,and Logistic regression model was used to analyze the influencing factors of drug resistance in TB patients.Results Among 1 321 patients,there were 936 males and 385 females,with an average age of(52.65±18.09)years.The rates of mono-resistant,multidrug-resistant(MDR),extensively drug-resistant(XDR)and total drug resistance were 19.00%,11.58%,11.66%and 42.24%,respectively.The highest resistance rates were observed for streptomycin(7.27%),isoniazid(4.69%),and isoniazid+streptomycin(4.47%).The drug resistance spectrum presented diversity and com-plexity.Compared to females,males had a higher proportion of drug resistance,and the difference was statistically significant(P<0.001).The proportion of patients who were sensitive to anti-tuberculosis drugs increased with age(P<0.05).Among different age groups,the proportion of drug-resistant patients was higher in the 20-40 age group,40-60 age group,and 60 and above age group compared to the 0-20 age group(P<0.05).Addi-tionally,the proportion of drug-resistant patients was higher in the 20-40 age group and 40-60 age group com-pared to the 60 and above age group(P<0.05).Moreover,the proportion of drug-resistant and multi-drug re-sistant patients was higher among patients undergoing retreatment compared to those undergoing initial treatment(P<0.001).Multivariate Logistic regression analysis showed that male gender(OR=1.48,95%CI:1.02-2.14),age 20-40 years(OR=2.64,95%CI:1.05-6.60),retreatment(OR=2.34,95%CI:1.70-3.22),and outpatient follow-up(OR=1.56,95%CI:1.05-2.33)were independent risk factors for drug-resistant tuber-culosis.Conclusion Inner Mongolia has a high prevalence of MDR and overall drug-resistant tuberculosis among patients.The drug resistance profile exhibits diversity and complexity.Risk factors that contribute to drug resist-ance include being male,aged between 20 and 40,undergoing retreatment,and receiving outpatient follow-up.Therefore,it is necessary to further improve clinical diagnosis and treatment,promote rational use of first-line anti-tuberculosis drugs,prioritize individualized treatment,enhance health education,improve the medical insurance system,and optimize patient management approaches in order to enhance patient compliance.

Objective To investigate the nutritional status and dietary structure of tuberculosis patients among dif-ferent populations, analyze the factors influencing the nutritional status of tuberculosis patients, and provide theo-retical basis for improving clinical nutrition and related issues in tuberculosis patients.Methods Tuberculosis pa-tients, non-tuberculosis patients, and healthy individuals were randomly selected for a questionnaire survey.De-scriptive analysis was conducted using SPSS 20.0 software.Statistical description was performed using rates and composition ratios, and qualitative data were described using relative numbers.Chi-square test was used to compare overall rates and composition ratios among different health conditions groups, with a significance level of α=0.05.Independent factors analysis of nutritional status body mass index (BMI) was conducted using multiple Logistic re-gression analysis for variables with statistically significant differences in the univariate analysis.Results There were differences in the nutritional status (x2 =62.184, P<0.05) and dietary diversity score (x2 =64.049, P<0.05) among tuberculosis patients, non-tuberculosis patients, and healthy individuals.Univariate analysis of nutri-tional status BMI showed statistically significant differences in gender, smoking, meat-based diet, vegetable-based diet, moderate diet diversity score, and 6 other variables for tuberculosis patients (P <0.05) , and in gender, age, ethnicity, marital status, occupation, education level, smoking, drinking white wine, drinking beer, meat-based diet, moderate diet, and 11 other variables for healthy individuals (P<0.05) .The variables with statisti-cally significant differences in the univariate analysis were included in the multiple ordinal logistic regression analy-sis model for both tuberculosis patients and healthy individuals.The results showed that the level of education, veg-etable intake, moderate food diversity score (DDS) of 4-6 were independent influencing factors of nutritional sta-tus BMI among tuberculosis patients (P<0.05);marital status was an independent influencing factor of nutritional status BMI among non-tuberculosis patients (P<0.05);while gender and occupation were independent influencing factors of nutritional status BMI among healthy individuals (P<0.05).Conclusion The dietary nutritional status of the three population groups varied.Targeted health education should be conducted, especially for tuberculosis patients, to address the issue of uneven dietary intake and promote good dietary habits among local tuberculosis pa-tients.