BACKGROUND:Rapid and accurate segmentation of brain tissue in medical images is of great significance for three-dimensional biomechanical modeling and diagnosis of craniocerebral injuries.Currently,artificial intelligence(AI)-based baseline models exhibit excellent generalization capabilities on large-scale datasets.However,due to the specificity and complexity of craniocerebral tissues,these models have certain limitations in their application to craniocerebral tissue segmentation.Additionally,the scarcity of craniocerebral tissue samples makes it difficult for baseline models to achieve precise segmentation results through fine-tuning.OBJECTIVE:To construct a craniocerebral tissue segmentation model based on texture feature retrieval enhancement to improve segmentation accuracy under a small number of samples.METHODS:Segment Anything in Medical Images(MedSAM)model was selected as the basic framework,and texture features were combined with deep learning to build a brain tissue segmentation model based on texture feature retrieval enhancement(DP-MedSAM).Dice Coefficient and mean intersection over union(MIoU)were selected to evaluate the efficiency of image segmentation results.In comparison with the original MedSAM model,the ablation experiment systematically evaluated the influence of key components on the model performance.The sensitivities of MedSAM,the Segment Anything Model(SAM)for medical image segmentation(SAM-Med2D)and DP-MedSAM in the mandible,left optic nerve,and left parotid gland were compared.RESULTS AND CONCLUSION:(1)By verifying the impact of the number of point prompts on segmentation results on the HaN-Seg dataset,the experimental results indicated that the optimal Dice score was achieved with the addition of three points.(2)DP-MedSAM demonstrated performance improvements compared with MedSAM and SAM-Med2D on two datasets(HaN and Public Domain Database for Computational Anatomy).Especially on the Public Domain Database for Computational Anatomy dataset,in terms of the MIoU metric,DP-MedSAM outperformed MedSAM by 6.59％and SAM-Med2D by 37.35％;in terms of the Dice metric,DP-MedSAM outperformed MedSAM and SAM-Med2D by 4.34％and 25.32％,respectively.(3)The ablation experiment results showed that removing the texture feature extraction module in the DP-MedSAM model,relying solely on original image features,led to a significant decrease in results on the test set.Furthermore,removing the vector cache database and its retrieval enhancement function from the model,which deprived the ability of the model to perform similarity retrieval using an external knowledge base,further reduced model performance.(4)Under conditions of limited data resources,the DP-MedSAM model outperformed the other two models in all evaluation metrics.The DP-MedSAM model performed excellently when processing simple and moderately difficult samples,demonstrating a clear advantage over the other two models and indicating good generalization ability.Processing the fine structures of difficult samples placed higher demands on the model's segmentation capabilities.Although the performance of the DP-MedSAM model declined slightly,it still outperformed the other two models.(5)This study proposes an innovative craniocerebral tissue segmentation model,DP-MedSAM,which improves the baseline model's performance in capturing local details and global structural information in medical images by introducing target region texture feature extraction.Through vector similarity retrieval technology,DP-MedSAM can retrieve the feature vector most similar to the current target region from a pre-constructed vector database,providing more precise guiding information for the segmentation process.

Aging has emerged as a cutting edge and hotspot in global life science field， with anti-aging and geriatric disease prevention and treatment becoming critical issues urgently demanding solutions in international medical communities. In the face of the challenge of accelerating global population aging， in-depth exploration of aging mechanisms and the development of effective intervention strategies hold significant scientific and clinical value. This study supported by the national key research and development program of China， employed the essence-Qi-spirit theory of Qiluo doctrine as its guiding framework， focusing on the key scientific issue of the core traditional Chinese pathogenesis of aging， namely "depletion of kidney essence， deficiency of primordial Qi， and impairment of body and spirit". The treatment principle of "tonifying the kidney to replenish essence， harmonizing Yin and Yang， warming and invigorating primordial Qi， and nourishing the body and spirit" was established. Centered on holistic aging， systemic aging， and aging-related diseases， the research integrated multidisciplinary research approaches to construct multi-modal aging models and a multi-dimensional evaluation system， and it utilized multi-omics technologies to deeply analyze aging mechanisms. By systematically reviewing historical kidney-tonifying and anti-aging formulas and combining big data with artificial intelligence technologies， an information database of anti-aging traditional Chinese medicine substance was developed to reveal the differences and synergistic effects of various treatment methods and formulas on anti-aging. Based on this treatment method， the research integrated two millennia of kidney-tonifying medicinal experience to develop the innovative anti-aging traditional Chinese medicine， namely Bazhi Bushen capsules. It was validated that this capsule can delay holistic and systemic aging through multiple targets and mechanisms， thereby elucidating the scientific connotation of the essence-Qi-spirit theory of Qiluo doctrine in guiding anti-aging research from multiple dimensions and providing robust support for leveraging the advantages of traditional Chinese medicine to occupy the commanding heights of international anti-aging research.

BACKGROUND:Rapid and accurate segmentation of brain tissue in medical images is of great significance for three-dimensional biomechanical modeling and diagnosis of craniocerebral injuries.Currently,artificial intelligence(AI)-based baseline models exhibit excellent generalization capabilities on large-scale datasets.However,due to the specificity and complexity of craniocerebral tissues,these models have certain limitations in their application to craniocerebral tissue segmentation.Additionally,the scarcity of craniocerebral tissue samples makes it difficult for baseline models to achieve precise segmentation results through fine-tuning.OBJECTIVE:To construct a craniocerebral tissue segmentation model based on texture feature retrieval enhancement to improve segmentation accuracy under a small number of samples.METHODS:Segment Anything in Medical Images(MedSAM)model was selected as the basic framework,and texture features were combined with deep learning to build a brain tissue segmentation model based on texture feature retrieval enhancement(DP-MedSAM).Dice Coefficient and mean intersection over union(MIoU)were selected to evaluate the efficiency of image segmentation results.In comparison with the original MedSAM model,the ablation experiment systematically evaluated the influence of key components on the model performance.The sensitivities of MedSAM,the Segment Anything Model(SAM)for medical image segmentation(SAM-Med2D)and DP-MedSAM in the mandible,left optic nerve,and left parotid gland were compared.RESULTS AND CONCLUSION:(1)By verifying the impact of the number of point prompts on segmentation results on the HaN-Seg dataset,the experimental results indicated that the optimal Dice score was achieved with the addition of three points.(2)DP-MedSAM demonstrated performance improvements compared with MedSAM and SAM-Med2D on two datasets(HaN and Public Domain Database for Computational Anatomy).Especially on the Public Domain Database for Computational Anatomy dataset,in terms of the MIoU metric,DP-MedSAM outperformed MedSAM by 6.59％and SAM-Med2D by 37.35％;in terms of the Dice metric,DP-MedSAM outperformed MedSAM and SAM-Med2D by 4.34％and 25.32％,respectively.(3)The ablation experiment results showed that removing the texture feature extraction module in the DP-MedSAM model,relying solely on original image features,led to a significant decrease in results on the test set.Furthermore,removing the vector cache database and its retrieval enhancement function from the model,which deprived the ability of the model to perform similarity retrieval using an external knowledge base,further reduced model performance.(4)Under conditions of limited data resources,the DP-MedSAM model outperformed the other two models in all evaluation metrics.The DP-MedSAM model performed excellently when processing simple and moderately difficult samples,demonstrating a clear advantage over the other two models and indicating good generalization ability.Processing the fine structures of difficult samples placed higher demands on the model's segmentation capabilities.Although the performance of the DP-MedSAM model declined slightly,it still outperformed the other two models.(5)This study proposes an innovative craniocerebral tissue segmentation model,DP-MedSAM,which improves the baseline model's performance in capturing local details and global structural information in medical images by introducing target region texture feature extraction.Through vector similarity retrieval technology,DP-MedSAM can retrieve the feature vector most similar to the current target region from a pre-constructed vector database,providing more precise guiding information for the segmentation process.

Background Under intensive dairy farming conditions, Enterococcus spp. can be transmitted between animals, farm workers, and the environment via multiple vectors such as feces, soil, water, air, and farming equipment, posing a potential threat to public health. Objective To elucidate the prevalence, distribution, and antimicrobial resistance profiles of Enterococcus faecalis (E. faecalis) and Enterococcus faecium (E. faecium) among farm workers, dairy cattle, and the farm environment in Xinjiang, and to assess the risk of their cross-host transmission. Methods From May 2024 to January 2025, a total of 317 samples were collected from 11 large-scale dairy farms in Xinjiang, China, including feces from farm workers (n=130) and dairy cattle (n=154), and environmental samples (n=33). E. faecalis and E. faecium were isolated and identified, followed by antimicrobial susceptibility testing and multilocus sequence typing (MLST) to analyze their molecular characteristics. Results A total of 183 Enterococcus isolates were obtained (66 E. faecalis and 117 E. faecium isolated). The isolation rates of both species showed statistically significant differences among the three sources (χ²=29.21, P=0.003). Antimicrobial resistance analysis revealed that E. faecalis generally exhibited higher resistance rates across multiple antibiotic classes than E. faecium. High resistance to rifampicin was observed across all sources (50.00%–81.25%), with statistical variation among origins (χ²=8.03, P=0.024). Multidrug-resistant strains accounted for 69.10% of the isolates. Multidrug resistance patterns in E. faecium varied significantly by source (χ²=27.19, P=0.014), and one isolate displayed resistance to eight antibiotic classes. MLST indicated high genetic diversity; E. faecalis was dominated by ST472 and ST227 of which the distrubution was significantly different among sources, while E. faecium primarily clustered into clonal complexes CC94 (centered on ST94) and CC17 (centered on ST22). Conclusion Resistant Enterococcus strains exhibit cross-transmission among farm workers, animals, and the environment. Under the "One Health" framework, standardized farming protocols and prudent antimicrobial use are essential to disrupt the transmission chain of resistant clones and mitigate the spread of antimicrobial resistance at its source.

Background Under intensive dairy farming conditions, Enterococcus spp. can be transmitted between animals, farm workers, and the environment via multiple vectors such as feces, soil, water, air, and farming equipment, posing a potential threat to public health. Objective To elucidate the prevalence, distribution, and antimicrobial resistance profiles of Enterococcus faecalis (E. faecalis) and Enterococcus faecium (E. faecium) among farm workers, dairy cattle, and the farm environment in Xinjiang, and to assess the risk of their cross-host transmission. Methods From May 2024 to January 2025, a total of 317 samples were collected from 11 large-scale dairy farms in Xinjiang, China, including feces from farm workers (n=130) and dairy cattle (n=154), and environmental samples (n=33). E. faecalis and E. faecium were isolated and identified, followed by antimicrobial susceptibility testing and multilocus sequence typing (MLST) to analyze their molecular characteristics. Results A total of 183 Enterococcus isolates were obtained (66 E. faecalis and 117 E. faecium isolated). The isolation rates of both species showed statistically significant differences among the three sources (χ²=29.21, P=0.003). Antimicrobial resistance analysis revealed that E. faecalis generally exhibited higher resistance rates across multiple antibiotic classes than E. faecium. High resistance to rifampicin was observed across all sources (50.00%–81.25%), with statistical variation among origins (χ²=8.03, P=0.024). Multidrug-resistant strains accounted for 69.10% of the isolates. Multidrug resistance patterns in E. faecium varied significantly by source (χ²=27.19, P=0.014), and one isolate displayed resistance to eight antibiotic classes. MLST indicated high genetic diversity; E. faecalis was dominated by ST472 and ST227 of which the distrubution was significantly different among sources, while E. faecium primarily clustered into clonal complexes CC94 (centered on ST94) and CC17 (centered on ST22). Conclusion Resistant Enterococcus strains exhibit cross-transmission among farm workers, animals, and the environment. Under the "One Health" framework, standardized farming protocols and prudent antimicrobial use are essential to disrupt the transmission chain of resistant clones and mitigate the spread of antimicrobial resistance at its source.

OBJECTIVE To evaluate the effectiveness and safety of warfarin anticoagulation therapy guided by gene test in patients undergoing left ventricular assist device （LVAD） implantation， and to analyze the influencing factors of warfarin anticoagulation efficacy. METHODS Patients who underwent LVAD implantation at the Heart and Vascular Center of Northern Jiangsu People’s Hospital from January 2023 to October 2024 and required warfarin anticoagulant therapy were selected as the study subjects. They were divided into genetic testing group （n＝51） and empirical treatment group （n＝17） based on whether they underwent CYP2C9 and VKORC1 gene test. The gene test group was given warfarin based on the predicted dose calculated by gene test， while the empirical treatment group was given warfarin by clinical doctors based on international normalized ratio （INR） experience， all patients were given warfarin once a day. Follow-up observation was conducted for 6 months to compare the effectiveness [time in therapeutic range（TTR）， the time required to reach INR for the first time， the incidence of embolic events， the incidence of INR＜1.5 events] and safety （the incidence of major and minor bleeding events，the incidence of INR＞3.5 events） of warfarin treatment between two groups of patients. According to whether the patient’s TTR was ≥60%， they were divided into TTR≥60% group （n＝20） and TTR＜60% group （n＝48）. Univariate and multivariate binary Logistic regression analysis were used to determine the factors affecting the anticoagulant effect of warfarin in patients. RESULTS The TTR of patients in the gene test group was significantly higher than that in the empirical treatment group （P＜0.05）. The incidence of INR＜1.5 events in the gene test group was significantly lower than in the empirical treatment group （P＜0.05）. The incidence of minor bleeding events and INR＞3.5 events in the gene test group were lower than in the empirical treatment group， but the difference was not statistically significant （P＞0.05）. The results of multivariate binary Logistic regression analysis showed that gene test was an independent protective factor for warfarin anticoagulant therapy [odds ratio （OR）＝10.842， 95% confidence interval （CI）： 1.211-27.037， P＝0.033]， and the combination of statins was an independent risk factor for warfarin anticoagulant therapy [OR＝0.196， 95%CI： 0.045-0.861， P＝0.031]. CONCLUSIONS Under the guidance of gene test， warfarin anticoagulation therapy for LVAD patients after implantation can improve TTR， shorten the anticoagulation target time， and has good safety； meanwhile， it should be noted that the combination of statins may enhance the anticoagulant effect of warfarin， thereby increasing the risk of bleeding in patients.

OBJECTIVE: To observe the clinical efficacy of Fu's subcutaneous needling based on "multi-joint muscle spiral balance chain" theory for cervical vertigo (CV) and its effect on blood flow velocity of vertebral artery. METHODS: A total of 60 patients with CV were randomized into a Fu's subcutaneous needling group and a medication group, 30 cases in each one. In the Fu's subcutaneous needling group, Fu's subcutaneous needling was delivered at Dazhui (GV14), the flexible tube was retained for 5 min after sweeping manipulation, and the treatment was given once every other day, 3 times a week for 3 weeks. In the medication group, betahistine mesylate tablet and diclofenac sodium dual-release enteric capsule were taken orally for continuous 3 weeks. Before treatment, after treatment, and in follow-up of one month after treatment completion, the scores of dizziness handicap inventory (DHI) and visual analogue scale (VAS) were observed; before and after treatment, the blood flow velocity of vertebral artery was measured by transcranial Doppler, and the clinical efficacy was evaluated after treatment in the two groups. RESULTS: After treatment and in follow-up, each item scores and total scores of DHI were decreased compared with those before treatment in the two groups (P<0.05); the VAS scores after treatment in the two groups, as well as the VAS score in follow-up of the Fu's subcutaneous needling group, were decreased compared with those before treatment (P<0.05). In the Fu's subcutaneous needling group, after treatment and in follow-up, the physical scores and the total scores of DHI, and the VAS scores were lower than those in the medication group (P<0.05); in follow-up, the emotional and functional scores of DHI were lower than those in the medication group (P<0.05). After treatment, the mean blood flow velocity (Vm) of the left vertebral artery (LVA) and the right vertebral artery (RVA) was increased compared with that before treatment in the two groups (P<0.05), and the Vm of LVA and RVA in the Fu's subcutaneous needling group was higher than that in the medication group (P<0.05). The total effective rate was 100.0% (30/30) in the Fu's subcutaneous needling group, which was superior to 73.3% (22/30) in the medication group (P<0.05). CONCLUSION Fu's subcutaneous needling based on the "multi-joint muscle spiral balance chain" theory can effectively alleviate the vertigo and neck pain, and improve the blood flow velocity of vertebral artery in CV patients, and has a long-term therapeutic effect.
Humans ; Female ; Male ; Middle Aged ; Acupuncture Therapy/instrumentation* ; Vertebral Artery/physiopathology* ; Adult ; Vertigo/physiopathology* ; Aged ; Blood Flow Velocity ; Treatment Outcome ; Acupuncture Points ; Young Adult

Artificial intelligence (AI), particularly deep learning, has demonstrated remarkable performance in medical imaging across a variety of modalities, including X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET), and pathological imaging. However, most existing state-of-the-art AI techniques are task-specific and focus on a limited range of imaging modalities. Compared to these task-specific models, emerging foundation models represent a significant milestone in AI development. These models can learn generalized representations of medical images and apply them to downstream tasks through zero-shot or few-shot fine-tuning. Foundation models have the potential to address the comprehensive and multifactorial challenges encountered in clinical practice. This article reviews the clinical applications of both task-specific and foundation models, highlighting their differences, complementarities, and clinical relevance. We also examine their future research directions and potential challenges. Unlike the replacement relationship seen between deep learning and traditional machine learning, task-specific and foundation models are complementary, despite inherent differences. While foundation models primarily focus on segmentation and classification, task-specific models are integrated into nearly all medical image analyses. However, with further advancements, foundation models could be applied to other clinical scenarios. In conclusion, all indications suggest that task-specific and foundation models, especially the latter, have the potential to drive breakthroughs in medical imaging, from image processing to clinical workflows.
Humans ; Artificial Intelligence ; Deep Learning ; Diagnostic Imaging/methods* ; Magnetic Resonance Imaging ; Tomography, X-Ray Computed ; Positron-Emission Tomography

This study identified blood-entering components of Anshen Dropping Pills and explored their anti-insomnia effects and mechanisms. The main blood-entering components of Anshen Dropping Pills were detected and identified by UPLC-Q-TOF-MS/MS. The rationality of the formula was assessed by using enrichment analysis based on the relationship between drugs and symptoms, and core targets of its active components were selected as the the potential anti-insomnia targets of Anshen Dropping Pills through network pharmacology analysis. Furthermore, protein-protein interaction(PPI) network, Gene Ontology(GO) enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway analysis were performed on the core targets. An active component-core target network for Anshen Dropping Pills was constructed. Finally, the effects of low-, medium-, and high-dose groups of Anshen Dropping Pills on sleep episodes, sleep duration, and sleep latency in mice were measured by supraliminal and subliminal pentobarbital sodium experiments. Moreover, total scores of the Pittsburgh sleep quality index(PSQI) scale was used to evaluate the changes before and after the treatment with Anshen Dropping Pills in a clinical study. The enrichment analysis based on the relationship between drugs and symptoms verified the rationality of the Anshen Dropping Pills formula, and nine blood-entering components of Anshen Dropping Pills were identified by UPLC-Q-TOF-MS/MS. The network proximity revealed a significant correlation between eight components and insomnia, including magnoflorine, liquiritin, spinosin, quercitrin, jujuboside A, ginsenoside Rb_3, glycyrrhizic acid, and glycyrrhetinic acid. Network pharmacology analysis indicated that the major anti-insomnia pathways of Anshen Dropping Pills involved substance and energy metabolism, neuroprotection, immune system regulation, and endocrine regulation. Seven core genes related to insomnia were identified: APOE, ALB, BDNF, PPARG, INS, TP53, and TNF. In summary, Anshen Dropping Pills could increase sleep episodes, prolong sleep duration, and reduce sleep latency in mice. Clinical study results demonstrated that Anshen Dropping Pills could decrease total scores of PSQI scale. This study reveals the pharmacodynamic basis and potential multi-component, multi-target, and multi-pathway effects of Anshen Dropping Pills, suggesting that its anti-insomnia mechanisms may be associated with the regulation of insomnia-related signaling pathways. These findings offer a theoretical foundation for the clinical application of Anshen Dropping Pills.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Tandem Mass Spectrometry/methods* ; Sleep Initiation and Maintenance Disorders/metabolism* ; Mice ; Network Pharmacology ; Male ; Chromatography, High Pressure Liquid ; Humans ; Protein Interaction Maps/drug effects* ; Sleep/drug effects* ; Female ; Adult

Panax species are mostly valuable medicinal plants. While some species' seeds are sensitive to dehydration, the dehydration tolerance of seeds from other Panax species remains unclear. The phospholipase D(PLD) gene plays an important role in plant responses to dehydration stress. However, the characteristics of the PLD gene family and their mechanisms of response to dehydration stress in seeds of Panax species with different dehydration tolerances are not well understood. This study used seeds from eight Panax species to measure the germination rates and PLD activity after dehydration and to analyze the correlation between dehydration tolerance and seed traits. Bioinformatics analysis was also conducted to characterize the PnPLD and PvPLD gene families and to evaluate their expression patterns under dehydration stress. The dehydration tolerance of Panax seeds was ranked from high to low as follows: P. ginseng, P. zingiberensis, P. quinquefolius, P. vietnamensis var. fuscidiscus, P. japonicus var. angustifolius, P. japonicus, P. notoginseng, and P. stipuleanatus. A significant negative correlation was found between dehydration tolerance and seed shape(three-dimensional variance), with flatter seeds exhibiting stronger dehydration tolerance(r=-0.792). Eighteen and nineteen PLD members were identified in P. notoginseng and P. vietnamensis var. fuscidiscus, respectively. These members were classified into five isoforms: α, β, γ, δ, and ζ. The gene structures, subcellular localization, physicochemical properties, and other characteristics of PnPLD and PvPLD were similar. Both promoters contained regulatory elements associated with plant growth and development, hormone responses, and both abiotic and biotic stress. During dehydration, the PLD enzyme activity in P. notoginseng seeds gradually increased as the water content decreased, whereas in P. vietnamensis var. fuscidiscus, PLD activity first decreased and then increased. The expression of PLDα and PLDδ in P. notoginseng seeds initially increased and then decreased, whereas in P. vietnamensis var. fuscidiscus, the expression of PLDα and PLDδ consistently decreased. In conclusion, the dehydration tolerance of Panax seeds showed a significant negative correlation with seed shape. The dehydration tolerance in P. vietnamensis var. fuscidiscus and dehydration sensitivity of P. notoginseng seeds may be related to differences in PLD enzyme activity and the expression of PLDα and PLDδ genes. This study provided the first systematic comparison of dehydration tolerance in Panax seeds and analyzed the causes of tolerance differences and the optimal water content for long-term storage at ultra-low temperatures, thus providing a theoretical basis for the short-term and ultra-low temperature long-term storage of medicinal plant seeds with varying dehydration tolerances.
Seeds/metabolism* ; Panax/physiology* ; Plant Proteins/metabolism* ; Gene Expression Regulation, Plant ; Phospholipase D/metabolism* ; Plants, Medicinal/enzymology* ; Germination ; Multigene Family ; Water/metabolism* ; Dehydration ; Phylogeny