Esophageal cancer is a prevalent malignant tumor of the digestive tract in China, and radical surgery remains the cornerstone of its comprehensive treatment. However, multifactorial challenges such as postoperative gastrointestinal tract reconstruction, traumatic stress, and tumor-related metabolic disturbances render esophageal cancer patients highly susceptible to malnutrition. Perioperative nutritional support therapy plays a crucial role in enhancing surgical safety, improving clinical outcomes, and elevating patients' quality of life by regulating metabolic homeostasis, preserving organ function, and optimizing the immune microenvironment. This article reviews the mechanisms underlying malnutrition in esophageal cancer, methods for nutritional status assessment, and precision intervention pathways based on multi-omics evaluations. The aim is to strengthen clinicians' awareness of standardized perioperative nutritional management for esophageal cancer patients and promote its clinical implementation, thereby facilitating postoperative recovery and improving long-term quality of life.

ObjectiveTranscranial focused ultrasound (tFUS) has emerged as a promising noninvasive neuromodulation technique capable of modulating neural activity with high spatial specificity. However, whether tFUS stimulation of the primary somatosensory cortex (S1) can reliably evoke conscious tactile perception without external peripheral sensory input remains unclear. Therefore, this study examined the feasibility of inducing hand-related tactile perception through targeted S1 stimulation and further investigated how sonication duration (SD), a key temporal parameter, influences the consistency of this perceptual response. The findings may provide empirical evidence for optimizing tFUS parameters in human somatosensory modulation. MethodsForty-eight healthy adults participated in the study and were assigned to two experiments. Experiment 1 included 24 participants and was designed to preliminarily examine whether S1-targeted tFUS could evoke hand-related tactile sensations. This experiment comprised two sub-experiments, Experiment 1a and Experiment 1b, with 12 participants in each. Specifically, Experiment 1a used a randomized active stimulation protocol targeting the bilateral S1 to assess the initial feasibility of inducing contralateral tactile perception, whereas Experiment 1b employed an alternating hemispheric design incorporating both active and sham stimulation to verify the specificity of the tFUS-induced sensations. Experiment 2 enrolled another 24 participants and used a real-sham dual-probe design to control for spatial tactile cues that might arise from probe-scalp contact. Five SD levels, including 200, 400, 600, 800, and 1 000 ms, were systematically tested to evaluate how stimulation duration influenced the behavioral response. The primary behavioral outcome was the perceptual consistency rate, quantified according to the accuracy of contralateral tactile localization. ResultsIn Experiment 1, 91.7% of participants reported reliable hand-related tactile sensations during active tFUS stimulation, including numbness, tingling, or similar sensory experiences. Contralateral localization accuracy under active stimulation was significantly higher than the0.50 chance level (P<0.05). In contrast, localization performance under sham stimulation did not differ from chance, suggesting that the observed tactile responses were mainly induced by active S1-targeted tFUS stimulation rather than nonspecific stimulation cues. In Experiment 2, perceptual consistency rates significantly exceeded the chance level under the 400 and 1 000 ms SD conditions(P<0.05), whereas no significant effect was observed at 200, 600, or 800 ms. Pairwise comparisons further showed that the 400 and 1 000 ms conditions produced significantly higher perceptual consistency rates than the 200 ms condition (P<0.05). ConclusiontFUS stimulation of S1 can elicit distinct hand-related tactile sensations without peripheral sensory input, demonstrating the feasibility of using targeted cortical ultrasound stimulation to induce conscious somatosensory perception. The reliability of this perceptual effect was modulated by sonication duration, indicating that temporal stimulation parameters play an important role in shaping both the occurrence and consistency of behavioral responses. Among the tested conditions, 400 ms appeared to provide a favorable temporal window for generating stable tactile perception, whereas excessively short stimulation may be insufficient to produce consistent responses. These findings highlight the sensitivity of somatosensory networks to ultrasonic modulation and provide useful parameter guidance for the application of tFUS in noninvasive sensory enhancement and cortical functional mapping.

This study aims to investigate the therapeutic effect and mechanism of Daotan Xixin Decoction on APP/PS1 mice. Twelve APP/PS1 male mice were randomized into four groups: APP/PS1 and low-, medium-, and high-dose Daotan Xixin Decoction. Three C57BL/6 wild-type mice were used as the control group. The learning and memory abilities of mice in each group were examined by the Morris water maze test. The pathological changes of hippocampal nerve cells were observed by hematoxylin-eosin staining and Nissl staining. Immunohistochemistry was employed to detect the expression of β-amyloid(Aβ)_(1-42) in the hippocampal tissue. The high-dose Daotan Xixin Decoction group with significant therapeutic effects and the model group were selected for high-throughput sequencing. The differentially expressed gene(DEG) analysis, Gene Ontology(GO) analysis, Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis, and Gene Set Variation Analysis(GSVA) were performed on the sequencing results. RT-qPCR and Western blot were conducted to determine the mRNA and protein levels, respectively, of some DEGs. Compared with the APP/PS1 group, Daotan Xixin Decoction at different doses significantly improved the learning and memory abilities of APP/PS1 mice, ameliorated the neuropathological damage in the CA1 region of the hippocampus, increased the number of neurons, and decreased the deposition of Aβ_(1-42) in the brain. A total of 1 240 DEGs were screened out, including 634 genes with up-regulated expression and 606 genes with down-regulated expression. The GO analysis predicted the biological processes including RNA splicing and protein folding, the cellular components including spliceosome complexes and nuclear spots, and the molecular functions including unfolded protein binding and heat shock protein binding. The KEGG pathway enrichment analysis revealed the involvement of neurodegenerative disease pathways, amyotrophic lateral sclerosis, and splicing complexes. Further GSVA pathway enrichment analysis showed that the down-regulated pathways involved nuclear factor-κB(NF-κB)-mediated tumor necrosis factor-α(TNF-α) signaling pathway, UV response, and unfolded protein response, while the up-regulated pathways involved the Wnt/β-catenin signaling pathway. The results of RT-qPCR and Western blot showed that compared with the APP/PS1 group, Daotan Xixin Decoction at different doses down-regulated the mRNA and protein levels of signal transducer and activator of transcription 3(STAT3), NF-κB, and interleukin-6(IL-6) in the hippocampus. In conclusion, Daotan Xixin Decoction can improve the learning and memory abilities of APP/PS1 mice by regulating the STAT3/NF-κB/IL-6 signaling pathway.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Mice ; Male ; Alzheimer Disease/metabolism* ; Computational Biology ; Mice, Inbred C57BL ; High-Throughput Nucleotide Sequencing ; Amyloid beta-Protein Precursor/metabolism* ; Hippocampus/metabolism* ; Mice, Transgenic ; Presenilin-1/metabolism* ; Humans ; Memory/drug effects* ; Maze Learning/drug effects* ; Amyloid beta-Peptides/genetics* ; Disease Models, Animal

Osteoporosis(OP) is a senile bone disease characterized by an imbalance between bone remodeling and bone formation. Targeting pathogenesis of kidney deficiency, spleen deficiency, and blood stasis, Bushen Jianpi Huoxue Decoction has a significant effect on the treatment of OP by tonifying kidney, invigorating spleen, and activating blood circulation. MicroRNA(miRNA) and the anti-apoptotic protein B-cell lymphoma-2-like protein 1(BCL2L1) are closely related to bone cell metabolism. Therefore, in this study, the binding of miR-140-5p to BCL2L1 was detected by dual luciferase assay and polymerase chain reaction(PCR). After silencing or overexpressing miR-140-5p, the apoptosis, autophagy, and osteogenic function of human fetal osteoblast cell line 1.19(HFOB1.19) were observed by flow cytometry and Western blot. Bushen Jianpi Huoxue Decoction-containing serum was prepared by intragastric administration of Bushen Jianpi Huoxue Decoction in rats. Different concentrations of Bushen Jianpi Huoxue Decoction-containing serum were used to treat HFOB1.19 with or without miR-140-5p mimic. The expression of osteogenic proteins in each group was observed, and the role of miR-140-5p/BCL2L1 in apoptosis and autophagy of HFOB1.19 was studied, along with the effect of Bushen Jianpi Huoxue Decoction on these processes. As indicated by the dual luciferase assay, miR-140-5p bound to BCL2L1. Flow cytometry and Western blot showed that miR-140-5p promoted apoptosis and inhibited autophagy in HFOB1.19. After intervention with high, medium, and low doses of Bushen Jianpi Huoxue Decoction-medicated serum, compared with the miR-140-5p NC group, the expression of osteocalcin(OCN), osteopontin(OPN), Runt-related transcription factor 2(RUNX2), and transforming growth factor beta 1(TGF-β1) decreased in the miR-140-5p mimic group, while the expression of bone morphogenetic protein 2(BMP2) showed no significant difference under high-dose intervention. Therefore, miR-140-5p/BCL2L1 can promote apoptosis and inhibit autophagy in HFOB1.19. Bushen Jianpi Huoxue Decoction can affect the osteogenic effect of miR-140-5p through BMP2.
MicroRNAs/metabolism* ; Autophagy/drug effects* ; Apoptosis/drug effects* ; Humans ; Drugs, Chinese Herbal/administration & dosage* ; Animals ; Cell Line ; bcl-X Protein/metabolism* ; Osteoblasts/metabolism* ; Rats ; Osteoporosis/physiopathology* ; Male ; Rats, Sprague-Dawley ; Osteogenesis/drug effects*

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective To investigate the factors influencing international normalized ratio (INR)>3.0 in patients undergoing warfarin anticoagulation therapy after mechanical heart valve replacement. Methods A retrospective analysis was performed on the clinical data of patients who underwent mechanical heart valve replacement surgery and received warfarin anticoagulation therapy at West China Hospital of Sichuan University from January 1, 2011 to June 30, 2022. Based on the discharge INR values, patients were divided into two groups: an INR≤3.0 group and an INR>3.0 group. The factors associated with INR>3.0 at the time of discharge were analyzed. Results A total of 8901 patients were enrolled, including 3409 males and 5492 females, with a median age of 49.3 (43.5, 55.6) years. The gender, body mass index (BMI), New York Heart Association (NYHA) cardiac function grading, INR, glutamic oxaloacetic transaminase, and preoperative prothrombin time (PT) were statistically different between the two groups (P<0.05). Multivariate logistic regression analysis revealed that lower BMI, preoperative PT>15 s, and mitral valve replacement were independent risk factors for INR>3.0 at discharge (P<0.05). Conclusion BMI, preoperative PT, and surgical site are factors influencing INR>3.0 at discharge in patients undergoing warfarin anticoagulation therapy after mechanical heart valve replacement. Special attention should be given to patients with lower BMI, longer preoperative PT, and mitral valve replacement to avoid excessive anticoagulation therapy.

BACKGROUND: The global rise in diabetes prevalence is a pressing concern. Despite initiatives like "The Healthy Beijing Action 2020-2030" advocating for increased awareness, treatment, and control, the specific situation in Beijing remains unexplored. This study aimed to analyze the trends in diabetes prevalence, awareness, treatment, and control among Beijing adults. METHODS: Through a stratified multistage probability cluster sampling method, a series of representative cross-sectional surveys were conducted in Beijing from 2005 to 2022, targeting adults aged 18-79 years. A face-to-face questionnaire, along with body measurements and laboratory tests, were administered to 111,943 participants. Data from all survey were age- and/or gender-standardized based on the 2020 Beijing census population. Annual percentage rate change (APC) or average annual percentage rate change (AAPC) was calculated to determine prevalence trends over time. Complex sampling logistic regression models were employed to explore the relationship between various characteristics and diabetes. RESULTS: From 2005 to 2022, the total prevalence of diabetes among Beijing adults aged 18-79 years increased from 9.6% (95% CI: 8.8-10.4%) to 13.9% (95% CI: 13.1-14.7%), with an APC/AAPC of 2.1% (95% CI: 1.1-3.2%, P <0.05). Significant increases were observed among adults aged 18-39 years and rural residents. Undiagnosed diabetes rose from 3.5% (95% CI: 3.2-4.0%) to 7.2% (95% CI: 6.6-7.9%) with an APC/AAPC of 4.1% (95% CI: 0.5-7.3%, P <0.05). However, diabetes awareness and treatment rates showed annual declines of 1.4% (95% CI: -3.0% to -0.2%, P <0.05) and 1.3% (95% CI: -2.6% to -0.2%, P <0.05), respectively. The diabetes control rate decreased from 21.5% to 19.1%, although not statistically significant (APC/AAPC = -1.5%, 95% CI: -5.6% to 1.9%). Overweight and obesity were identified as risk factors for diabetes, with ORs of 1.65 (95% CI: 1.38-1.98) and 2.48 (95% CI: 2.07-2.99), respectively. CONCLUSIONS The prevalence of diabetes in Beijing has significantly increased between 2005 and 2022, particularly among young adults and rural residents. Meanwhile, there has been a concerning decrease in diabetes awareness and treatment rates, while control rates have remained stagnant. Regular blood glucose testing, especially among adults aged 18-59 years, should be warranted. Furthermore, being male, elderly, overweight, or obese was associated with higher diabetes risk, suggesting the needs for targeted management strategies.
Humans ; Adult ; Middle Aged ; Male ; Female ; Aged ; Adolescent ; Young Adult ; Cross-Sectional Studies ; Diabetes Mellitus/epidemiology* ; Beijing/epidemiology* ; Prevalence ; China/epidemiology* ; Surveys and Questionnaires

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.