ObjectiveTo construct and validate a clinical prediction model for Qi deficiency and blood stasis syndrome in chronic heart failure （CHF），aiming to assist clinical diagnosis and provide tools and methods for individualized treatment of CHF. MethodsThe clinical data of patients with chronic heart failure treated at Dongzhimen Hospital of Beijing University of Chinese Medicine from January 2022 to January 2024 were retrospectively collected. The patients were randomly divided into a training group and a validation group with a ratio of 7∶3. First， the least absolute shrinkage and selection operator （LASSO） regression analysis was used to preliminarily screen the predictive factors affecting the diagnosis of Qi deficiency and blood stasis syndrome in CHF. Subsequently， the Logistic regression method was applied to conduct a more in-depth and detailed analysis of these factors. Variables with P<0.05 in the results of the multi-factor Logistic regression were carefully selected and included. Based on the regression coefficients obtained from this analysis， a model was constructed， and a nomogram was accurately drawn. Using R software，the receiver operating characteristic （ROC） curve，calibration curve，and decision curve analysis （DCA） were precisely drawn. These analyses were used to comprehensively evaluate the model from three crucial aspects： discrimination，calibration，and clinical applicability. Additionally， the accuracy，specificity，sensitivity，positive predictive value，and negative predictive value of the model were meticulously calculated to conduct a more all-round and comprehensive assessment. ResultsIn total， 168 cases were successfully obtained in the training group， and 71 cases were included in the validation group. After a thorough comparison， it was found that there were no statistically significant differences in the baseline data between the two groups. After being rigorously screened by the LASSO-multivariate logistic regression method， dark red tongue，smoking history，cardiac troponin I，and N-terminal pro-B-type natriuretic peptide （NT-ProBNP） were identified as the influencing factors for diagnosing patients with the Qi deficiency and blood stasis syndrome in CHF. The constructed model demonstrated an area under the curve （AUC） of 0.812 in the training group and 0.719 in the validation group. The calibration curve showed that the predicted curve of the model was close to the actual observed curve. DCA indicated that the model could provide substantial clinical benefits for patients at the decision thresholds ranging from 0.2 to 0.9. ConclusionThe clinical prediction model for Qi deficiency and blood stasis syndrome in chronic heart failure constructed in this study shows good performance. It has certain application value in clinical practice， which may contribute to the improvement of the diagnosis and treatment of CHF patients with this syndrome.

Objective: To determine the clinical efficacy of platelet-rich plasma (PRP) injection combined with warm acupuncture and moxibustion for treating patients with knee osteoarthritis and cold dampness obstruction syndrome. Methods: One hundred and twenty-eight patients with knee osteoarthritis and cold dampness obstruction syndrome who visited the Rehabilitation Department and Orthopedics Department of the Second Affiliated Hospital of Anhui University of Chinese Medicine from January 2023 to March 2024 and who met the inclusion and exclusion criteria were randomly divided into an experimental (n=64) and control group (n=64) using the random number table method. The experimental group was treated with PRP injection combined with warm acupuncture and moxibustion, whereas the control group was treated with normal saline injection combined with warm acupuncture and moxibustion treatment. PRP and normal saline injections were administered once every two weeks, a total of four times. Patients were treated with warm acupuncture and moxibustion once a day, six times a week, for four consecutive weeks. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Traditional Chinese Medicine (TCM) syndrome, visual analog scale (VAS), and Lysholm scores were determined before treatment, at week 4 and week 8 of treatment, and week 16 of follow-up. Serum interleukin-6 (IL-6), matrix metalloproteinase-3 (MMP-3), tumor necrosis factor-α (TNF-α), osteoprotegerin (OPG), bone gla protein(BGP), and cartilage oligomeric matrix protein (COMP) levels were compared between the two groups before and after 8 weeks of treatment. The clinical efficacy and safety indicators between the two groups were also compared. Results: There was no statistical difference in baseline data such as gender, age, disease duration, and body mass index between the two groups of patients. Compared with before treatment, both groups showed decreased WOMAC total and subscale, TCM syndrome total score and symptom scores, and VAS scores, and an increase in Lysholm scores at 4, 8, and 16 weeks after treatment. After treatment, serum IL-6, MMP-3, TNF-α, and COMP levels decreased, whereas serum OPG and BGP levels increased (P<0.05). Compared with the control group, patients in the experimental group showed decreased WOMAC total and subscale, TCM syndrome total score and symptom scores, and VAS scores, and an increase in Lysholm score at 4, 8, and 16 weeks after treatment. Compared with the control group, patients in the experimental group showed decreased serum IL-6, MMP-3, TNF-α, and COMP levels and an increase in serum OPG and BPG levels after treatment (P<0.05). The total effective rate of the experimental group was 91.94%, higher than that of the control group (81.97%; P<0.05). Conclusion PRP injection combined with warm acupuncture and moxibustion can improve various TCM symptoms, improve knee joint function and bone metabolism, and reduce inflammation in patients with knee osteoarthritis and cold dampness obstruction syndrome.

This paper aimed to investigate the therapeutic effect and mechanism of action of the Yiqi Fumai Formula(YQFM), a kind of traditional Chinese medicine(TCM), on mice with experimental heart failure based on the "heart-gut axis" theory. Based on the network pharmacology integrated with the group collaboration algorithm, the active ingredients were screened, a "component-target-disease" network was constructed, and the potential pathways regulated by the formula were predicted and analyzed. Next, the model of experimental heart failure was established by intraperitoneal injection of adriamycin at a single high dose(15 mg·kg~(-1)) in BALB/c mice. After intraperitoneal injection of YQFM(lyophilized) at 7.90, 15.80, and 31.55 mg·d~(-1) for 7 d, the protective effects of the formula on cardiac function were evaluated using indicators such as ultrasonic electrocardiography and myocardial injury markers. Combined with inflammatory factors in the cardiac and colorectal tissue, as well as targeted assays, the relevant indicators of potential pathways were verified. Meanwhile, 16S rDNA sequencing was performed on mouse fecal samples using the Illumina platform to detect changes in gut flora and analyze differential metabolic pathways. The results show that the administration of injectable YQFM(lyophilized) for 7 d significantly increased the left ventricular end-systolic internal diameter, fractional shortening, and ejection fraction of cardiac tissue of mice with experimental heart failure(P<0.05). Moreover, markers of myocardial injury were significantly decreased(P<0.05), indicating improved cardiac function, along with significantly suppressed inflammatory responses in cardiac and intestinal tissue(P<0.05). Additionally, the species of causative organisms was decreased, and the homeostasis of gut flora was improved, involving a modulatory effect on PI3K-Akt signaling pathway-related inflammation in cardiac and colorectal tissue. In conclusion, YQFM can affect the "heart-gut axis" immunity through the homeostasis of the gut flora, thereby exerting a therapeutic effect on heart failure. This finding provides a reference for the combination of TCM and western medicine to prevent and treat heart failure based on the "heart-gut axis" theory.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Heart Failure/microbiology* ; Mice ; Mice, Inbred BALB C ; Male ; Disease Models, Animal ; Gastrointestinal Microbiome/drug effects* ; Heart/physiopathology* ; Humans ; Signal Transduction/drug effects*

BackgroundCurrently， the problem of depressed mood in college students is becoming more prominent. The experience of childhood maltreatment is a significant contributor to depression among college students. Although the association between the two has been confirmed， the specific psychosocial mechanisms underlying how childhood maltreatment affects college students' mental health remain insufficiently evidenced. ObjectiveTo explore the mediating role of emotion regulation difficulties in the relationship between childhood maltreatment and depression among college students， and to investigate the moderated effects of psychological resilience and family socioeconomic status， aiming to provide references for improving depressive symptoms in college students. MethodsOn 14 March 2024， a cluster sampling method was employed to recruit 751 college students from a university in Heilongjiang Province. Participants were assessed with Childhood Trauma Questionnaire （CTQ）， Difficulties in Emotion Regulation Scale （DERS）， Patients' Health Questionnaire Depression Scale-9 item （PHQ-9）， 10-item Connor-Davidson Resilience Scale （CD-RISC-10） and Family Socioeconomic Status Questionnaire. Pearson correlation analysis was adopted to examine the correlation between the scores of scales. Model 4 and model 7 in Process 4.2 were used to test the mediating effects of emotional regulation difficulties and the moderated effects of psychological resilience and family socioeconomic status. Results① A total of 712 （94.81%） valid questionnaires were collected. ② College students' CTQ score was positively correlated with DERS score and PHQ-9 score （r=0.296， 0.507， P<0.01）， and negatively correlated with CD-RISC-10 score and Family Socioeconomic Status Questionnaire score （r=-0.148， -0.229， P<0.01）. ③ The indirect effect value of difficulties in emotion regulation on the relationship between childhood maltreatment and depression was 0.091 （95% CI： 0.018~0.046）， accounting for 17.95% of the total effect. ④ The first half of the mediation model "childhood maltreatment → difficulties in emotion regulation → depression" （childhood maltreatment → difficulties in emotion regulation） was moderated by psychological resilience （β=-0.030， t=-6.147， 95% CI： -0.040~-0.020） and family socioeconomic status （β=-0.051， t=-3.929， 95% CI： -0.077~-0.026）. ConclusionChildhood maltreatment exerts both a direct effect on college students' depression and an indirect effect through emotion regulation difficulties. The childhood maltreatment → emotion regulation difficulties pathway in this mediation model is moderated by psychological resilience and family socioeconomic status. ［Funded by Qiqihar Medical University Graduate Student Innovation Fund Project （number， QYYCX2023-48）； Special Research Fund Project for Young Doctors of Qiqihar Academy of Medical Sciences （number， QMSI2021B-08）］

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.

Increasing evidence shows that the early lesions of Parkinson's disease (PD) originate from gut, and correction of microbiota dysbiosis is a promising therapy for PD. FLZ is a neuroprotective agent on PD, which has been validated capable of alleviating microbiota dysbiosis in PD mice. However, the detailed mechanisms still need elucidated. Through metabolomics and 16S rRNA analysis, we identified glycoursodeoxycholic acid (GUDCA) was the most affected differential microbial metabolite by FLZ treatment, which was specially and negatively regulated by Clostridium innocuum, a differential microbiota with the strongest correlation to GUDCA production, through inhibiting bile salt hydrolase (BSH) enzyme. The protection of GUDCA on colon and brain were also clarified in PD models, showing that it could activate Nrf2 pathway, further validating that FLZ protected dopaminergic neurons through promoting GUDCA production. Our study uncovered that FLZ improved PD through microbiota-gut-brain axis, and also gave insights into modulation of microbial metabolites may serve as an important strategy for treating PD.

Although enteric glial cell (EGC) abnormal activation is reported to be involved in the pathogenesis of Parkinson's disease (PD), and inhibition of EGC gliosis alleviated gut and dopaminergic neuronal dysfunction was verified in our previous study, the potential role of gut microbiota on EGC function in PD still need to be addressed. In the present study, fecal microbiota transplantation revealed that EGC function was regulated by gut microbiota. By employing 16S rRNA and metabolomic analysis, we identified that 3-indolepropionic acid (IPA) was the most affected differential microbial metabolite that regulated EGC gliosis. The protective effects of IPA on PD were validated in rotenone-stimulated EGCs and rotenone (30 mg/kg i.g. for 4 weeks)-induced PD mice, as indicated by decreased inflammation, improved intestinal and brain barrier as well as dopaminergic neuronal function. Mechanistic study showed that IPA targeted pregnane X receptor (PXR) in EGCs, and inhibition of IL-13Rα1 involved cytokine-cytokine receptor interaction pathway, leading to inactivation of downstream JAK1-STAT6 pathway. Our data not only provided evidence that EGC gliosis was critical in spreading intestinal damage to brain, but also highlighted the potential role of microbial metabolite IPA in alleviating PD pathological damages through gut-brain axis.

Tumors exhibit abnormal glucose metabolism, consuming excessive glucose and excreting lactate, which constructs a tumor microenvironment that facilitates cancer progression and disrupts immunotherapeutic efficacy. Currently, tumor glucose metabolic dysregulation to reshape the immunosuppressive microenvironment and enhance immunotherapy efficacy is emerging as an innovative therapeutic strategy. However, glucose metabolism modulators lack specificity and still face significant challenges in overcoming tumor delivery barriers, microenvironmental complexity, and metabolic heterogeneity, resulting in poor clinical benefit. Nanomedicines, with their ability to selectively target tumors or immune cells, respond to the tumor microenvironment, co-deliver multiple drugs, and facilitate combinatorial therapies, hold significant promise for enhancing immunotherapy through tumor glucose metabolic reprogramming. This review explores the complex interactions between tumor glucose metabolism-specifically metabolite transport, glycolysis processes, and lactate-and the immune microenvironment. We summarize how nanomedicine-mediated reprogramming of tumor glucose metabolism can enhance immunotherapy efficacy and outline the prospects and challenges in this field.

[This corrects the article DOI: 10.1016/j.apsb.2025.02.029.].

OBJECTIVE: To explore the correlation between chromosome 8 open reading frame 76 (C8orf76) and cyclin-dependent kinase 4 (CDK4) and the potential predictive effect of C8orf76 and CDK4 on the prognosis of colorectal cancer (CRC). METHODS: We constructed a protein-protein interaction network of C8orf76-related genes and analyzed the prognostic signatures of C8orf76 and CDK4. Clinicopathological features of C8orf76 and CDK4 were visualized using a nomogram. RESULTS: C8orf76 and CDK4 levels were positively correlated in two independent human CRC cohorts ( n = 83 and n = 597). A consistent positive correlation was observed between C8orf76 and CDK4 expression in the CRC cell lines. The nomogram included prognostic genes (C8orf76 and CDK4) and pathological N and M stages. The concordance index (C-index) in our cohort was 0.776, which suggests that the ability of the indicators to predict the overall survival of patients with CRC in our cohort was strong. CONCLUSION We found that C8orf76 was positively correlated with CDK4 in both the cohorts as well as in CRC cell lines. Therefore, C8orf76 and CDK4 can be used as potential biomarkers to predict the prognosis of CRC.
Humans ; Colorectal Neoplasms/diagnosis* ; Cyclin-Dependent Kinase 4/metabolism* ; Prognosis ; Male ; Female ; Middle Aged ; Biomarkers, Tumor/genetics* ; Aged ; Cell Line, Tumor ; Gene Expression Regulation, Neoplastic