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.

Electromagnetic fields can regulate the fundamental biological processes involved in bone remodeling. As a non-invasive physical therapy, electromagnetic fields with specific parameters have demonstrated therapeutic effects on bone remodeling diseases, such as fractures and osteoporosis. Electromagnetic fields can be generated by the movement of charged particles or induced by varying currents. Based on whether the strength and direction of the electric field change over time, electromagnetic fields can be classified into static and time-varying fields. The treatment of bone remodeling diseases with static magnetic fields primarily focuses on fractures, often using magnetic splints to immobilize the fracture site while studying the effects of static magnetic fields on bone healing. However, there has been relatively little research on the prevention and treatment of osteoporosis using static magnetic fields. Pulsed electromagnetic fields, a type of time-varying field, have been widely used in clinical studies for treating fractures, osteoporosis, and non-union. However, current clinical applications are limited to low-frequency, and research on the relationship between frequency and biological effects remains insufficient. We believe that different types of electromagnetic fields acting on bone can induce various “secondary physical quantities”, such as magnetism, force, electricity, acoustics, and thermal energy, which can stimulate bone cells either individually or simultaneously. Bone cells possess specific electromagnetic properties, and in a static magnetic field, the presence of a magnetic field gradient can exert a certain magnetism on the bone tissue, leading to observable effects. In a time-varying magnetic field, the charged particles within the bone experience varying Lorentz forces, causing vibrations and generating acoustic effects. Additionally, as the frequency of the time-varying field increases, induced currents or potentials can be generated within the bone, leading to electrical effects. When the frequency and power exceed a certain threshold, electromagnetic energy can be converted into thermal energy, producing thermal effects. In summary, external electromagnetic fields with different characteristics can generate multiple physical quantities within biological tissues, such as magnetic, electric, mechanical, acoustic, and thermal effects. These physical quantities may also interact and couple with each other, stimulating the biological tissues in a combined or composite manner, thereby producing biological effects. This understanding is key to elucidating the electromagnetic mechanisms of how electromagnetic fields influence biological tissues. In the study of electromagnetic fields for bone remodeling diseases, attention should be paid to the biological effects of bone remodeling under different electromagnetic wave characteristics. This includes exploring innovative electromagnetic source technologies applicable to bone remodeling, identifying safe and effective electromagnetic field parameters, and combining basic research with technological invention to develop scientifically grounded, advanced key technologies for innovative electromagnetic treatment devices targeting bone remodeling diseases. In conclusion, electromagnetic fields and multiple physical factors have the potential to prevent and treat bone remodeling diseases, and have significant application prospects.

Objective To investigate the effectiveness of the integrated schistosomiasis control programme in Sichuan Province during the stage moving from transmission interruption to elimination (2015—2023), so as to provide insights into formulation of the schistosomiasis control measures during the post-elimination stage. Methods Schistosomiasis control data were retrospectively collected from departments of health, agriculture and rural affairs, forestry and grassland, water resources, and natural resources in Sichuan Province from 2015 to 2023, and a database was created to document examinations and treatments of human and livestock schistosomiasis, and snail survey and control, conversion of paddy fields to dry fields, ditch hardening, rivers and lakes management and building of forests for snail control and schistosomiasis prevention. The completion of schistosomiasis control measures was investigated, and the effectiveness was evaluated. Results A total of 20 545 155 person-times received human schistosomiasis examinations in Sichuan Province during the period from 2015 to 2023, and 232 157 person-times were seropositive, with a reduction in the seroprevalence from 2.10% (44 299/2 107 003) in 2015 to 1.12% (9 361/837 896) in 2023 (χ² = 7.68, P < 0.001). The seroprevalence of human schistosomiasis appeared a tendency towards a decline in Sichuan Province over years from 2015 to 2023 (b = −8.375, t = −10.052, P < 0.001); however, no egg positive individuals were identified during the period from 2018 to 2023, with the prevalence of human Schistosoma japonicum infections maintained at 0. Expanded chemotherapy was administered to 2 754 515 person-times, and medical assistance of advanced schistosomiasis was given to 6 436 persontimes, with the treatment coverage increasing from 46.80% (827/1 767) in 2015 to 64.87% (868/1 338) in 2023. Parasitological tests for livestock schistosomiasis were performed in 35 113 herd-times, and expanded chemotherapy was administered to 513 043 herd-times, while the number of fenced livestock decreased from 121 631 in 2015 to 103 489 in 2023, with a reduction of 14.92%. Snail survey covered 433 621.80 hm² in Sichuan Province from 2015 to 2023, with 204 602.81 hm² treated by chemical control and 4 637.74 hm² by environmental modifications. The area of snail habitats decreased from the peak of 5 029.80 hm² in 2016 to 3 709.72 hm² in 2023, and the actual area of snail habitats decreased from the peak of 8 585.48 hm² in 2016 to 473.09 hm² in 2023. The mean density of living snails remained low across the study period except in 2017 (0.62 snails/0.1 m²). Schistosomiasis control efforts by departments of agriculture and rural affairs in Sichuan Province included conversion of paddy fields to dry fields covering 153 346.93 hm², hardening of 6 110.31 km ditches, building of 70 356 biogas digesters, replacement of cattle with 227 161 sets of machines, and captive breeding of 21 161 070 livestock from 2015 to 2023, and the control efforts by departments of water resources included rivers and lakes management measuring 5 676.92 km and renovation of 2 331 irrigation areas, while the control efforts by departments of forestry and grassland included building of forests for snail control and schistosomiasis prevention covering 23 913.33 hm², renovation of snail control forests covering 8 720 hm² and newly building of shelterbelts covering 764 686.67 hm². All 63 endemic counties (cities and districts) had achieved the criterion for schistosomiasis elimination criteria in Sichuan Province by the end of 2023. Conclusion Following the integrated control efforts from 2015 to 2023, remarkable achievements have been obtained in the schistosomiasis control programme in Sichuan Province, with all endemic counties successfully attaining the schistosomiasis elimination target at the county level.

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.

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*

Antibiotic resistance is spreading at a faster rate than new antibiotic agents applied for clinical remedies. It is an urgent need to discover potential compounds to combat multidrug-resistant (MDR) bacteria. Marine fungi offer a promising avenue for mining antibiotic-like molecules with chemical diversity. To discover structurally novel and antibiotic metabolites, we screened the in-house marine fungus genome library and found a fungus Stephanonectria keithii LZD-10-1 containing a non-ribosomal peptide synthetase (NRPS) cluster with 18 modules to synthesize a new subfamily of peptaibols with effective eradication against MDR pathogens. Targeting isolation of the cultured fungus afforded six new peptaibols, which exhibit the ability to kill MDR bacteria by targeting bacterial membrane phospholipids, especially phosphatidylglycerol (PG), leading to the dysfunction of bacterial membranes. Furthermore, their efficacies against methicillin-resistant Staphylococcus aureus (MRSA) in both Galleria mellonella and mouse wound infection models were observed. This study underscores the significance of employing genome-guided approaches to identify untapped marine fungi as potential sources for novel antibiotic candidates with unique scaffolds.

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.

Objective: To compare quality control (relative purity and specific activity) and process control [plasminogen (Pg) antigen recovery and potency recovery] indexes of samples before and after adding the Q chromatography step to the full chromatography process of human Pg, thereby determining whether the addition of this step could improve Pg recovery by affinity chromatography. Methods: A Q chromatography step was added before the Pg affinity chromatography in the original Pg chromatography process. The loading solution, flow through solution and eluate of Q chromatography and Pg affinity chromatography were collected. The potency of coagulation factor Ⅱ (FⅡ), Ⅶ (FⅦ), Ⅷ (FⅧ), Ⅸ (FⅨ), and Ⅹ(FⅩ) were detected by the coagulation method, the total protein content was detected by the BCA method, and the Pg potency was detected by the chromogenic substrate method. The content of specific plasma proteins was detected by immunoturbidimetry, the potency recovery of coagulation factors was calculated, and the flow direction of coagulation factors was analyzed. The recovery of different plasma protein antigens were calculated, and the distribution of impurity proteins was analyzed. The relative purity and specific activity of Pg, antigen content, and potency recovery in the target fractions were calculated and compared with the original process indicators, so as to determine the effect of adding Q chromatography on the original process. Furthermore, the reproducibility after process modification was assessed. Results: 100% of FⅡ, FⅩ, and FⅨ, 87.81% of FⅧ, and 40.44% of FⅦ in filtered plasma were removed by Q chromatography. The residual FⅦ (53.26%) and FⅧ (13.30%) in Q flow-through fraction were completely removed by Pg affinity chromatography. In both the original process (without Q-chromatography) and the modified process (with Q-chromatography), non-target plasma proteins mainly existed in the flow-through fraction of Pg affinity chromatography. The antigen recovery of IgM, ceruloplasmin (CER), and fibronectin (FNC) in Q-chromatography flow-through fraction were reduced. In contrast, antigen recovery of other plasma proteins [IgG, IgA, Pg, albumin (AlB), alpha-1-antitrypsin (AAT), and fibrinogen (Fg)] were all >90%, which were consistent with the protein composition and proportion in the original affinity chromatography loading solution. Compared with the recovery rate of Pg antigen in the original process (74.4%), the total recovery of Pg antigen in the modified process was significantly increased (89.97%). Compared with the recovery of IgG (97.48%) and Fg (95.32%) in the Pg affinity flows-through fraction of the original process, the modified process resulted in a slight reduction in the recovery of IgG (94.60%), while the recovery of Fg was not affected (95.05%). The potency recovery rate, specific activity, and relative purity of Pg after Q chromatography were 99.3%, 0.016 U/mg, and 0.15%. These values were the same as those of Pg affinity chromatography loading solution by the original process, indicating that introduction of Q chromatography did not affect subsequent Pg affinity chromatography. Compared with the recovery of Pg antigen in three batches of the original process (66.49±1.02)%, the recovery of Pg antigen in the affinity chromatography eluent of the modified process [five batches; (77.43±4.43)%] was significantly improved. Furthermore, the potency recovery was (86.80±4.28)%, the relative purity was (81.99±1.25)%, the specific activity was (8.679±1.073)U/mg, and the process was reproducible. Conclusion: The addition of Q chromatography could improve the recovery of Pg affinity chromatography in the full chromatography process.

Objective To investigate the correlation between the expression of the activator of HSP90 ATPase-1(AHA1),lysyl oxidase like-2 protein(LOXL2)in osteosarcoma tissues with mRNA expression of invasion and metastasis genes and their clinical significance.Methods A total of 90 osteosarcoma patients diagnosed and treated in North China Medical and Health Group Fengfeng General Hospital from February 2016 to March 2017 were selected as the research object.The expression of AHA1 mRNA,LOXL2 mRNA and invasion and metastasis genes Wnt family member 9A(Wnt9a)mRNA,zinc finger E-box binding homologous box 1(ZEB1)mRNA,zinc finger E-box binding homologous box 1(ZEB2)mRNA,N-cadherin(N-cad)mRNA,and vimentin(Vim)mRNA in tissues were detected by real-time fluorescence quantitative PCR.Pearson correlation analysis was used for correlation analysis.The differences in expression of AHA1 mRNA and LOXL2 mRNA in osteosarcoma patients among different clinical characteristics were compared.Kaplan-Meier survival analysis was used to analyze the effect of AHA1 mRNA and LOXL2 mRNA on the prognosis of osteosarcoma patients.The prognostic factors of osteosarcoma patients were analyzed by univariate and multivariate COX regression.Results The expressions of AHA1 mRNA(3.16±0.59),LOXL2 mRNA(2.84±0.44)and invasion and metastasis genes[Wnt9a mRNA(3.23±0.42),ZEB1 mRNA(2.73±0.39),ZEB2 mRNA(2.52±0.56),N-cad mRNA(2.71±0.65)and Vim mRNA(2.81±0.73)]in osteosarcoma tissues were higher than those in paracancerous tissues(1.10±0.21,0.95±0.18,0.79±0.15,0.64±0.11,0.98±0.19,0.68±0.14,0.72±0.15),and the differences were statissically significant(t=31.206,37.716,51.903,48.931,24.706,28.964,26.605,all P<0.05).There was a significant positive correlation between AHA1 mRNA and LOXL2 mRNA expression in osteosarcoma(r=0.712,P<0.05).The expressions of AHA1 mRNA and LOXL2 mRNA were significantly positively correlated with the expressions of invasion and metastasis genes(Wnt9a,ZEB1,ZEB2,N-cad,and Vim mRNA)in tumor tissue of osteosarcoma group(r=0.504～0.720,all P<0.05).The expressions of AHA1 mRNA and LOXL2 mRNA in osteosarcoma tissues with Eneeking stage Ⅲ,soft tissue infiltration,and lung metastasis were higher than those in patients with Eneeking stage Ⅰ～Ⅱ,no soft tissue infiltration,and no lung metastasis,with significant differences(t=14.122～171.054,all P<0.05).The 5-year survival rates of patients in the AHA1 mRNA high expression group and low expression group were 36.36%(16/44)and 78.26%(36/46),respectively.The 5-year cumulative survival rate of patients in the AHA1 mRNA high expression group was significantly lower than that in the low expression group(Log-rank χ2=16.081,P<0.05).The 5-year survival rates of patients with high and low expression of LOXL2 mRNA were 34.88%(15/43)and 78.72%(37/47),respectively.The 5-year cumulative survival rate of patients in the LOXL2 mRNA high expression group was significantly lower than that in the low expression group(Log-rank χ2=15.880,P<0.05).Lung metastasis(OR=1.921,P<0.05),Eneeking stage Ⅲ(OR=1.906,P<0.05),AHA1 mRNA high expression(OR=1.405,P<0.05),and LOXL2 mRNA high expression(OR=1.733,P<0.05)were independent risk factors affecting the poor survival prognosis of osteosarcoma patients.Conclusion The expressions of AHA1 mRNA and LOXL2 mRNA in osteosarcoma were increased,and they were correlated with the expression of invasion and metastasis genes,indicating they may be independent risk factors affecting the poor survival and prognosis of osteosarcoma patients.

As the foremost among the four examinations in traditional Chinese medicine （TCM）， inspection and related equipment research face challenges in landing and transformation due to variations in evidence quality， lack of standardization， insufficient algorithm transparency， and poor reliability and stability of decision-making. Against the backdrop of rapid development of emerging technologies such as big data， the internet of things， and artificial intelligence， coupled with macro policy support from the government， digital and intelligent generalized inspection in TCM has emerged， with the aim of utilizing digital technologies to overcome the limitations of naked-eye inspection and comprehensively perceive and analyze facial and bodily expressions. The research in this field intelligently correlates Zang-fu organ functions with health conditions and disease progression and establishes a technical system for digital and intelligent inspection， multi-dimensional and multimodal perception， fusion analysis， and decision-making. This system aims to enhance the accuracy of disease risk warning and diagnosis， bridging the gap between inspection equipment and assistance in clinical decision-making. From an evidence-based perspective， this paper systematically examines the research ideas of digital and intelligent inspection and the development of related equipment， deeply explores how to propose clinical practice-oriented key scientific issues， comprehensively acquire and co-apply multi-dimensional data， establish precise inspection models driven by digital intelligence， optimize standards to enhance equipment interoperability and reliability， construct post-effect evaluation mechanisms to promote improvement， and actively address potential risks such as the black box nature and information security in the application of intelligent technology. This paper not only demonstrates the tremendous potential of digital technologies in improving the accuracy and clinical application efficiency of inspection but also provides new perspectives and ideas for the modernization of inspection in TCM， paving the way for the application of inspection in the global medical and health field.