ObjectiveThis study aims to explore the mechanism of action of Huangqi Guizhi Wuwutang in treating rheumatoid arthritis （RA）-associated sarcopenia by regulating autophagy and improving skeletal muscle homeostasis based on network pharmacology，bioinformatics，machine learning，and animal experiments. MethodsActive ingredients and targets of Huangqi Guizhi Wuwutang were screened using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform （TCMSP），PubChem，and SwissTargetPrediction databases. RA-related datasets were retrieved from the GEO database，and differential genes were screened. Sarcopenia-related targets were searched through GeneCards and the Comparative Toxicology Database （CTD），and autophagy-related gene sets were downloaded from the Human Autophagy Database （HADb）. Their intersection was analyzed to identify autophagy-related therapeutic targets，followed by enrichment analysis. A protein-protein interaction （PPI） network was constructed using the STRING database，and key targets were selected using multiple methods. Machine learning was applied to predict models based on the expression profiles of intersecting targets，and nomogram models were constructed based on key targets. Molecular docking of the top four active ingredients with key targets was performed using AutoDockVina. A collagen-induced arthritis （CIA） rat model was established using bovine type Ⅱ collagen，with SD rats divided into groups including a blank group，a model group，and low-，medium-，and high-dose groups of Huangqi Guizhi Wuwutang （2.44，4.88，and 9.76 g·kg^-1） and administered for five consecutive weeks. Joint scores and gastrocnemius muscle mass were recorded and analyzed after modeling. Hematoxylin and eosin （HE） staining and Masson's staining were used to observe pathological changes in muscle tissue. Immunofluorescence staining was applied to observe the protein expression levels of myosin heavy chain （MYHC） and insulin-like growth factor-1 （IGF-1） in skeletal muscle. Western blot was used to detect the protein expression levels of autophagy-related proteins ATG5，Beclin1，LC3B，muscle-specific proteins （MuRF1），MaFbx，and MYHC. Real-time quantitative reverse transcription PCR （Real-time PCR） was performed to measure the mRNA expression levels of ATG5，Beclin1，LC3B，MuRF1，MaFbx，and MYHC in muscle tissue. ResultsNetwork pharmacology revealed that Huangqi Guizhi Wuwutang shared 25 common targets with autophagy genes related to RA-associated sarcopenia. The PPI network and machine learning identified six key targets，which were primarily involved in autophagy and inflammatory pathways. Animal experiments showed that compared to the blank group，the model group had significantly higher joint scores （P<0.01） and lower gastrocnemius muscle index （P<0.01）. HE staining indicated a significant reduction in the cross-sectional area of gastrocnemius muscle fibers，with notable inflammatory cell infiltration and muscle atrophy in the model group. Masson's staining revealed obvious collagen fiber proliferation and deposition，with significant muscle fibrosis in the model group. The protein and mRNA expression levels of ATG5，Beclin1，LC3B，MuRF1，and MaFbx were significantly increased （P<0.01），while the protein expression of MYHC and IGF1 was significantly downregulated （P<0.01）. Compared with the model group，the high-dose group of Huangqi Guizhi Wuwutang showed significantly reduced protein and mRNA expression levels of ATG5，Beclin1，LC3B，MuRF1，and MaFbx （P<0.01） and increased protein expression levels of MYHC and IGF1 （P<0.01）. The cross-sectional area of muscle fibers increased，and the muscle cell morphology approached normal. Moreover，pathological abnormalities in the gastrocnemius muscle were significantly improved，with reduced collagen fiber proliferation （P<0.01）. ConclusionHuangqi Guizhi Wuwutang can mediate autophagy by regulating the expression of ATG5，Beclin1，LC3B，and IGF1，thereby reducing skeletal muscle catabolism and improving skeletal muscle homeostasis，which contributes to the treatment of RA-associated sarcopenia. The findings provide insight into the mechanisms underlying the effects of Huangqi Guizhi Wuwutang in the treatment of RA-related sarcopenia and offer a reference for its enhanced clinical application.

Objective:To develop an ultrasound multi-view fusion recognition model and evaluate its clinical value in diagnosing fetal conotruncal defects (CTD).Methods:This prospective study collected cardiac ultrasound images from fetuses at 20-32 weeks of gestation undergoing prenatal ultrasound at Dongguan Maternal and Child Health Hospital between September 2022 and May 2024. The case group comprised fetuses diagnosed with CTD, while controls with normal cardiac structures were collected at a 1∶2 ratio. Both groups were divided into modeling training and validation sets at a 3∶1 ratio. One optimal standard image each from the four-chamber view, left ventricular outflow tract view, right ventricular outflow tract view, and three vessels and trachea view was included per fetus. A deep learning-based multi-view fusion recognition model was developed to differentiate normal conotruncal anatomy from CTD. Model performance was validated against post-abortion pathology or postnatal echocardiography results. SAS software was used for statistical analysis to calculate the sensitivity and specificity of three fusion models (based on positivity in any two, three, or four views, and were designated as Fusion Model 1, Fusion Model 2, and Fusion Model 3, respectively), with the optimal model determined by the maximum Youden index. Senior, intermediate, and junior prenatal sonologists independently diagnosed cases in the validation set under blinding conditions. Their diagnostic results were compared with those of the optimal model. Paired Chi-square test (Cochran's Q test) was employed to compare the differences between the diagnostic accuracy rates of sonologists at different experience levels and the sensitivity of the optimal model, thereby analyzing the auxiliary diagnostic value of the multi-view fusion recognition model. Results:The study included 88 CTD cases, excluding six cases (non-CTD diagnosed by post-abortion pathology or postnatal echocardiography or poor image quality), divided into 60 training and 22 validation cases (12 tetralogy of Fallot, four double outlet right ventricle, three transposition of great arteries, three persistent truncus arteriosus). The control group included 176 cases, excluding 15 cases (other cardiac abnormalities confirmed postnatally or poor image quality after re-evaluation), divided into 120 training and 41 validation cases. The sensitivities of Fusion Model 1, Fusion Model 2, and Fusion Mudel 3 were 0.86, 0.64, and 0.27, while their specificities were 0.76, 0.95, and 1.00, respectively. Fusion Model 1 demonstrated the highest Youden index (0.62) and was selected as optimal. Its diagnostic sensitivity showed no significant difference from senior sonologists [86% vs. 91% (20/22), Bonferroni-corrected P>0.999], but was significantly higher than intermediate [55% (12/22), Bonferroni-corrected P=0.049] and junior sonologists [32% (7/22), Bonferroni-corrected P=0.003]. Conclusion:The deep learning multi-view fusion model achieved diagnostic performance comparable to senior sonologists, demonstrating potential value in assisting CTD diagnosis, training less experienced sonologists, and supporting research and education.

Objective:To develop an ultrasound multi-view fusion recognition model and evaluate its clinical value in diagnosing fetal conotruncal defects (CTD).Methods:This prospective study collected cardiac ultrasound images from fetuses at 20-32 weeks of gestation undergoing prenatal ultrasound at Dongguan Maternal and Child Health Hospital between September 2022 and May 2024. The case group comprised fetuses diagnosed with CTD, while controls with normal cardiac structures were collected at a 1∶2 ratio. Both groups were divided into modeling training and validation sets at a 3∶1 ratio. One optimal standard image each from the four-chamber view, left ventricular outflow tract view, right ventricular outflow tract view, and three vessels and trachea view was included per fetus. A deep learning-based multi-view fusion recognition model was developed to differentiate normal conotruncal anatomy from CTD. Model performance was validated against post-abortion pathology or postnatal echocardiography results. SAS software was used for statistical analysis to calculate the sensitivity and specificity of three fusion models (based on positivity in any two, three, or four views, and were designated as Fusion Model 1, Fusion Model 2, and Fusion Model 3, respectively), with the optimal model determined by the maximum Youden index. Senior, intermediate, and junior prenatal sonologists independently diagnosed cases in the validation set under blinding conditions. Their diagnostic results were compared with those of the optimal model. Paired Chi-square test (Cochran's Q test) was employed to compare the differences between the diagnostic accuracy rates of sonologists at different experience levels and the sensitivity of the optimal model, thereby analyzing the auxiliary diagnostic value of the multi-view fusion recognition model. Results:The study included 88 CTD cases, excluding six cases (non-CTD diagnosed by post-abortion pathology or postnatal echocardiography or poor image quality), divided into 60 training and 22 validation cases (12 tetralogy of Fallot, four double outlet right ventricle, three transposition of great arteries, three persistent truncus arteriosus). The control group included 176 cases, excluding 15 cases (other cardiac abnormalities confirmed postnatally or poor image quality after re-evaluation), divided into 120 training and 41 validation cases. The sensitivities of Fusion Model 1, Fusion Model 2, and Fusion Mudel 3 were 0.86, 0.64, and 0.27, while their specificities were 0.76, 0.95, and 1.00, respectively. Fusion Model 1 demonstrated the highest Youden index (0.62) and was selected as optimal. Its diagnostic sensitivity showed no significant difference from senior sonologists [86% vs. 91% (20/22), Bonferroni-corrected P>0.999], but was significantly higher than intermediate [55% (12/22), Bonferroni-corrected P=0.049] and junior sonologists [32% (7/22), Bonferroni-corrected P=0.003]. Conclusion:The deep learning multi-view fusion model achieved diagnostic performance comparable to senior sonologists, demonstrating potential value in assisting CTD diagnosis, training less experienced sonologists, and supporting research and education.

Background/Aims: Gastric intestinal metaplasia (GIM), a common precancerous lesion of gastric cancer, can be caused by bile acid reflux. GATA binding protein 4 (GATA4) is an intestinal transcription factor involved in the progression of gastric cancer. However, the expression and regulation of GATA4 in GIM has not been clarified. Methods: The expression of GATA4 in bile acid-induced cell models and human specimens was examined. The transcriptional regulation of GATA4 was investigated by chromatin immunoprecipitation and luciferase reporter gene analysis. An animal model of duodenogastric reflux was used to confirm the regulation of GATA4 and its target genes by bile acids. Results: GATA4 expression was elevated in bile acid-induced GIM and human specimens.GATA4 bound to the promoter of mucin 2 (MUC2) and stimulate its transcription. GATA4 and MUC2 expression was positively correlated in GIM tissues. Nuclear transcription factor-κB activation was required for the upregulation of GATA4 and MUC2 in bile acid-induced GIM cell models. GATA4 and caudal-related homeobox 2 (CDX2) reciprocally transactivated each other to drive the transcription of MUC2. In chenodeoxycholic acid-treated mice, MUC2, CDX2, GATA4, p50, and p65 expression levels were increased in the gastric mucosa. Conclusions GATA4 is upregulated and can form a positive feedback loop with CDX2 to transactivate MUC2 in GIM. NF-κB signaling is involved in the upregulation of GATA4 by chenodeoxycholic acid.

Combined radiation and burn injury (CRBI) is a severe syndrome, which is induced by the simultaneous or successive radiation and burn; but no appropriate clinical therapies are available. Loong oil (LO) is a traditional Chinese medicine oil composed of the oil extracts of cuttlebone, safflower, walnut oil, and rapeseed oil, which has been demonstrated to own anti-radiation and tissue healing functions. In this study, glyceryl monostearate (GMO) was used for the preparation of lyotropic liquid crystals that loaded LO to obtain Loong oil-lyotropic liquid crystals (LOL) for the treatment of skin CRBI. The hexagonal phase structure of LOL was proved by small X-ray scattering (SAXS) analysis with an approximate 7:9:11 ratio of peaks and under the polarizing microscope with the birefringence phenomenon. LOL had a high LO loading capacity (4%) and good bio-adhesive force, favoring skin administration. Animal experiments were approved by the Ethics Committee of the Beijing Institute of Radiation Medicine, Academy of Military Medical Sciences and the experiments were conducted in accordance with relevant guidelines and regulations (approval number: IACUC-DWZX-2022-834). Compared to triethanolamine creams, LOL had higher skin permeability with total skin penetration within 1 h, benefiting the treatment of deep CRBI. A deep II degree burn CRBI mouse model was established after whole-body irradiation with 5 Gy ⁶⁰Co γ-ray and the next 6-second 100 ℃ burn injury. LOL promoted LO skin penetration, improved epithermal cell proliferation and the formation of hair follicles, accelerated wound healing, and inhibited scarring, which benefitted the therapy of CRBI. In this study, LO application fields are expanded and LO is a promising medication for the treatment of CRBI.

Objective To develop a GIS-based 3D playback system for the flight human-plane data to realize the fusion of pilots'airborne flight data and physiological data.Methods The 3D playback system was developed with the Browser/Server(B/S)architecture,micro-server model,Java language and Spring Cloud technology framework,which was composed of three functional modules for flight process reproduction,physiological situational awareness and critical event calibration analysis.Results The system developed achieved time synchronization and data fusion of airborne flight data and physiological data with a time synchronization frequency of 1 Hz and a refresh rate of not less than 120 frames/s.Conclusion The system developed with high safety,stability,reliability and accuracy facilitates pilot in-flight physiological monitoring and fusion and simultaneous display of airborne flight data and physiological data,which can be used as an important platform for decision-making support in flight training.[Chinese Medical Equipment Journal,2024,45(10):14-19]

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

Objective Naturally,Aedes albopictus carries both the A and B supergroups of Wolbachia strains,which are symbiotic bacteria that can influence the transmission of mosquito-borne diseases by inhibiting the ability of their host to transmit viruses or by reducing the population density of their host,and the strength of this effect is closely related to its density in host cells.In this study,an accurate qPCR-based method for detecting Wolbachia genotype and density in mosquitoes was established.Methods The wsp gene of Wolbachia carried by Ae.albopictus was cloned and sequenced,and specific primers for the non-conserved regions of the w AlbA and w AlbB wsp genes were designed.Results The application of this method showed great sensitivity and specificity.The primers did not cross-react between the two Wolbachia supergroups.Further,with this method,it was possible to detect decreases in Wolbachia density in host cells following treatment with tetracycline at different concentrations,and the optimal tetracycline treatment concentration was determined to be 0.1 mg/mL.Conclusion This method can provide technical supports for studies of the effects of Wolbachia on physiological reproduction and innate immunity in mosquitoes.

The Baimai Ointment with the effect of relaxing sinew and activating collaterals demonstrates a definite effect on Baimai disease with pain, spasm, stiffness and other symptoms, while the pharmacodynamic characteristics and mechanism of this agent remain unclear. In this study, a rat model of chronic compression of L4 dorsal root ganglion(CCD) was established by lumbar disc herniation, and the efficacy and mechanism of Baimai Ointment in the treatment of CCD were preliminarily explored by behavioral tests, side effect evaluation, network analysis, antagonist and molecular biology verification. The pharmacodynamic experiment indicated that Baimai Ointment significantly improved the pain thresholds(mechanical pain, thermal pain, and cold pain) and gait behavior of CCD model rats without causing tolerance or obvious toxic and side effects. Baimai Ointment inhibited the second-phase nociceptive response of mice in the formalin test, increased the hot plate threshold of normal mice, and down-regulated the expression of inflammatory cytokines in the spinal cord. Network analysis showed that Baimai Ointment had synergistic effect in the treatment of CCD and was related to descending inhibition/facilitation system and neuroinflammation. Furthermore, behavioral tests, Western blot, and immunofluorescence assay revealed that the pain-relieving effect of Baimai Ointment on CCD may be related to the regulation of the interaction between neuroactive ligand and receptors(neuroligands) such as CHRNA7, ADRA2A, and ADRB2, and the down-regulation of the expression of NOS2/pERK/PI3K, the core regulatory element of HIF-1 signaling pathway in spinal microglia. The findings preliminarily reveal the mechanism of relaxing sinew and activating collaterals of Baimai Ointment in the treatment of Baimai disease, providing a reference for the rational drug use and further research of this agent.
Rats ; Mice ; Animals ; Chronic Pain/metabolism* ; Rats, Sprague-Dawley ; Ganglia, Spinal/metabolism* ; Ligands ; Signal Transduction ; Hyperalgesia/metabolism* ; Drugs, Chinese Herbal

Humans ; Multiple Myeloma/drug therapy* ; Thalidomide/therapeutic use* ; Dexamethasone/therapeutic use* ; Antineoplastic Combined Chemotherapy Protocols ; Neoplasm Recurrence, Local/drug therapy*