Objective: This study aimed to construct an animal model of heart failure with preserved ejection fraction (HFpEF) that integrates disease and syndrome based on the "deficiency-blood stasis-toxin" pathogenesis and to evaluate it comprehensively. Methods: The HFpEF mouse model was constructed using a combination of Nω-nitro-L-arginine methyl ester (L-NAME) and a high-fat diet. According to the random number table method, SPF-grade male C57BL/6J mice were randomly assigned to the control, L-NAME, high-fat diet, and model groups, 10 in each group. Comprehensive observations and data collection on macroscopic signs (e.g., fur condition, mental state, stool and urine, oral and nasal condition, paw and body condition, etc.) and cardiac function were performed after 10 and 16 weeks of model induction. Additionally, the syndrome evolution was elucidated based on diagnostic criteria for clinical syndromes of heart failure. Furthermore, pathological and molecular biological examinations of myocardial tissue were performed to assess the stability and reliability of the model. Results: Mice in the model group showed typical characteristics of syndrome of qi deficiency and blood stasis, as well as syndrome of internal heat accumulation, including lethargy, slow response, dull paw color and oral/nasal color, exercise intolerance, abnormal platelet activation, dry feces, and dark yellow urine. The time window for these syndromes was between 10 and 16 weeks post-modeling. Cardiac function assessments revealed severe diastolic dysfunction, concentric myocardial hypertrophy, and myocardial fibrosis in the model group. Pathological examinations showed a significantly increased collagen deposition in the myocardial interstitium, enlarged cross-sectional area of cardiomyocytes, and sparse coronary microvasculature in the model group. Molecular biological analyses indicated marked activation of the inducible nitric oxide synthase/nuclear factor kappa-light-chain-enhancer of activated B cells/NOD-like receptor family pyrin domain containing 3 inflammatory pathway and significantly elevated inflammation levels in the myocardial tissue of the model group. Although mice in the L-NAME and high-fat diet groups also showed certain manifestations of qi deficiency syndrome, the substantial cardiac damage was relatively limited compared to the control group. Conclusion This study has constructed an animal model of HFpEF that integrates disease and syndrome based on the "deficiency-blood stasis-toxin" pathogenesis. The macroscopic and microscopic characteristics of this model are consistent with the manifestations of syndrome of qi deficiency and blood stasis, toxin syndrome, and syndrome of internal heat accumulation. Moreover, it can stably simulate the HFpEF state and reflect phenotypic changes in human disease. This model provides a suitable experimental platform to explore the pathogenesis of HFpEF, evaluate the effectiveness of traditional Chinese medicine (TCM) treatment regimens, and promote in-depth research on TCM syndromes of heart failure.

OBJECTIVE: To observe the clinical efficacy of acupuncture combined with thunder-fire moxibustion in treating low back pain with cold-damp. METHODS: Seventy-two patients of low back pain with cold-damp were randomly divided into an observation group (36 cases, 1 case was eliminated) and a control group (36 cases, 1 case dropped out). The control group received acupuncture at Jizhong (GV6), Yaoyangguan (GV3), ashi points, bilateral Shenshu (BL23), Dachangshu (BL25), and Weizhong (BL40) for 30 min daily. The observation group was treated with thunder-fire moxibustion in addition to the same acupuncture regimen as the control group, once daily. Both groups were treated for 6 consecutive days followed by one rest day, for a total duration of 4 weeks. The visual analog scale (VAS) score, Oswestry disability index (ODI) score, Japanese Orthopedic Association (JOA) score, present pain intensity (PPI) score, and serum levels of β-endorphin (β-EP), 5-hydroxytryp tamin (5-HT), and substance P (SP) were compared before and after treatment, and the clinical efficacy was also compared between the two groups. RESULTS: Compared before treatment, the VAS scores, ODI scores, PPI scores, and serum levels of 5-HT and SP were decreased (P<0.01), while JOA scores and serum levels of β-EP were increased (P<0.01) in both groups after treatment. The observation group showed lower VAS, ODI, and PPI scores and serum levels of 5-HT and SP than those in the control group (P<0.05), as well as higher JOA score and serum level of β-EP (P<0.05). The total effective rate in the observation group was 94.3% (33/35), higher than 82.9% (29/35) in the control group (P<0.05). CONCLUSION Acupuncture combined with thunder-fire moxibustion could effectively alleviate pain and improve lumbar function in patients of low back pain with cold-damp, possibly by regulating β-EP, 5-HT, and SP levels.
Humans ; Moxibustion ; Low Back Pain/blood* ; Male ; Female ; Adult ; Middle Aged ; Acupuncture Therapy ; Acupuncture Points ; Treatment Outcome ; Combined Modality Therapy ; beta-Endorphin/blood* ; Young Adult ; Aged

Objective To construct an animal model of dilated cardiomyopathy(DCM)with heart failure toxin syndrome that conforms to the characteristics of traditional Chinese medicine(TCM)syndrome and identify potential biomarkers or intervention targets for DCM with heart failure toxin syndrome.Methods Fifteen male SD rats were divided into a blank control,doxorubicin,or DCM with heart failure toxin syndrome group using a random number table method,with five rats per group.The doxorubicin group received intraperitoneal injection of doxorubicin at a dose of 1.25 mg/kg,administered on the first and fourth days of each week,along with a standard diet.The DCM with heart failure toxin syndrome group,in addition to the doxorubicin treatment,was given 42％white liquor(10 mL/kg)via gavage every other day,along with a 45％high-fat feed and 10％fructose water.The blank control group received intraperitoneal injection of an equivalent volume of phosphate-buffered saline at the same time points as the doxorubicin group,along with a standard diet.The model was established for 10 weeks.At the fourth and tenth weeks of modeling,echocardiography was performed to measure left ventricular ejection fraction(LVEF),fractional shortening(FS),systolic left ventricular posterior wall thickness(LVPWs),diastolic left ventricular posterior wall thickness,systolic left ventricular internal diameter(LVIDs),and diastolic left ventricular internal diameter(LVIDd);macroscopic changes in fur color of the rats were assessed using the red-green-blue colorimetric method.After modeling,the open field test was conducted to evaluate the exercise tolerance of the rats,and the grip strength test was performed to assess changes in forelimb grip strength.Hematoxylin-eosin,Masson,and wheat germ agglutinin staining were used to evaluate pathological changes in cardiac tissue.Bulk RNA sequencing analysis was performed to identify differentially expressed genes(DEGs)in the hearts of rats between the blank control and the DCM with heart failure toxin syndrome groups.Using DCM,the Blue value of rat fur color,and forelimb grip strength as phenotypic traits,weighted gene co-expression network analysis(WGCNA)was performed to screen for characteristic module gene sets(MEs)associated with DCM with heart failure toxin syndrome.Overlapping analysis was performed on DEGs,immune-related gene sets,and MEs,and the intersecting genes were identified as potential biomarkers or intervention targets for DCM with heart failure toxin syndrome.The sensitivity and specificity of these targets were evaluated using receiver operating characteristic(ROC)curve analysis.Results Compared with the blank control group,at the tenth week of modeling,the LVEF,FS,and LVPWs of rats in the doxorubicin group and the DCM with heart failure toxin syndrome group decreased,whereas LVIDs and LVIDd increased,and the movement distance of the open field test and forelimb grip strength were reduced(P＜0.05).At the 10th week of modeling,the Blue value of fur color in the DCM with heart failure toxin syndrome group was significantly lower than that of the blank control and doxorubicin groups(P＜0.05).Compared with the blank control group,rats in the doxorubicin and DCM with heart failure toxin syndrome groups exhibited significant cardiac dilation and increased immune cell infiltration in cardiac tissue,accompanied by collagen deposition and cardiomyocyte hypertrophy.Bulk RNA sequencing identified 2,003 DEGs,including 1,082 downregulated genes and 921 upregulated genes.WGCNA results revealed that the MEturquoise module had the strongest positive correlation with DCM and the strongest negative correlation with the Blue value and forelimb grip strength.The overlapping analysis identified four intersecting genes:bone morphogenetic protein 6(Bmp6),serine-threonine-protein kinase 1(Pak1),proto-oncogene JunD(JunD),and S100 calcium-binding protein A3(S100A3).ROC curve analysis demonstrated that these four genes exhibited high sensitivity and specificity for DCM with heart failure toxin syndrome.Conclusion The rat model constructed by intraperitoneal injection of doxorubicin combined with a high-fat feed,fructose water,and white liquor gavage closely aligns with the characteristics of the DCM with heart failure toxin syndrome.Bmp6,JunD,Pak1,and S100A3 are potential biomarkers or therapeutic targets for DCM heart failure toxin syndrome.

Objective To development and validate a predictive model for distinguishing between malignant pleural effusion(MPE)and benign pleural effusion(BPE).Methods A total of 428 patients diagnosed with pleural effusion(PE)and hospitalized at the First Hospital of Huai'an Affiliated to Nanjing Medical University from July 2020 to May 2022 were selected.The patients were divided into BPE group(211 cases)and MPE group(217 cases)according to diagnostic criteria.The basic information and clinical data of these patients were collected.Boruta method was used for univariate screening,followed by multivariate Logistic regression to construct a basic nomogram model.Bootstrap method was used for internal validation to evaluate the performance of the nomogram,including dis-crimination,accuracy,and clinical applicability.Results The model included 8 key variables:dyspnea,chest pain,general symp-toms,X-ray/CT with malignant tumor features,serum carcinoembryonic antigen,serum neuron-specific enolase,pleural lactate dehy-drogenase,and pleural carcinoembryonic antigen.Internal validation showed that the area under the receiver operating characteristic curve(AUCROC)of the model was 0.933(95%confidence interval:0.912-0.954),with good accuracy(P＞0.05).Decision curve a-nalysis(DCA)indicated that this predictive model for predicting MPE risk had a significant net benefit when the probability threshold exceeded 1%.Conclusion The constructed prediction model could effectively distinguish between MPE and BPE.

Objective:To screen risk factors for ICU-acquired weakness in patients with mechanical ventilation and construct a predictive model, so as to provide a basis for the health management of patients with mechanical ventilation.Methods:Convenience sampling was used to select 312 ICU patients with mechanical ventilation admitted to the Tenth People's Hospital of Tongji University from October 2019 to August 2020 for the study. Patients were divided into training set ( n=220) and test set ( n=92) in a 7∶3 ratio. Based on machine learning algorithms, decision random forest (DRF), extremely-randomized trees (XRT) and generalized linear model (GLM) were used to construct three ICU-acquired weakness risk prediction models for patients with mechanical ventilation, respectively. The performance of the prediction model was evaluated using the area under the receiver operating characteristic curve ( AUC), the area under the precision-recall curve ( AUPRC), and the root mean square error ( RMSE) . Results:There were 7 predictors of risk of ICU-acquired weakness in patients with mechanical ventilation, including age, gender, braking, duration of mechanical ventilation, blood glucose, lactic acid, and parenteral nutrition. Test set and training set validation showed that AUC and AUPRC of GLM prediction model were greater than those of DRF, XRT prediction model. Test set validation indicated that the RMSE, logarithmic loss of GLM prediction model was less than those of DRF, XRT prediction model. Conclusions:Machine learning algorithm based GLM prediction model has good prediction performance. Healthcare professionals can construct evidence-based decisions for interventions in areas such as braking, duration of mechanical ventilation, and blood glucose management.

Acute respiratory distress syndrome (ARDS) is a common respiratory emergency, but current clinical treatment remains at the level of symptomatic support and there is a lack of effective targeted treatment measures. Our previous study confirmed that inhalation of hydrogen gas can reduce the acute lung injury of ARDS, but the application of hydrogen has flammable and explosive safety concerns. Drinking hydrogen-rich liquid or inhaling hydrogen gas has been shown to play an important role in scavenging reactive oxygen species and maintaining mitochondrial quality control balance, thus improving ARDS in patients and animal models. Coral calcium hydrogenation (CCH) is a new solid molecular hydrogen carrier prepared from coral calcium (CC). Whether and how CCH affects acute lung injury in ARDS remains unstudied. In this study, we observed the therapeutic effect of CCH on lipopolysaccharide (LPS) induced acute lung injury in ARDS mice. The survival rate of mice treated with CCH and hydrogen inhalation was found to be comparable, demonstrating a significant improvement compared to the untreated ARDS model group. CCH treatment significantly reduced pulmonary hemorrhage and edema, and improved pulmonary function and local microcirculation in ARDS mice. CCH promoted mitochondrial peripheral division in the early course of ARDS by activating mitochondrial thioredoxin 2 (Trx2), improved lung mitochondrial dysfunction induced by LPS, and reduced oxidative stress damage. The results indicate that CCH is a highly efficient hydrogen-rich agent that can attenuate acute lung injury of ARDS by improving the mitochondrial function through Trx2 activation.

Occupational pneumoconiosis remains the most common occupational disease in China, with occupational mineral dust exposure being its primary causative factor. Although national standards for online monitoring and early warning systems of coal mine dust concentrations have been established, national occupational health standards for rapid and online monitoring of dust concentration and particle size distribution in other industries are still limited. Among dust concentration sensor technologies, the light scattering method is the preferred choice for online dust monitoring owing to its wide measurement range and low cost. The beta-ray absorption method is mature but highly sensitive to humidity. The electrostatic induction method offers high sensitivity, simple structure, and low maintenance costs but exhibits high errors in low-concentration dust monitoring. The tapered element oscillating microbalance method is highly sensitive but costly. Multi-sensor data fusion technology can improve monitoring reliability, however, mature domestic products are not yet available. For monitoring dust particle size distribution, sieving and sedimentation methods are cumbersome. The aerodynamic method shows broad prospects in the online monitoring of respirable dust but has obvious measurement errors for larger dust particles. The use of optical measurement method is limited by dust morphology and is not suitable for monitoring coal dust particle size distribution. The electrical mobility method is primarily applicable to submicron dust. Future research should focus on promoting the application of monitoring technology for respirable dust particle size distribution in online monitoring of industrial dust. By integrating Internet of Things, data mining, and artificial intelligence technologies, along with multi-sensor data fusion and numerical simulation, dust concentration prediction models can be established to achieve accurate dust concentration monitoring and early warning of exceedances. The advancements of technologies will provide scientific support for the assessment of industrial dust hazards and the prevention and control of occupational pneumoconiosis.

Objective:To develop an artificial intelligence system using fully convolutional neural networks（FCN）to assist echocardiographers in the quantitative assessment of mitral regurgitation（MR）severity.Methods:From August 2021 to June 2024，echocardiographic images of 441 patients with MR were prospectively collected from Renmin Hospital of Wuhan University and the Central Hospital of Wuhan. After screening，a total of 269 patients（4 917 frames）were included in the study. Of these，3 644 frames（128 patients）of apical four-chamber color Doppler MR flow convergence images from Renmin Hospital of Wuhan University were selected as the training/validation set，while images from 121 patients（813 frames）were used as the internal test set. Additionally，images from 20 patients（460 frames）from the Central Hospital of Wuhan were selected as the external test set. The FCN algorithm was employed to capture features and segment the MR color region on the left atrial side，simultaneously outputting the regurgitant radius（r）for the calculation of the effective regurgitant orifice area and regurgitant volume. The severity of MR was then classified according to the 2017 guidelines of the American Society of Echocardiography. The segmentation and classification performance of the model was evaluated，and the measurement results of the AI system was compared with that of both senior and junior physicians.Results:In the internal test set，the accuracy of r identification for cases classified as Grade Ⅰ to Ⅳ was 0.48，0.81，0.86，and 0.87，respectively. In the external test set，the accuracy of r identification for cases classified as Grade Ⅰ to Ⅳ was 0.60，0.77，0.64，and 0.77，respectively. The average accuracy of MR classification in the internal and external test sets was 0.91 and 0.88，respectively.Conclusions:The FCN model is capable of segmenting the left atrial side regurgitant areas in apical four-chamber heart color Doppler images，aiding physicians in obtaining quantitative assessment parameters for MR，and assisting junior physicians in accurately assessing the severity of MR.

Objective To construct an animal model of dilated cardiomyopathy(DCM)with heart failure toxin syndrome that conforms to the characteristics of traditional Chinese medicine(TCM)syndrome and identify potential biomarkers or intervention targets for DCM with heart failure toxin syndrome.Methods Fifteen male SD rats were divided into a blank control,doxorubicin,or DCM with heart failure toxin syndrome group using a random number table method,with five rats per group.The doxorubicin group received intraperitoneal injection of doxorubicin at a dose of 1.25 mg/kg,administered on the first and fourth days of each week,along with a standard diet.The DCM with heart failure toxin syndrome group,in addition to the doxorubicin treatment,was given 42％white liquor(10 mL/kg)via gavage every other day,along with a 45％high-fat feed and 10％fructose water.The blank control group received intraperitoneal injection of an equivalent volume of phosphate-buffered saline at the same time points as the doxorubicin group,along with a standard diet.The model was established for 10 weeks.At the fourth and tenth weeks of modeling,echocardiography was performed to measure left ventricular ejection fraction(LVEF),fractional shortening(FS),systolic left ventricular posterior wall thickness(LVPWs),diastolic left ventricular posterior wall thickness,systolic left ventricular internal diameter(LVIDs),and diastolic left ventricular internal diameter(LVIDd);macroscopic changes in fur color of the rats were assessed using the red-green-blue colorimetric method.After modeling,the open field test was conducted to evaluate the exercise tolerance of the rats,and the grip strength test was performed to assess changes in forelimb grip strength.Hematoxylin-eosin,Masson,and wheat germ agglutinin staining were used to evaluate pathological changes in cardiac tissue.Bulk RNA sequencing analysis was performed to identify differentially expressed genes(DEGs)in the hearts of rats between the blank control and the DCM with heart failure toxin syndrome groups.Using DCM,the Blue value of rat fur color,and forelimb grip strength as phenotypic traits,weighted gene co-expression network analysis(WGCNA)was performed to screen for characteristic module gene sets(MEs)associated with DCM with heart failure toxin syndrome.Overlapping analysis was performed on DEGs,immune-related gene sets,and MEs,and the intersecting genes were identified as potential biomarkers or intervention targets for DCM with heart failure toxin syndrome.The sensitivity and specificity of these targets were evaluated using receiver operating characteristic(ROC)curve analysis.Results Compared with the blank control group,at the tenth week of modeling,the LVEF,FS,and LVPWs of rats in the doxorubicin group and the DCM with heart failure toxin syndrome group decreased,whereas LVIDs and LVIDd increased,and the movement distance of the open field test and forelimb grip strength were reduced(P＜0.05).At the 10th week of modeling,the Blue value of fur color in the DCM with heart failure toxin syndrome group was significantly lower than that of the blank control and doxorubicin groups(P＜0.05).Compared with the blank control group,rats in the doxorubicin and DCM with heart failure toxin syndrome groups exhibited significant cardiac dilation and increased immune cell infiltration in cardiac tissue,accompanied by collagen deposition and cardiomyocyte hypertrophy.Bulk RNA sequencing identified 2,003 DEGs,including 1,082 downregulated genes and 921 upregulated genes.WGCNA results revealed that the MEturquoise module had the strongest positive correlation with DCM and the strongest negative correlation with the Blue value and forelimb grip strength.The overlapping analysis identified four intersecting genes:bone morphogenetic protein 6(Bmp6),serine-threonine-protein kinase 1(Pak1),proto-oncogene JunD(JunD),and S100 calcium-binding protein A3(S100A3).ROC curve analysis demonstrated that these four genes exhibited high sensitivity and specificity for DCM with heart failure toxin syndrome.Conclusion The rat model constructed by intraperitoneal injection of doxorubicin combined with a high-fat feed,fructose water,and white liquor gavage closely aligns with the characteristics of the DCM with heart failure toxin syndrome.Bmp6,JunD,Pak1,and S100A3 are potential biomarkers or therapeutic targets for DCM heart failure toxin syndrome.

Objective To development and validate a predictive model for distinguishing between malignant pleural effusion(MPE)and benign pleural effusion(BPE).Methods A total of 428 patients diagnosed with pleural effusion(PE)and hospitalized at the First Hospital of Huai'an Affiliated to Nanjing Medical University from July 2020 to May 2022 were selected.The patients were divided into BPE group(211 cases)and MPE group(217 cases)according to diagnostic criteria.The basic information and clinical data of these patients were collected.Boruta method was used for univariate screening,followed by multivariate Logistic regression to construct a basic nomogram model.Bootstrap method was used for internal validation to evaluate the performance of the nomogram,including dis-crimination,accuracy,and clinical applicability.Results The model included 8 key variables:dyspnea,chest pain,general symp-toms,X-ray/CT with malignant tumor features,serum carcinoembryonic antigen,serum neuron-specific enolase,pleural lactate dehy-drogenase,and pleural carcinoembryonic antigen.Internal validation showed that the area under the receiver operating characteristic curve(AUCROC)of the model was 0.933(95%confidence interval:0.912-0.954),with good accuracy(P＞0.05).Decision curve a-nalysis(DCA)indicated that this predictive model for predicting MPE risk had a significant net benefit when the probability threshold exceeded 1%.Conclusion The constructed prediction model could effectively distinguish between MPE and BPE.