INTRODUCTION: The diagnosis of sarcopenia relies on key indicators such as handgrip strength, walking speed and muscle mass. Developing a composite index that integrates these measures could enhance clinical evaluation in older adults. This study aimed to standardise and combine these metrics to establish a z score for the sarcopenia composite index (ZoSCI) tailored for the ageing population. Additionally, we explore the risk factors associated with ZoSCI to provide insights into early prevention and intervention strategies. METHOD: This retrospective study analysed data between January 2017 and December 2021 from an elderly health programme in Taiwan, applying the Asian Working Group for Sarcopenia criteria to assess sarcopenia. ZoSCI was developed by standardising handgrip strength, walking speed and muscle mass into z scores and integrating them into a composite index. Receiver operating characteristic (ROC) curve analysis was used to determine optimal cut-off values, and multiple regression analysis identified factors influencing ZoSCI. RESULTS: Among the 5047 participants, the prevalence of sarcopenia was 3.7%, lower than the reported global prevalence of 3.9-15.4%. ROC curve analysis established optimal cut-off points for distinguishing sarcopenia in ZoSCI: -1.85 (sensitivity 0.91, specificity 0.88) for males and -1.97 (sensitivity 0.93, specificity 0.88) for females. Factors associated with lower ZoSCI included advanced age, lower education levels, reduced exercise frequency, lower body mass index and creatinine levels. CONCLUSION This study introduces ZoSCI, a new compo-site quantitative indicator for identifying sarcopenia in older adults. The findings highlight specific risk factors that can inform early intervention. Future studies should validate ZoSCI globally, with international collaborations to ensure broader applicability.
Humans ; Sarcopenia/physiopathology* ; Male ; Aged ; Female ; Retrospective Studies ; Hand Strength ; Taiwan/epidemiology* ; ROC Curve ; Aged, 80 and over ; Risk Factors ; Walking Speed ; Geriatric Assessment/methods* ; Prevalence ; Muscle, Skeletal ; Middle Aged

In the present study, a mouse model of coronary artery ligation was employed to evaluate the effects of Jiming Powder on mitophagy in the mouse model of myocardial infarction and elucidate its underlying mechanisms. A mouse model of myocardial infarction post heart failure was constructed by ligating the left anterior descending branch of the coronary artery. The therapeutic efficacy of Jiming Powder was assessed from multiple perspectives, including ultrasonographic imaging, hematoxylin-eosin(HE) staining, Masson staining, and serum cardiac enzyme profiling. Dihydroethidium(DHE) staining was employed to evaluate the oxidative stress levels in the hearts of mice from each group. Mitophagy levels were assessed by scanning electron microscopy and immunofluorescence co-localization. Western blot was employed to determine the levels of key proteins involved in mitophagy, including Bcl-2-interacting protein beclin 1(BECN1), sequestosome 1(SQSTM1), microtubule-associated protein 1 light chain 3 beta(LC3B), PTEN-induced putative kinase 1(PINK1), phospho-Parkinson disease protein(p-Parkin), and Parkinson disease protein(Parkin). The results demonstrated that compared with the model group, high and low doses of Jiming Powder significantly reduced the left ventricular internal diameter in systole(LVIDs) and left ventricular internal diameter in diastole(LVIDd) and markedly improved the left ventricular ejection fraction(LVEF) and left ventricular fractional shortening(LVFS), effectively improving the cardiac function in post-myocardial infarction mice. Jiming Powder effectively reduced the levels of myocardial injury markers such as creatine kinase(CK), creatine kinase isoenzyme(CK-MB), and lactate dehydrogenase(LDH), thereby protecting ischemic myocardium. HE staining revealed that Jiming Powder attenuated inflammatory cell infiltration after myocardial infarction. Masson staining indicated that Jiming Powder effectively inhibited ventricular remodeling. Western blot results showed that Jiming Powder activated the PINK1-Parkin pathway, up-regulated the protein level of BECN1, down-regulated the protein level of SQSTM1, and increased the LC3Ⅱ/LC3Ⅰ ratio to promote mitophagy. In conclusion, Jiming Powder exerts therapeutic effects on myocardial infarction by inhibiting ventricular remodeling. The findings pave the way for subsequent pharmacological studies on the active components of Jiming Powder.
Animals ; Myocardial Infarction/physiopathology* ; Mitophagy/drug effects* ; Mice ; Drugs, Chinese Herbal/administration & dosage* ; Protein Kinases/genetics* ; Male ; Ubiquitin-Protein Ligases/genetics* ; Humans ; Disease Models, Animal ; Mice, Inbred C57BL ; Signal Transduction/drug effects*

This study employed a mouse model of coronary artery ligation to assess the effect and mechanism of Jiming Powder on mitochondrial autophagy in mice with myocardial infarction. The mouse model of heart failure post-myocardial infarction was established by ligating the left anterior descending coronary artery. The pharmacological efficacy of Jiming Powder was evaluated through echocardiographic imaging, hematoxylin-eosin(HE) staining, and Masson staining. The levels of malondialdehyde(MDA), Fe~(2+), reduced glutathione(GSH), and superoxide dismutase(SOD) in heart tissues, as well as MDA immunofluorescence of heart tissues, were measured to assess lipid peroxidation and Fe~(2+) levels in the hearts of mice in different groups. Ferroptosis levels in the groups were evaluated using scanning electron microscopy and Prussian blue staining. Western blot analysis was conducted to detect the levels of key ferroptosis-related proteins, including nuclear factor erythroid 2-related factor 2(NRF2), ferritin heavy chain(FTH), glutathione peroxidase 4(GPX4), solute carrier family 7 member 11(SLC7A11), heme oxygenase 1(HO-1), and Kelch-like ECH-associated protein 1(KEAP1). The results showed that compared with the model group, both the high-and low-dose Jiming Powder groups exhibited significantly reduced left ventricular internal diameter in systole(LVIDs) and left ventricular internal diameter in diastole(LVIDd), while the left ventricular ejection fraction(EF) and left ventricular fractional shortening(FS) were significantly improved, effectively enhancing cardiac function in mice post-myocardial infarction. HE staining revealed that Jiming Powder attenuated myocardial inflammatory cell infiltration post-infarction, and Masson staining indicated that Jiming Powder effectively reduced fibrosis in the infarct margin area. Treatment with Jiming Powder reduced the levels of MDA and Fe~(2+), indicators of lipid peroxidation post-myocardial infarction, while increasing GSH and SOD levels, thus protecting ischemic myocardium. Western blot results demonstrated that Jiming Powder reduced KEAP1 protein accumulation, activated the NRF2/HO-1/GPX4 pathway, and up-regulated the protein expression of FTH and SLC7A11, exerting an inhibitory effect on ferroptosis. This study reveals that Jiming Powder exerts a therapeutic effect on myocardial infarction by inhibiting ferroptosis through the NRF2/HO-1/GPX4 pathway, providing a foundation for subsequent research on the pharmacological effects of Jiming Powder.
Animals ; Ferroptosis/drug effects* ; Myocardial Infarction/physiopathology* ; NF-E2-Related Factor 2/genetics* ; Mice ; Drugs, Chinese Herbal/administration & dosage* ; Male ; Heme Oxygenase-1/genetics* ; Phospholipid Hydroperoxide Glutathione Peroxidase/genetics* ; Humans ; Mice, Inbred C57BL ; Signal Transduction/drug effects* ; Disease Models, Animal

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective To investigate whether the combination of conventional respiratory training and articulatory visual feedback training can improve respiratory function and diaphragmatic function in stroke patients.Methods This single-blind randomized controlled trial recruited a total of 30 stroke patients who were admitted to Department of Rehabilitation Medicine,Zhongshan Hospital,Fudan University,from Nov 2022 to Aug 2023,and divided them into two groups:a experimental group(n=15)and a control group(n=15).The experimental group received conventional respiratory training combined with articulatory visual feedback training,and the control group received conventional respiratory training.The training in the 2 groups was conducted 5 times per week for 4 weeks.Results Both groups significantly improved in maximum inspiratory pressure(MIP),peak inspiratory flow(PIF),maximum phonation time(MPT),maximum counting ability(MCA),and peak expiratory flow(PEF)in each of the two groups improved significantly after training(P<0.05).After training,compared with the control group,the experimental group showed significant differences in MIP[(46.04±13.58)cmH2O vs.(63.46±16.96)cmH2O;P=0.004;95%CI:-28.91,-5.93;effect size(ES)=1.13],PIF[(144.00±43.81)L/min vs.(190.20±75.01)L/min;P=0.049;95%CI:-1.54,0;ES=0.75],MCA[(7.06±3.25)s vs.(10.30±4.89)s;P=0.041;95%CI:-6.34,-0.13;ES=0.77],forced vital capacity(FVC)[(1.74±0.76)L vs.(2.26±0.57)L;P=0.04;95%CI:-1.03,-0.03;ES=0.77],forced expiratory volume in one second(FEV1)[(1.10±0.40)L vs.(1.60±0.50)L;P=0.004;95%CI:-0.85,-0.18;ES=1.1],and PEF[(83.40(55.80)L/min vs.171.12(94.80)L/min;P=0.012)].However,there were no statistically significant differences after training between the two groups in the maximum phonation time(MPT),vital capacity(VC),maximum voluntary ventilation(MVV),diaphragm mobility of the nonparetic side and paretic side,thickening fraction of the nonparetic side and paretic side.Conclusion Compared with conventional respiratory training alone,the combination of articulatory visual feedback training with conventional respiratory training is more effective in enhancing respiratory and lung function in stroke.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

【Objective】 To perform pre-transfusion examination and major crossmatch test using CD47 anti-idiotypic antibody (CD47 AID) (method 1) and reagent lack of anti-IgG4 anti-human globulin(method 2) in patients treated with CD47 monoclonal antibodies, and evaluate the feasibility of method 1 by comparing the transfusion efficacy of patients after cross matching with two methods. 【Methods】 Post-drug samples were collected from 18 clinical subjects treated with CD47 monoclonal antibody in our hospital. Antibody screening and major crossmatch test were performed using method 1 and method 2, and the difference of ΔHb (post-transfusion Hb minus pre-transfusion Hb) was compared after transfusion. The differences in ΔHb after transfusion were analyzed between the test group using method 1 and the control group without CD47 monoclonal antibody using ordinary microcolumn gel method. 【Results】 There was no significant difference in ΔHb between the test group using method 1 and test group using method 2 (8.40±0.71 vs 7.36±0.94, P>0.05). No significant difference was noticed in ΔHb between the test group using method 1 and the control group without CD47 monoclonal antibody (8.40±0.71 vs 6.59±0.77, P>0.05). 【Conclusion】 In the test group, major crossmatch test with method 1 has the same transfusion efficacy as the test with method 2. Method 1 is simple and easy to operate, and the results are objective and accurate. It is recommended to use method 1 for pre-transfusion antibody screening and major crossmatch tests for patients using CD47 monoclonal antibody.

Objective To evaluate the safety and effectiveness of excessive dynamic airway collapse(EDAC)treated by laser.Methods 13 patients with EDAC confirmed by bronchoscopy from January 2018 to August 2022 were selected and divided into a simple EDAC group(6 cases)and an EDAC combined with tracheobronchomalacia(TBM)group(7 cases)based on whether they were combined with TBM.All patients underwent laser tracheobronchoplasty under bronchoscope.Symptoms,airway collapse,oxygenation index,modified version of British Medical Research Council dyspnoea scale(mMRC)and 6 min walking test before and after treatment were compared to evaluate the therapeutic effect.Results 13 patients underwent 17 times of laser tracheobronchoplasty with laser power of 8～15 W,and 4 patients underwent 2 times of laser tracheobronchoplasty.After treatment,the clinical symptoms of cough,sputum,shortness of breath and dyspnea were improved in all patients.1 week post-treatment,the EDAC group showed a significant improvement in airway lumen stenosis,with a significant statistical difference(P<0.05),1 month post-treatment,enhancements were observed in airway stenosis,oxygenation index,FEV1%,6-minute walk test,and mMRC,which remained stable over a 6 month follow-up.In the EDAC + TBM group,significant enhancements in airway stenosis,oxygenation index,and mMRC were noted 1 week post-treatment,with statistical significance(P<0.05).Between 8 d～6 months post-treatment,some patients exhibited a recurrence of airway stenosis,necessitating comprehensive interventions like balloon dilation,cryotherapy,and stent insertion.Local necrosis and granuloma occurred in some patients after laser therapy,and no serious complications associated with laser intervention were found in all patients.Conclusion Laser tracheobronchoplasty is a safe and effective technique for the treatment of EDAC.For patients with EDAC alone,the therapeutic effect is good,but for patients with EDAC combined with TBM,the long-term effect is not good.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.