To investigate the association between serpin family E member 1 (SERPINE1) -844 A/G and -675 4G/5G polymorphisms and chronic obstructive pulmonary disease (COPD) in a Chinese Han population. SERPINE1 -844 A/G and -675 4G/5G polymorphisms were assessed by polymerase chain reaction-restriction fragment length polymorphism sequencing of genomic DNA from patients with COPD and healthy smoking controls. Out of 140 patients with COPD and 100 controls, all SERPINE1 -844 and -675 polymorphisms were in Hardy-Weinberg equilibrium. Differences in SERPINE1 -675 4G and 5G allele frequencies were statistically significant between the COPD and control groups (odds ratio [OR] 1.45, 95% confidence interval [CI] 1.00, 2.09), but there was no significant between-group difference in SERPINE1 -844 A and G allele frequencies. The SERPINE1 -675 4G/4G genotype was associated with COPD (OR 1.87, 95% CI 1.06, 3.32 [binary logistic regression]). Haplotype analysis showed that COPD was associated with SERPINE1 -844G/4G (OR 2.11, 95% CI 1.32, 3.38) and SERPINE1 -844G/5G (OR 0.66, 95% CI 0.45, 0.95). The SERPINE1 -675 polymorphism, but not SERPINE1 -844 polymorphism, was associated with susceptibility to COPD in a Chinese Han population.

Background: Impedance cardiography (ICG) offers a potential alternative for hemodynamic assessment in pulmonary arterial hypertension (PAH) as a non-invasive technique. Methods: A total of 132 patients who underwent right heart catheterization (RHC) were included. Cardiac output (CO) and stroke volume (SV) measured by thermodilution during RHC (COTD) and ICG (COICG) were compared. The capacity of ICG in PAH risk stratification and clinical deterioration prediction was also analyzed. Results: Ninety-three pre-capillary pulmonary hypertension patients were enrolled, 54 (58.06%) patients belong to Group 1 PAH, and 39 (41.94%) patients were diagnosed with chronic thromboembolic pulmonary hypertension. The mean COTD was 4.93 ± 1.06 L/min, while the COICG was 4.41 ± 1.23 L/min, showing a moderate correlation (r = 0.49, P < .001). In Group 1 PAH patients, the COTD was 5.13 ± 1.10 L/min, and COICG was 4.57 ± 1.22 L/min (r = 0.52, P < .001). Bland–Altman analysis indicated a mean difference of 0.52 L/min and limits of agreement from −1.76 to 2.80 L/min. The mean SVTD was 64.63 ± 17.10 mL, and the SVICG was 60.94 ± 18.03 mL (r = 0.53, P < .001) with a mean difference of 3.69 mL. After a 1-year follow-up, the CIICG and SVIICG showed potential power in predicting clinical deterioration in PAH patients, with area under the curves of 0.76 and 0.81, respectively. Conclusion: Impedance cardiography measured CO and SV presented an acceptable correlation with RHC in PAH patients. Stroke volume index and cardiac index measured by ICG is potent to identify the low-risk status and predict clinical deterioration in PAH patients.

Aim. To investigate the effect of Tongxinluo (Txl), a Chinese herbal compound, on diabetic peripheral neuropathy (DPN). Methods and Results. Diabetic rat model was established by peritoneal injection of streptozotocin (STZ). Txl ultrafine powder treatment for 16 weeks from the baseline significantly reversed the impairment of motor nerve conductive velocity (MNCV), mechanical hyperalgesia, and nerve structure. We further proved that Tongxinluo upregulates PGC‐1α and its downstream factors including COX IV and SOD, which were involved in mitochondrial biogenesis. Conclusion. Our study indicates that the protective effect of Txl in diabetic neuropathy may be attributed to the induction of PGC‐1α and its downstream targets. This finding may further illustrate the pleiotropic effect of the medicine.

Background: Exploring accurate and noninvasive methods for detecting pulmonary hypertension (PH) has always been a focal point of research. Owing to its exceptional spatiotemporal resolution, magnetocardiography (MCG) has demonstrated potential value in cardiovascular diseases. Aims: This exploratory study aims to investigate the characteristics of MCG variations in PH patients and evaluate their potential utility in distinguishing healthy subjects from those with PH. Methods: This study analyzed 175 PH patients and 333 healthy subjects who underwent MCG examination. The training cohort consisted of patients with PH previously diagnosed by right heart catheterization (RHC) and age‐frequency‐matched healthy controls (HC). The testing cohort comprised age‐ and frequency‐matched HC and PH patients who underwent both MCG and RHC on the same day. Nine MCG parameters were included. Logistic regression was used to screen for significant parameters and develop a model. Results: By comparing the pseudo‐current density maps, it was found that the current vector at the R‐wave peak of HC points toward the lower‐left quadrant, whereas in PH patients, it points toward the lower‐right quadrant. The MCG detection model demonstrated robust performance, achieving a sensitivity of 86.1% and a specificity of 94.1% in the testing cohort. Compared to the ECG of PH patients, MCG demonstrated greater sensitivity; however, it exhibited slightly lower specificity. Furthermore, MCG can detect PH in patients with normal ECG findings. Conclusion: MCG demonstrates highly promising potential for the noninvasive detection of PH.

Accurate detection and risk assessment are crucial for the prognosis of patients with pulmonary hypertension (PH). This study aimed to develop a detection model by employing ultra‐high‐sensitivity magnetocardiography (MCG) combined with machine learning while exploring the potential of MCG features in enhancing prognostic evaluation. PH patients were allocated into the exploratory and external validation cohort according to enrollment period in a single‐center. The control group comprises patients with symptoms including dyspnea, chest tightness, chest pain, or fatigue in the absence of PH. Outpatient follow‐up is performed for PH patients in the exploratory cohort. Seven machine learning algorithms and 15 out of 40 MCG features were employed to develop PH detection models. Cox regression is used to construct a risk assessment model. The exploratory cohort includes 312 subjects (156 PH patients), whereas the external validation cohort includes 162 subjects (81 PH patients). The Random Forest algorithm demonstrates superior performance, achieving sensitivities of 79.6% and 84.0% and specificities of 92.4% and 91.4% in different datasets, respectively. During the average follow‐up period of 33 weeks, 14 patients experienced clinical deterioration events. Compared to the baseline model developed using three parameters from the guideline‐recommended model, the combined model incorporating 2 MCG features performs better in this small cohort to detect deterioration events. In conclusion, the Random Forest‐based MCG model demonstrated robust detection accuracy for PH. The integration of 2 MCG features with clinical baseline parameters may improve short‐term risk assessment.