Objective: Executive function correlates with the parasympathetic nervous system (PNS) based on static heart rate variability (HRV) measurements. Our study advances this understanding by employing dynamic assessments of the PNS to explore and quantify its relationship with inhibitory control (IC). Methods: We recruited 31 men aged 20–35 years. We monitored their electrocardiogram (ECG) signals during the administration of the Conners’ Continuous Performance Test-II (CCPT-II) on a weekly basis over 2 weeks. HRV analysis was performed on ECG-derived RR intervals using 5-minute windows, each overlapping for the next 4 minutes to establish 1-minute intervals. For each time window, the HRV metrics extracted were: mean RR intervals, standard deviation of NN intervals (SDNN), low-frequency power with logarithm (lnLF), and high-frequency power with logarithm (lnHF). Each value was correlated with detectability and compared to the corresponding baseline value at t0. Results: Compared with the baseline level, SDNN and lnLF showed marked decreases during CCPT-II. The mean values of HRV showed significant correlation with d’, including mean SDNN (R=0.474, p=0.012), mean lnLF (R=0.390, p=0.045), and mean lnHF (R=0.400, p=0.032). In the 14th time window, the significant correlations included SDNN (R=0.578, p=0.002), lnLF (R=0.493, p=0.012), and lnHF (R=0.432, p=0.031). Significant correlation between d’ and HRV parameters emerged only during the initial CCPT-II. Conclusion A significant correlation between PNS and IC was observed in the first session alone. The IC in the repeated CCPT-II needs to consider the broader neural network.

Objective: Executive function correlates with the parasympathetic nervous system (PNS) based on static heart rate variability (HRV) measurements. Our study advances this understanding by employing dynamic assessments of the PNS to explore and quantify its relationship with inhibitory control (IC). Methods: We recruited 31 men aged 20–35 years. We monitored their electrocardiogram (ECG) signals during the administration of the Conners’ Continuous Performance Test-II (CCPT-II) on a weekly basis over 2 weeks. HRV analysis was performed on ECG-derived RR intervals using 5-minute windows, each overlapping for the next 4 minutes to establish 1-minute intervals. For each time window, the HRV metrics extracted were: mean RR intervals, standard deviation of NN intervals (SDNN), low-frequency power with logarithm (lnLF), and high-frequency power with logarithm (lnHF). Each value was correlated with detectability and compared to the corresponding baseline value at t0. Results: Compared with the baseline level, SDNN and lnLF showed marked decreases during CCPT-II. The mean values of HRV showed significant correlation with d’, including mean SDNN (R=0.474, p=0.012), mean lnLF (R=0.390, p=0.045), and mean lnHF (R=0.400, p=0.032). In the 14th time window, the significant correlations included SDNN (R=0.578, p=0.002), lnLF (R=0.493, p=0.012), and lnHF (R=0.432, p=0.031). Significant correlation between d’ and HRV parameters emerged only during the initial CCPT-II. Conclusion A significant correlation between PNS and IC was observed in the first session alone. The IC in the repeated CCPT-II needs to consider the broader neural network.

Objective: Executive function correlates with the parasympathetic nervous system (PNS) based on static heart rate variability (HRV) measurements. Our study advances this understanding by employing dynamic assessments of the PNS to explore and quantify its relationship with inhibitory control (IC). Methods: We recruited 31 men aged 20–35 years. We monitored their electrocardiogram (ECG) signals during the administration of the Conners’ Continuous Performance Test-II (CCPT-II) on a weekly basis over 2 weeks. HRV analysis was performed on ECG-derived RR intervals using 5-minute windows, each overlapping for the next 4 minutes to establish 1-minute intervals. For each time window, the HRV metrics extracted were: mean RR intervals, standard deviation of NN intervals (SDNN), low-frequency power with logarithm (lnLF), and high-frequency power with logarithm (lnHF). Each value was correlated with detectability and compared to the corresponding baseline value at t0. Results: Compared with the baseline level, SDNN and lnLF showed marked decreases during CCPT-II. The mean values of HRV showed significant correlation with d’, including mean SDNN (R=0.474, p=0.012), mean lnLF (R=0.390, p=0.045), and mean lnHF (R=0.400, p=0.032). In the 14th time window, the significant correlations included SDNN (R=0.578, p=0.002), lnLF (R=0.493, p=0.012), and lnHF (R=0.432, p=0.031). Significant correlation between d’ and HRV parameters emerged only during the initial CCPT-II. Conclusion A significant correlation between PNS and IC was observed in the first session alone. The IC in the repeated CCPT-II needs to consider the broader neural network.

Objective: Executive function correlates with the parasympathetic nervous system (PNS) based on static heart rate variability (HRV) measurements. Our study advances this understanding by employing dynamic assessments of the PNS to explore and quantify its relationship with inhibitory control (IC). Methods: We recruited 31 men aged 20–35 years. We monitored their electrocardiogram (ECG) signals during the administration of the Conners’ Continuous Performance Test-II (CCPT-II) on a weekly basis over 2 weeks. HRV analysis was performed on ECG-derived RR intervals using 5-minute windows, each overlapping for the next 4 minutes to establish 1-minute intervals. For each time window, the HRV metrics extracted were: mean RR intervals, standard deviation of NN intervals (SDNN), low-frequency power with logarithm (lnLF), and high-frequency power with logarithm (lnHF). Each value was correlated with detectability and compared to the corresponding baseline value at t0. Results: Compared with the baseline level, SDNN and lnLF showed marked decreases during CCPT-II. The mean values of HRV showed significant correlation with d’, including mean SDNN (R=0.474, p=0.012), mean lnLF (R=0.390, p=0.045), and mean lnHF (R=0.400, p=0.032). In the 14th time window, the significant correlations included SDNN (R=0.578, p=0.002), lnLF (R=0.493, p=0.012), and lnHF (R=0.432, p=0.031). Significant correlation between d’ and HRV parameters emerged only during the initial CCPT-II. Conclusion A significant correlation between PNS and IC was observed in the first session alone. The IC in the repeated CCPT-II needs to consider the broader neural network.

Objective: Executive function correlates with the parasympathetic nervous system (PNS) based on static heart rate variability (HRV) measurements. Our study advances this understanding by employing dynamic assessments of the PNS to explore and quantify its relationship with inhibitory control (IC). Methods: We recruited 31 men aged 20–35 years. We monitored their electrocardiogram (ECG) signals during the administration of the Conners’ Continuous Performance Test-II (CCPT-II) on a weekly basis over 2 weeks. HRV analysis was performed on ECG-derived RR intervals using 5-minute windows, each overlapping for the next 4 minutes to establish 1-minute intervals. For each time window, the HRV metrics extracted were: mean RR intervals, standard deviation of NN intervals (SDNN), low-frequency power with logarithm (lnLF), and high-frequency power with logarithm (lnHF). Each value was correlated with detectability and compared to the corresponding baseline value at t0. Results: Compared with the baseline level, SDNN and lnLF showed marked decreases during CCPT-II. The mean values of HRV showed significant correlation with d’, including mean SDNN (R=0.474, p=0.012), mean lnLF (R=0.390, p=0.045), and mean lnHF (R=0.400, p=0.032). In the 14th time window, the significant correlations included SDNN (R=0.578, p=0.002), lnLF (R=0.493, p=0.012), and lnHF (R=0.432, p=0.031). Significant correlation between d’ and HRV parameters emerged only during the initial CCPT-II. Conclusion A significant correlation between PNS and IC was observed in the first session alone. The IC in the repeated CCPT-II needs to consider the broader neural network.

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.

Background/Aims: Chronic hepatitis C (CHC) patients who failed antiviral therapy are at increased risk for hepatocellular carcinoma (HCC). This study assessed the potential role of metformin and statins, medications for diabetes mellitus (DM) and hyperlipidemia (HLP), in reducing HCC risk among these patients. Methods: We included CHC patients from the T-COACH study who failed antiviral therapy. We tracked the onset of HCC 1.5 years post-therapy by linking to Taiwan’s cancer registry data from 2003 to 2019. We accounted for death and liver transplantation as competing risks and employed Gray’s cumulative incidence and Cox subdistribution hazards models to analyze HCC development. Results: Out of 2,779 patients, 480 (17.3%) developed HCC post-therapy. DM patients not using metformin had a 51% increased risk of HCC compared to non-DM patients, while HLP patients on statins had a 50% reduced risk compared to those without HLP. The 5-year HCC incidence was significantly higher for metformin non-users (16.5%) versus non-DM patients (11.3%; adjusted sub-distribution hazard ratio [aSHR]=1.51; P=0.007) and metformin users (3.1%; aSHR=1.59; P=0.022). Statin use in HLP patients correlated with a lower HCC risk (3.8%) compared to non-HLP patients (12.5%; aSHR=0.50; P<0.001). Notably, the increased HCC risk associated with non-use of metformin was primarily seen in non-cirrhotic patients, whereas statins decreased HCC risk in both cirrhotic and non-cirrhotic patients. Conclusions Metformin and statins may have a chemopreventive effect against HCC in CHC patients who failed antiviral therapy. These results support the need for personalized preventive strategies in managing HCC risk.

In this study, untargeted metabolomics was conducted using the liquid chromatography-tandem mass spectrometry(LC-MS/MS) technique to analyze the potential biomarkers in the plasma of mice with heart failure with preserved ejection fraction(HFpEF) induced by a high-fat diet(HFD) and nitric oxide synthase inhibitor(Nω-nitro-L-arginine methyl ester hydrochloride, L-NAME) and explore the pharmacological effects and mechanism of Jiming Powder in improving HFpEF. Male C57BL/6N mice aged eight weeks were randomly assigned to a control group, a model group, an empagliflozin(10 mg·kg~(-1)·d~(-1)) group, and high-and low-dose Jiming Powder(14.3 and 7.15 g·kg~(-1)·d~(-1)) groups. Mice in the control group were fed on a low-fat diet, and mice in the model group and groups with drug intervention were fed on a high-fat diet. All mice had free access to water, with water in the model group and Jiming Powder groups being supplemented with L-NAME(0.5 g·L~(-1)). Drugs were administered on the first day of modeling, and 15 weeks later, blood pressure and cardiac function of the mice in each group were measured. Heart tissues were collected for hematoxylin-eosin(HE) staining to observe pathological changes and Masson's staining to observe myocardial collagen deposition. Untargeted metabolomics analysis was performed on the plasma collected from mice in each group, and metabolic pathway analysis was conducted using MetaboAnalyst 5.0. The results showed that the blood pressure was significantly lower and the myocardial concentric hypertrophy and left ventricular diastolic dysfunction were significantly improved in both the high-dose and low-dose Jiming Powder groups as compared with those in the model group. HE and Masson staining showed that both high-dose and low-dose Jiming Powder significantly alleviated myocardial fibrosis. In the metabolomics experiment, 23 potential biomarkers were identified and eight strongly correlated metabolic pathways were enriched, including linoleic acid metabolism, histidine metabolism, alpha-linolenic acid metabolism, glycerophospholipid metabolism, purine metabolism, porphyrin and chlorophyll metabolism, arachidonic acid metabolism, and pyrimidine metabolism. The study confirmed the pharmacological effects of Jiming Powder in lowering blood pressure and ameliorating HFpEF and revealed the mechanism of Jiming Powder using the metabolomics technique, providing experimental evidence for the clinical application of Jiming Powder in treating HFpEF and a new perspective for advancing and developing TCM therapy for HFpEF.
Male ; Mice ; Animals ; Heart Failure/metabolism* ; Powders ; Stroke Volume/physiology* ; Chromatography, Liquid ; NG-Nitroarginine Methyl Ester/therapeutic use* ; Mice, Inbred C57BL ; Tandem Mass Spectrometry ; Metabolomics ; Biomarkers ; Water

Periarticular fracture of the shoulder is a common type of fractures in the elderly. Postoperative adverse events such as internal fixation failure, humeral head ischemic necrosis and upper limb dysfunction occur frequently, which seriously endangers the exercise and health of the elderly. Compared with the fracture with normal bone mass, the osteoporotic periarticular fracture of the shoulder is complicated with slow healing and poor rehabilitation, so the clinical management becomes more difficult. At present, there is no targeted guideline or consensus for this type of fracture in China. In such context, experts from Youth Osteoporosis Group of Chinese Orthopedic Association, Orthopedic Expert Committee of Geriatrics Branch of Chinese Association of Gerontology and Geriatrics, Osteoporosis Group of Youth Committee of Chinese Association of Orthopedic Surgeons and Osteoporosis Committee of Shanghai Association of Chinese Integrative Medicine developed the Chinese expert consensus on the diagnosis and treatment of osteoporotic periarticular fracture of the shoulder in the elderly ( version 2023). Nine recommendations were put forward from the aspects of diagnosis, treatment strategies and rehabilitation of osteoporotic periarticular fracture of the shoulder, hoping to promote the standardized, systematic and personalized diagnosis and treatment concept and improve functional outcomes and quality of life in elderly patients with osteoporotic periarticular fracture of the shoulder.

Objective: To observe the effects of exosomes derived from human umbilical cord mesenchymal stem cells on the proliferation and invasion of pancreatic cancer cells, and to analyze the contents of exosomes and explore the mechanisms affecting pancreatic cancer cells. Methods: Exosomes extracted from human umbilical cord mesenchymal stem cells were added to pancreatic cancer cells BxPC3, Panc-1 and mouse models of pancreatic cancer, respectively. The proliferative activity and invasion abilities of BxPC3 and Panc-1 cells were measured by cell counting kit-8 (CCK-8) and Transwell assays. The expressions of miRNAs in exosomes were detected by high-throughput sequencing. GO and KEGG were used to analyze the related functions and the main metabolic pathways of target genes with high expressions of miRNAs. Results: The results of CCK-8 cell proliferation assay showed that the absorbance of BxPC3 and Panc-1 cells in the hucMSCs-exo group was significantly higher than that in the control group [(4.68±0.09) vs. (3.68±0.01), P<0.05; (5.20±0.20) vs. (3.45±0.17), P<0.05]. Transwell test results showed that the number of invasion cells of BxPC3 and Panc-1 in hucMSCs-exo group was significantly higher than that in the control group (129.40±6.02) vs. (89.40±4.39), P<0.05; (134.40±7.02) vs. (97.00±6.08), P<0.05. In vivo experimental results showed that the tumor volume and weight in the exosomes derived from human umbilical cord mesenchymal stem cells (hucMSCs-exo) group were significantly greater than that in the control group [(884.57±59.70) mm(3) vs. (695.09±57.81) mm(3), P<0.05; (0.94±0.21) g vs. (0.60±0.13) g, P<0.05]. High-throughput sequencing results showed that miR-148a-3p, miR-100-5p, miR-143-3p, miR-21-5p and miR-92a-3p were highly expressed. GO and KEGG analysis showed that the target genes of these miRNAs were mainly involved in the regulation of glucosaldehylation, and the main metabolic pathways were ascorbic acid and aldehyde acid metabolism, which were closely related to the development of pancreatic cancer. Conclusion: Exosomes derived from human umbilical cord mesenchymal stem cells can promote the growth of pancreatic cancer cells and the mechanism is related to miRNAs that are highly expressed in exosomes.
Mice ; Animals ; Humans ; MicroRNAs/metabolism* ; Exosomes/genetics* ; Sincalide/metabolism* ; Pancreatic Neoplasms/metabolism* ; Carcinoma, Pancreatic Ductal/genetics* ; Mesenchymal Stem Cells/metabolism* ; Umbilical Cord