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.

Objective: Anxious depression is a prevalent characteristic observed in Asian psychiatric patients diagnosed with major depressive disorder (MDD). This study aims to investigate the prevalence and clinical presentation of anxious depression in Taiwanese individuals diagnosed with MDD. Methods: We recruited psychiatric outpatients aged over 18 who had been diagnosed with MDD through clinical interviews. This recruitment took place at five hospitals located in northern Taiwan. We gathered baseline clinical and demographic information from the participants. Anxious depression was identified using a threshold of an anxiety/somatization factor score ≥7 on the 21-item Hamilton Rating Scale for Depression (HAM-D). Results: In our study of 399 patients (84.21% female), 64.16% met the criteria for anxious depression. They tended to be older, married, less educated, with more children, and an older age of onset. Anxious depression patients had higher HAM-D and Clinical Global Impression–Severity scale score, more panic disorder (without agoraphobia), and exhibited symptoms like agitation, irritability, concentration difficulties, psychological and somatic anxiety, somatic complaints, hypochondriasis, weight loss, and increased insight. Surprisingly, their suicide rates did not significantly differ from non-anxious depression patients. This highlights the importance of recognizing and addressing these unique characteristics. Conclusion Our study findings unveiled that the prevalence of anxious depression among Taiwanese outpatients diagnosed with MDD was lower compared to inpatients but substantially higher than the reported rates in European countries and the United States. Furthermore, patients with anxious depression exhibited a greater occurrence of somatic symptoms.

Methamphetamine (Meth) abuse can cause serious mental disorders, including anxiety and depression. The gut microbiota is a crucial contributor to maintaining host mental health. Here, we aim to investigate if microbiota participate in Meth-induced mental disorders, and the potential mechanisms involved. Here, 15 mg/kg Meth resulted in anxiety- and depression-like behaviors of mice successfully and suppressed the Sigma-1 receptor (SIGMAR1)/BDNF/TRKB pathway in the hippocampus. Meanwhile, Meth impaired gut homeostasis by arousing the Toll-like receptor 4 (TLR4)-related colonic inflammation, disturbing the gut microbiome and reducing the microbiota-derived short-chain fatty acids (SCFAs). Moreover, fecal microbiota from Meth-administrated mice mediated the colonic inflammation and reproduced anxiety- and depression-like behaviors in recipients. Further, SCFAs supplementation optimized Meth-induced microbial dysbiosis, ameliorated colonic inflammation, and repressed anxiety- and depression-like behaviors. Finally, Sigmar1 knockout (Sigmar1^-/-) repressed the BDNF/TRKB pathway and produced similar behavioral phenotypes with Meth exposure, and eliminated the anti-anxiety and -depression effects of SCFAs. The activation of SIGMAR1 with fluvoxamine attenuated Meth-induced anxiety- and depression-like behaviors. Our findings indicated that gut microbiota-derived SCFAs could optimize gut homeostasis, and ameliorate Meth-induced mental disorders in a SIGMAR1-dependent manner. This study confirms the crucial role of microbiota in Meth-related mental disorders and provides a potential preemptive therapy.

Disease Outbreaks ; Humans ; Monkeypox/epidemiology*

BACKGROUND: The prevalence of skin diseases and diabetes mellitus (DM) are prominent around the world. The current scope of knowledge regarding the prevalence of skin diseases and comorbidities with type 2 DM (T2DM) is limited, leading to limited recognition of the correlations between skin diseases and T2DM. METHODS: We collected 383 subjects from the Da Qing Diabetes Study during the period from July 9th to September 1st, 2016. The subjects were categorized into three groups: Normal glucose tolerance (NGT), impaired glucose tolerance (IGT), and T2DM. The prevalence and clinical characteristics of skin diseases were recorded and investigated. RESULTS: In this cross-sectional study, 383 individuals with ages ranging from 53 to 89-year-old were recruited. The overall prevalence of skin diseases was 93.5%, and 75.7% of individuals had two or more kinds of skin diseases. Additionally, there were 47 kinds of comorbid skin diseases in patients with T2DM, of which eight kinds of skin diseases had a prevalence >10%. The prevalence of skin diseases in NGT, IGT, and T2DM groups were 93.3%, 91.5%, and 96.6%, respectively; stratified analysis by categories showed a statistically significant difference in "disturbances of pigmentation" and "neurological and psychogenic dermatoses". The duration of T2DM also significantly associated with the prevalence of "disturbances of pigmentation" and "neurological and psychogenic dermatoses". Subsequently, the prevalence of "disturbances of pigmentation" was higher in males than females in NGT (P < 0.01) and T2DM (P < 0.01) groups. In addition, the difference in the prevalence of "disturbances of pigmentation" was also significant in NGT and T2DM groups (P < 0.01). CONCLUSIONS There was a high prevalence of skin diseases in the Da Qing Diabetes Study. To address the skin diseases in the Da Qing Diabetes Study, increased awareness and intervention measures should be implemented.
Aged ; Aged, 80 and over ; Blood Glucose ; Cross-Sectional Studies ; Diabetes Mellitus, Type 2/epidemiology* ; Female ; Glucose Intolerance/epidemiology* ; Glucose Tolerance Test ; Humans ; Male ; Middle Aged ; Skin Diseases/epidemiology*

INTRODUCTION: The COVID-19 pandemic has affected the world for more than a year, with multiple waves of infections resulting in morbidity, mortality and disruption to the economy and society. Response measures employed to control it have generally been effective but are unlikely to be sustainable over the long term. METHODS: We examined the evidence for a vaccine-driven COVID-19 exit strategy including academic papers, governmental reports and epidemiological data, and discuss the shift from the current pandemic footing to an endemic approach similar to influenza and other respiratory infectious diseases. RESULTS: A desired endemic state is characterised by a baseline prevalence of infections with a generally mild disease profile that can be sustainably managed by the healthcare system, together with the resumption of near normalcy in human activities. Such an endemic state is attainable for COVID-19 given the promising data around vaccine efficacy, although uncertainty remains around vaccine immunity escape in emergent variants of concern. Maintenance of non-pharmaceutical interventions remains crucial until high vaccination coverage is attained to avoid runaway outbreaks. It may also be worthwhile to de-escalate measures in phases, before standing down most measures for an endemic state. If a variant that substantially evades immunity emerges, it will need to be managed akin to a new disease threat, with pandemic preparedness and response plans. CONCLUSION An endemic state for COVID-19, characterised by sustainable disease control measures, is likely attainable through vaccination.
COVID-19 ; Disease Outbreaks/prevention & control* ; Humans ; Influenza, Human/prevention & control* ; Pandemics/prevention & control* ; SARS-CoV-2