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.

Humans ; Herpesvirus 4, Human ; Epstein-Barr Virus Infections ; Carcinoma, Small Cell ; Nasopharynx/pathology* ; Carcinoma, Neuroendocrine/pathology* ; Nasopharyngeal Neoplasms/pathology*

Platelets are reprogrammed by cancer via a process called education, which favors cancer development. The transcriptional profile of tumor-educated platelets (TEPs) is skewed and therefore practicable for cancer detection. This intercontinental, hospital-based, diagnostic study included 761 treatment-naïve inpatients with histologically confirmed adnexal masses and 167 healthy controls from nine medical centers (China, n = 3; Netherlands, n = 5; Poland, n = 1) between September 2016 and May 2019. The main outcomes were the performance of TEPs and their combination with CA125 in two Chinese (VC1 and VC2) and the European (VC3) validation cohorts collectively and independently. Exploratory outcome was the value of TEPs in public pan-cancer platelet transcriptome datasets. The AUCs for TEPs in the combined validation cohort, VC1, VC2, and VC3 were 0.918 (95% CI 0.889-0.948), 0.923 (0.855-0.990), 0.918 (0.872-0.963), and 0.887 (0.813-0.960), respectively. Combination of TEPs and CA125 demonstrated an AUC of 0.922 (0.889-0.955) in the combined validation cohort; 0.955 (0.912-0.997) in VC1; 0.939 (0.901-0.977) in VC2; 0.917 (0.824-1.000) in VC3. For subgroup analysis, TEPs exhibited an AUC of 0.858, 0.859, and 0.920 to detect early-stage, borderline, non-epithelial diseases and 0.899 to discriminate ovarian cancer from endometriosis. TEPs had robustness, compatibility, and universality for preoperative diagnosis of ovarian cancer since it withstood validations in populations of different ethnicities, heterogeneous histological subtypes, and early-stage ovarian cancer. However, these observations warrant prospective validations in a larger population before clinical utilities.
Humans ; Female ; Blood Platelets/pathology* ; Biomarkers, Tumor/genetics* ; Ovarian Neoplasms/pathology* ; China

Purinergic P2Y Receptor Antagonists ; Consensus ; Cardiovascular System ; Cardiology ; Asia

Objective: To investigate the clinicopathological features, immunophenotype and prognosis of nuclear protein in testis (NUT) midline carcinoma. Methods: Twenty-four resection cases of NUT midline carcinoma diagnosed at the Department of Pathology, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China from January 2018 to September 2022, were collected, and retrospectively analyzed for their clinicopathological characteristics. Relevant literature was reviewed. Results: All 24 cases of NUT midline carcinoma occurred in the chest or head and neck, including 14 men and 10 women, with a median age of 40 years. Histological examination showed that the tumors were poorly differentiated, with solid nested or sheet-like arrangement, small to medium-sized cells, sparse cytoplasm and coarse granular chromatin, including 5 cases with abrupt squamous epithelial differentiation. Immunohistochemistry showed that all 24 cases were positive for NUT protein, while 16 cases were p63 positive, 19 cases were p40 positive, 15 out of 18 cases were CK5/6 positive. Follow-up data were obtained for 21 patients (follow-up time range, 1-21 months), of which 11 survived, 10 died, and 3 were lost to follow-up. Conclusions: NUT midline carcinoma is a rare and highly aggressive malignancy with unique histological, immunophenotypic and molecular features. It has a poor prognosis.
Male ; Humans ; Female ; Adult ; Neoplasm Proteins/genetics* ; Nuclear Proteins/genetics* ; Retrospective Studies ; Carcinoma/surgery* ; Testicular Neoplasms

Objective: To investigate the clinicopathological features and molecular genetics of diffuse large B-cell lymphomas (DLBCL) with concurrent or secondary to nodal T-follicular helper cell lymphoma, angioimmunoblastic-type (nTFHL-AI). Methods: The clinicopathological features and molecular genetics of DLBCL associated with nTFHL-AI diagnosed between January 2015 and October 2022 at the First Affiliated Hospital of Zhengzhou University were analyzed using histology, immunohistochemistry, PCR, EBV-encoded RNA in situ hybridization and fluorescence in situ hybridization (FISH). Clinical information was collected and analyzed. Results: A total of 6 cases including 3 nTFHL-AI with secondary DLBCL and 3 composite lymphomas were reviewed. There were 4 male and 2 female patients, whose ages ranged from 40 to 74 years (median 57 years). All patients presented with nodal lesions at an advanced Ann Arbor stage Ⅲ/Ⅳ (6/6). Bone marrow involvement was detected in 4 patients. All cases showed typical histologic and immunophenotypic characteristics of nTFHL-AI. Among them, 5 cases of DLBCL with concurrent nTFHL-AI exhibited numerous large atypical lymphoid cells and the tumor cells were CD20 and CD79α positive. The only case of DLBCL secondary to nTFHL-AI showed plasma cell differentiation and reduced expression of CD20. All of cases were activated B-cell (ABC)/non-germinal center B-cell (non-GCB) subtype. Three of the 6 cases were EBV positive with>100 positive cells/high power field, meeting the diagnostic criteria of EBV⁺DLBCL. The expression of MYC and CD30 protein in the DLBCL region was higher than that in the nTFHL-AI region (n=5). C-MYC, bcl-6 and bcl-2 translocations were not detected in the 4 cases that were subject to FISH. Four of the 6 patients received chemotherapy after diagnosis. For the DLBCL cases of nTFHL-AI with secondary DLBCL, the interval was between 2-20 months. During the follow-up period ranging from 3-29 months, 3 of the 6 patients died of the disease. Conclusions: DLBCL associated with nTFHL-AI is very rare. The expansion of EBV-infected B cells in nTFHL-AI may progress to secondary EBV⁺DLBCL. However, EBV-negative cases have also been reported, suggesting possible other mechanisms. The up-regulation of MYC expression in these cases suggests a possible role in B-cell lymphomagenesis. Clinicians should be aware that another biopsy is still necessary to rule out concurrent or secondary DLBCL when nodal and extranodal lesions are noted after nTFHL-AI treatment.
Female ; Male ; Humans ; In Situ Hybridization, Fluorescence ; Lymphoma, Large B-Cell, Diffuse ; B-Lymphocytes ; Biopsy ; T-Lymphocytes, Helper-Inducer