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.

[摘 要] 目的：基于生物信息学分析和体内、体外实验研究长链非编码RNA00511（LINC00511）敲减对非小细胞肺癌细胞增殖、凋亡、侵袭等恶性生物学行为的影响，并初步探究其作用机制。方法：通过基因表达谱交互分析（GEPIA）数据库分析LINC00511在非小细胞肺癌的表达水平，及其与患者肿瘤分期、生存期等临床特征、肿瘤细胞恶性生物学行为有关基因的相关性；利用shRNA慢病毒载体构建LINC00511敲减的H1299肺癌细胞株，克隆形成实验、划痕愈合实验和流式细胞术分别检测对H1299细胞增殖、迁移、细胞周期和凋亡能力的影响，qRT-PCR检测相关调控基因表达，WB法检测肿瘤相关蛋白的表达；构建裸鼠皮下移植瘤模型，取瘤组织进行免疫组织化学实验检测Ki67表达情况。结果：GEPIA数据库分析表明LINC00511在非小细胞肺癌组织中表达水平升高，且与该病的临床分期情况相关（P < 0.05），LINC00511与肺癌中CASP3、CCNB1、CDK4等多种基因表达均有相关性（P < 0.01）；LINC00511敲减可抑制细胞的克隆形成和迁移能力、促进肺癌细胞凋亡并影响细胞周期进展（P < 0.05，P < 0.01）；LINC00511敲减可下调肺癌细胞CCNB、CDK4、TGF-β1基因的表达（P < 0.01），对CCND1、VEGFA基因表达无明显影响，LINC00511敲减可抑制细胞内MMP9、CTNNB1表达，上调CASP3的表达（P < 0.05，P < 0.01）；裸鼠体内实验证实，LINC00511敲减可抑制移植瘤体组织内Ki67的表达（P < 0.01）。结论：LINC00511在非小细胞肺癌组织中呈高表达，与肺癌临床分期和多种基因表达具有相关性，LINC00511敲减可能通过影响相关基因、蛋白的表达，抑制肺癌H1299细胞的恶性生物学行为。

The original version of this article (Yang et al., 2023) unfortunately contained a mistake. In Acknowledgments, the funding information for the Zhejiang Provincial Natural Science Foundation of China (No. LBY21H160001) was wrong. The correct funding should be the Zhejiang Health Science and Technology Project (No. 2022KY682), China.

Direct electrical stimulation of the human cortex can produce subjective visual sensations, yet these sensations are unstable. The underlying mechanisms may stem from differences in electrophysiological activity within the distributed network outside the stimulated site. To address this problem, we recruited 69 patients who experienced visual sensations during invasive electrical stimulation while intracranial electroencephalography (iEEG) data were recorded. We found significantly flattened power spectral slopes in distributed regions involving different brain networks and decreased integrated information during elicited visual sensations compared with the non-sensation condition. Further analysis based on minimum information partitions revealed that the reconfigured network interactions primarily involved the inferior frontal cortex, posterior superior temporal sulcus, and temporoparietal junction. The flattened power spectral slope in the inferior frontal gyrus was also correlated with integrated information. Taken together, this study indicates that the altered electrophysiological signatures provide insights into the neural mechanisms underlying subjective visual sensations.
Humans ; Male ; Female ; Adult ; Visual Perception/physiology* ; Electric Stimulation ; Middle Aged ; Young Adult ; Electrocorticography ; Electroencephalography ; Brain Mapping

Bronchoalveolar lavage(BAL)has become an important technique in the diagnosis and treatment of respiratory diseases in children.In order to standardize the clinical application of BAL in children,the Branch of Pediatric Critical Care Physicians of Chinese Medical Association,in collaboration with other institutions,has developed the"Clinical practice guidelines for bronchoalveolar lavage in Chinese children(2024)"based on the principles of the World Health Organization guidelines and the formulation/revision principles of the Chinese clinical practice guidelines(2022 edition).This guideline provides 30 recommendations to guide the operational procedures of BAL in children.

[摘 要] 目的：利用昆虫杆状病毒表达系统（昆虫BEVS）表达糖酵解酶α-烯醇化酶（ENO1）及其3种酶活性位点缺失突变的ENO1蛋白ENO1-M1、ENO1-M2和ENO1-M3，为后续宫颈癌的代谢治疗研究奠定基础。方法：利用分子克隆技术将优化后ENO1序列插入pFastBacTM1载体，获得含有目的基因的重组质粒pFastBac-ENO1。分别缺失ENO1发挥糖酵解酶功能的3个活性位点，进行优化后将其插入pFastBacTM1载体，获得3个活性位点缺失的重组质粒pFastBac-M1、pFastBac-M2和pFastBac-M3。通过转座、转染后获得重组杆状病毒rBV-ENO1、rBV-M1、rBV-M2和rBV-M3，利用WB法对目的蛋白的表达及特异性进行检测。结果：成功扩增重组杆粒rBacmid-ENO1、rBacmid-M1、rBacmid-M2和rBacmid-M3，获得大小约2 000 bp的基因片段，与预期大小相符。昆虫BEVS可表达ENO1蛋白及其3个酶活位点缺失的重组蛋白ENO1-M1、ENO1-M2和ENO1-M3，其分子量约为52 000，与预期相符。WB法鉴定这些蛋白能与特异性标签His-tag发生反应。结论：通过昆虫BEVS成功表达目的蛋白ENO1及其酶活性位点缺失蛋白ENO1-M1、ENO1-M2和ENO1-M3，这些蛋白具有反应原性，为后续测定这些蛋白与ENO1单抗亲和力创造了条件。