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。

[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。

[摘 要] 目的：探讨同源重组修复（HRR）基因突变对晚期非小细胞肺癌（NSCLC）患者免疫治疗疗效和预后的影响。方法：收集2018年3月至2023年4月间在郑州大学第一附属医院接受PD-1抑制剂治疗的124例晚期NSCLC患者的临床资料。根据有无HRR基因突变将患者分为突变组（n=57例）和野生组（n=67例），采用卡方检验或Fisher’s精确检验比较两组患者的临床特征及免疫治疗疗效差异，采用Kaplan-Meier方法比较两组患者的PFS，采用单因素和多因素Cox回归分析PFS的影响因素。结果：HRR基因突变组中鳞癌及肿瘤突变负荷（TMB）≥10 mut/Mb的占比显著多于野生组（54.4% vs 32.8%，61.4% vs 29.9%，均P<0.05）。HRR基因突变组与野生组患者的ORR分别为17.5%和10.4%（P=0.252），DCR分别为86.0%和73.1%（P=0.080）。HRR基因突变组与野生组的PFS比较差异具有统计学意义（6.8个月 vs 3.9个月，P<0.001）。多因素分析结果显示，有无HRR基因突变[HR=0.550，95%CI（0.352, 0.860）, P=0.009]与免疫治疗线数[HR=0.468，95%CI（0.312, 0.702）, P<0.001]和PFS显著相关。结论：HRR基因突变组患者的免疫治疗疗效优于野生组患者，HRR基因突变是晚期NSCLC患者免疫治疗预后的独立保护因素。

[摘 要] 目的：探讨精氨酸-甘氨酸-天冬氨酸（RGD）修饰对肿瘤抑素19肽（T-19）抗肝癌活性的影响，比较分析T-19及RGD修饰的T-19（RGD-T-19）对肝癌SK-Hep-1细胞增殖、侵袭和迁移能力的影响。方法：用Fmoc固相法合成T-19及RGD-T-19，用高效液相色谱仪和质谱进行分离、鉴定。常规培养SK-Hep-1细胞，用0、50、100、150、200、250 mg/mL的T-19及RGD-T-19分别处理细胞，分为0 mg/mL（对照）组、50 mg/mL组、100 mg/mL组、150 mg/mL组、200 mg/mL组、250 mg/mL组。CCK-8法、克隆形成实验、划痕愈合实验和Tanswell小室实验、WB法和qPCR法分别检测SK-Hep-1细胞的增殖、迁移、侵袭能力，以及环氧合酶-2（COX-2）、基质金属蛋白酶-2（MMP-2）、MMP-9、组织基质金属蛋白酶抑制剂-1（TIMP-1）、TIMP-2蛋白和MMP-1、MMP-2 mRNA的表达。结果：经质谱鉴定，用Fmoc固相法合成的T-19及RGD-T-19纯度高。T-19和RGD-T-19均能显著抑制SK-Hep-1细胞的增殖、迁移、侵袭能力，抑制COX-2蛋白、MMP-2和MMP-9蛋白及mRNA的表达、促进TIMP-1、TIMP-2蛋白的表达（P < 0.05, P < 0.01, P < 0.001），RGD-T-19的抑制或促进效应均明显强于T-19（均P < 0.05）。结论：利用Fmoc固相法合成了纯度高、活性好的T-19及RGD-T-19，两种肽均能抑制SK-Hep-1细胞增殖、侵袭和迁移能力，RGD-T-19作用明显强于T-19。

[摘 要] 目的：探究染色体结构维持蛋白4（SMC4）在乳腺癌中的表达及其评估患者预后的价值。方法： 利用TIMER、GEPIA等数据库数据分析SMC4在乳腺癌组织中的表达及其与患者临床病理特征间的相关性；用免疫组化法检测SMC4蛋白在乳腺癌组织中的表达，并探讨其与乳腺癌临床病理特征之间的关系；通过Cox回归模型、Kaplan-Meier生存曲线、GEPIA数据库数据评估SMC4表达与乳腺癌患者生存期之间的关系。结果：生物信息学分析结果显示，SMC4 mRNA在乳腺癌组织中的表达水平明显高于癌旁组织（P < 0.01），并与临床分期、淋巴结转移及化疗耐药密切相关（均P < 0.05）。免疫组化法检测结果显示，在乳腺癌组织中SMC4的阳性及强阳性表达率均显著高于癌旁组织（均P < 0.05）；SMC4蛋白的高表达与乳腺癌患者年龄、临床分期和淋巴结转移密切相关（均P < 0.05）。SMC4蛋白低表达组的患者累积生存率显著高于SMC4蛋白高表达组，尤其是在HER2阳性乳腺癌患者中（均P < 0.05），且SMC4是乳腺癌患者预后的独立危险因素。结论：SMC4在乳腺癌组织中呈高表达，SMC4高表达与乳腺癌患者的淋巴结转移、临床分期及预后密切相关，可作为乳腺癌诊断与预后评判的潜在标志物。