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.

三级淋巴结构（TLS）是指非生理条件下由于长期发生炎症而形成的异位淋巴组织，是架构在成纤维细胞网络上的淋巴细胞聚集体，包含两个重要的结构区域——T细胞区和滤泡B细胞区。TLS在肿瘤中以不同的成熟状态存在，最终形成生发中心，其内含有T滤泡辅助细胞和滤泡树突状细胞，并且与B细胞紧密联系。近年研究明确了新生的 TLS的关键特征、相关的生物标志物及检测手段，进一步阐述了TLS通过刺激相关的肿瘤抗原调控淋巴细胞的浸润，以增强抗肿瘤免疫效应的作用机制。对TLS与肿瘤患者临床获益之间的相关性研究结果表明，TLS可作为包括免疫治疗在内的生物治疗的良好预后和预测因素。目前，研究者正在开发诱导TLS形成的技术，包括使用趋化因子、细胞因子、抗体、抗原提呈细胞或合成支架等。在“冷肿瘤”和“热肿瘤”中诱导TLS新生联合抑制炎性环境的治疗剂或免疫检查点抑制剂的方案，代表了肿瘤治疗的新希望。

[摘 要] 目的：探讨多结节非小细胞肺癌（NSCLC）组织中的驱动基因突变情况与临床病理特征的关系，为多结节NSCLC患者治疗提供分子诊断依据。方法：本研究共纳入2018年1月至2023年10月间云南省肿瘤医院分子诊断中心检测的121例多结节NSCLC患者的253个肺结节肿瘤组织标本，以第二代测序（NGS）技术或扩增阻滞突变系统PCR（ARMS-PCR）技术检测多结节NSCLC 组织中驱动基因突变情况，分析其与患者临床病理特征的关系，比较不同结节间肺癌驱动基因的突变异质性。结果：与非“宣威”NSCLC相比，“宣威”多结节NSCLC患者驱动基因突变具有显著的地域特点，表现在“宣威”患者具有较低（20%）的EGFR敏感突变（L858R、19-del）及较高（27.26%）的EGFR少见突变（主要为G719/S768I、G719）；“宣威”多结节NSCLC患者的KRAS突变率（27.27%）亦显著高于非“宣威”患者突变率（12.59%）（P<0.05）。此外，“宣威”多结节NSCLC患者驱动基因突变不一致率高达69.23%，远高于非“宣威”患者驱动基因突变不一致率（55.07%）（P<0.05）。结论：“宣威”多结节NSCLC患者具有较高的EGFR少见突变及KRAS突变率，同一患者不同病灶之间存在更高的驱动基因突变异质性，本研究将为“宣威”多结节NSCLC的诊疗策略提供更多的选择。

[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。