液晶数字显示角度仪在全髋关节置换术中的应用价值

目的:探讨在全髋关节置换术中使用液晶数字显示角度仪控制前倾角的应用价值。方法:回顾分析自2018年1月至2019年12月83例行初次全髋关节置换术的患者,其中男28例,女55例;年龄42~81(70.4±7.9)岁。股骨颈骨折63例,股骨头缺血性坏死20例。所有患者术中使用液晶数显角度仪控制髋臼杯假体的前倾角,术后采用CT扫描,测量髋臼杯的前倾角,两者进行比较,了解使用液晶数显角度仪的准确性。结果:术后CT测量提示患者的髋臼前倾角均位于Lewinnek提倡的安全区内,术中使用液晶数显角度仪测量髋臼杯的前倾角度中位数为14.20°(12.80~15.40)°,术后CT扫描测量的髋臼杯的前倾角中位数为14.20°(13.40~15.50)°,两者比较差异无统计学意义(Z=-1.725,P=0.085)。结论:应用液晶数显角仪器对术中控制髋臼杯的前倾角是一种准确可靠的方法,具有良好的辅助参考价值。     

Shortcomings on genetic testing of Familial hypercholesterolemia (FH) in India: Can we collaborate to establish Indian FH registry?

Familial hypercholesterolemia (FH) is a common autosomal dominant disorder that affects ～1 in 250–500 individuals globally. The only prevalence study in India shows FH in 15% of patients with premature CAD in North Indians. There are only 6 genetic studies in India of the total mutations, 32% are LDLR mutations, 4% are ApoB, 2% are PCSK9 mutations and the mutational spectrum for 37% is unknown. This calls for widespread genetic screening which could help identify definite FH patients.European Atherosclerosis Society-Familial Hypercholesterolemia Studies Collaboration (EAS- FHSC) has taken an initiative to develop a worldwide registry of FH. India is also a part of the collaboration and 3 groups from Mumbai, Delhi and Chennai are actively contributing to this registry. We believe this review might help to understand the Indian scenario of FH and investigators across India can contribute in managing FH in India and further help in the detection, diagnosis and treatment.     

动脉三级淋巴组织与动脉粥样硬化关系的研究进展

学者们对动脉血管的研究常聚焦在血管内膜动脉粥样硬化(As)斑块的形成,而很少关注血管外膜的免疫反应及其对疾病病程的影响。前期研究发现,老龄的ApoE－/－小鼠动脉外膜出现免疫细胞的有序集聚,形成类似淋巴结的异位淋巴组织,称之为动脉三级淋巴组织(ATLO),其形成对内膜As具有明显的调控作用。ATLO的发现及研究为As的研究指明了新的方向,并为其他疾病状况下三级淋巴组织(TLO)的研究提供了很好的范例。因此,阐明TLO的特点及形成机制对As的防治具有重要的意义,并为其在其他疾病中的临床运用提供坚实的基础。     

青年人群近距离工作后眼部参数的变化及恢复时间

目的：观察青年人群近距离用眼后眼部生理及功能性参数的变化及恢复时间。

方法：前瞻性研究。随机选取2019-12/2020-06在我院进行医学验光的患者69例138眼，根据主觉验光结果分为正视组(+0.75D≤等效球镜度≤-0.50D，18例36眼)、低度近视组(-0.75D≤等效球镜度≤-3.00D，25例50眼)和中度近视组(-3.25D≤等效球镜度≤-6.00D，26例52眼)。所有受试者近距离阅读20min后远眺放松20min，分别于近距离用眼前、近距离用眼20min后、远眺5、10、15、20min时测量受试者眼部生理性参数\〖前房深度(ACD)、眼轴长度(AL)\〗和功能性参数\〖正相对调节(PRA)、调节反应(BCC)\〗，分析各参数的达极时间和恢复时间。

结果：近距离用眼后眼轴变长，前房变浅，PRA绝对值变大，BCC无明显变化，75%(52/69)的受试者AL在近距离用眼20min后达极，87%(60/69)的受试者ACD在远眺5min时达极，96%(66/69)的受试者PRA在近距离用眼20min后达极，且以上参数均在远眺10min后逐渐恢复至初始状态。

结论：近距离用眼后眼部参数发生改变，眼轴变长，前房变浅，PRA绝对值增大，但均在远眺放松过程中逐渐回退，且均需要10min以上才能恢复至初始状态。     

飞秒激光辅助超声乳化联合Ahmed青光眼引流阀植入术治疗合并难治性青光眼的白内障

目的：评估飞秒激光辅助超声乳化联合Ahmed青光眼引流阀植入术治疗合并难治性青光眼的白内障的有效性和安全性。

方法：回顾性病例对照研究。2019-10/2021-10入院合并难治性青光眼的白内障患者53例53眼，依据自愿选择分为飞秒激光辅助白内障超声乳化(FLACS)组26例26眼和常规白内障超声乳化(CPCS)组27例27眼。两组分别行FLACS和CPCS联合Ahmed青光眼引流阀植入术。比较两组患者术中超声乳化能量释放量(CDE)、有效超声时间(EPT)的差异和术前与术后抗青光眼药物数量的变化，以及术后观察不同时期(1d，1wk，1、3mo)在提高最佳矫正视力(BCVA)，降低眼压、角膜内皮细胞损伤程度和手术并发症及成功率状况。

结果：FLACS组术中CDE和EPT明显低于CPCS组(t=8.50、5.16； P<0.01、=0.001)。两组术后抗青光眼药物较术前均明显减少(t=9.12、7.76； P=0.011、0.016)，但两组间无差异(t=1.79，P=0.082)。两组术后BCVA均较术前改善，眼压均较术前降低(P<0.05)。FLACS组在术后早期(1d，1wk)BCVA的改善较CPCS组更显著(t=9.74、8.49； P=0.008、0.012)，但在术后1、3mo的BCVA改善程度并无不同(t=0.62、0.44； P=1.415、2.021)。CPCS组在术后随访不同时期的角膜内皮细胞损伤较FLACS组更明显(P<0.05)。术后随访的不同时期FLACS组和CPCS组在控制眼压方面无差异(F_组间=0.64，P_组间=0.421)。FLACS组的手术并发症发生率27%(7/26)较CPCS组89%(24/27)低(χ²=20.95，P<0.01)，其中角膜水肿(8% vs 41%)、前囊撕裂(0 vs 11%)在FLACS组中明显低于CPCS组，后囊破裂(0 vs 7%)、玻璃体脱出(0 vs 4%)及人工晶状体偏位(0 vs 7%)也均发生在CPCS组。但两组的治疗总成功率相近(P=28.718)。

结论：飞秒激光辅助超声乳化联合Ahmed青光眼引流阀植入术可充分发挥联合手术的精准微创可控优势，帮助合并难治性青光眼的白内障患者有效降低眼压及更早获得视力恢复。     

Development of deep learning segmentation models for coronary X-ray angiography: Quality assessment by a new global segmentation score and comparison with human performance

Introduction and objectivesAlthough automatic artificial intelligence (AI) coronary angiography (CAG) segmentation is arguably the first step toward future clinical application, it is underexplored. We aimed to (1) develop AI models for CAG segmentation and (2) assess the results using similarity scores and a set of criteria defined by expert physicians.MethodsPatients undergoing CAG were randomly selected in a retrospective study at a single center. Per incidence, an ideal frame was segmented, forming a baseline human dataset (BH), used for training a baseline AI model (BAI). Enhanced human segmentation (EH) was created by combining the best of both. An enhanced AI model (EAI) was trained using the EH. Results were assessed by experts using 11 weighted criteria, combined into a Global Segmentation Score (GSS: 0–100 points). Generalized Dice Score (GDS) and Dice Similarity Coefficient (DSC) were also used for AI models assessment.Results1664 processed images were generated. GSS for BH, EH, BAI and EAI were 96.9+/-5.7; 98.9+/-3.1; 86.1+/-10.1 and 90+/-7.6, respectively (95% confidence interval, p<0.001 for both paired and global differences). The GDS for the BAI and EAI was 0.9234±0.0361 and 0.9348±0.0284, respectively. The DSC for the coronary tree was 0.8904±0.0464 and 0.9134±0.0410 for the BAI and EAI, respectively. The EAI outperformed the BAI in all coronary segmentation tasks, but performed less well in some catheter segmentation tasks.ConclusionsWe successfully developed AI models capable of CAG segmentation, with good performance as assessed by all scores.