重庆市万州区274家用人单位职业病危害项目申报现况分析

[摘要]　目的　分析新型冠状病毒肺炎疫情（新冠疫情）暴发前、后2个年度，安庆市流行性感冒（流感）流行的强度和特征。方法　在“中国流感监测信息系统”中选取安庆市2019—2021年度监测数据，分析我市新冠疫情发生前、后的流感样病例就诊百分比、病原学阳性率和聚集性疫情的变化。结果　新冠疫情前、后2个年度，流感样病例就诊百分比分别为4.17%和3.78%，差异有统计学意义（χ2=56.652，P=0.000）；病原学阳性率分别为23.43%和1.15%，差异有统计学意义（χ2=538.694，P=0.000）。新冠疫情前的流感病毒活动呈现双峰分布，新冠疫情后全年仅检出27份流感病毒阳性样本。结论　新冠疫情中采取的严格非药物预防措施，对流感的季节性特征有重要影响。应通过疫苗接种和非药物预防等措施，做好流感防控工作。     

安庆市新型冠状病毒肺炎疫情暴发前后流行性感冒流行特征分析

[摘要]　目的　分析新型冠状病毒肺炎疫情（新冠疫情）暴发前、后2个年度，安庆市流行性感冒（流感）流行的强度和特征。方法　在“中国流感监测信息系统”中选取安庆市2019—2021年度监测数据，分析我市新冠疫情发生前、后的流感样病例就诊百分比、病原学阳性率和聚集性疫情的变化。结果　新冠疫情前、后2个年度，流感样病例就诊百分比分别为4.17%和3.78%，差异有统计学意义（χ2=56.652，P=0.000）；病原学阳性率分别为23.43%和1.15%，差异有统计学意义（χ2=538.694，P=0.000）。新冠疫情前的流感病毒活动呈现双峰分布，新冠疫情后全年仅检出27份流感病毒阳性样本。结论　新冠疫情中采取的严格非药物预防措施，对流感的季节性特征有重要影响。应通过疫苗接种和非药物预防等措施，做好流感防控工作。     

2011—2020年呼和浩特市流行性腮腺炎流行特征分析

目的 分析呼和浩特市2011—2020年流行性腮腺炎(流腮)流行病学特征,为制定有针对性防控措施提供参考依据。方法 对呼和浩特市2011—2020年流行性腮腺炎的发病数据进行监测分析,描述其分布及趋势。 呼和浩特市2011—2020年共报告流腮病例6 176例,年均发病率20.60/10万,其中2012年发病率最高为40.17/10万,2020年发病率最低为7.08/10万,2012年和2020年发病率差异有统计学意义(χ2=717.641,P<0.001),10年间流腮发病呈波动下降趋势。流腮发病具有明显季节性,11月至次年1月和4—7月出现2个发病高峰。年龄分布以儿童和青少年为主,占总病例数60.83%。流腮病例最多人群是学生,占总病例数的61.08%。 流腮暴发疫情主要发生在中小学校,应加强儿童入托、入学预防接种证查验和流腮疫苗查漏补种工作,必要时开展6～15岁学生为目标人群的应急接种。     

朝鲜淫羊藿酸性多糖的理化特性及对HepG2细胞脂质堆积的改善作用＊

目的 对比研究朝鲜淫羊藿酸性多糖酯化还原前后的理化特性，并探讨其改善油酸诱导的肝癌HepG2细胞脂质堆积活性的差异。方法 采用高效凝胶渗透色谱法测定朝鲜淫羊藿酸性多糖（EFPA）的均一性和分子量，高效液相色谱法测定EFPA和酯化还原后朝鲜淫羊藿酸性多糖（EFPA-R）的单糖组成；采用油酸（OA）处理HepG2细胞诱导建立脂质蓄积模型，不同浓度EFPA与EFPA-R（10、30、100、300 μg·mL^-1）分别和OA共同作用于细胞24 h，采用CCK-8试剂盒测定细胞存活率，油红O染色观察细胞内脂滴蓄积情况，并采用试剂盒测定细胞内总胆固醇（TC）、甘油三酯（TG）含量。结果 EFPA为成分均一的多糖组分，分子量为125.8 kDa，由甘露糖、葡萄糖、半乳糖、葡萄糖醛酸和阿拉伯糖组成，摩尔比为1.7∶7.4∶1.4∶1.8∶1.0，葡萄糖占比最大，EFPA-R由甘露糖、葡萄糖、半乳糖和阿拉伯糖组成，摩尔比为0.8∶10.6∶2.1∶1.0；在10-300 μg·mL^-1范围内，EFPA和EFPA-R对HepG2细胞的抑制作用较弱，作为给药浓度；与空白组相比，模型组细胞中TC、TG含量显著升高（P < 0.01），细胞内红色脂滴显著增多，与模型组相比，EFPA可显著降低细胞中TC、TG含量（P < 0.01），明显减少细胞内红色脂滴（P < 0.05或P < 0.01），EFPA-R干预后细胞则无明显变化。结论 EFPA可明显改善HepG2细胞脂质堆积情况，且呈现剂量依赖性，而半乳糖醛酸（GalA）的存在可能是其抑制HepG2细胞脂质蓄积的关键因素。     

视网膜大动脉瘤的多模态眼底影像和疾病 特征

目的：分析视网膜大动脉瘤（RAM）的多模态眼底影像学特点及疾病特征。方法：回顾性研究。连续 纳入2003年5月至2020年1月在北京协和医院眼科确诊的RAM患者57例（58眼）。分析患者彩色眼底 照相、荧光素血管造影（FFA）、吲哚菁绿血管造影（ICGA）及光学相干断层扫描（OCT）的影像学特点， 并总结疾病特征。结果：纳入患者年龄为（69.0±9.3）岁，其中女性占75%，单眼发病患者占98%， 共发现瘤体65个。彩色眼底照示95%的瘤体位于颞侧动脉，32%位于动脉主干，29%位于视网膜动 脉分支处或动静脉交叉处。85%伴不同程度出血，86%伴硬性渗出。FFA的瘤体检出率为92%，动脉 期可呈圆点状或梭状强荧光，周围出血或硬性渗出遮蔽荧光，晚期荧光增强并出现渗漏。ICGA的瘤 体检出率为95%，可透过出血的遮蔽荧光见视网膜动脉壁上瘤体高荧光，造影晚期瘤体荧光仍较背 景荧光强。OCT的瘤体检出率为63%，可见RAM局部高隆起，病灶处可见圆形强反射瘤体腔壁。结论： RAM常见于老年女性。多模态影像学检查可从多角度显示RAM的瘤体形态、活动性以及伴随的出 血、渗出表现。其中，ICGA对于瘤体的检出率更高，显示RAM更为清晰。     

Automated assessment of the substantia nigra on susceptibility map-weighted imaging using deep convolutional neural networks for diagnosis of Idiopathic Parkinson's disease

ObjectivesDespite its use in determining nigrostriatal degeneration, the lack of a consistent interpretation of nigrosome 1 susceptibility map-weighted imaging (SMwI) limits its generalized applicability. To implement and evaluate a diagnostic algorithm based on convolutional neural networks for interpreting nigrosome 1 SMwI for determining nigrostriatal degeneration in idiopathic Parkinson's disease (IPD).MethodsIn this retrospective study, we enrolled 267 IPD patients and 160 control subjects (125 patients with drug-induced parkinsonism and 35 healthy subjects) at our institute, and 24 IPD patients and 27 control subjects at three other institutes on approval of the local institutional review boards. Dopamine transporter imaging served as the reference standard for the presence or absence of abnormalities of nigrosome 1 on SMwI. Diagnostic performance was compared between visual assessment by an experienced neuroradiologist and the developed deep learning-based diagnostic algorithm in both internal and external datasets using a bootstrapping method with 10000 re-samples by the “pROC” package of R (version 1.16.2).ResultsThe area under the receiver operating characteristics curve (AUC) (95% confidence interval [CI]) per participant by the bootstrap method was not significantly different between visual assessment and the deep learning-based algorithm (internal validation, .9622 [0.8912–1.0000] versus 0.9534 [0.8779-0.9956], P = .1511; external validation, 0.9367 [0.8843-0.9802] versus 0.9208 [0.8634-0.9693], P = .6267), indicative of a comparable performance to visual assessment.ConclusionsOur deep learning-based algorithm for assessing abnormalities of nigrosome 1 on SMwI was found to have a comparable performance to that of an experienced neuroradiologist.     

Influence of coronary stenosis location on diagnostic performance of machine learning-based fractional flow reserve from CT angiography

BackgroundCompared with invasive fractional flow reserve (FFR), coronary CT angiography (cCTA) is limited in detecting hemodynamically relevant lesions. cCTA-based FFR (CT-FFR) is an approach to overcome this insufficiency by use of computational fluid dynamics. Applying recent innovations in computer science, a machine learning (ML) method for CT-FFR derivation was introduced and showed improved diagnostic performance compared to cCTA alone. We sought to investigate the influence of stenosis location in the coronary artery system on the performance of ML-CT-FFR in a large, multicenter cohort.MethodsThree hundred and thirty patients (75.2% male, median age 63 years) with 502 coronary artery stenoses were included in this substudy of the MACHINE (Machine Learning Based CT Angiography Derived FFR: A Multi-Center Registry) registry. Correlation of ML-CT-FFR with the invasive reference standard FFR was assessed and pooled diagnostic performance of ML-CT-FFR and cCTA was determined separately for the following stenosis locations: RCA, LAD, LCX, proximal, middle, and distal vessel segments.ResultsML-CT-FFR correlated well with invasive FFR across the different stenosis locations. Per-lesion analysis revealed improved diagnostic accuracy of ML-CT-FFR compared with conventional cCTA for stenoses in the RCA (71.8% [95% confidence interval, 63.0%–79.5%] vs. 54.8% [45.7%–63.8%]), LAD (79.3 [73.9–84.0] vs. 59.6 [53.5–65.6]), LCX (84.1 [76.0–90.3] vs. 63.7 [54.1–72.6]), proximal (81.5 [74.6–87.1] vs. 63.8 [55.9–71.2]), middle (81.2 [75.7–85.9] vs. 59.4 [53.0–65.6]) and distal stenosis location (67.4 [57.0–76.6] vs. 51.6 [41.1–62.0]).ConclusionIn a multicenter cohort with high disease prevalence, ML-CT-FFR offered improved diagnostic performance over cCTA for detecting hemodynamically relevant stenoses regardless of their location.     

Go-Ichi-Ni-San 2: A potential biomarker and therapeutic target in human cancers

Cancer incidence and mortality are increasing globally, leading to its rising status as a leading cause of death. The Go-Ichi-Ni-San (GINS) complex plays a crucial role in DNA replication and the cell cycle. The GINS complex consists of four subunits encoded by the GINS1, GINS2, GINS3, and GINS4 genes. Recent findings have shown that GINS2 expression is upregulated in many diseases, particularly tumors. For example, increased GINS2 expression has been found in cervical cancer, gastric adenocarcinoma, glioma, non-small cell lung cancer, and pancreatic cancer. It correlates with the clinicopathological characteristics of the tumors. In addition, high GINS2 expression plays a pro-carcinogenic role in tumor development by promoting tumor cell proliferation and migration, inhibiting tumor cell apoptosis, and blocking the cell cycle. This review describes the upregulation of GINS2 expression in most human tumors and the pathway of GINS2 in tumor development. GINS2 may serve as a new marker for tumor diagnosis and a new biological target for therapy.