糖尿病视网膜病变光凝术后角膜知觉的改变

目的 探讨糖尿病视网膜病变患者在全视网膜光凝术后的角膜知觉改变及影响因素。方法 32例(32只眼)糖尿病视网膜病变患者应用倍频ND：YAG激光行全视网膜光凝，光凝斑大小：200～300／μm，光凝时间：0．2～0．4秒，光凝斑强度以达到中等强为标准。采用Cochet--Bonnnnet角膜知觉测量仪测量光凝前及光凝后1、2、3、4、5、6周及2个月的角膜知觉。结果 糖尿病视网膜病变患者术前的角膜知觉为2．27cm，术后1、2、3、4、5、6周及2个月的角膜知觉分别为1．42cm、1．60cm、1．58cm、1．92cm、1．71cm、2．19cm和2．23cm。与术前相比，术后1～5周的角膜知觉减退值与术前的角膜知觉的相关系数为0．82。结论 糖尿病视网膜病变行全视网膜光凝后的角膜知觉有明显减退，在光凝后6周恢复至术前水平，角膜知觉的减退值与光凝的范围及术前的角膜知觉有关。     

降糖起萎合剂对糖尿病微血管病变大鼠阴茎海绵体糖基化终产物含量的影响

目的探讨降糖起萎合剂对糖尿病微血管并发症大鼠阴茎海绵体沉积的糖基化终产物(AGEs)的抑制作用.方法根据AGEs具有产生荧光的特点,通过测定其荧光强度,间接表示阴茎海绵体内AGEs的含量.结果及结论降糖起萎合剂对糖尿病微血管并发症大鼠阴茎海绵体沉积的荧光AGEs有较强的抑制作用.     

两种药物治疗糖尿病视网膜病变的成本-效果分析

目的 :研究进口与国产羟苯磺酸钙治疗糖尿病视网膜病变的成本 -效果。方法 :采用疗效观察和成本 -效果分析方法对进口与国产羟苯磺酸钙进行经济学评价。结果与结论 :进口与国产羟基磺酸钙治疗糖尿病视网膜病变的效果相近 ,但国产品是更经济的选择。     

视网膜光凝对糖尿病视网膜病变患者神经纤维层厚度的影响

目的：探讨分析糖尿病视网膜病变(diabetic retinopathy,DR)患者全视网膜激光光凝术(panretinal photocoagulation,PRP)治疗后1a视网膜神经纤维层(retinal nerve fiber layer,RNFL)厚度的变化。

方法：选取2014-05/2017-06在我院行PRP治疗的92例92眼DR患者,采用OCT的DISC CIRCLE程序,测量分析以视盘为中心、直径3.45mm范围DR患者PRP术前和术后1a(采用Follow-Up程序自动追踪模式)视盘周围RNFL厚度,按视盘上方(S)、下方(I)、鼻侧(N)、颞侧(T)4个象限分区所得的数值进行统计分析。

结果：DR患者PRP术后1a较术前视盘周围上方、下方、鼻侧、颞侧和全周平均RNFL厚度明显变薄,且差异有统计学意义(均P<0.05)。

结论：PRP治疗可导致视网膜RNFL变薄,临床上应充分重视和考虑RNFL的损伤,更好地保护视网膜神经细胞的机能对DR治疗具有重要意义。     

A Survey on Intelligent Screening for Diabetic Retinopathy

Diabetic retinopathy (DR) is one of the leading causes of vision loss and can be effectively avoided by screening, early diagnosis and treatment. In order to increase the universality and efficiency of DR screening, many efforts have been invested in developing intelligent screening, and there have been great advances. In this paper, we survey DR screening from four perspectives: 1) public color fundus image datasets of DR; 2) DR classification and related lesion-extraction approaches; 3) existing computer-aided systems for DR screening; and 4) existing issues, challenges, and research trends. Our goal is to provide insights for future research directions on DR intelligent screening.     

单核细胞/淋巴细胞比值与糖尿病足患者下肢动脉病变的相关性研究

目的 探讨单核细胞/淋巴细胞比值（monocytes to lymphocytes ratio,MLR）与糖尿病足患者下肢动脉病变（lower extremity arterial disease,LEAD）的相关性。方法 选取2020年4月至2023年4月于陆军军医大学第一附属医院住院治疗的2 型糖尿病足患者243例,分为下肢动脉病变组（LEAD,n=103）和非下肢动脉病变组（non-lower extremity arterial disease,NLEAD,n=140）,分析MLR与LEAD的相关性。结果 LEAD组MLR显著高于NLEAD组［0.46（0.29,0.63） vs. 0.38（0.26,0.58）（P<0.05）］。logistic回归分析结果显示,MLR、年龄、胆固醇、血肌酐是糖尿病足患者发生下肢血管病变的影响因素（P<0.05）。受试者工作特征曲线分析表明,MLR预测糖尿病足患者下肢血管病变的最佳切点为0.34,敏感度和特异度分别为69.9%和46.4%。结论 MLR与2型糖尿病足患者LEAD的发生相关。     

中西药联合应用治疗糖尿病足的护理

随着糖尿病发病率的升高,其慢性并发症逐渐对患者的健康和生活质量构成严重威胁.其中糖尿病足患病率达6%～20%[1].我科2000年1月～2006年10月,共收治2级糖尿病足的患者42例.其中21例给予中西药联合治疗,疗效明显,现介绍如下.     

Diabetic retinopathy identification based on multi-source-free domain adaptation

AIM: To address the challenges of data labeling difficulties, data privacy, and necessary large amount of labeled data for deep learning methods in diabetic retinopathy (DR) identification, the aim of this study is to develop a source-free domain adaptation (SFDA) method for efficient and effective DR identification from unlabeled data. METHODS: A multi-SFDA method was proposed for DR identification. This method integrates multiple source models, which are trained from the same source domain, to generate synthetic pseudo labels for the unlabeled target domain. Besides, a softmax-consistence minimization term is utilized to minimize the intra-class distances between the source and target domains and maximize the inter-class distances. Validation is performed using three color fundus photograph datasets (APTOS2019, DDR, and EyePACS). RESULTS: The proposed model was evaluated and provided promising results with respectively 0.8917 and 0.9795 F1-scores on referable and normal/abnormal DR identification tasks. It demonstrated effective DR identification through minimizing intra-class distances and maximizing inter-class distances between source and target domains. CONCLUSION: The multi-SFDA method provides an effective approach to overcome the challenges in DR identification. The method not only addresses difficulties in data labeling and privacy issues, but also reduces the need for large amounts of labeled data required by deep learning methods, making it a practical tool for early detection and preservation of vision in diabetic patients.