Objective To examine the expression of stromal cell-derived factor-1 (SDF-1) in a rat model of retinal ischemia-reperfusion injury (RIRI),and investigate the protective effect of 17β-Estradiol (E2) on RIRI and explore the mechanism.Methods The RIRI model was established in Sprague-Dawley rats by increasing the intraocular pressure.Relative expression levels of SDF-1 mRNA and protein in the retina at 6 hours,12 hours and 24 hours following reperfusion was determined by RT-PCR and Western blot,respectively.E2 was administered to investigate the effects of estrogen on SDF-1 expression,and the estrogen receptor antagonist ICI 182-780 was administered to investigate the effect of estrogen receptor on the expression of SDF-1.Results SDF-1 expression in RIRI 6 hours group,12 hours group and 24 hours group was increased compared with normal control group (all P ＜ 0.05),with maximum expression at RIRI 12 hours group.As expected,pretreatment of RIRI rats with E2 had a protection on RIRI retina;SDF-1 expression was increased in RIRI + E2 group compared with IR control group and RIRI + vehicle group (all P ＜ 0.05).RIRI + E2 + ICI 182-780 group could decrease SDF-1 expression compared with RIRI + E2 group(all P ＜ 0.05).Conclusion E2 offers protection against RIRI by inducing an up-regulation in SDF-1 expression through activation of the estrogen receptor.

Small cell lung cancer （SCLC） accounts for about 15% in lung cancer and is highly malignant， heterogeneous and invasive. Etoposide combined with platinum-based chemotherapy is the basis of standard first-line treatment for extensive-stage SCLC， but suffers from the problem of susceptibility to drug resistance and relapse. In recent years， the emergence of new immunological drugs and novel cytotoxic drugs has improved the survival of SCLC patients to a certain extent， especially bringing therapeutic hope to patients with relapsed/refractory SCLC. In this paper， we review the current clinical drug regimens and the new progress of potential target drug therapeutic regimens for the treatment of SCLC. At present， the first-， second- and third-line schemes of SCLC include etoposide+carboplatin， atezolizumab+etoposide+platinum， adebrelimab， topotecan， docetaxel， etc.； the current drug targets for the treatment of SCLC mainly focus on topoisomerase Ⅱ/Ⅰ， DNA， the immune checkpoint molecules programmed death-1/programmed death-ligand 1， tubulin， etc. The potential target drug therapeutic options include alisertib+ paclitaxel， rovalpituzumab， APG-1252， etc.， and mainly focus on DNA damage response pathways and immune pathways， which can achieve the prolongation of patient survival by exerting anti-tumor effects through aurora kinase A and other potential targets.

Corona virus disease 2019 (COVID-19) is an acute respiratory infectious disease with strong contagiousness, strong variability, and long incubation period. The probability of misdiagnosis and missed diagnosis can be significantly decreased with the use of automatic segmentation of COVID-19 lesions based on computed tomography images, which helps doctors in rapid diagnosis and precise treatment. This paper introduced the level set generalized Dice loss function (LGDL) in conjunction with the level set segmentation method based on COVID-19 lesion segmentation network and proposed a dual-path COVID-19 lesion segmentation network (Dual-SAUNet++) to address the pain points such as the complex symptoms of COVID-19 and the blurred boundaries that are challenging to segment. LGDL is an adaptive weight joint loss obtained by combining the generalized Dice loss of the mask path and the mean square error of the level set path. On the test set, the model achieved Dice similarity coefficient of (87.81 ± 10.86)%, intersection over union of (79.20 ± 14.58)%, sensitivity of (94.18 ± 13.56)%, specificity of (99.83 ± 0.43)% and Hausdorff distance of 18.29 ± 31.48 mm. Studies indicated that Dual-SAUNet++ has a great anti-noise capability and it can segment multi-scale lesions while simultaneously focusing on their area and border information. The method proposed in this paper assists doctors in judging the severity of COVID-19 infection by accurately segmenting the lesion, and provides a reliable basis for subsequent clinical treatment.
Humans ; COVID-19/diagnostic imaging* ; Respiratory Rate ; Tomography, X-Ray Computed