The long-term pulmonary function and related physiological characteristics of COVID-19 survivors have not been studied in depth, thus many aspects are not understood. COVID-19 survivors were recruited for high resolution computed tomography (HRCT) of the thorax, lung function and serum levels of SARS-CoV-2 IgG antibody tests 3 months after discharge. The relationship between the clinical characteristics and the pulmonary function or CT scores were investigated. Fifty-five recovered patients participated in this study. SARS-CoV-2 infection related symptoms were detected in 35 of them and different degrees of radiological abnormalities were detected in 39 patients. Urea nitrogen concentration at admission was associated with the presence of CT abnormalities (P = 0.046, OR 7.149, 95% CI 1.038 to 49.216). Lung function abnormalities were detected in 14 patients and the measurement of D-dimer levels at admission may be useful for prediction of impaired diffusion defect (P = 0.031, OR 1.066, 95% CI 1.006 to 1.129). Of all the subjects, 47 of 55 patients tested positive for SARS-CoV-2 IgG in serum, among which the generation of Immunoglobulin G (IgG) antibody in female patients was stronger than male patients in infection rehabilitation phase. Radiological and physiological abnormalities were still found in a considerable proportion of COVID-19 survivors without critical cases 3 months after discharge. Higher level of D-dimer on admission could effectively predict impaired DLCO after 3 months discharge. It is necessary to follow up the COVID-19 patients to appropriately manage any persistent or emerging long-term sequelae. Key Scientific Research Projects of Henan Higher Education Institutions

Certain high-risk factors related to the death of COVID-19 have been reported, however, there were few studies on a death prediction model. This study was conducted to delineate the clinical characteristics of patients with coronavirus disease 2019 (covid-19) of different degree and establish a death prediction model. In this multi-centered, retrospective, observational study, we enrolled 523 COVID-19 cases discharged before February 20, 2020 in Henan Province, China, compared clinical data, screened for high-risk fatal factors, built a death prediction model and validated the model in 429 mild cases, six fatal cases discharged after February 16, 2020 from Henan and 14 cases from Wuhan. Out of the 523 cases, 429 were mild, 78 severe survivors, 16 non-survivors. The non-survivors with median age 71 were older and had more comorbidities than the mild and severe survivors. Non-survivors had a relatively delay in hospitalization, with higher white blood cell count, neutrophil percentage, D-dimer, LDH, BNP, and PCT levels and lower proportion of eosinophils, lymphocytes and albumin. Discriminative models were constructed by using random forest with 16 non-survivors and 78 severe survivors. Age was the leading risk factors for poor prognosis, with AUC of 0.907 (95% CI 0.831–0.983). Mixed model constructed with combination of age, demographics, symptoms, and laboratory findings at admission had better performance (p = 0.021) with a generalized AUC of 0.9852 (95% CI 0.961–1). We chose 0.441 as death prediction threshold (with 0.85 sensitivity and 0.987 specificity) and validated the model in 429 mild cases, six fatal cases discharged after February 16, 2020 from Henan and 14 cases from Wuhan successfully. Mixed model can accurately predict clinical outcomes of COVID-19 patients.

Mucormycosis is an angioinvasive fungal infection, associated with high mortality. The aim of our study was to explore the high-risk factors and predict the death of hematological disease complicated with mucormycosis. We retrospectively analyzed clinical data of 31 patients with hematological disease complicated with mucormycosis, adopted random forest to establish the death prediction model, and validated the model in another 15 patients. The median age of the 31 cases was 46 (28–51) years, male to female ratio 1.38:1, and 90-day mortality rate 54.8%. The most common underlying disease was acute myeloid leukemia (58.1%). The main clinical symptoms were fever (100%), cough (87.1%), sputum (80.6%), chest pain (61.3%), and hemoptysis (19.4%). Reversed halo sign (83.9%) was the most common computed tomography sign. A total of 48.4% of patients also had aspergillus or bacterial infections. Discriminative models were constructed by random forest with 17 non-survivors and 14 survivors. Procalcitonin, the duration of intravenous administration of amphotericin B or amphotericin B liposomes, and neutropenia at death or 90 days of survival were the leading risk factors for poor prognosis, with area under the curve of 0.975 (95% CI 0.934–1). We chose 0.6775 as death prediction threshold (with 82.3% sensitivity and 100% specificity) and validated the model successfully in another 15 patients. Chest pain and reversed halo sign are specific clinical and image signs of hematological disease complicated with mucormycosis. Neutropenia, elevated procalcitonin, and insufficient use time of amphotericin B or amphotericin B liposomes are risk factors for death.

背景与目的 癌症患者的营养状况与预后之间的关系已成为研究关注的热点。本研究旨在探索老年营养风险指数（geriatric nutritional risk index, GNRI）在老年非小细胞肺癌（non-small cell lung cancer, NSCLC）患者预后评估中的应用价值，构建预测老年NSCLC患者预后的列线图模型。方法 回顾性分析郑州大学第一附属医院于2016年1月至2019年12月初治的、≥65岁的153例NSCLC患者的资料。使用受试者工作特征（receiver operating characteristic, ROC）曲线确定GNRI的最佳截断值，把患者分为高GNRI组和低GNRI组，通过Kaplan-Meier曲线和Log-rank检验对两组患者总生存期（overall survival, OS）进行比较。使用单因素和多因素Cox回归分析探讨老年NSCLC患者不良预后的危险因素，利用R软件构建预测老年NSCLC患者生存率的列线图模型，并验证该模型。结果 高GNRI组和低GNRI组在年龄、性别、体重指数（body mass index, BMI）、组织学类型、白蛋白水平、治疗方式、中性粒细胞与淋巴细胞比值（neutrophil to lymphocyte ratio, NLR）、预后营养指数（prognostic nutritional index, PNI）、全身免疫炎症指数（systemic immune-inflammation index, SII）和细胞角蛋白19片段（cytokeratin 19 fragment, CYFRA21-1）方面存在统计学差异（P<0.05）。Kaplan-Meier曲线显示低GNRI组的患者总生存期较短。多因素Cox回归分析显示CYFRA21-1>3.3 ng/mL是影响NSCLC患者OS的独立危险因素，GNRI>97.09是保护因素[风险比（hazard ratio, HR）=0.52，95%置信区间（confidence interval, CI）：0.34-0.79，P<0.05]。IV期患者发生死亡的风险是I期患者的1.98倍（95%CI: 1.02-3.86, P<0.05）。与行综合治疗的患者相比，行单一化疗的患者发生死亡的风险增加3.58倍（95%CI: 2.03-6.32, P<0.05）。基于GNRI构建的列线图模型对老年NSCLC患者OS进行预测，其一致性指数（concordance index, C-index）为0.70（95%CI: 0.65-0.76），预测1、2年生存率的曲线下面积（area under the curve, AUC）分别为0.93（95%CI: 0.87-0.98）、0.72（95%CI: 0.63-0.80），校准曲线显示该模型的预测符合度较好。结论 高GNRI评分与改善老年NSCLC患者的生存率显著相关，依靠截断值可能提供恰当的营养支持时机。该研究构建的列线图可作为一种有效预测老年NSCLC患者生存率的工具，具有很强的临床实用性。

Background: Previous research has linked alterations in the composition of the gut microbiota to a variety of gynecologic tumors.Nevertheless, although the causal relationship between the gut microbiota and gynecologic tumors remains to be fully elucidated. Therefore, this study uses a two-sample Mendelian randomization analysis (MR) to explore the causal correlation between the gut microbiota community and prevalent gynecologic tumours. With the goal of identifying specific pathogenic bacterial communities that may be involved in gynecologic tumor development. Materials and Methods: We utilized data from the MiBioGen consortium’s Genome-Wide Association Study (GWAS) on gut microbiota as the exposure variable. Four common gynecologic neoplasms including uterine fibroids (UF), endometrial cancer (EC), ovarian cancer (OC) and cervical cancer (CC) were selected as the outcome variables. Single-nucleotide polymorphisms (SNPs) significantly associated with exposure were selected as the instrumental variables (IVs). The inverse variance-weighted (IVW) method was used as the principal MR analysis to assess the causal relationship between gut microbiota and these tumors, with the goal of identifying microbial communities associated with gynecologic tumor development. An independent validation cohort was used for further validation. We conducted sensitivity analyses to ensure robustness of the findings. Lastly, we performed reverse MR analysis to assess the potential for reverse causation. Results: Combining the results from the discovery and validation cohorts, we found that higher relative abundance of Lachnospiraceae is associated with lower risk of UF (OR: 0.882, 95% CI: 0.793-0.999, P = 0.982). Conversely, a higher incidence of OC is associated with a higher relative abundance of Lachnospiraceae (OR: 1.329, 95% CI: 1.019-1.732, P= 0.036). Sensitivity analyses confirmed the reliability of these results. Furthermore, the results of the reverse MR analysis showed no evidence of a reverse cause-and-effect relationship between UF, OC, and Lachnospiraceae. Conclusion: In this study, a causal relationship between Lachnospiraceae and both UF and OC was established. This provides new insights into the role of gut microbiota in the mechanism of gynecological tumor development.