Effect of the COVID-19 pandemic on women's fertility intentions and its policy implications for China and the rest of the world: a perspective essay.

The COVID-19 pandemic and its social, economic, and health implications have generally reduced women's fertility intentions in different countries. In this article, we aimed to review studies of the impact of COVID-19 infection on women's fertility intentions and interventions to provide a theoretical basis and practical benchmark for the development of effective intervention strategies in China, which lifted its zero COVID system in early December 2022.

Aggravated pneumonia and diabetes in SARS-CoV-2 infected diabetic mice.

Multiple clinical and epidemiological studies have shown an interconnection between coronavirus disease 2019 (COVID-19) and diabetes, but experimental evidence is still lacking. Understanding the interplay between them is important because of the global health burden of COVID-19 and diabetes. We found that C57BL/6J mice were susceptible to the alpha strain of SARS-CoV-2. Moreover, diabetic C57BL/6J mice with leptin receptor gene deficiency (db/db mice) showed a higher viral load in the throat and lung and slower virus clearance in the throat after infection than C57BL/6J mice. Histological and multifactor analysis revealed more advanced pulmonary injury and serum inflammation in SARS-CoV-2 infected diabetic mice. Moreover, SARS-CoV-2 infected diabetic mice exhibited more severe insulin resistance and islet cell loss than uninfected diabetic mice. By RNA sequencing analysis, we found that diabetes may reduce the collagen level, suppress the immune response and aggravate inflammation in the lung after infection, which may account for the greater susceptibility of diabetic mice and their more severe lung damage after infection. In summary, we successfully established a SARS-CoV-2 infected diabetic mice model and demonstrated that diabetes and COVID-19 were risk factors for one another.

Factors affecting prolonged SARS-CoV-2 infection and development and validation of predictive nomograms.

Prolonged severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has received much attention since it is associated with mortality and is hypothesized as the cause of long COVID-19 and the emergence of a new variant of concerns. However, a prediction model for the accurate prediction of prolonged infection is still lacking. A total of 2938 confirmed patients with COVID-19 diagnosed by positive reverse transcriptase-polymerase chain reaction tests were recruited retrospectively. This study cohort was divided into a training set (70% of study patients; n = 2058) and a validation set (30% of study patients; n = 880). Univariate and multivariate logistic regression analyses were utilized to identify predictors for prolonged infection. Model 1 included only preadmission variables, whereas Model 2 also included after-admission variables. Nomograms based on variables of Model 1 and Model 2 were built for clinical use. The efficiency of nomograms was evaluated by using the area under the curve, calibration curves, and concordance indexes (C-index). Independent predictors of prolonged infection included in Model 1 were: age ≥75 years, chronic kidney disease, chronic lung disease, partially or fully vaccinated, and booster. Additional independent predictors in Model 2 were: treated with nirmatrelvir/ritonavir more than 5 days after diagnosis and glucocorticoid. The inclusion of after-admission variables in the model slightly improved the discriminatory power (C-index in the training cohort: 0.721 for Model 1 and 0.737 for Model 2; in the validation cohort: 0.699 for Model 1 and 0.719 for Model 2). In our study, we developed and validated predictive models based on readily available variables of preadmission and after-admission for predicting prolonged SARS-CoV-2 infection of patients with COVID-19.

A practical scoring model to predict the occurrence of critical illness in hospitalized patients with SARS-CoV-2 omicron infection.

Background: The variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have emerged repeatedly, especially the Omicron strain which is extremely infectious, so early identification of patients who may develop critical illness will aid in delivering proper treatment and optimizing use of resources. We aimed to develop and validate a practical scoring model at hospital admission for predicting which patients with Omicron infection will develop critical illness. Methods: A total of 2,459 patients with Omicron infection were enrolled in this retrospective study. Univariate and multivariate logistic regression analysis were performed to evaluate predictors associated with critical illness. Moreover, the area under the receiver operating characteristic curve (AUROC), continuous net reclassification improvement, and integrated discrimination index were assessed. Results: The derivation cohort included 1721 patients and the validation cohort included 738 patients. A total of 98 patients developed critical illness. Thirteen variables were independent predictive factors and were included in the risk score: age > 65, C-reactive protein > 10 mg/L, lactate dehydrogenase > 250 U/L, lymphocyte < 0.8*10^9/L, white blood cell > 10*10^9/L, Oxygen saturation < 90%, malignancy, chronic kidney disease, chronic cardiac disease, chronic obstructive pulmonary disease, diabetes, cerebrovascular disease, and non-vaccination. AUROC in the derivation cohort and validation cohort were 0.926 (95% CI, 0.903-0.948) and 0.907 (95% CI, 0.860-0.955), respectively. Moreover, the critical illness risk scoring model had the highest AUROC compared with CURB-65, sequential organ failure assessment (SOFA) and 4C mortality scores, and always obtained more net benefit. Conclusion: The risk scoring model based on the characteristics of patients at the time of admission to the hospital may help medical practitioners to identify critically ill patients and take prompt measures.

Dynamic Changes in Coagulation Function in Patients With Pneumonia Under Admission and Non-admission Treatment.

Objective: We aimed to explore the dynamic changes in coagulation function and the effect of age on coagulation function in patients with pneumonia under admission and non-admission treatment. Methods: We included 178 confirmed adult inpatients with COVID-19 from Wuhan Union Hospital Affiliated to Huazhong University of Science and Technology (Wuhan, China). Patients were classified into common types, and all were cured and discharged after hospitalization. We recorded the time of the first clinical symptoms of the patients and performed blood coagulation tests at the time of admission and after admission. In total, eight factors (TT, FIB, INR, APTT, PT, DD, ATIII, and FDP) were analyzed. Patients were classified into four groups according to the time from the first symptom onset to hospital admission for comparative analysis. The patients who were admitted within 2 weeks of disease onset were analyzed for the dynamic changes in their blood coagulation tests. Further division into two groups, one group comprising patients admitted to the hospital within 2 weeks after the onset of disease and the other comprising patients admitted to the hospital 2 weeks after disease onset, was performed to form two groups based on whether the patient ages were over or under 55 years. Chi-square tests and T tests were used to explore the dynamic changes in coagulation function and the influence of age on the results of coagulation function tests. Results: A total of 178 inpatients, 34 of whom underwent dynamic detection, were included in this analysis. We divided these patients into four groups according to the interval between the onset of COVID-19 pneumonia and the time to admission in the hospital: the 1-7 days (group 1), 8-14 days (group 2), 15-21 days (group 3), and >21-days (group 4). Eight factors all increased within 2 weeks after onset and gradually decreased to normal 2 weeks before the patient was admitted. The changes in coagulation function of patients admitted to the hospital were similar. After being admitted to the hospital, the most significant decreases among the eight factors were between week 2 and 3. There were distinct differences among the eight factors between people older than 55 years and those younger than 55 years. In the first 2 weeks after being admitted, the levels of the eight factors in patients >55 years were significantly higher than those in patients <55 years, and after another 2 weeks of treatment, the factor levels in both age groups returned to normal. Conclusion: The eight factors all increased within 2 weeks after onset and gradually decreased to normal after 2 weeks regardless of treatment. Compared with patients younger than 55 years, patients older than 55 years have greater changes in their blood coagulation test values.