Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

Metformin augments major cytoplasmic organization except for spindle organization in oocytes cultured under hyperglycemic and hyperlipidemic conditions: An in vitro study.

The present study aimed to investigate the role of antidiabetic drug metformin on the cytoplasmic organization of oocytes. Germinal vesicle (GV) stage oocytes were collected from adult female Swiss albino mice and subjected to in vitro maturation (IVM) in various experimental groups- control, vehicle control (0.3% ethanol), metformin (50 µg/mL), high glucose and high lipid (HGHL, 10 mM glucose; 150 µM palmitic acid; 75 µM stearic acid and 200 µM oleic acid in ethanol), and HGHL supplemented with metformin. The metaphase II (MII) oocytes were analyzed for lipid accumulation, mitochondrial and endoplasmic reticulum (ER) distribution pattern, oxidative and ER stress, actin filament organization, cortical granule distribution pattern, spindle organization and chromosome alignment. An early polar body extrusion was observed in the HGHL group. However, the maturation rate at 24 h did not differ significantly among the experimental groups compared to the control. The HGHL conditions exhibited significantly higher levels of oxidative stress, ER stress, poor actin filament organization, increased lipid accumulation, altered mitochondrial distribution, spindle abnormalities, and chromosome misalignment compared to the control. Except for spindle organization, supplementation of metformin to the HGHL conditions improved all the parameters (non-significant for ER and actin distribution pattern). These results show that metformin exposure in the culture media helped to improve the hyperglycemia and hyperlipidemia-induced cytoplasmic anomalies except for spindle organization. Given the crucial role of spindle organization in proper chromosome segregation during oocyte maturation and meiotic resumption, the implications of metformin's limitations in this aspect warrant careful evaluation and further investigation.

Correlation of Fetal Anterior Abdominal Wall Thickness and Other Standard Biometric Ultrasound Measurements to Predict Fetal Macrosomia in Gestational Diabetes.

Background: Gestational diabetes mellitus (GDM) is one of the most common medical conditions affecting pregnancy and significantly increasing the risk for maternal and perinatal complications. The aim of the present study is to study the correlation of fetal anterior abdominal wall thickness (FAAWT) and other standard fetal biometric parameters measured by ultrasound between 36 and 39 weeks of gestation with neonatal birth weight in pregnancies complicated by GDM. Methods: Prospective cohort study in a tertiary care center including 100 singleton pregnancies with GDM were subjected to ultrasound between 36 and 39 weeks of gestation. Standard fetal biometry (Biparietal diameter, Head Circumference, Abdominal circumference [AC], and Femur Length) and estimated fetal weight were calculated. FAAWT was measured at AC section and actual neonatal birth weights were recorded after delivery. Macrosomia was defined as an absolute birth weight more than 4000 g regardless of the gestational age. Statistical analysis was done and 95% confidence level was considered significant. Results: Among 100 neonates, 16 were macrosomic (16%) and third trimester mean FAAWT was significantly higher in macrosomic babies (6.36 ± 0.5 mm) as compared to nonmacrosomic babies (5.54 ± 0.61 mm) (P < 0.0001). FAAWT >6 mm (Receiver operating characteristic curve derived) provided a sensitivity of 87.5%, specificity of 75%, positive predictive value of 40%, and negative predictive value (NPV) of 96.9% for prediction of macrosomia. While other standard fetal biometric parameters did not correlate well with actual birth weight in macrosomic neonates, only FAAWT was found to have statistically significant correlation (correlation coefficient of 0.626, P = 0.009). Conclusion: The FAAWT was the only sonographic parameter to have a significant correlation with neonatal birth weight in macrosomic neonates of GDM mothers. We found a high sensitivity (87.5%), specificity (75%), and NPV (96.9%) suggesting that FAAWT < 6 mm can rule out macrosomia in pregnancies with GDM.

Evaluation of the Recombinant Bacterial Chitinases as Anti-proliferative and Anti-migratory Agents for the Human Breast Cancer Cell Line, MCF-7.

Chitinases, a glycosyl hydrolase family 18 members, have a wide distribution in both prokaryotes and eukaryotes, including humans. Regardless of the absence of endogenous chitin polymer, various chitinases and chitinase-like proteins (CLPs) have been reported in mammals. However, several other carbohydrate polymers, such as hyaluronic acid and heparan sulfate, show structural similarities with chitin, which could be a potential target of chitinase and CLPs. Heparan sulfate is part of the integral membrane proteins and involves in cell adherence and migration. Hence, to demonstrate the effect of chitinase on cancer cell progression, we selected two chitinases from Serratia marcescens, ChiB and ChiC, which function as exo- and endo-chitinase, respectively. The ChiB and ChiC proteins were produced recombinantly by cloning chiB and chiC genes from Serratia marcescens. The cell viability of the Michigan Cancer Foundation-7 (MCF-7) cells was studied using different concentrations of the purified recombinant proteins. Cell viability assay was performed using 3-(4, 5-dimethyl thiazolyl-2)-2, 5-diphenyltetrazolium bromide and water-soluble tetrazolium salt, and the effect of ChiB and ChiC on cell proliferation was studied by clonogenic assay. The cell migration study was analysed by wound healing, transwell migration, and invasion assays. Cell cycle analysis of propidium iodide-stained cells and cell proliferation markers such as pERK1/2, pAKT, and SMP30 were also done. It was observed that both ChiB and ChiC were able to impede cell viability, cell migration, and invasion significantly. These observations and our in silico molecular docking analysis suggest that ChiC is a potential anticancer agent and is more efficient than ChiB. Since the ChiC is able to inhibit both cancer cell proliferation and migration, it could be a potential candidate for the treatment of metastatic cancer.

Magnetic Resonance Imaging of Malformations of Cortical Development-A Comprehensive Review.

Malformations of cortical development (MCDs) are structural anomalies that disrupt the normal process of cortical development. These include microcephaly with simplified gyral pattern/microlissencephaly, hemimegalencephaly, focal cortical dysplasia, lissencephaly, heterotopia, polymicrogyria, and schizencephaly. They can present with intractable epilepsy, developmental delay, neurologic deficits, or cognitive impairment. Though the definitive diagnosis of MCD depends on histopathology, the pathologic tissue is rarely available; hence diagnosis begins with neuroimaging. This article shall briefly review the embryology, followed by specific magnetic resonance imaging features of MCD in an attempt to simplify the process of diagnosing these disorders with clinical and genetic correlation. A table has been included to highlight the embryologic, clinical, and genetic findings associated with various MCDs.

The United States COVID-19 Forecast Hub dataset.

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.