Maternal Neonatal Outcome in Relation to Placental Location, Dimensions in Early Pregnancy

Background: Placenta, which is the vital link between mother and fetus, is critical for maternal neonatal well-being. Its study in early pregnancy may provide information about maternal neonatal disorders.Aim: The study aimed to evaluate the relationship of placental location and dimensions in early pregnancy with maternal neonatal outcomes.Subjects and Methods: Primigravida (801) with singleton pregnancy at 10-weeks gestation and no past/present medical and obstetric disorder had ultrasonography for placental location and dimensions and were followed by ultrasonographic (USG) examination (at 20th week and 30th week), clinically for maternal-neonatal outcome. Statistical analysis was done by Epi 6 software (version 6.0, developed by Centres for Disease Control and Prevention, Atlanta, Georgia, USA) using Chi-square test and Fischer exact test for determining the statistical significance of the observations. P values of < 0.05 were considered as significant.Results: The number of primigravida with hypertensive disorders were 2.5% (5/200) with anterior, 20.5% (66/322) with fundal, and with posterior placenta 9.8% (12/123); Placental abruption 2.5% (5/200) with anterior, 6.8% (22/322) with fundal, and 3.3% (4/123) with posterior. With placental surface area <41 cm2 19.0% (37/195), with area 41-55 cm2 7.2% (30/416), and with area >55 cm2 6.8% (13/190), had hypertensive disorders. area < 41 cm2 11.3% (22/195), area 41-55 cm2 5.0% (21/416), and area >55 cm2 3.7% (7/190) had placental abruption. With thick placenta, 39.2% (58/148), thin, 9.4% (9/96), and normal placenta, 5.2% (29/562) had hypertensive disorders. With thick, 11.5% (17/148), thin 16.7% (16/96), and normal placenta 2.7% (15/562) had placental abruption. With anterior 0.5% (1/200), posterior 14.6% (18/123), fundal placenta 10.55% had preterm births. With anterior 7.5% (15/200), posterior 23.6% (29/123), fundal placenta 18% (58/322) had CS.With placental surface area <41 cm2 28.7% (56/195), area 41-55 cm2 14.2% (58/406), with > 55 cm2 14% (28/200) had CS. With thin 27% (25/91), with thick 36.1% (53/148), with normal placenta none had CS for fetal distress.Conclusions: Study of placental location and dimensions in early pregnancy is useful in identifying risks.  Keywords: Dimensions, early pregnancy, location, maternal-neonatal outcome, placent

PYODERMA GANGRENOSUM SECONDARY TO TAKAYASU'S ARTERITIS

Pyoderma gangrenosum (PG) is an uncommon & noninfectious neutrophilic dermatosis commonly associated with underlying systemic disease in more than 50% of cases. About 20-30% of cases are commenced and aggravated by minor trauma or surgery, a phenomenon named pathergy. PG is associated with many systemic diseases, but association with Takayasu's arteritis (TA) / pulseless disease, which is chronic inflammatory and stenotic disease of large sized arteries is less common. These conditions responded well with systemic corticosteroids. The association of PG with TA has been less reported in the literature so far. KEYWORDS: Pyoderma gangrenosum; Takayasu's arteritis;Pulseless disease

Design and Fabrication of Garlic Peeler

Garlic is an important and economical plant. It has many uses in medicinal, culinary and ayurvedic purposes. Garlic peeling is a tedious, time consuming and laborious work. So far, traditional peeling methods are used for garlic peeling. These methods are observed to be unhygienic, laborious and caused high damage to garlic segments. Mechanical peelers available are costly and not affordable to small scale industries. Since, the traditional methods are laborious and mechanical methods are costly there is a need to develop low cost, mechanical peeler that will reduce the drudgery. Angular iron and flat iron was used for main frame and supporting the main units. A food grade rubber and mild steel pipe was used for rubber roller. Iron bar was used for shaft. A wire mesh was used as screen. Dimensions of garlic segments were measured using digital vernier callipers and weight of each garlic segment was measured using electronic balance. Moisture content, orthogonal dimensions, weight, geometric mean diameter, sphericity, equivalent mean diameter, shape factor, terminal velocity and drag coefficient of garlic segments were found. Moisture content of garlic segments was 59.36±0.87% (w.b). At this moisture content, average length, width and thickness of garlic segments were found to be 25.818 3.743 mm, 10.116 2.209 mm and 7.34 1.638 mm, respectively. Average weight of individual garlic segments were found to be 1.159 g. Geometric mean diameter, sphericity, equivalent mean diameter and shape factor of garlic segments were found out to be 12.422 mm, 0.481, 13.03 mm and 0.218, respectively. The terminal velocity and drag coefficient were 18.941 m/s and 0.416 at moisture content of 59.08 ± 0.82% (w.b.). Cost of peeler was estimated to be about ₹ 10,005/-

Association of Genetic Variation with Keratoconus

Importance: Keratoconus is a condition in which the cornea progressively thins and protrudes in a conical shape, severely affecting refraction and vision. It is a major indication for corneal transplant. To discover new genetic loci associated with keratoconus and better understand the causative mechanism of this disease, we performed a genome-wide association study on patients with keratoconus.Objective: To identify genetic susceptibility regions for keratoconus in the human genome.Design, Setting, and Participants: This study was conducted with data from eye clinics in Australia, the United States, and Northern Ireland. The discovery cohort of individuals with keratoconus and control participants from Australia was genotyped using the Illumina HumanCoreExome single-nucleotide polymorphism array. After quality control and data cleaning, genotypes were imputed against the 1000 Genomes Project reference panel (phase III; version 5), and association analyses were completed using PLINK version 1.90. Single-nucleotide polymorphisms with P -6 were assessed for replication in 3 additional cohorts. Control participants were drawn from the cohorts of the Blue Mountains Eye Study and a previous study of glaucoma. Replication cohorts were from a previous keratoconus genome-wide association study data set from the United States, a cohort of affected and control participants from Australia and Northern Ireland, and a case-control cohort from Victoria, Australia. Data were collected from January 2006 to March 2019.Main Outcomes and Measures: Associations between keratoconus and 6 252 612 genetic variants were estimated using logistic regression after adjusting for ancestry using the first 3 principal components.Results: The discovery cohort included 522 affected individuals and 655 control participants, while the replication cohorts included 818 affected individuals (222 from the United States, 331 from Australia and Northern Ireland, and 265 from Victoria, Australia) and 3858 control participants (2927 from the United States, 229 from Australia and Northern Ireland, and 702 from Victoria, Australia). Two novel loci reached genome-wide significance (defined as P -8), with a P value of 7.46 × 10-9 at rs61876744 in patatin-like phospholipase domain-containing 2 gene (PNPLA2) on chromosome 11 and a P value of 6.35 × 10-12 at rs138380, 2.2 kb upstream of casein kinase I isoform epsilon gene (CSNK1E) on chromosome 22. One additional locus was identified with a P value less than 1.00 × 10-6 in mastermind-like transcriptional coactivator 2 (MAML2) on chromosome 11 (P = 3.91 × 10-7). The novel locus in PNPLA2 reached genome-wide significance in an analysis of all 4 cohorts (P = 2.45 × 10-8).Conclusions and Relevance: In this relatively large keratoconus genome-wide association study, we identified a genome-wide significant locus for keratoconus in the region of PNPLA2 on chromosome 11

Analysis of performance in Depth Based Routing for Underwater Wireless Sensor Networks

In the last decade, Underwater Wireless Sensor Networks (UWSNs) have been widely studied because of their peculiar aspects that distinguish them from common wireless terrestrial networks. In fact, most UWSNs use acoustic instead of radio-frequency based communications, and nodes are subject to high mobility caused by water currents. As a consequence, specialized routing algorithms have been developed to tackle this challenging scenario. Depth based Routing (DBR) is one of the first protocols that have been developed to this aim, and is still widely adopted in actual implementations of UWSNs. In this paper we propose a stochastic analysis that aims at evaluating the performance of UWSNs using DBR in terms of expected energy consumption and expected end-to-end delay. Under a set of assumptions, we give expressions for these performance indices that can be evaluated efficiently, and hence they can be adopted as the basis for optimizing the configuration parameters of the protocol

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US

This article contains supporting information online at http://www.pnas.org/lookup/suppl/doi:10.1073/pnas.2113561119/-/DCSupplemental.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 multi-model ensemble forecast that combined predictions from dozens of different research 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-week horizon 3-5 times larger than when predicting at a 1-week 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.Integrative Biolog

A multi-ethnic genome-wide association study implicates collagen matrix integrity and cell differentiation pathways in keratoconus

Keratoconus is characterised by reduced rigidity of the cornea with distortion and focal thinning that causes blurred vision, however, the pathogenetic mechanisms are unknown. It can lead to severe visual morbidity in children and young adults and is a common indication for corneal transplantation worldwide. Here we report the first large scale genome-wide association study of keratoconus including 4,669 cases and 116,547 controls. We have identified significant association with 36 genomic loci that, for the first time, implicate both dysregulation of corneal collagen matrix integrity and cell differentiation pathways as primary disease-causing mechanisms. The results also suggest pleiotropy, with some disease mechanisms shared with other corneal diseases, such as Fuchs endothelial corneal dystrophy. The common variants associated with keratoconus explain 12.5% of the genetic variance, which shows potential for the future development of a diagnostic test to detect susceptibility to disease

IMI - Myopia Genetics Report

The knowledge on the genetic background of refractive error and myopia has expanded dramatically in the past few years. This white paper aims to provide a concise summary of current genetic findings and defines the direction where development is needed. We performed an extensive literature search and conducted informal discussions with key stakeholders. Specific topics reviewed included common refractive error, any and high myopia, and myopia related to syndromes. To date, almost 200 genetic loci have been identified for refractive error and myopia, and risk variants mostly carry low risk but are highly prevalent in the general population. Several genes for secondary syndromic myopia overlap with those for common myopia. Polygenic risk scores show overrepresentation of high myopia in the higher deciles of risk. Annotated genes have a wide variety of functions, and all retinal layers appear to be sites of expression. The current genetic findings offer a world of new molecules involved in myopiagenesis. As the missing heritability is still large, further genetic advances are needed. This Committee recommends expanding large-scale, in-depth genetic studies using complementary big data analytics, consideration of gene-environment effects by thorough measurement of environmental exposures, and focus on subgroups with extreme phenotypes and high familial occurrence. Functional characterization of associated variants is simultaneously needed to bridge the knowledge gap between sequence variance and consequence for eye growth

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

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