Background: Perinatal arterial ischemic stroke (PAIS) is a focal brain injury in term neonates, identified postnatally but presumed to occur around birth. Early risk detection and targeted treatments are limited. We developed and validated a diagnostic risk prediction model from common clinical factors to predict a term neonate’s probability of PAIS. Methods: A diagnostic prediction model was developed using multivariable logistic regression. Common pregnancy, delivery, and neonatal clinical factors were collected across four registries. Variable selection was based on peer-reviewed literature. Participant inclusion criteria were term birth and no underlying predisposition to stroke. The primary outcome was discriminative accuracy of the model predicting PAIS, measured by the concordance (C-) statistic. Results: 2571 participants (527 cases, 2044 controls) were eligible for analysis. Nine variables were included in the model – maternal age, tobacco exposure, recreational drug exposure, pre-eclampsia, chorioamnionitis, maternal fever, emergency c-section, low 5-minute Apgar score, and sex – to predict the risk of PAIS in a term neonate. This model demonstrated good discrimination between cases and controls (C-statistic 0.73) and model fit (Hosmer-Lemeshow p=0.20). Conclusions: Clinical variables can be used to develop and internally validate a model of PAIS risk prediction. Identifying high-risk neonates for early screening and treatment could reduce lifelong morbidity.

Background: Perinatal arterial ischemic stroke (PAIS) is a leading cause of hemiparetic cerebral palsy. Multiple risk factors are associated with PAIS but studies are limited by small sample sizes and complex interactions. Unbiased machine learning applied to larger datasets may enable the development of robust predictive models. We aimed to use machine learning to identify risk factors predictive of PAIS and compare these to the existing literature. Methods: Common data elements of maternal, delivery, and neonatal factors were collected from three perinatal stroke registries and one control sample over a 7-year period. Inclusion criteria were MRI-confirmed PAIS, term birth, and idiopathic etiology. Random forest machine learning in combination with feature selection was used to develop a predictive model of PAIS. Results: Total of 2571 neonates were included (527 cases, 2044 controls). Risk factors uniquely identified through machine learning were infertility, miscarriage, primigravida, and meconium. When compared, factors identified through both literature-based selection and machine learning included maternal age, fetal tobacco exposure, intrapartum fever, and low 5-minute APGAR. Conclusions: Machine learning offers a novel, less biased method to identify PAIS predictors and complex pathophysiology. Our findings support known associations with concepts of placental disease and difficult fetal transition and may support early screening for PAIS.

The National Center for Advancing Translational Science (NCATS) seeks to improve upon the translational process to advance research and treatment across all diseases and conditions and bring these interventions to all who need them. Addressing the racial/ethnic health disparities and health inequities that persist in screening, diagnosis, treatment, and health outcomes (e.g., morbidity, mortality) is central to NCATS’ mission to deliver more interventions to all people more quickly. Working toward this goal will require enhancing diversity, equity, inclusion, and accessibility (DEIA) in the translational workforce and in research conducted across the translational continuum, to support health equity. This paper discusses how aspects of DEIA are integral to the mission of translational science (TS). It describes recent NIH and NCATS efforts to advance DEIA in the TS workforce and in the research we support. Additionally, NCATS is developing approaches to apply a lens of DEIA in its activities and research – with relevance to the activities of the TS community – and will elucidate these approaches through related examples of NCATS-led, partnered, and supported activities, working toward the Center’s goal of bringing more treatments to all people more quickly.

The sustainability of rigid packaging can be increased by using biocomposites in packaging. Existing frameworks have some limitations such as are made to assess a few aspects, conventional packaging parameters are considered, etc. Biocomposite has a slightly different scenario at various life cycle stages, like the end-of-life cycle process. To assess the sustainability of biocomposite rigid packaging, we must consider parameters related to the biocomposite-based rigid packaging materials life cycle. These are categorised into different aspects of sustainability and life cycle phases.

Substantial progress has been made in the standardization of nomenclature for paediatric and congenital cardiac care. In 1936, Maude Abbott published her Atlas of Congenital Cardiac Disease, which was the first formal attempt to classify congenital heart disease. The International Paediatric and Congenital Cardiac Code (IPCCC) is now utilized worldwide and has most recently become the paediatric and congenital cardiac component of the Eleventh Revision of the International Classification of Diseases (ICD-11). The most recent publication of the IPCCC was in 2017. This manuscript provides an updated 2021 version of the IPCCC.

The International Society for Nomenclature of Paediatric and Congenital Heart Disease (ISNPCHD), in collaboration with the World Health Organization (WHO), developed the paediatric and congenital cardiac nomenclature that is now within the eleventh version of the International Classification of Diseases (ICD-11). This unification of IPCCC and ICD-11 is the IPCCC ICD-11 Nomenclature and is the first time that the clinical nomenclature for paediatric and congenital cardiac care and the administrative nomenclature for paediatric and congenital cardiac care are harmonized. The resultant congenital cardiac component of ICD-11 was increased from 29 congenital cardiac codes in ICD-9 and 73 congenital cardiac codes in ICD-10 to 318 codes submitted by ISNPCHD through 2018 for incorporation into ICD-11. After these 318 terms were incorporated into ICD-11 in 2018, the WHO ICD-11 team added an additional 49 terms, some of which are acceptable legacy terms from ICD-10, while others provide greater granularity than the ISNPCHD thought was originally acceptable. Thus, the total number of paediatric and congenital cardiac terms in ICD-11 is 367. In this manuscript, we describe and review the terminology, hierarchy, and definitions of the IPCCC ICD-11 Nomenclature. This article, therefore, presents a global system of nomenclature for paediatric and congenital cardiac care that unifies clinical and administrative nomenclature.

The members of ISNPCHD realize that the nomenclature published in this manuscript will continue to evolve. The version of the IPCCC that was published in 2017 has evolved and changed, and it is now replaced by this 2021 version. In the future, ISNPCHD will again publish updated versions of IPCCC, as IPCCC continues to evolve.

We assessed the impact of an embedded electronic medical record decision-support matrix (Cerner software system) for the reduction of hospital-onset Clostridioides difficile. A critical review of 3,124 patients highlighted excessive testing frequency in an academic medical center and demonstrated the impact of decision support following a testing fidelity algorithm.

There are two different views regarding the genesis of gibbsite in tropical acid soils: (1) direct weathering of primary Al-silicate minerals; and (2) transformation through clay mineral intermediates. We investigated the genesis of gibbsite in two representative Ultisols from northeastern India. Gibbsite in these Ultisols appears to be the remnant of earlier weathering products of aluminosilicate minerals formed in a neutral to alkaline pedochemical environment. The mere presence of gibbsite in these soils, therefore, does not indicate their advanced stage of weathering. The formation of typically rod-shaped and well-crystallized gibbsite in both the coarse and fine soil fractions in the presence of large amounts of 2:1 minerals indicates that the anti-gibbsite hypothesis may not be tenable in these tropical acid soils. A schematic model for the formation of gibbsite and kaolin in Ultisols is proposed.

In this paper, we study the relative perturbation bounds for jointeigenvalues of commuting tuples of normal $n\times n$ matrices. Some Hoffman–Wielandt-type relative perturbationbounds are proved using the Clifford algebra technique. We also extend aresult for diagonalisable matrices which improves a relative perturbationbound for single matrices.

Background: Granulomatosis with polyangitis (GPA) is a rare systemic vasculitis with a prevalence of 0.6 per million in the pediatric population. CNS involvement occurs in 7-18% of cases. Pituitary involvement is only noted in 1% of cases. Methods: A 16-year-old girl with newly-diagnosed GPA presented to our hospital with progressive debilitating headaches, polyuria, and polydipsia. Results: Initial MRI showed changes to the pituitary. Lumbar puncture (LP) revealed opening pressure of 26. She developed central diabetes insipidus (DI) and visual changes. Repeat head imaging showed adenohypophysitis. The GPA was previously treated with steroids and cyclophosphamide, followed by Cellcept. Once the pituitary involvement was discovered, she was given re-induction therapy with Rituximab and steroid dose was increased. DI is being treated with DDAVP. Her headaches are improving. Conclusions: CNS inflammatory diseases are rare in childhood. Pituitary involvement is extremely rare in GPA. Induction therapy for adults with GPA and pituitary involvement includes glucocorticoids and cyclophosphamide, which often leads to improvement of MRI abnormalities but is not effective in resolving pituitary dysfunction. Our patient had already received this treatment when she developed the CNS findings. This case demonstrates that cerebral involvement is often resistant to classic therapy, and one should be vigilant in looking for CNS inflammation in these patients.

An internationally approved and globally used classification scheme for the diagnosis of CHD has long been sought. The International Paediatric and Congenital Cardiac Code (IPCCC), which was produced and has been maintained by the International Society for Nomenclature of Paediatric and Congenital Heart Disease (the International Nomenclature Society), is used widely, but has spawned many “short list” versions that differ in content depending on the user. Thus, efforts to have a uniform identification of patients with CHD using a single up-to-date and coordinated nomenclature system continue to be thwarted, even if a common nomenclature has been used as a basis for composing various “short lists”. In an attempt to solve this problem, the International Nomenclature Society has linked its efforts with those of the World Health Organization to obtain a globally accepted nomenclature tree for CHD within the 11th iteration of the International Classification of Diseases (ICD-11). The International Nomenclature Society has submitted a hierarchical nomenclature tree for CHD to the World Health Organization that is expected to serve increasingly as the “short list” for all communities interested in coding for congenital cardiology. This article reviews the history of the International Classification of Diseases and of the IPCCC, and outlines the process used in developing the ICD-11 congenital cardiac disease diagnostic list and the definitions for each term on the list. An overview of the content of the congenital heart anomaly section of the Foundation Component of ICD-11, published herein in its entirety, is also included. Future plans for the International Nomenclature Society include linking again with the World Health Organization to tackle procedural nomenclature as it relates to cardiac malformations. By doing so, the Society will continue its role in standardising nomenclature for CHD across the globe, thereby promoting research and better outcomes for fetuses, children, and adults with congenital heart anomalies.

During the Cretaceous and Paleogene, the Indian subcontinent was isolated as it migrated north from the east coast of Africa to collide with Asia. As it passed over the Reunion hotspot in the late Maastrichtian–early Danian, a series of lava flows extruded, known as the Deccan Traps. Also during this interval, there was a major mass-extinction event at the Cretaceous–Paleogene boundary, punctuated by a meteorite impact at Chicxulub, Mexico. What were the biological implications of these changes in paleogeography and the extensive volcanism in terms of biodiversity, evolution, and biogeography? By combining chronostratigraphic, paleosol, and paleobotanical data, an understanding of how the ecosystems and climates changed and the relative contributions of the Chicxulub impact, Deccan Traps volcanism, and paleogeographic isolation can be gained. Understanding relative ages of paleobotanical localities is crucial to determining floristic changes, and is challenging because different methods (e.g., magnetostratigraphy, radiometric dating, vertebrate and microfossil biostratigraphy) sometimes give conflicting answers, or have not been done for paleobotanical localities. Climatic data can be obtained quantitatively by studying paleosol geochemistry, as well as qualitatively by examining functional traits and nearest living relatives of fossil plants. An additional challenge is revising macrofossil data, which includes some confidently identified taxa and others with uncertain affinities. This is important for understanding ecosystem composition both spatially and temporally, as well as the biogeographic implications of an isolated India.

There is increased interest towards the application of carbon based nanomaterials to biosensors since these can be used to quickly detect presence of the toxins in food, agricultural and environmental systems. The accurate, faster and early detection of food toxins is presently very important for ensuring safety and shelf life of agricultural commodities resulting from food contamination. The carbon materials (CNTs) and recently discovered graphene have been predicted to be promising candidates in the development of electrochemical biosensor owing to their exceptionally large surface area and interesting electrochemical properties. We focus on some of the recent results obtained in our laboratories pertaining to the development of biosensors based on multi-walled carbon nanotubes and graphene for mycotoxin(aflatoxin ) detection.

The objective of this study was to develop emission factors (EF) for methane (CH4) emissions from enteric fermentation in cattle native to Benin. Information on livestock characteristics and diet practices specific to the Benin cattle population were gathered from a variety of sources and used to estimate EF according to Tier 2 methodology of the 2006 Intergovernmental Panel on Climate Change (IPCC) Guidelines for National Greenhouse Gas Inventories. Most cattle from Benin are Bos taurus represented by Borgou, Somba and Lagune breeds. They are mainly multi-purpose, being used for production of meat, milk, hides and draft power and grazed in open pastures and crop lands comprising tropical forages and crops. Estimated enteric CH4 EFs varied among cattle breeds and subcategory owing to differences in proportions of gross energy intake expended to meet maintenance, production and activity. EFs ranged from 15.0 to 43.6, 16.9 to 46.3 and 24.7 to 64.9 kg CH4/head per year for subcategories of Lagune, Somba and Borgou cattle, respectively. Average EFs for cattle breeds were 24.8, 29.5 and 40.2 kg CH4/head per year for Lagune, Somba and Borgou cattle, respectively. The national EF for cattle from Benin was 39.5 kg CH4/head per year. This estimated EF was 27.4% higher than the default EF suggested by IPCC for African cattle with the exception of dairy cattle. The outcome of the study underscores the importance of obtaining country-specific EF to estimate global enteric CH4 emissions.