bluuebunny/crossref_metadata_2025_split huggingface dataset:
Socioeconomic disparities in epilepsy care
Authors: Blank Leah J. 
Published: January 2022

Purpose of review
Epilepsy is a common, chronic neurologic disease with continued disparities in care. The COVID-19 pandemic and recent social movements have drawn greater attention to social determinants of health and our progress (or lack thereof) toward delivering more equitable care.

Recent findings
Recent studies continue to document racial and economic disparities in diagnosis, treatment, and overall care of epilepsy and associated conditions. Notably, an increasing number of studies are attempting to design healthcare pathways and other interventions to improve access and equity in epilepsy care.

Summary
The present literature highlights the importance of identifying and addressing the particular needs of vulnerable persons with epilepsy. Practitioners and researchers should continue to develop interventions aimed at improving care for all patients and, crucially, measure the impact of their changes to ensure that any interventions are truly advancing health equity.
Novel treatments in autism spectrum disorder
Authors: Baribeau Danielle , Vorstman Jacob , Anagnostou Evdokia 
Published: January 2022

Purpose of review
There are currently no approved medications for the core symptoms of autism spectrum disorder (ASD), and only limited data on the management of co-occurring mental health and behavioural symptoms. The purpose of this review is to synthesize recent trials on novel treatments in ASD, with a focus on research trends in the past 2 years.

Recent findings
No new pharmacologic agents received regulatory approval for use in ASD. Several large randomized controlled trials (RCTs) had negative or ambiguous results (e.g. fluoxetine, oxytocin). A cross-over RCT of an oral cannabinoid suggested possible benefits for disruptive behaviours. Two large-scale multicentre trials of bumetanide were terminated early for lack of efficacy. Multicenter trials using repetitive transcranial magnetic stimulation are underway. Recent meta-analyses indicate that specific behavioural and psychological interventions can support social communication and treat anxiety. Numerous novel treatment targets informed by biological mechanisms are under investigation.

Summary
Recent data support the use of behavioural and psychological interventions for social communication and anxiety in ASD; data are more limited regarding pharmacotherapy for core and associated symptoms. Next steps include replication of early findings, trials of new molecular targets, and the identification of novel biomarkers, including genetic predictors, of treatment response.
Ornithinimicrobium laminariae sp. nov., isolated from the kelp Laminaria japonica
Authors: Cao Wen-Rui , Yu Xin-Ran , Sun Yuan-yuan , Jiang Ming-Yu , Xu Xiao-Dong , Li Ying-Jie 
Published: January 2022

A Gram-stain-positive, aerobic, non-sporulating, yellow-pigmented and rod or cocci-shaped bacterium, designated Arc0846-15T, was isolated from the kelp Laminaria japonica. Strain Arc0846-15T was found to grow at 16–35 °C (optimum, 28 °C), at pH 6.0–9.5 (optimum, 7.0) and in the presence of 0–6 % (w/v) NaCl (optimum, 2 %). Cells were positive for catalase and negative for oxidase activity. Phylogenetic analyses, based on 16S rRNA gene sequence comparisons, revealed that the nearest phylogenetic neighbour strains of strain Arc0846-15T were

Ornithinimicrobium murale

01 Gi-040T (96.2 %),

Ornithinimicrobium kibberense

K22-20T (96.1 %) and

Ornithinimicrobium humiphilum

HKI 0124T (95.2 %). Based on phylogenomic analysis, the average nucleotide identity values between strain Arc0846-15T and the neighbour strains were 69.8, 69.7 and 69.8 %, respectively; the digital DNA–DNA hybridization values between strain Arc0846-15T and its three closest neighbour strains were 18.8, 19.1 and 19.3 %, respectively. The predominant menaquinone was MK-8 (H4). The dominant cellular fatty acids were C17 : 1
ω8c, iso-C15 : 0, iso-C16 : 0 and C17 : 0. The polar lipids contained diphosphatidylglycerol, phosphatidylglycerol, phosphatidylinositol, glycolipid, one unidentified aminolipid and four unidentified lipids. The DNA G+C content of strain Arc0846-15T was 61.6 mol% based on the whole genome sequence. Based on the phylogenetic and phenotypic characteristics, strain Arc0846-15T is considered to represent a novel species of the genus

Ornithinimicrobium

, for which the name Ornithinimicrobium laminariae sp. nov. is proposed, with Arc0846-15T (=KCTC 49655T=MCCC 1K06093T) as the type strain.
A protocol for locating and counting transgenic sequences from laboratory animals using a map-then-capture (MapCap) sequencing workflow: procedure and application of results
Authors: Dicke Sidney S. , Rubinstein C. Dustin , Speers James M. , Berres Mark E. , Pavelec Derek M. , Schueler Kathryn L. , Stapleton Donnie S. , Simonett Shane P. , Keller Mark P. , Attie Alan D. , Zanni Martin T. 
Published: January 2022

ABSTRACTTransgenic rodent models for human diseases have been widely used over the past 50 years and are a mainstay of many biomedical research programs. Oftentimes the sequence of the transgenic segment of DNA is carefully designed but incorporation of this DNA into the host genome is less well understood. Structural variation and insertional mutagenesis may occur at transgenic insertion sites. Here, we present a robust workflow including identification of the transgene locus via selective Illumina sequencing followed by Cas9-mediated target DNA enrichment of the locus, which successfully identified beginning and end sites of a large transgenic insertion into a murine model for human amylin-induced type II diabetes. Enriched sequences were mapped via Oxford Nanopore sequencing. Although the insertion was too long for a single mapped genetic sequence to encompass, the method provided multiple insights relevant to the animal model: a minimum number of forward- and reverse-facing transcript copies as well as characterization of an inversion point within the insertion site. The insertion start point containing both murine and human DNA was used to identify and separate animals hemizygous for the transgenic insertion from homozygous animals. This identification could be performed early in the rodent life cycle prior to maturation (i.e. breeding age), thus allowing for management of colony phenotypes and eliminating the need to “genotype by phenotype” later on (onset of amylin-induced type II diabetes does not occur until ~8-10 weeks of age for this model). We further confirmed our homozygous diabetic mice function the same as colonies established in other labs and present full antibody and fluorescent-staining protocols (available in SI). Lastly, we note that, due to our genotyping, a novel animal was identified within our colony: non-diabetic homozygous mice. Indeed, only 37% of homozygous mice bred in our colony became diabetic.AUTHOR SUMMARY (for broad audience)Transgenic rodent models are important to studying human diseases. When creating a new rodent model, one may insert new DNA into a well-characterized background genome. However, it is oftentimes not known where the new DNA was incorporated, how many times it was incorporated, or if any coding sequences or regulatory elements within native DNA were disrupted. Here, we have developed a method to characterize transgenic animals, and have applied it to a popular model for studying human amylin-induced type II diabetes.
JAX Animal Behavior System (JABS): A genetics informed, end-to-end advanced behavioral phenotyping platform for the laboratory mouse
Authors: Choudhary Anshul , Geuther Brian Q. , Sproule Thomas J. , Beane Glen , Kohar Vivek , Trapszo Jarek , Kumar Vivek 
Published: January 2022

AbstractAutomated detection of complex animal behavior remains a challenge in neuroscience. Developments in computer vision have greatly advanced automated behavior detection and allow high-throughput preclinical and mechanistic studies. An integrated hardware and software solution is necessary to facilitate the adoption of these advances in the field of behavioral neurogenetics, particularly for non-computational laboratories. We have published a series of papers using an open field arena to annotate complex behaviors such as grooming, posture, and gait as well as higher-level constructs such as biological age and pain. Here, we present our, integrated rodent phenotyping platform, JAX Animal Behavior System (JABS), to the community for data acquisition, machine learning-based behavior annotation and classification, classifier sharing, and genetic analysis. The JABS Data Acquisition Module (JABS-DA) enables uniform data collection with its combination of 3D hardware designs and software for real-time monitoring and video data collection. JABS-Active Learning Module (JABS-AL) allows behavior annotation, classifier training, and validation. We introduce a novel graph-based framework (ethograph) that enables efficient boutwise comparison of JABS-AL classifiers. JABS-Analysis and Integration Module (JABS-AI), a web application, facilitates users to deploy and share any classifier that has been trained on JABS, reducing the effort required for behavior annotation. It supports the inference and sharing of the trained JABS classifiers and downstream genetic analyses (heritability and genetic correlation) on three curated datasets spanning 168 mouse strains that we are publicly releasing alongside this study. This enables the use of genetics as a guide to proper behavior classifier selection. This open-source tool is an ecosystem that allows the neuroscience and genetics community for shared advanced behavior analysis and reduces the barrier to entry into this new field.
Data-driven research on eczema: systematic characterization of the field and recommendations for the future
Authors: Duverdier A. , Custovic A. , Tanaka R.J. 
Published: January 2022

ABSTRACTBackgroundThe past decade has seen a substantial rise in the employment of modern data-driven methods to study atopic dermatitis (AD) / eczema.ObjectiveTo summarise the past and future of data-driven AD research, and identify areas in the field that would benefit from the application of these methods.MethodsWe retrieved the publications that applied multivariate statistics (MS), artificial intelligence (AI, including machine learning-ML), and Bayesian statistics (BS) to AD and eczema research from the SCOPUS database over the last 50 years. We conducted a bibliometric analysis to highlight the publication trends and conceptual knowledge structure of the field, and applied topic modelling to retrieve the key topics in the literature.ResultsFive key themes of data-driven research on AD and eczema were identified: (1) allergic co-morbidities, (2) image analysis and classification, (3) disaggregation, (4) quality of life, and (5) risk factors and prevalence. ML&AI methods mapped to studies investigating quality of life, prevalence, risk factors, allergic co-morbidities and disaggregation of AD/eczema, but seldom in studies of therapies. MS was employed evenly between the topics, particularly in studies on risk factors and prevalence. BS was focused on three key topics: treatment, risk factors and allergy. The use of AD or eczema terms was not uniform, with studies applying ML&AI methods using the term eczema more often. Within MS, papers using cluster and factor analysis were often only identified with the term AD. In contrast, those using logistic regression and latent class/transition models were “eczema” papers.ConclusionsResearch areas that could benefit from the application of data-driven methods include the study of the pathogenesis of the condition and related risk factors, its disaggregation into validated subtypes, and personalized severity management and prognosis. We highlight Bayesian statistics as a new and promising approach in AD and eczema research.
Understanding evolution of COVID-19 driven mortality rate
Authors: Bhaumik Ishika , Sinha-Ray Suman , Chaudhary Anshul , Srivastava Abhishek , Kodgire Prashant 
Published: January 2022

ABSTRACTObjectiveCOVID-19 has resulted in the death of almost 4 million people till date1. However, the mortality rate across countries seems to be vastly different irrespective of their respective socio-economic backgrounds. It is well known now that COVID-19 is an acute inflammatory infectious disease that gets complicated by type-I interferon response2,3. However, the precise reason for variations in COVID-19 related mortality rates is unknown. A detailed understanding behind the evolution of mortality rate around the globe is needed.MethodsIn this article, we show that a biological science guided machine learning-based approach can predict the evolution of mortality rates across countries. We collected the publicly available data of all the countries in the world with regard to the mortality rate and the relevant biological and socio-economical causes. The data was analyzed using a novel FFT driven machine learning algorithm.ResultsOur results demonstrate how COVID-19 related mortality rate is closely dependent on a multitude of socio-economic factors (population density, GDP per capita, global health index and population above 65 years of age), environmental (PM2.5 air pollution) and lifestyle aka food habits (meat consumption per capita, alcohol consumption per capita, dairy product consumption per capita and sugar consumption per capita). Interestingly, we found that individually these parameters show no visible trend that can be generalized with mortality.ConclusionsWe anticipate that our work will initiate conversations between health officials, policymakers and world leaders towards providing preventative measures against COVID-19 and future coronavirus-based diseases and endemics/ pandemics by taking a holistic view.
Automatic colorectal cancer screening using deep-learning on spatial light interference microscopy data
Authors: Zhang Jingfang K. , Fanous Michael , Sobh Nahil , Balla Andre , Popescu Gabriel 
Published: January 2022

ABSTRACTThe surgical pathology workflow currently adopted in the clinic uses staining to reveal tissue architecture within thin sections. A trained pathologist then conducts a visual examination of these slices and, as the investigation is based on an empirical assessment, a certain amount of subjectivity is unavoidable. Furthermore, the reliance on such external contrast agents like hematoxylin and eosin (H&E), albeit a well-established method, makes it difficult to standardize color balance, staining strength, and imaging conditions, hindering automated computational analysis. In response to these challenges, we applied spatial light interference microscopy (SLIM), a label-free method that generates contrast based on the intrinsic tissue refractive index signatures. Thus, we reduce human bias and make the image data comparable across instruments and clinics. We applied a Mask R-CNN deep learning algorithm to the SLIM data to achieve an automated colorectal cancer screening procedure, i.e., classifying normal vs. cancer specimens. Our results obtained on a tissue microarray consisting of specimens from 132 patients, resulted in 91% accuracy for gland detection, 99.71% accuracy in gland-level classification, and 97% accuracy in core-level classification. A SLIM tissue scanner accompanied by an application-specific deep learning algorithm may become a valuable clinical tool, enabling faster and more accurate assessment by the pathologist.
Time to commencement of effective treatment in patients with drug-resistant tuberculosis diagnosed in the Torres Strait / Papua New Guinea cross-border region
Authors: Foster J’Belle , Mendez Diana , Marais Ben J. , Peniyamina Dunstan , McBryde Emma S. 
Published: January 2022

ABSTRACTIntroductionDelays between self-reported symptom onset and commencement of effective treatment contribute to ongoing tuberculosis (TB) transmission, which is a particular concern in patients with drug-resistant (DR)-TB. We assessed improvements in time to commencement of effective treatment in patients diagnosed with DR-TB in the Torres Strait / Papua New Guinea cross-border region.MethodsAll laboratory-confirmed DR-TB cases diagnosed in the Torres Strait between 1 March 2000 and 31 March 2020 were reviewed. We assessed the total time from self-reported onset of symptoms to effective treatment commencement in different programmatic time periods. Pairwise analyses and time to event proportional hazard calculations were used to explore the association between delays in median time to effective treatment, and selected variables. Data were further analysed to examine predictors of excessive treatment delay.ResultsThe median number of days from self-reported onset of symptoms to effective treatment commencement was 124 days (interquartile range 51-214) over two decades. Between 2006 and 2012, most (57%) cases exceeded this ‘grand median’ while the median ‘time to treat’ in the most recent time period (2016-2020) was significantly reduced to 29 days (p<0.001). Although there was a reduction in the median ‘time to treat’ with the introduction of Xpert MTB/RIF (135 days pre-Xpert vs 67 days post-Xpert) this was not statistically significant (p0.07). Establishment of the Torres and Cape TB Control Unit on Thursday Island (2016-2020) was significantly associated with reduced treatment delay, compared to previous TB program period (2000-2005p<0.04; 2006-2012p<0.001).ConclusionMinimising TB treatment delay in remote settings like the Torres Strait / Papua New Guinea cross-border region requires effective decentralised diagnosis and management structures. The results of this study suggest that the establishment of the Torres and Cape TB Control Unit on Thursday Island significantly improved time to commencement of effective TB treatment. Possible contributing factors include better TB education, cross-border communication and patient-centred care.
Use of unstructured text in prognostic clinical prediction models: a systematic review
Authors: Seinen Tom M. , Fridgeirsson Egill , Ioannou Solomon , Jeannetot Daniel , John Luis H. , Kors Jan A. , Markus Aniek F. , Pera Victor , Rekkas Alexandros , Williams Ross D. , Yang Cynthia , van Mulligen Erik , Rijnbeek Peter R. 
Published: January 2022

ABSTRACTObjectiveThis systematic review aims to assess how information from unstructured clinical text is used to develop and validate prognostic risk prediction models. We summarize the prediction problems and methodological landscape and assess whether using unstructured clinical text data in addition to more commonly used structured data improves the prediction performance.Materials and MethodsWe searched Embase, MEDLINE, Web of Science, and Google Scholar to identify studies that developed prognostic risk prediction models using unstructured clinical text data published in the period from January 2005 to March 2021. Data items were extracted, analyzed, and a meta-analysis of the model performance was carried out to assess the added value of text to structured-data models.ResultsWe identified 126 studies that described 145 clinical prediction problems. Combining text and structured data improved model performance, compared to using only text or only structured data. In these studies, a wide variety of dense and sparse numeric text representations were combined with both deep learning and more traditional machine learning methods. External validation, public availability, and explainability of the developed models was limited.ConclusionOverall, the use of unstructured clinical text data in the development of prognostic prediction models has been found beneficial in addition to structured data in most studies. The EHR text data is a source of valuable information for prediction model development and should not be neglected. We suggest a future focus on explainability and external validation of the developed models, promoting robust and trustworthy prediction models in clinical practice.
Systematic recovery of building plumbing-associated microbial communities after extended periods of altered water demand during the COVID-19 pandemic
Authors: Vosloo Solize , Huo Linxuan , Chauhan Umang , Cotto Irmarie , Gincley Benjamin , Vilardi Katherine J , Yoon Byungman , Pieper Kelsey J , Stubbins Aron , Pinto Ameet 
Published: January 2022

AbstractBuilding closures related to the coronavirus disease (COVID-19) pandemic resulted in increased water stagnation in commercial building plumbing systems that heightened concerns related to the microbiological safety of drinking water post re-opening. The exact impact of extended periods of reduced water demand on water quality is currently unknown due to the unprecedented nature of widespread building closures. We analyzed 420 tap water samples over a period of six months, starting the month of phased reopening (i.e., June 2020), from sites at three commercial buildings that were subjected to reduced capacity due to COVID-19 social distancing policies and four occupied residential households. Direct and derived flow cytometric measures along with water chemistry characterization were used to evaluate changes in plumbing-associated microbial communities with extended periods of altered water demand. Our results indicate that prolonged building closures impacted microbial communities in commercial buildings as indicated by increases in microbial cell counts, encompassing greater proportion cells with high nucleic acids. While flushing reduced cell counts and increased disinfection residuals, the microbial community composition in commercial buildings were still distinct from those at residential households. Nonetheless, increased water demand post-reopening enhanced systematic recovery over a period of months, as microbial community fingerprints in commercial buildings converged with those in residential households. Overall, our findings suggest that sustained and gradual increases in water demand may play a more important role in the recovery of building plumbing-associated microbial communities as compared to short-term flushing, after extended periods of altered water demand that result in reduced flow volumes.
Disruption of the MYC Super-Enhancer Complex by Dual Targeting of FLT3 and LSD1 in Acute Myeloid Leukemia
Authors: Yashar William M. , Curtiss Brittany M. , Coleman Daniel J. , Van-Campen Jake , Kong Garth , Macaraeg Jommel , Estabrook Joseph , Demir Emek , Long Nicola , Bottomly Dan , McWeeney Shannon K. , Tyner Jeffrey W. , Druker Brian J. , Maxson Julia E. , Braun Theodore P. 
Published: January 2022

ABSTRACTMutations in Fms-like tyrosine kinase 3 (FLT3) are common drivers in acute myeloid leukemia (AML) yet FLT3 inhibitors only provide modest clinical benefit. Prior work has shown that inhibitors of lysine-specific demethylase 1 (LSD1) enhance kinase inhibitor activity in AML. Here we show that combined LSD1 and FLT3 inhibition induces synergistic cell death in FLT3-mutant AML. Multi-omic profiling revealed that the drug combination disrupts STAT5, LSD1, and GFI1 binding at the MYC blood super-enhancer, suppressing super-enhancer activation as well as MYC expression and activity. The drug combination simultaneously results in the accumulation of repressive H3K9me1 methylation, an LSD1 substrate, at MYC target genes. We validated these findings in 72 primary AML samples with the nearly every sample demonstrating synergistic responses to the drug combination. Collectively, these studies provide preclinical rationale for the investigation of dual FLT3/LSD1 inhibition in a clinical trial.