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Although large-scale genetic association studies have proven useful for the delineation of neurodegenerative disease processes, we still lack a full understanding of the pathological mechanisms of these diseases, resulting in few appropriate treatment options and diagnostic challenges. To mitigate these gaps, the Neurodegenerative Disease Knowledge Portal (NDKP) was created as an open-science initiative with the aim to aggregate, enable analysis, and display all available genomic datasets of neurodegenerative disease, while protecting the integrity and confidentiality of the underlying datasets. The portal contains 218 genomic datasets, including genotyping and sequencing studies, of individuals across ten different phenotypic groups, including neurological conditions such as Alzheimer's disease, amyotrophic lateral sclerosis, Lewy body dementia, and Parkinson's disease. In addition to securely hosting large genomic datasets, the NDKP provides accessible workflows and tools to effectively utilize the datasets and assist in the facilitation of customized genomic analyses. Here, we summarize the genomic datasets currently included within the portal, the bioinformatics processing of the datasets, and the variety of phenotypes captured. We also present example use-cases of the various user interfaces and integrated analytic tools to demonstrate their extensive utility in enabling the extraction of high-quality results at the source, for both genomics experts and those in other disciplines. Overall, the NDKP promotes open-science and collaboration, maximizing the potential for discovery from the large-scale datasets researchers and consortia are expending immense resources to produce and resulting in reproducible conclusions to improve diagnostic and therapeutic care for neurodegenerative disease patients., Competing Interests: Declaration of interests H.L.L., D.V., H.I., and M.A.N.’s participation in this project was part of a competitive contract awarded to Data Tecnica International LLC by the National Institutes of Health to support open science research. M.A.N. also currently serves on the scientific advisory board for Clover Therapeutics and is a scientific founder at Neuron23 Inc, he also owns stocks.

Copy Number Variations (CNVs) play pivotal roles in the etiology of complex diseases and are variable across diverse populations. Understanding the association between CNVs and disease susceptibility is of significant importance in disease genetics research and often requires analysis of large sample sizes. One of the most cost-effective and scalable methods for detecting CNVs is based on normalized signal intensity values, such as Log R Ratio (LRR) and B Allele Frequency (BAF), from Illumina genotyping arrays. In this study, we present CNV-Finder, a novel pipeline integrating deep learning techniques on array data, specifically a Long Short-Term Memory (LSTM) network, to expedite the large-scale identification of CNVs within predefined genomic regions. This facilitates the efficient prioritization of samples for subsequent, costly analyses such as short-read and long-read whole genome sequencing. We focus on five genes-Parkin ( PRKN ), Leucine Rich Repeat And Ig Domain Containing 2 ( LINGO2 ), Microtubule Associated Protein Tau ( MAPT ), alpha-Synuclein ( SNCA ), and Amyloid Beta Precursor Protein ( APP )-which may be relevant to neurological diseases such as Alzheimer's disease (AD), Parkinson's disease (PD), or related disorders such as essential tremor (ET). By training our models on expert-annotated samples and validating them across diverse cohorts, including those from the Global Parkinson's Genetics Program (GP2) and additional dementia-specific databases, we demonstrate the efficacy of CNV-Finder in accurately detecting deletions and duplications. Our pipeline outputs app-compatible files for visualization within CNV-Finder's interactive web application. This interface enables researchers to review predictions and filter displayed samples by model prediction values, LRR range, and variant count in order to explore or confirm results. Our pipeline integrates this human feedback to enhance model performance and reduce false positive rates. Through a series of comprehensive analyses and validations using both short-read and long-read sequencing data, we demonstrate the robustness and adaptability of CNV-Finder in identifying CNVs with regions of varied sparsity, noise, and size. Our findings highlight the significance of contextual understanding and human expertise in enhancing the precision of CNV identification, particularly in complex genomic regions like 17q21.31. The CNV-Finder pipeline is a scalable, publicly available resource for the scientific community, available on GitHub (https://github.com/GP2code/CNV-Finder; DOI 10.5281/zenodo.14182563). CNV-Finder not only expedites accurate candidate identification but also significantly reduces the manual workload for researchers, enabling future targeted validation and downstream analyses in regions or phenotypes of interest.

GenoTools, a Python package, streamlines population genetics research by integrating ancestry estimation, quality control (QC), and genome-wide association studies (GWAS) capabilities into efficient pipelines. By tracking samples, variants, and quality-specific measures throughout fully customizable pipelines, users can easily manage genetics data for large and small studies. GenoTools' "Ancestry" module renders highly accurate predictions, allowing for high-quality ancestry-specific studies, and enables custom ancestry model training and serialization specified to the user's genotyping or sequencing platform. As the genotype processing engine that powers several large initiatives, including the NIH's Center for Alzheimer's and Related Dementias (CARD) and the Global Parkinson's Genetics Program (GP2), GenoTools was used to process and analyze the UK Biobank and major Alzheimer's Disease (AD) and Parkinson's Disease (PD) datasets with over 400,000 genotypes from arrays and 5,000 whole genome sequencing (WGS) samples and has led to novel discoveries in diverse populations. It has provided replicable ancestry predictions, implemented rigorous QC, and conducted genetic ancestry-specific GWAS to identify systematic errors or biases through a single command. GenoTools is a customizable tool that enables users to efficiently analyze and scale genotyping and sequencing (WGS and exome) data with reproducible and scalable ancestry, QC, and GWAS pipelines., (Published by Oxford University Press on behalf of The Genetics Society of America 2024.)

In this paper, we leveraged Large Language Models(LLMs) to accelerate data wrangling and automate labor-intensive aspects of data discovery and harmonization. This work promotes interoperability standards and enhances data discovery, facilitating AI-readiness in biomedical science with the generation of Common Data Elements (CDEs) as key to harmonizing multiple datasets. Thirty-one studies, various ontologies, and medical coding systems served as source material to create CDEs from which available metadata and context was sent as an API request to 4th-generation OpenAI GPT models to populate each metadata field. A human-in-the-loop (HITL) approach was used to assess quality and accuracy of the generated CDEs. To regulate CDE generation, we employed ElasticSearch and HITL to avoid duplicate CDEs and instead, added them as potential aliases for existing CDEs. The generated CDEs are foundational to assess the interoperability potential of datasets by determining how many data set column headers can be correctly mapped to CDEs as well as quantifying compliance with permissible values and data types. Subject matter experts reviewed generated CDEs and determined that 94.0% of generated metadata fields did not require manual revisions. Data tables from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Global Parkinson's Genetic Program (GP2) were used as test cases for interoperability assessments. Column headers from all test cases were successfully mapped to generated CDEs at a rate of 32.4% via elastic search.The interoperability score, a metric for dataset compatibility to CDEs and other connected datasets, based on relevant criteria such as data field completeness and compliance with common harmonization standards averaged 53.8 out of 100 for test cases. With this project, we aim to automate the most tedious aspects of data harmonization, enhancing efficiency and scalability in biomedical research while decreasing activation energy for federated research.

Background: Latin America's diverse genetic makeup, shaped by centuries of admixture, presents a unique opportunity to study Alzheimer's disease dementia (AD) and frontotemporal dementia (FTD). Our aim is to identify genetic variations associated with AD and FTD within this population., Methods: The Multi-Partner Consortium to Expand Dementia Research in Latin America (ReDLat) recruited 2,162 participants with AD, FTD, and healthy controls from six Latin American countries (Argentina, Brazil, Chile, Colombia, Mexico, and Peru). All participants underwent array, exome, and/or whole-genome sequencing. Population structure was analyzed using Principal Component Analysis and ADMIXTURE, projecting the ReDLat population onto the 1000 Genomes Project database. To identify genes associated with autosomal dominant, autosomal recessive, or X-linked forms of adult-onset dementia, we searched the Online Mendelian Inheritance in Man database and analyzed pedigree information. Variant interpretation followed guidelines from the American College of Medical Genetics and Genomics, and the Guerreiro algorithm was applied for the PSEN1 and PSEN2 genes., Results: Global ancestry analysis of the ReDLat cohort revealed a predominant mix of American, African, and European ancestries. Uniquely, Brazil displayed an additional East Asian component accurately reflecting the historical admixture patterns from this region. We identified 17 pathogenic variants, a pathogenic C9orf72 expansion, and 44 variants of uncertain significance. Among our cohort, 70 families exhibited autosomal dominant inheritance of neurodegenerative diseases, with 48 families affected by AD and 22 by FTD. In families with AD, We discovered a novel variant in the PSEN1 gene, c.519G>T (p.Leu173Phe), along with other previously described variants seen in the region, such as c.356C>T (p.Thr119Ile). In families with FTD, the most commonly associated gene was GRN , followed by MAPT . Notably, we identified a patient meeting criteria for FTD who carried a pathogenic variant in SOD1 , c.388G>A (p.Phe21Leu), which had previously been reported in another FTD patient from the same geographical region., Conclusions: This study provides the first snapshot of genetic contributors to AD and FTD in a multisite cohort across Latin America. It will be critical to evaluate the generalizability of genetic risk factors for AD and FTD across diverse ancestral backgrounds, considering distinct social determinants of health and accounting for modifiable risk factors that may influence disease risk and resilience across different cultures., Competing Interests: Competing interests J.S.Y and K.S.K collaborate with the scientific advisory board of the Epstein Family Alzheimer’s Research Collaboration. C.J., M.A.N., H.L., D.V. and M.J.K.’s participation in this project was part of a competitive contract awarded to DataTecnica LLC by the National Institutes of Health to support open science research. M.A.N. also owns stock from Character Bio Inc and Neuron23 Inc.

Background: Commercial genome-wide genotyping arrays have historically neglected coverage of genetic variation across populations., Objective: We aimed to create a multi-ancestry genome-wide array that would include a wide range of neuro-specific genetic content to facilitate genetic research in neurological disorders across multiple ancestral groups, fostering diversity and inclusivity in research studies., Methods: We developed the Illumina NeuroBooster Array (NBA), a custom high-throughput and cost-effective platform on a backbone of 1,914,934 variants from the Infinium Global Diversity Array and added custom content comprising 95,273 variants associated with more than 70 neurological conditions or traits, and we further tested its performance on more than 2000 patient samples. This novel platform includes approximately 10,000 tagging variants to facilitate imputation and analyses of neurodegenerative disease-related genome-wide association study loci across diverse populations., Results: In this article, we describe NBA's potential as an efficient means for researchers to assess known and novel disease genetic associations in a multi-ancestry framework. The NBA can identify rare genetic variants and accurately impute more than 15 million common variants across populations. Apart from enabling sample prioritization for further whole-genome sequencing studies, we envisage that NBA will play a pivotal role in recruitment for interventional studies in the precision medicine space., Conclusions: From a broader perspective, the NBA serves as a promising means to foster collaborative research endeavors in the field of neurological disorders worldwide. Ultimately, this carefully designed tool is poised to make a substantial contribution to uncovering the genetic etiology underlying these debilitating conditions. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA., (© 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

Background and Objectives: Acute pancreatitis (AP) carries the risk of subsequent nutritional deficiencies. The prevalence of these deficiencies following a single episode of AP in children is unknown. We aimed to determine prevalence of anthropometric and laboratory-based measures of nutritional status in children following their first (index) admission for AP., Methods: Prospective observational cohort study of patients ≤21 years of age with first episode of confirmed AP. Anthropometric and laboratory values were obtained at time of AP onset and at follow up time points of 3 and 12 months (m) post AP. AP attack was classified as either: mild, moderately severe or severe (which were combined in one group (SAP))., Results: 181 patients met criteria and were followed prospectively with 52% male, a median age of 13.7 years (IQR 9.4-16.0) and median Body Mass Index (BMI) Z-score of 0.6 (IQR -0.5, 1.6). Most patients had mild AP (140, 77%), with 23% meeting criteria for moderate or severe (41/181). 6 (3%) had diabetes mellitus (DM) predating AP and were excluded from further analysis. BMI Z-score remained stable during the follow up period. 13% of patients developed pre-DM or DM at 3m or 12m. Nearly one third of patients had low ferritin at 3m (29%) or 12m (29%). At 12m, 8% of patients had Vitamin A deficiency. 6% of patients had low Vitamin E levels at 3m and 5% at 12m. Over half of patients at both 3m and 12m had 25 OH Vitamin D insufficiency or deficiency (56% and 56%). Prolonged International Normalized Ratio (INR) (>1.3) was seen in 9% of patients at 12m. Very low albumin (<3.5 g/dL) was found in 24% of patients at 3m and 18% at 12m (Table 1). Patients with very low albumin at 3m were younger (median 10.7 vs. 14.2 years, p = 0.04), however sex, BMI Z-score and AP severity were not associated with albumin level. Although BMI Z-score did not differ between the groups, those with SAP had a significant decrease in BMI Z-score from first attack compared to mild AP at 3m (-0.4 vs. 0.0, p = 0.0002, Figure 2). At 3m, Vitamin E deficiency in SAP versus mild AP was found in 20% vs 2% (p = 0.04) and SAP had a lower median hematocrit (35.8 vs. 37.6, p = 0.046). There were no other laboratory significant differences at 3m in mild versus SAP groups. At 12m, those with SAP were more likely to have pre-DM or DM compared to mild AP (31% vs. 7%, p = 0.002). No other significant laboratory differences occurred at 12m., Conclusions: After the first AP attack patients experience nutritional deficiencies, including ferritin, all fat-soluble vitamins, and low albumin. SAP is associated with a decrease in BMI Z-score, increased prevalence of vitamin E deficiency at 3m, and an increase in pre-diabetes and diabetes at 12m. Serial monitoring of vitamin and mineral values post AP is warranted and further prospective studies are needed., Competing Interests: Declaration of competing interest The authors have no conflicts of interest relevant to this article to disclose., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Pathogenic variants in the LRRK2 gene represent the most common cause of autosomal dominant Parkinson's disease (PD) worldwide. We identified the LRRK2 p.L1795F variant in 14 White/European ancestry PD patients, including two families with multiple affected carriers and seven additional affected individuals with familial PD using genotyping and sequencing data from more than 50,000 individuals through GP2, AMP-PD, PDGENEration, and CENTOGENE. All variant carriers were of White/European ancestry, and those with available genotyping data shared a common haplotype. The clinical presentation of p.L1795F carriers resembles that of other LRRK2 pathogenic variant carriers. Combined with published functional evidence showing strongly enhanced LRRK2 kinase activity, our findings provide conclusive evidence that the LRRK2 p.L1795F variant is pathogenic. It represents a rare cause of PD in the European population but needs to be included in genetic testing efforts and considered for ongoing gene-specific clinical trials., Competing Interests: Competing interests L.M.L., N.A., K. Lo., and L.A.H. declare no competing interests.

Over the last few decades, scientists have recognized the critical role that various components of the extracellular matrix (ECM) play in maintaining homeostatic immunity. Besides, dysregulation in the synthesis or degradation levels of these components directly impacts the mechanisms of immune response during tissue injury caused by tumor processes or the regeneration of the tissue itself in the event of damage. ECM is a complex network of protein compounds, proteoglycans and glycosaminoglycans (GAGs). Hyaluronic acid (HA) is one of the major GAGs of this network, whose metabolism is strictly physiologically regulated and quickly altered in injury processes, affecting the behavior of different cells, from stem cells to differentiated immune cells. In this revision we discuss how the native or chemically modified HA interacts with its specific receptors and modulates intra and intercellular communication of immune cells, focusing on cancer and tissue regeneration conditions., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Genotyping single nucleotide polymorphisms (SNPs) is fundamental to disease research, as researchers seek to establish links between genetic variation and disease. Although significant advances in genome technology have been made with the development of bead-based SNP genotyping and Genome Studio software, some SNPs still fail to be genotyped, resulting in "no-calls" that impede downstream analyses. To recover these genotypes, we introduce Cluster Buster, a genotyping neural network and visual inspection system designed to improve the quality of neurodegenerative disease (NDD) research. Concordance analysis with whole genome sequencing (WGS) and imputed genotypes validated the reliability of predicted genotypes, with dozens of high-performing SNPs across LRRK2 , APOE , and GBA loci achieving at least 90% concordance per SNP location. Further analysis of concordance between Genome Studio genotypes and imputed and WGS genotypes revealed discrepancies between the genotyping technologies, highlighting the need for selective application of Cluster Buster on SNP locations based on concordance rates. Cluster Buster's implementation significantly reduces manual labor for recovering no-call SNPs, refining genotype quality for the Global Parkinson's Genetics Program (GP2). This system facilitates better imputation and GWAS outcomes, ultimately contributing to a deeper understanding of genetic factors in NDDs., Competing Interests: KL, DV, MAN, and MJK participated in this project under a competitive contract awarded to Data Tecnica International LLC by the National Institutes of Health to support open-access scientific research. MAN also owns stock from Character Bio Inc and Neuron23 Inc.

The iDA Project (iPSCs to Study Diversity in Alzheimer's and Alzheimer's Disease-related Dementias) is generating 200 induced pluripotent stem cell lines from Alzheimer's Disease Neuroimaging Initiative participants. These lines are sex balanced, include common APOE genotypes, span disease stages, and are ancestrally diverse. Cell lines and characterization data will be shared openly., Competing Interests: Declaration of interests A.J.S. has received support from Avid Radiopharmaceuticals, a subsidiary of Eli Lilly (in-kind contribution of PET tracer precursor), and has participated in scientific advisory boards (Bayer Oncology, Eisai, Novo Nordisk, and Siemens Medical Solutions USA, Inc) and an observational study monitoring board (MESA, NIH NHLBI). He also serves as editor-in-chief for Brain Imaging and Behavior, a Springer-Nature journal, and serves on four other NIA external advisory committees. M.A.N. currently serves on the scientific advisory board for Character Bio Inc. and is a scientific founder at Neuron23 Inc., (Published by Elsevier Inc.)

While machine learning (ML) research has recently grown more in popularity, its application in the omics domain is constrained by access to sufficiently large, high-quality datasets needed to train ML models. Federated learning (FL) represents an opportunity to enable collaborative curation of such datasets among participating institutions. We compare the simulated performance of several models trained using FL against classically trained ML models on the task of multi-omics Parkinson's disease prediction. We find that FL model performance tracks centrally trained ML models, where the most performant FL model achieves an AUC-PR of 0.876 ± 0.009, 0.014 ± 0.003 less than its centrally trained variation. We also determine that the dispersion of samples within a federation plays a meaningful role in model performance. Our study implements several open-source FL frameworks and aims to highlight some of the challenges and opportunities when applying these collaborative methods in multi-omics studies., Competing Interests: B.P.D., A.D., D.V., M.A.N., and F.F. declare the following competing financial interests, as their participation in this project was part of a competitive contract awarded to Data Tecnica LLC by the National Institutes of Health to support open science research. M.A.N. also currently serves on the scientific advisory board for Character Bio and is an advisor to Neuron23 Inc. The study’s funders had no role in the study design, data collection, data analysis, data interpretation, or writing of the report. F.F. takes final responsibility for the decision to submit the paper for publication., (© 2024 The Authors.)

Introduction: Biomarker testing is mandatory for the clinical management of patients with advanced non-small cell lung cancer (NSCLC). Myriads of technical platforms are now available for biomarker analysis with differences in terms of multiplexing capability, analytical sensitivity, and turnaround time (TAT). We evaluated the technical performance of the diagnostic workflows of 24 representative Italian institutions performing molecular tests on a series of artificial reference specimens built to mimic routine diagnostic samples., Methods: Sample sets of eight slides from cell blocks of artificial reference specimens harboring exon 19 EGFR (epidermal growth factor receptor) p.E746&#95;AT50del, exon 2 KRAS (Kirsten rat sarcoma viral oncogene homologue) p.G12C, ROS1 (c-ros oncogene 1)-unknown gene fusion, and MET (MET proto-oncogene, receptor tyrosine kinase) Δ exon 14 skipping were distributed to each participating institution. Two independent cell block specimens were validated by the University of Naples Federico II before shipment. Methodological and molecular data from reference specimens were annotated., Results: Overall, a median DNA concentration of 3.3 ng/µL (range 0.1-10.0 ng/µL) and 13.4 ng/µL (range 2.0-45.8 ng/µL) were obtained with automated and manual technical procedures, respectively. RNA concentrations of 5.7 ng/µL (range 0.2-11.9 ng/µL) and 9.3 ng/µL (range 0.5-18.0 ng/µL) were also detected. KRAS exon 2 p.G12C, EGFR exon 19 p.E736&#95;A750del hotspot mutations, and ROS1 aberrant transcripts were identified in all tested cases, whereas 15 out of 16 (93.7%) centers detected MET exon 14 skipping mutation., Conclusions: Optimized technical workflows are crucial in the decision-making strategy of patients with NSCLC. Artificial reference specimens enable optimization of diagnostic workflows for predictive molecular analysis in routine clinical practice., (© 2024. The Author(s).)

Treatments for neurodegenerative disorders remain rare, but recent FDA approvals, such as lecanemab and aducanumab for Alzheimer disease (MIM: 607822), highlight the importance of the underlying biological mechanisms in driving discovery and creating disease modifying therapies. The global population is aging, driving an urgent need for therapeutics that stop disease progression and eliminate symptoms. In this study, we create an open framework and resource for evidence-based identification of therapeutic targets for neurodegenerative disease. We use summary-data-based Mendelian randomization to identify genetic targets for drug discovery and repurposing. In parallel, we provide mechanistic insights into disease processes and potential network-level consequences of gene-based therapeutics. We identify 116 Alzheimer disease, 3 amyotrophic lateral sclerosis (MIM: 105400), 5 Lewy body dementia (MIM: 127750), 46 Parkinson disease (MIM: 605909), and 9 progressive supranuclear palsy (MIM: 601104) target genes passing multiple test corrections (p SMR&#95;multi  < 2.95 × 10 -6 and p HEIDI  > 0.01). We created a therapeutic scheme to classify our identified target genes into strata based on druggability and approved therapeutics, classifying 41 novel targets, 3 known targets, and 115 difficult targets (of these, 69.8% are expressed in the disease-relevant cell type from single-nucleus experiments). Our novel class of genes provides a springboard for new opportunities in drug discovery, development, and repurposing in the pre-competitive space. In addition, looking at drug-gene interaction networks, we identify previous trials that may require further follow-up such as riluzole in Alzheimer disease. We also provide a user-friendly web platform to help users explore potential therapeutic targets for neurodegenerative diseases, decreasing activation energy for the community., Competing Interests: Declaration of interests C.X.A., D.V., K.L., H.L.L., F.F., and M.A.N. declare that they are consultants employed by Data Tecnica International, whose participation in this is part of a consulting agreement between the US National Institutes of Health and said company. M.A.N. is also an advisor to Neuron23, Inc. and Character Biosciences., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Although over 90 independent risk variants have been identified for Parkinson's disease using genome-wide association studies, most studies have been performed in just one population at a time. Here we performed a large-scale multi-ancestry meta-analysis of Parkinson's disease with 49,049 cases, 18,785 proxy cases and 2,458,063 controls including individuals of European, East Asian, Latin American and African ancestry. In a meta-analysis, we identified 78 independent genome-wide significant loci, including 12 potentially novel loci (MTF2, PIK3CA, ADD1, SYBU, IRS2, USP8, PIGL, FASN, MYLK2, USP25, EP300 and PPP6R2) and fine-mapped 6 putative causal variants at 6 known PD loci. By combining our results with publicly available eQTL data, we identified 25 putative risk genes in these novel loci whose expression is associated with PD risk. This work lays the groundwork for future efforts aimed at identifying PD loci in non-European populations., (© 2023. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Background: In the last decades, the use of the helmet interface to deliver noninvasive respiratory support has steadily increased. When delivering oxygen therapy, conditioning of inspired gas is mandatory, as the water content of medical gas is neglectable. To minimize the risk of airway damage, it is recommended to achieve an absolute humidity greater than 10 mg H2O/L. The primary aim of the study was to assess the performance of 3 different heated humidifiers to condition gas during helmet continuous positive airway pressure and to compare them with a setting without active humidification., Methods: We performed a crossover randomized physiological study in healthy volunteers. Absolute humidity, relative humidity, and temperature were measured during 4 steps (no humidification and the 3 heated humidifiers, performed in a randomized order) and at 3 time points (after 1, 5, and 10 minutes)., Results: Eight subjects participated to the study. Without active humidification, absolute humidity was constantly below the recommended level. All humidifiers conditioned the inspired gases to an average absolute humidity greater than 10 mg H2O/L. Overall, the best performance, in terms of absolute humidity, was obtained with H900 (19.74 ± 0.50 mg H2O/L), followed by TurbH2O (-6.82 mg H2O/L vs H900; 95% confidence interval, -8.49 to -5.14; P < .05) and F&P 810 (-9.03 mg H2O/L vs H900; 95% confidence interval, -10.70 to -7.35; P < .05)., Conclusions: Active humidification is required to achieve adequate conditioning of inspired gas during helmet continuous positive airway pressure. The choice between different humidifiers available on the market should be made according to the local needs and expertise., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

A robust and efficient extraction method was developed to detect a broad range of pollutants of emerging interest in three freshwater invasive species: American red crab (Prokambarus clarkii), Asian clam (Corbicula fluminea), and pumpkinseed fish (Lepomis gibbosus). One native species, "petxinot" clam (Anodonta cygnea), was also evaluated. Invasive species are often more resistant to contamination and could be used in biomonitoring studies to assess the effect of contaminants of emerging concern on aquatic ecosystems while preserving potentially threatened native species. So far, most extraction methods developed for this purpose have focused on analyzing fish and generally focus on a limited number of compounds, especially analyzing compounds from the same family. In this sense, we set out to optimize a method that would allow the simultaneous extraction of 87 PhACs, 11 flame retardants, 21 per- and poly-fluoroalkyl substances, and 54 pesticides. The optimized method is based on ultrasound-assisted solvent extraction. Two tests were performed during method development, one to choose the extraction solvent with the best recovery efficiencies and one to select the best clean-up. The analysis was performed by high-performance liquid chromatography coupled to high-resolution mass spectrometry. The method obtained recoveries between 40 and 120% and relative standard deviations of less than 25% for 85% of the analytes in the four validated matrices. Limits of quantification between 0.01 ng g -1 and 22 ng g -1 were obtained. Application of the method on real samples from the Albufera Natural Park of Valencia (Spain) confirmed the presence of contaminants of emerging concern in all samples, such as acetaminophen, hydrochlorothiazide, tramadol, PFOS, carbendazim, and fenthion. PFAS were the group of compounds with the highest mean concentrations. C. fluminea was the species with the highest detection frequency, and P. clarkii had the highest average concentrations, so its use is prioritized for biomonitoring studies., (© 2023. The Author(s).)

Overlapping symptoms and co-pathologies are common in closely related neurodegenerative diseases (NDDs). Investigating genetic risk variants across these NDDs can give further insight into disease manifestations. In this study we have leveraged genome-wide single nucleotide polymorphisms and genome-wide association study summary statistics to cluster patients based on their genetic status across identified risk variants for five NDDs (Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, Lewy body dementia and frontotemporal dementia). The multi-disease and disease-specific clustering results presented here provide evidence that NDDs have more overlapping genetic aetiology than previously expected and how neurodegeneration should be viewed as a spectrum of symptomology. These clustering analyses also show potential subsets of patients with these diseases that are significantly depleted for any known common genetic risk factors suggesting environmental or other factors at work. Establishing that NDDs with overlapping pathologies share genetic risk loci, future research into how these variants might have different effects on downstream protein expression, pathology and NDD manifestation in general is important for refining and treating NDDs., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Parkinson's disease has a large heritable component and genome-wide association studies have identified over 90 variants with disease-associated common variants, providing deeper insights into the disease biology. However, there have not been large-scale rare variant analyses for Parkinson's disease. To address this gap, we investigated the rare genetic component of Parkinson's disease at minor allele frequencies <1%, using whole genome and whole exome sequencing data from 7184 Parkinson's disease cases, 6701 proxy cases and 51 650 healthy controls from the Accelerating Medicines Partnership Parkinson's disease (AMP-PD) initiative, the National Institutes of Health, the UK Biobank and Genentech. We performed burden tests meta-analyses on small indels and single nucleotide protein-altering variants, prioritized based on their predicted functional impact. Our work identified several genes reaching exome-wide significance. Two of these genes, GBA1 and LRRK2, have variants that have been previously implicated as risk factors for Parkinson's disease, with some variants in LRRK2 resulting in monogenic forms of the disease. We identify potential novel risk associations for variants in B3GNT3, AUNIP, ADH5, TUBA1B, OR1G1, CAPN10 and TREML1 but were unable to replicate the observed associations across independent datasets. Of these, B3GNT3 and TREML1 could provide new evidence for the role of neuroinflammation in Parkinson's disease. To date, this is the largest analysis of rare genetic variants in Parkinson's disease., (Published by Oxford University Press on behalf of the Guarantors of Brain 2023.)

Background: An understanding of the genetic mechanisms underlying diseases in ancestrally diverse populations is an important step towards development of targeted treatments. Research in African and African admixed populations can enable mapping of complex traits, because of their genetic diversity, extensive population substructure, and distinct linkage disequilibrium patterns. We aimed to do a comprehensive genome-wide assessment in African and African admixed individuals to better understand the genetic architecture of Parkinson's disease in these underserved populations., Methods: We performed a genome-wide association study (GWAS) in people of African and African admixed ancestry with and without Parkinson's disease. Individuals were included from several cohorts that were available as a part of the Global Parkinson's Genetics Program, the International Parkinson's Disease Genomics Consortium Africa, and 23andMe. A diagnosis of Parkinson's disease was confirmed clinically by a movement disorder specialist for every individual in each cohort, except for 23andMe, in which it was self-reported based on clinical diagnosis. We characterised ancestry-specific risk, differential haplotype structure and admixture, coding and structural genetic variation, and enzymatic activity., Findings: We included 197 918 individuals (1488 cases and 196 430 controls) in our genome-wide analysis. We identified a novel common risk factor for Parkinson's disease (overall meta-analysis odds ratio for risk of Parkinson's disease 1·58 [95% CI 1·37-1·80], p=2·397 × 10 -14 ) and age at onset at the GBA1 locus, rs3115534-G (age at onset β=-2·00 [SE=0·57], p=0·0005, for African ancestry; and β=-4·15 [0·58], p=0·015, for African admixed ancestry), which was rare in non-African or non-African admixed populations. Downstream short-read and long-read whole-genome sequencing analyses did not reveal any coding or structural variant underlying the GWAS signal. The identified signal seems to be associated with decreased glucocerebrosidase activity., Interpretation: Our study identified a novel genetic risk factor in GBA1 in people of African ancestry, which has not been seen in European populations, and it could be a major mechanistic basis of Parkinson's disease in African populations. This population-specific variant exerts substantial risk on Parkinson's disease as compared with common variation identified through GWAS and it was found to be present in 39% of the cases assessed in this study. This finding highlights the importance of understanding ancestry-specific genetic risk in complex diseases, a particularly crucial point as the Parkinson's disease field moves towards targeted treatments in clinical trials. The distinctive genetics of African populations highlights the need for equitable inclusion of ancestrally diverse groups in future trials, which will be a valuable step towards gaining insights into novel genetic determinants underlying the causes of Parkinson's disease. This finding opens new avenues towards RNA-based and other therapeutic strategies aimed at reducing lifetime risk of Parkinson's disease., Funding: The Global Parkinson's Genetics Program, which is funded by the Aligning Science Across Parkinson's initiative, and The Michael J Fox Foundation for Parkinson's Research., Competing Interests: Declaration of interests DV, HI, HLL, KSL, CXA, and MAN's participation in this project was part of a competitive contract awarded to Data Tecnica International by the US National Institutes of Health (NIH) to support open science research. MAN also currently serves on the scientific advisory board for Character Biosciences and Neuron 23. KH is employed by 23andMe and holds stock or stock options in 23andMe. AS, MBM, PAJ, CB, KD, MJK, JJK, and PWC are employed by the NIH. AS also declares funding for the present work from the Michael J Fox Foundation (MJFF) and Aligning Science Across Parkinson's (ASAP); royalties for a diagnostic for stroke (unrelated to the current work); honoraria for associate editorial work for the journals Movement Disorders and npj Parkinson's Disease; and travel support from the Chan Zuckerberg Initiative to attend annual investigators’ meeting, MJFF to attend Parkinson's Progression Marker Initiative annual meeting, and Weill Cornell to give grand rounds. The spouse of AS is an employee of GeneDx. OOk, IE, HI, JHK, and DV declare funding from the NIH (1ZIA AG000534-04). HI also declares honoraria from GP2 for a steering committee meeting and from MJFF for a data community meeting. DGS declares support for the present work from ASAP (for the BLAAC-PD study); research support from the NIH (P50 108675), the MJFF, the Parkinson Foundation of the National Capital Area, the American Parkinson Disease Association, AbbVie, and Genentech; book royalties from McGraw Hill; consulting fees from AbbVie, Curium Pharma, F Hoffman-La Roche, Appello Pharma, and Blue Rock Therapeutics; participation on a data safety monitoring board or advisory board for Sanofi-Aventis, Theravance, and Alnylam Pharmaceuticals; being Deputy Editor of the journal Movement Disorders; and being Chair of the Scientific Advisory Board of the American Parkinson Disease Association. DAH declares support for the present work from the NIH, the MJFF, the CHDI Foundation, Parkinson's Foundation, Lundbeck, Uniqure, and Neurocrine. TX declares research funding from the MJFF, the American Parkinson's Disease Association, and the NIH; and consulting fees from Parkinson's Foundation and CVS Caremark. HH and JH declare research funding by the Medical Research Council (UK), the Wellcome Trust, the MSA Trust, NIHR University College London Hospitals Biomedical Research Centre (NIHR-BRC), the MJFF, the Fidelity Trust, the Rosetrees Trust, the Guarantors of Brain, SOLVE-RD, and the Dolby Family Fund. HRM declares support from the MJFF related to this work; grants not related to this work from PSP Association, CBD Solutions, Drake Foundation, and Cure Parkinson's Trust; consulting fees from Roche, Amylyx, and Aprinoia; speaker's honoraria from Kyowa-Kirin, the British Medical Journal, and the Movement Disorders Society; travel support from the MJFF and the Movement Disorders Society; and being on the advisory board for Cure PSP Association, the Association of British Neurologists Movement Disorders special interest group, and the Association of British Neurologists Neurogenetics advisory group. HRM is a co-applicant on a patent application related to C9ORF72—Method for diagnosing a neurodegenerative disease (PCT/GB2012/052140). ES declares funding for the present work from the US National Human Genome Research Institute (NHGRI) Intramural Research Program; grants from ASAP and MJFF; and a cooperative research and development agreement with Roche. EAS declares funding for the present work from the MJFF; and travel support from the MJFF. MND declares funding for the present work from MJFF; grant support from ASAP; speaker's honoraria from the Parkinson's Foundation; and is a member of the Parkinson's Foundation Gulf Coast advisory board. Z-HF declares salary support from the MJFF. OOOj declares a study grant from the NIHR; honoraria for educational courses from the International Parkinson and Movement Disorder Society (IPMDS); travel support from P2 for an annual investigators’ meeting and from the IPMDS for congress attendance; and is a member of the executive committee of the IPMDS. NUO declares a study grant from the NIHR; travel support and honoraria for educational courses from the IPMDS; and being Chair of the IPMDS Africa Section. UA declares a study grant from the NIHR. RA declares grant support from the NIH (U01HG010273, U19AG074865), the UK Royal Society and the African Academy of Sciences Future Leaders—African Independent Research (FLR/R1/191813, FCG/R1/201034), and the Global Brain Health Institute, Alzheimer's Association, and Alzheimer's Society UK (GBHI ALZ UK-21- 24204). TF declares speaker's honoraria from Roche; travel support from the Alzheimer's Association; and is a member of the Alzheimer's Disease International medical and scientific advisory panel. CB declares support from ASAP. KD declares a Japan Society for the Promotion of Science Research Fellowship. JJK declares participation in the graduate school programme for Queen Mary University London (London, UK). PAJ declares participation in the graduate school programme for University College London (London, UK). MBM declares participation in a summer internship at Genentech/Roche (unrelated to the current work). All other authors declare no competing interests., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The Global Parkinson's Genetics Program (GP2) will genotype over 150,000 participants from around the world, and integrate genetic and clinical data for use in large-scale analyses to dramatically expand our understanding of the genetic architecture of PD. This report details the workflow for cohort integration into the complex arm of GP2, and together with our outline of the monogenic hub in a companion paper, provides a generalizable blueprint for establishing large scale collaborative research consortia., (© 2023. Springer Nature Limited.)

Wastewater-based epidemiology (WBE) approach provides objective, quantitative, near real-time profiles of illicit drug consumption by monitoring the concentration of unchanged parent drugs or their metabolites entering the municipal sewage system. Valencia is the third most populous city in Spain (an important country for the use and transit of several of these drugs). Estimations of consumption over long periods of time will help get better understanding of spatial and temporal trends in the use of licit and illicit drugs. Accordingly, applying the "best practice" protocol, 16 drugs of abuse and metabolites were monitored in this study, and 8 were daily measured during one-two weeks between 2011 and 2020 at the inlet of three wastewater treatment plants of Valencia City. Analysis of the selected compounds was performed by liquid chromatography-triple quadrupole mass spectrometry, and the concentrations obtained were used to back-calculate the consumption data. Cannabis, tobacco, and cocaine were the most consumed drugs whereas opioids were less used. Cannabis and cocaine consumption are on average 2.7-23.4 and 1.1-2.3 g/day/1000inh, respectively, and their use tended to increase since 2018. Weekly profiles were characterized by higher consumption of cocaine, ecstasy, and heroin during weekends compared to weekdays. Similarly, during "Las Fallas" (main local festivity), increased use of cocaine and amphetamine-type stimulants, mainly MDMA, was measured. WBE proved to be an objective and useful methodology to get more insight on temporal drugs of abuse consumption, and the changes derived from local festivities., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Genome-wide association studies (GWAS) of Alzheimer's disease are predominantly carried out in European ancestry individuals despite the known variation in genetic architecture and disease prevalence across global populations. We leveraged published GWAS summary statistics from European, East Asian, and African American populations, and an additional GWAS from a Caribbean Hispanic population using previously reported genotype data to perform the largest multi-ancestry GWAS meta-analysis of Alzheimer's disease and related dementias to date. This method allowed us to identify two independent novel disease-associated loci on chromosome 3. We also leveraged diverse haplotype structures to fine-map nine loci with a posterior probability >0.8 and globally assessed the heterogeneity of known risk factors across populations. Additionally, we compared the generalizability of multi-ancestry- and single-ancestry-derived polygenic risk scores in a three-way admixed Colombian population. Our findings highlight the importance of multi-ancestry representation in uncovering and understanding putative factors that contribute to risk of Alzheimer's disease and related dementias., (© 2023. The Author(s).)

Background: Biallelic pathogenic variants in GBA1 are the cause of Gaucher disease (GD) type 1 (GD1), a lysosomal storage disorder resulting from deficient glucocerebrosidase. Heterozygous GBA1 variants are also a common genetic risk factor for Parkinson's disease (PD). GD manifests with considerable clinical heterogeneity and is also associated with an increased risk for PD., Objective: The objective of this study was to investigate the contribution of PD risk variants to risk for PD in patients with GD1., Methods: We studied 225 patients with GD1, including 199 without PD and 26 with PD. All cases were genotyped, and the genetic data were imputed using common pipelines., Results: On average, patients with GD1 with PD have a significantly higher PD genetic risk score than those without PD (P = 0.021)., Conclusions: Our results indicate that variants included in the PD genetic risk score were more frequent in patients with GD1 who developed PD, suggesting that common risk variants may affect underlying biological pathways. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA., (© 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

Objective: Identification of genetic risk factors for Parkinson disease (PD) has to date been primarily limited to the study of single nucleotide variants, which only represent a small fraction of the genetic variation in the human genome. Consequently, causal variants for most PD risk are not known. Here we focused on structural variants (SVs), which represent a major source of genetic variation in the human genome. We aimed to discover SVs associated with PD risk by performing the first large-scale characterization of SVs in PD., Methods: We leveraged a recently developed computational pipeline to detect and genotype SVs from 7,772 Illumina short-read whole genome sequencing samples. Using this set of SV variants, we performed a genome-wide association study using 2,585 cases and 2,779 controls and identified SVs associated with PD risk. Furthermore, to validate the presence of these variants, we generated a subset of matched whole-genome long-read sequencing data., Results: We genotyped and tested 3,154 common SVs, representing over 412 million nucleotides of previously uncatalogued genetic variation. Using long-read sequencing data, we validated the presence of three novel deletion SVs that are associated with risk of PD from our initial association analysis, including a 2 kb intronic deletion within the gene LRRN4., Interpretation: We identified three SVs associated with genetic risk of PD. This study represents the most comprehensive assessment of the contribution of SVs to the genetic risk of PD to date. ANN NEUROL 2023;93:1012-1022., (© 2023 The Authors. Annals of Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

Open science and collaboration are necessary to facilitate the advancement of Parkinson's disease (PD) research. Hackathons are collaborative events that bring together people with different skill sets and backgrounds to generate resources and creative solutions to problems. These events can be used as training and networking opportunities, thus we coordinated a virtual 3-day hackathon event, during which 49 early-career scientists from 12 countries built tools and pipelines with a focus on PD. Resources were created with the goal of helping scientists accelerate their own research by having access to the necessary code and tools. Each team was allocated one of nine different projects, each with a different goal. These included developing post-genome-wide association studies (GWAS) analysis pipelines, downstream analysis of genetic variation pipelines, and various visualization tools. Hackathons are a valuable approach to inspire creative thinking, supplement training in data science, and foster collaborative scientific relationships, which are foundational practices for early-career researchers. The resources generated can be used to accelerate research on the genetics of PD., (© 2023. The Author(s).)

While the vaccination campaign against COVID-19 is having its positive impact, we retrospectively analyze the causal impact of some decisions made by the Italian government on the second outbreak of the SARS-CoV-2 pandemic in Italy, when no vaccine was available. First, we analyze the causal impact of reopenings after the first lockdown in 2020. In addition, we also analyze the impact of reopening schools in September 2020. Our results provide an unprecedented opportunity to evaluate the causal relationship between the relaxation of restrictions and the transmission in the community of a highly contagious respiratory virus that causes severe illness in the absence of prophylactic vaccination programs. We present a purely data-analytic approach based on a Bayesian methodology and discuss possible interpretations of the results obtained and implications for policy makers., Competing Interests: Conflict of interestThe authors have no conflict of interest to disclose., (© The Author(s) 2023.)

Human induced pluripotent stem cell (iPSC) lines are a powerful tool for studying development and disease, but the considerable phenotypic variation between lines makes it challenging to replicate key findings and integrate data across research groups. To address this issue, we sub-cloned candidate human iPSC lines and deeply characterized their genetic properties using whole genome sequencing, their genomic stability upon CRISPR-Cas9-based gene editing, and their phenotypic properties including differentiation to commonly used cell types. These studies identified KOLF2.1J as an all-around well-performing iPSC line. We then shared KOLF2.1J with groups around the world who tested its performance in head-to-head comparisons with their own preferred iPSC lines across a diverse range of differentiation protocols and functional assays. On the strength of these findings, we have made KOLF2.1J and its gene-edited derivative clones readily accessible to promote the standardization required for large-scale collaborative science in the stem cell field., Competing Interests: Declaration of interests S.W.S. is on the scientific advisory council of the Lewy Body Dementia Association and the MSA Coalition. S.W.S. is an editorial board member for the Journal of Parkinson Disease and JAMA Neurology. S.W.S. received research support from Cerevel Therapeutics. M.K. serves on the scientific advisory boards of Engine Biosciences, Casma Therapeutics, Cajal Neuroscience, and Alector and is a consultant to Modulo Bio and Recursion Therapeutics. Participation by researchers from Data Tecnica International, LLC in this project was part of a competitive contract awarded to Data Tecnica International, LLC by the National Institutes of Health to support open science research. M.A.N. also currently serves on the scientific advisory board for Clover Therapeutics and is an advisor to Neuron23 Inc. E.A. is founder, shareholder, and scientific advisor of Cholestenix, Ltd., (Published by Elsevier Inc.)

Refugees, immigrants, and migrants (RIM) in the United States (US) have been identified as an underimmunized population prior to the COVID-19 pandemic. Vaccine acceptance is critical to combat the public health threat incited by COVID-19 and other vaccine-preventable disease. To better understand escalating vaccine hesitancy among US RIM, a comprehensive evaluation of the problem and solutions is necessary. In this systematic review, we included 57 studies to describe vaccination rates, barriers, and interventions addressing vaccine hesitancy over the past decade. Meta-analysis was performed among 22 studies, concluding that RIM represent an underimmunized population compared to the general US population. Narrative synthesis and qualitative methods were used to identify critical barriers, including gaps in knowledge, poor access to medical care, and heightened distrust of the medical system. Our results demonstrate the need for effective, evidence-based interventions to increase vaccination rates among diverse RIM populations.

Background: We report the characteristics, timing, and factors related to the decision to perform a tracheostomy in patients with confirmed COVID-19 infection admitted to eight Italian intensive care units (ICUs)., Materials and Methods: Prospective observational cohort study of patients with COVID-19 disease on mechanical ventilation. Long-term functional impairment (up to 180 days' post-hospital discharge) was assessed using the Karnofsky scale. Kaplan-Meier analysis assessed differences in survival and freedom from tracheostomy in relation to ICU stay. Cox regression model was used to assess which variables impacted on tracheostomy as a categorical outcome., Results: A total of 248 patients were recruited in the eight participating ICUs. Patients undergoing tracheostomy ( n = 128) had longer ICU (25 (18-36) vs. 10 (7-16), P = 0.001) and hospital (37 (26.5-50) vs. 19 (8.5-34.5) P = 0.02) stays. ICU and hospital mortality of patients tracheostomized was 34% and 37%, respectively. Cumulative survival Kaplan-Meier analysis documented improved survival rates in patients undergoing tracheostomy (Log-Rank, Mantel-Cox = 4.8, P = 0.028). Median Karnofsky scale values improved over time but were similar between survivors receiving or not receiving tracheostomy. No healthcare worker involved in the tracheostomy procedure developed COVID-19 infection during the study period., Conclusions: Patients with COVID-19 infection who underwent tracheostomy had a better cumulative survival but similar long-term functional outcomes at 30, 60, and 180 days after hospital discharge., Competing Interests: No conflicts of interest to declare.

Although research biobanks are among the most promising tools to fight disease and improve public health, there are a range of risks biobanks may face that mainly need to be assessed in an attempt to be relieved. We conducted a strategic insurance review of an institutional cancer biobank with the aim of both identifying the insurable risks of our own Biobank and gathering useful evidence of primary exposure to insurable risks. In this practical scenario, risks have been outlined and categorized into inherent and residual risks, along with their possible impact on biobank maintenance. Results at the Biobank of the Cancer Institute of Bari showed evidence of potentially significant and intrinsic risk due to highly relevant threats, along with already implemented improvements that significantly reduce risks to a range of relative acceptability.

Background: Traumatic brain injury (TBI) is one of the most prevalent diagnoses among trauma populations and places significant strain on valuable rural hospital resources. Limited studies show safety and efficacy of implementation of a Brain Injury Guideline (BIG) protocol at a Department of Defense (DoD) Level 1 trauma center., Materials and Methods: Data from patients diagnosed with traumatic brain injury during the study period were collected from our institutional trauma database. A retrospective review was performed on patients identified in the database to collect demographic and injury related data. All primary and secondary outcome data were analyzed using two-tailed Fischer's exact tests, Pearson Chi-square tests, and non-parametric Mann Whitney U tests., Results: A total of 354 patients were included in the study, 189 pre-implementation and 165 post-implementation. Demographics, head injury severity, initial HCT findings, and BIG classification distributions were well-matched. There was a significant reduction in neurosurgical consultations (NSC) (98.4% pre- to 77.0% post-implementation, P<0.001) and ICU admissions (84.1% pre-, 74.5% post-implementation, P=0.025) following protocol implementation. There were no differences between groups in ICU LOS (P=0.239), incidence of worsening findings on RHCT (P=0.894), or in-hospital mortality (P=0.814). There was a slight reduction in hospital LOS from 4.0d pre-implementation to 3.0d post-implementation (P=0.043)., Conclusions: Implementation of a BIG protocol at our Level 1 trauma center suggested at a relationship with fewer NSCs and ICU admissions. Management of mild and moderate TBI by acute care and trauma surgeons without direct neurosurgical oversight is safe and implies a reduction in utilization of hospital resources., (Published by Elsevier Inc.)

Personalized medicine promises individualized disease prediction and treatment. The convergence of machine learning (ML) and available multimodal data is key moving forward. We build upon previous work to deliver multimodal predictions of Parkinson's disease (PD) risk and systematically develop a model using GenoML, an automated ML package, to make improved multi-omic predictions of PD, validated in an external cohort. We investigated top features, constructed hypothesis-free disease-relevant networks, and investigated drug-gene interactions. We performed automated ML on multimodal data from the Parkinson's progression marker initiative (PPMI). After selecting the best performing algorithm, all PPMI data was used to tune the selected model. The model was validated in the Parkinson's Disease Biomarker Program (PDBP) dataset. Our initial model showed an area under the curve (AUC) of 89.72% for the diagnosis of PD. The tuned model was then tested for validation on external data (PDBP, AUC 85.03%). Optimizing thresholds for classification increased the diagnosis prediction accuracy and other metrics. Finally, networks were built to identify gene communities specific to PD. Combining data modalities outperforms the single biomarker paradigm. UPSIT and PRS contributed most to the predictive power of the model, but the accuracy of these are supplemented by many smaller effect transcripts and risk SNPs. Our model is best suited to identifying large groups of individuals to monitor within a health registry or biobank to prioritize for further testing. This approach allows complex predictive models to be reproducible and accessible to the community, with the package, code, and results publicly available., (© 2022. This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.)

Purpose: To investigate the prevalence, incidence and characteristics of bacterial infections and their impact on outcome in critically ill patients infected with COVID-19., Methods: We conducted a prospective observational study in eight Italian ICUs from February to May 2020; data were collected through an interactive electronic database. Kaplan-Meier analysis (limit product method) was used to identify the occurrence of infections and risk of acquisition., Results: During the study period 248 patients were recruited in the eight participating ICUs. Ninety (36.3%) patients developed at least one episode of secondary infection. An ICU length of stay between 7 and 14 days was characterized by a higher occurrence of infectious complications, with ventilator-associated pneumonia being the most frequent. At least one course of antibiotic therapy was given to 161 (64.9%) patients. Overall ICU and hospital mortality were 33.9% and 42.9%, respectively. Patients developing bacteremia had a higher risk of ICU mortality [45.9% vs. 31.6%, odds ratio 1.8 (95% CI 0.9-3.7), p = 0.069] and hospital mortality [56.8% vs. 40.3%, odds ratio 1.9 (95% CI 1.1-3.9), p = 0.04]., Conclusion: In critically ill patients infected with COVID-19 the incidence of bacterial infections is high and associated with worse outcomes. Regular microbiological surveillance and strict infection control measures are mandated., (© 2021. Springer-Verlag GmbH Germany, part of Springer Nature.)