bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimal Prescriptive Treatments for Ovarian Cancer with Genetic Data
Authors: Mertzios Alkiviadis , Gjika Matea , Xu Xidan , Guha Samayita , Bardhan Neelkanth M. , Kumar Subodha , Belcher Angela , Perakis Georgia 
Published: October 2024

AbstractAmong the cancers affecting the female population, Ovarian Cancer (OC), while being relatively rare, is the leading cause of gynecological cancer-related deaths, with overall 5-year survival rates of approximately 50% for all stages combined. This is because of the challenges associated with the diagnosis, resulting in detection at advanced stages of OC, coupled with the slow progress in effective treatment options since the approval of platinum-based chemotherapy in the late 1970s. There has been a relative lack of sophisticated methods based on Machine Learning (ML) models that use genetic data for better prediction of Ovarian Cancer outcomes and result in more effective treatment recommendations. Therefore, there is an unmet clinical need to create models that allow physicians to make informed decisions based on all available data, including patient demographic, social, health, and genomic data. Hence, we develop new techniques for leveraging genetic information in prescribing optimal treatments for patients with OC, using a publicly available dataset from the Prostate, Lung, Colorectal and Ovarian Cancer (PLCO) trial. Our approach is able to transform genotype sequencing information into a simple tabular form that can then be used as the input to any ML model. Coupled with the recorded treatment regimen and clinical parameters of matched patients from the genetic dataset, we estimate the treatment effect in terms of mortality prediction and use it to prescribe the optimal treatment for any given patient. By including the genetic features engineered through our proposed method, our models have a higher accuracy than the models without genetic information embedded. The increase in predictive accuracy demonstrates the improved efficacy of our method in the predictive setting. Furthermore, in the prescriptive setting, the models including genetic features output different treatment choices for patients, showing the impact of their inclusion. This is further highlighted by the feature importance of the genetic features such as mutations in the FAT3, BRCA1, BRCA2, and NF1 genes, where they rank highly with a tighter aggregation of the top features, relative to the sharp drop-off in feature importance after the top feature in the models without genetic data. Taken together, in summary, our models will allow oncologists to make more informed and accurate decisions, incorporating a patient’s genetic data with all other available clinical information, which has the potential for improved prognosis and better long-term survival outcomes for Ovarian Cancer patients.
Genomic ascertainment to quantify prevalence and cancer risk in adults with pathogenic and likely pathogenic germline variants in RASopathy genes
Authors: Kim Jung , Ney Gina , Frone Megan N. , Haley Jeremy S. , Mirshahi Uyenlinh L. , Astiazaran-Symonds Esteban , Shandrina Mariya , Urban Gretchen , Rao H. Shanker , Stahl Rick , Golden Alicia , Yohe Marielle E. , Gross Andrea M. , Ding Yi , Carey David J. , Gelb Bruce D. , Stewart Douglas R. 
Published: October 2024

AbstractPurposeGenomic ascertainment of electronic health record-linked exome data in two large biobanks was used to quantify germline pathogenic/likely pathogenic (P/LP) variant prevalence, cancer prevalence, and survival in adults with non-NF1RAS/mitogen-activated protein kinase genes (RASopathies).Patients and MethodsGermline RASopathy variants were examined from adult participants in UK Biobank (UKBB; n=469,802), Geisinger MyCode (n=167,050) and Mount Sinai BioMe(n=30,470). Variants were classified as per American College of Medical Genetics/Association for Molecular Pathology criteria and reviewed by a RASopathy variant expert. Heterozygotes harbored a RASopathy pathogenic/likely pathogenic variant; controls harbored wild type or benign/likely benign RASopathy variation. To distinguish germline variants from clonal hematopoiesis, benign tissues were Sanger sequenced. Tumor phenotype and demographic data were retrieved from MyCode and UKBB.ResultsPathogenic variants in Noonan syndrome-associated genes (excluding known Noonan syndrome with multiple lentigines variants) were the most common with an estimated prevalence that ranged between 1:1,772–1:3,330 in the three cohorts. Pathogenic variants in cardiofaciocutaneous syndrome-associated genes had an estimated prevalence of 1:41,762– 1:55,683 in two cohorts. Pathogenic variants inSPRED1(Legius syndrome) were more frequent in UKBB (1:19,567 [95%CI: 1:13,150–1:29,116]) compared to MyCode (1:41,762 [95%CI: 1:15,185–1:130,367]). InSPRED1-heterozygotes, cancer prevalence was significantly increased in UKBB (OR:3.8 [95% CI: 2.48–8.64]; p=1.2×10-3) but not in the MyCode cohort. Pathogenic variants inHRAS(Costello syndrome) were not identified. In MyCode and UKBB cohorts, there was no significant increase in cancer prevalence in individuals with Noonan-,CBL-and CFC syndrome-associated pathogenic variants.ConclusionGenomic ascertainment from two large biobanks did not show evidence of elevated cancer risk in adult Noonan syndrome heterozygotes. There may be an increased cancer risk for adultSPRED1heterozygotes.
Recent advances in the clinical applications of machine learning in proton therapy
Authors: Wildman Vanessa L. , Wynne Jacob F. , Kesarwala Aparna H. , Yang Xiaofeng 
Published: October 2024

ABSTRACTThe present systematic review is an effort to explore the different clinical applications and current implementations of machine/deep learning in proton therapy. It will assist as a reference for scientists, researchers, and other health professionals who are working in the field of proton radiation therapy and need up-to-date knowledge regarding recent technological advances. This review utilized Pubmed and Embase to search for and identify research studies of interest published between 2019 and 2024. This systematic literature review utilized PubMed and Embase to search for and identify studies pertinent to machine learning in proton therapy. The time period of 2019 to 2024 was chosen to capture the most recent signficant advances. An initial search on PubMed was made with the search strategy “‘proton therapy’, ‘machine learning’, ‘deep learning’”, with filters including only research articles from 2019 to 2024, returning 84 results. Next, “(“proton therapy”) AND (“machine learning” OR “deep learning”)” was searched on Embase, retrieving 546 results. When filtered between 2019 to 2024 and to only research articles, 250 results were retrieved on Embase. Reviews, editorials, technical notes, and articles in any language other than English were excluded from the broad search on both databases. Filtering by title, papers were chosen based on two inclusion factors: explicit application to, or mention of, proton therapy, and inclusion of a machine learning algorithm. Assessing by abstract, works irrelevant to specific aspects of the proton therapy workflow in the scope of the review were excluded. Upon assessing and evaluating full texts for quality, studies were excluded that lacked a clear explanation of model architecture. If multiple studies of the same architecture applied to the same workflow step were identified, chronologically only the most recent advancement in application was included. An additional 5 studies that met all inclusion criteria were identified from references of chosen papers. In total, 38 relevant studies have been summarized and incorporated into this review. This is the first systematic review to comprehensively cover all current and potential areas of application of machine learning to the proton therapy clinical workflow.
An evaluation of combined objective neurophysiologic markers to aid assessment of prolonged disorders of consciousness (PDoC)
Authors: du Bois N. , Hill J. , Korik A. , Hoad D. , Bradley L. , Judge S. , Vaughan T. M. , Wolpaw J. R. , Coyle D. 
Published: October 2024

AbstractObjectiveClinical assessments of individuals with Cognitive-Motor Dissociation (CMD) following brain injury are challenging and prone to errors. This prompts investigation of objective, movement-independent neurophysiological markers using electroencephalography (EEG)-based Brain-Computer Interface (BCI) technology. The current pilot study involving adults with prolonged disorders of consciousness (PDoC) investigated the combination of Motor-Imagery BCI (MI-BCI) training and auditory evoked Event Related Potentials (ERPs) using an oddball paradigm to produce complementary biomarkers to improve evaluation of awareness in PDoC.ApproachEEG data (16 channels) were collected from participants with Unresponsive Wakefulness Syndrome (UWS,n= 2), Minimally Conscious State (MCS,n= 3), and Locked-In Syndrome (LIS,n= 4). The MI-BCI involved assessing sensorimotor rhythm modulation, motor-imagery training with and without auditory feedback, and motor-imagery responses to closed questions over 12 sessions each lasting ∼1hour. The oddball protocol was also deployed in 2-3 of those sessions, with ∼10 days between first and last, featuring auditory stimuli, comprising two 5-minute sets of standard, deviant beeps plus novel sounds, in a structured ratio. We expected those with the lowest levels of awareness would have reduced ERP components, with highest latencies to peak, as well as lowest accuracy in the motor imagery BCI protocol – and that trends across these metrics would be observed across the three patient groups based on their clinical diagnoses.Main resultsSignificant differences in mean N1 component latencies and mean MI Decoding Accuracies (DA, for significant runs) occurred between groups – with shorter N1 latencies for the LIS and MCS groups than for the UWS group (LIS vs. UWS and MCS vs. UWS,p< 0.001), and higher DA for the LIS group compared to MCS and UWS (p< 0.001). Mean DA were found to have a significant negative correlation with mean N1 latencies (two-tailed,p= 0.017).SignificanceThe results indicate that neurophysiological markers from the concomitant application of an MI-BCI and auditory-oddball paradigm can augment standard clinical assessments by providing objective measures that produce robust evidence of awareness in people with PDoC.
REVISITING TUBERCULOSIS (TB) DIAGNOSIS: THE PROSPECT OF URINE LIPOARABINOMANNAN (LAM) ASSAY AS A DIAGNOSTIC TOOL IN PEOPLE LIVING WITH HIV
Authors: Adedokun Ayodele Isaac , Bala Gaius , Elehamer Nafisa M.K. , Yohanna Raphael , Oyelabi Boluwatife Deborah , Gana Samuel Eniola 
Published: October 2024

ABSTRACTBackgroundTuberculosis (TB), since its discovery has become a global health burden, continuously spreading across the nations of the earth with increased mortality. Its effect on human immunodeficiency virus (HIV) progression and immune cell deterioration cannot be overlooked. The widespread use of traditional and molecular TB diagnostic methods has been hindered by several factors. Nonetheless, the development of a rapid, sensitive, timely, non-invasive diagnostic protocol could revolutionize the diagnostic era and halt the spread of this infectious agent. It could also help in the early diagnosis of the infection in poorly developed areas. The detection efficacy of urine lipoarabinomannan assay as a prospect in tuberculosis diagnosis in HIV-positive patients was assessed in this study. Urine samples were collected into a wide- mouthed universal container from HIV-positive patients with signs and symptom of tuberculosis and HIV advanced disease. Alere Determine urine lateral flow - LAM test was used for the detection of LAM antigen in urine samples of HIV-positive patients. The CD4 count of the HIV- positive patients was also documented.ResultsA total of 85 HIV-positive subjects were included in the analysis based on the inclusion criteria. Urinary LAM was reactive in 29 (34.1%) of 85 HIV-positive patients and non-reactive in 56 (69.1%). The majority of the participants had a CD4+count < 200 cells/µL 60 (70.6%) while others had CD4+count ≥ to 200 cells/µL 25 (29.4%). There was no statistically significant difference between CD4 count and LAM positivity. Most of the affected age groups in the study were 31-45 years, and females had preponderance in comparison to male subjects.ConclusionsThe use of urinary LAM in HIV-positive patients is a helpful tool for TB screening. It also has a promising ability for early TB diagnosis in the study group and other TB-associated disease conditions in resource-deprived settings. For the general populace, further research is required to certify its sensitivity and specificity.
Danicamtiv reduces myosin’s working stroke but enhances contraction by activating the thin filament
Authors: Scott Brent , Greenberg Lina , Squarci Caterina , Campbell Kenneth S. , Greenberg Michael J. 
Published: October 2024

ABSTRACTHeart failure is a leading cause of death worldwide, and even with current treatments, the 5-year transplant-free survival rate is only ∼50-70%. As such, there is a need to develop new treatments for patients that improve survival and quality of life. Recently, there have been efforts to develop small molecules for heart failure that directly target components of the sarcomere, including cardiac myosin. One such molecule, danicamtiv, recently entered phase II clinical trials; however, its mechanism of action and direct effects on myosin’s mechanics and kinetics are not well understood. Using optical trapping techniques, stopped flow transient kinetics, andin vitroreconstitution assays, we found that danicamtiv reduces the size of cardiac myosin’s working stroke, and in contrast to studies in muscle fibers, we found that it does not affect actomyosin detachment kinetics at the level of individual crossbridges. We demonstrate that danicamtiv accelerates actomyosin association kinetics, leading to increased recruitment of myosin crossbridges and subsequent thin filament activation at physiologically-relevant calcium concentrations. Finally, we computationally model how the observed changes in mechanics and kinetics at the level of single crossbridges contribute to increased cardiac contraction and improved diastolic function compared to the related myotrope, omecamtiv mecarbil. Taken together, our results have important implications for the design of new sarcomeric-targeting compounds for heart failure.SIGNIFICANCE STATEMENTHeart failure is a leading cause of death worldwide, and there is a need to develop new treatments that improve outcomes for patients. Recently, the myosin-binding small molecule danicamtiv entered clinical trials for heart failure; however, its mechanism at the level of single myosin crossbridges is not well understood. We determined the molecular mechanism of danicamtiv and showed how drug-induced molecular changes can mechanistically increase heart contraction. Moreover, we demonstrate fundamental differences between danicamtiv and the related myosin-binding small molecule omecamtiv mecarbil that explain the improved diastolic function seen with danicamtiv. Our results have important implications for the design of new therapeutics for heart failure.
Randomization based evaluation of distinct topological and cancer expression characteristics of mutually acting gene pairs
Authors: Dalgıç Ertuğrul , Çelebi-Çınar Muazzez , Vural-Özdeniz Merve , Konu Özlen 
Published: October 2024

AbstractSmall scale molecular network patterns and motifs are crucial for systems level understanding of cellular information transduction. A subgroup of such patterns consists of only 2 molecules, which we named as mutually acting pairs. Using randomizations, we statistically explored such gene pair designs, i.e. mutually positive (PP) or negative (NN) and positive-negative (PN) pairs, in two comprehensive and distinct large-scale molecular networks from literature; the human protein signaling network (PSN) and the human gene regulatory network (GRN). While the numbers of NN and PN pairs were significantly higher, the number of PP pairs was significantly lower than randomly expected values. Genes participating in mutual pairs were more connected than other genes. The connectivity of mutual gene pairs differed between the two networks. NN genes were more connected than PP and PN in GRN for all types of degree values, including in, out, positive or negative connections, but less connected for in-degree and more connected for out-degree values in PSN. They also had significantly high number of intersections with each other and PN pairs than randomly expected values, indicating potential cooperative mechanisms. The three mutual interaction designs we examined had distinct RNA and protein expression correlation characteristics. NN protein pairs were uniquely over-represented across normal tissue samples, whose negative correlations were lost across cancer tissue samples. PP and PN pairs showed non-random positive RNA or protein expression correlation across normal or cancer tissue samples. Moreover, we developed an online tool, i.e., MGPNet, for further user specific analysis of mutual gene pairs. For example, we identified SNCA with significantly enriched negatively correlated NN pairs with ubiquitin-proteasome members only in normal tissues. Unique non-random characteristics of mutual gene/protein pairs identified in two different comprehensive human molecular networks could provide valuable information for a better understanding of molecular design principles between normal and cancer states.
Natural disturbances and connectivity shape the seasonal variability of aquatic macroinvertebrate communities across Europe
Authors: Chalmandrier Loic , Cunillera-Montcusi David , Lopez-Rojo Naiara , Canedo-Arguelles Miguel , Csabai Zoltan , Foulquier Arnaud , Jabot Franck , Milisa Marko , Mykra Heikki , Paril Petr , Pernecker Balint , Polovic Luka , Sarremejane Romain , Snare Henna , Soria Maria , Datry Thibault , Bonada Nuria , Munoz Francois 
Published: October 2024

AbstractUnderstanding the joint influence of natural disturbance, spatial connectivity and biogeography on biodiversity is essential to forecast its responses to climate change. Macroinvertebrate communities in drying river networks constitute an ideal study system to understand the interplay of these ecological processes. We analyze the taxonomic and functional structure of macroinvertebrate communities sampled across 126 reaches with perennial and intermittent streamflow, surveyed in six drying river networks (DRN) across Europe, six times over one year.Drying frequency decreased community richness and functional diversity of communities, whereas spatio-temporal connectivity increased community richness in intermittent reaches. Communities experiencing a high drying frequency increased the proportion of taxa with K-strategies and drying resistance traits. Communities experiencing a long drying duration compensated by high spatio-temporal connectivity had more taxa with a r-strategy and high dispersal ability. Perennial communities varied from taxa-poor communities of r-strategists in spring and autumn and taxa-rich communities of K-strategists in summer and had a constant functional diversity throughout the year. When drying frequency increased, communities showed a similar pattern except in autumn when they shifted towards species-poor communities of K-strategists. Functional diversity then peaked in summer. Community trait structure and in particular optimal drying resistance traits changed across biogeographical scales. It opposed communities from mountainous DRN (with more r-strategies and high dispersal ability) to non-mountainous DRN (with more K-strategies).Drying frequency, drying duration, and spatio-temporal connectivity drive divergent community structures, suggesting the presence of an ecological threshold that explains the variability of disturbed ecosystems across broad spatial scales. These factors also shaped seasonal community variations, particularly after summer, with intermittent communities influenced by stochastic recolonization events in spring and autumn. Spatial-temporal connectivity proved crucial for maintaining diversity in communities subjected to intense drying. Lastly, the effectiveness of drying resistance traits was dependent on the biogeographical and environmental conditions of drying river networks.
Discovery of new and variantPotyvirusesinAsparagalesplants from a Dutch urban botanic garden
Authors: Dekker Rob J. , Rauwerda Han , de Leeuw Wim C. , van Olst Marina , Ensink Wim A. , van Leeuwen Selina , Meijs Claartje , Baksi Moezammin M.M. , Breit Timo M. , Jonker Martijs J. 
Published: October 2024

AbstractAlthough viruses play an important role in human health and plant health, most viruses are still undetected. One of the reasons is that they may evolve in distinct natural habitats that are not yet extensively sampled for virus analysis. An example of such a habitat is urban botanic gardens. We analyzed 25Asparagalesplants with a mild to severe disease phenotype from a Dutch urban botanic garden for the presence of known an unknown virus types and virus variants by smallRNA-seq and RNA-seq. We found in all samples evidence for (past)Potyviridaepresence, mostly Ornithogalum virus (OV) and Ornithogalum mosaic virus (OrMV), as well as a new Iris mild mosaic virus variant (IMMV) and a yet unknown species of Potyvirus. Also, presence of a new Betaflexivirus, a new Polerovirus and a new Phenuivirus were detected. Most analyzed plants harbored multiple viruses, 18 out of 25 plants showed evidence for three to seven viruses and 12 out of 13 viruses were present in four to 11 samples. In this study, we describe the characteristics of a newly discovered Potyvirus and identify several variants of known potyviruses. We place these findings in the context of known viruses. However, we were unable to link these potyviruses to any specific disease phenotype.
Novel amylase genes enable utilisation of resistant starch by bifidobacteria relevant to early-life microbiome development
Authors: Millar Molly E. , Abele Miriam , Harris Hannah C. , Koev Todor T. , Telatin Andrea , Kiu Raymond , Van Sinderen Douwe , Khimyak Yaroslav Z. , Ludwig Christina , Hall Lindsay J. , Warren Frederick J. 
Published: October 2024

AbstractBifidobacteriumspecies and strains are key members of the gut microbiota, appearing soon after birth and persisting into adulthood. Resistant starch is an important dietary substrate for adult-associated bifidobacteria, where its fermentation supports host health. However, little is known about how different starch structures interact with bifidobacteria across various ages and ecological niches. To address this, we carried out detailed growth kinetics screening ofBifidobacteriumreference strains and unique isolates from breast-fed infants, testing their metabolic interaction with a variety of starch structures.1H NMR metabolomics as well as analysis of CAZyme profiles from genomes were generated for eachBifidobacterium-starch combination. For a subset of resistant starch-utilising isolates, we integrated multi-omics approaches to attain further mechanistic interaction insights. Our results revealed that bifidobacterial starch hydrolysis capabilities are closely associated with their CAZyme profiles and appear to be connected to the niche they occupy. Notably, in one isolate ofBifidobacterium pseudolongum, we identified a novel gene cluster containing three multi-functional amylase enzymes complemented by several starch binding modules which were significantly upregulated in response to resistant starch. This gene cluster was also found in the genomes of bifidobacterial isolates from weaning infants and adults. These findings provide new insights into their participation in the maturation process of the infant gut microbiota. Uncovering mechanisms of metabolic interaction between starch structures and bifidobacteria underscores the importance of this ecological function and potential health implications.