bluuebunny/crossref_metadata_2025_split huggingface dataset:
A repeatedly evolved mutation in Cryptochrome-1 of subterranean animals alters behavioral and molecular circadian rhythms
Authors: Swaminathan Amruta , Kenzior Alexander , McCoin Colin , Price Andrew , Weaver Kyle , Hintermann Aurélie , Morris NatiCia , Keene Alex C. , Rohner Nicolas 
Published: September 2024

ABSTRACTThe repeated evolution of similar phenotypes in independent lineages often occurs in response to similar environmental pressures, through similar or different molecular pathways. Recently, a repeatedly occurring mutation R263Q in a conserved domain of the protein Cryptochrome-1 (CRY1) was reported in multiple species inhabiting subterranean environments. Cryptochromes regulate circadian rhythms, and glucose and lipid metabolism. Subterranean species show changes to their circadian rhythm and metabolic pathways, making it likely that this mutation in CRY1 contributes to adaptive phenotypic changes. To identify the functional consequences of the CRY1 R263Q mutation, we generated a mouse model homozygous for this mutation. Indirect calorimetry experiments revealed delayed energy expenditure, locomotor activity and feeding patterns of mutant mice in the dark phase, but no further metabolic phenotypes – unlike a full loss of function of CRY1. Gene expression analyses showed altered expression of several canonical circadian genes in the livers of the mutant mice, fortifying the notion that CRY1 R263Q impacts metabolism. Our data provide the first characterization of a novel mutation that has repeatedly evolved in subterranean environments, supporting the idea that shared environmental constraints can drive the evolution of similar phenotypes through similar genetic changes.
Modelling dynamics of human NDPK hexamer structure, stability and interactions
Authors: Lim Yee Ying , Natarajan Kedar Nath 
Published: September 2024

AbstractNucleoside diphosphate kinases (NDPKs) are evolutionarily conserved multifunctional enzymes involved in energy metabolism and gene regulation. NDPKs primarily regulate nucleotide pool turnover by catalyzing the transfer between nucleoside triphosphate (NTPs) and their deoxy derivatives, maintaining cellular homeostasis. The NDPK hexameric assembly is needed for kinase activity, but its precise assembly into homo-/hetero-oligomeric complexes remains poorly understood. How quaternary structure affects NDPK activity is limited by high subunit homology, experimental challenges in isolatingin vivoheterohexamers and subunit abundances across cellular compartments. We identify conserved Arg27 across group I NDPKs (NME1-4) as the key residue for hexamer assembly. The Arg27 ensures similar hexameric assembly across subunits and mediates inter- and intra-molecular monomeric interactions, while Arg27 mutation leads to decreased binding affinity, dynamics, and complex destabilization. The double and triple Arg NME4 mutations destabilize hexamer into dimer due to shorter C-terminal region. Simulating NME1-3 with Arg mutations and shortened C-terminal recapitulates hexameric destabilization, highlighting role of the C-terminal region in stabilizing NDPK hexamers. Comparing heterohexameric complexes, we report NME1-NME2 (A1B5) complex as most stable and abundant, owing to predominant subunit nuclear localization. We propose that Arginine residues, C-terminal sequence and subunit abundances contribute to formation and stabilization of NDPK heterohexameric complexes.
Evaluating the Radiation Sensitivity Index and 12-chemokine gene expression signature for clinical use in a CLIA laboratory
Authors: Berglund Anders , Puskas John , Yoder Sean , Smith Andrew T. , Marchion Douglas C. , Qian Dahui , Mulé James J. , Torres-Roca Javier F. , Eschrich Steven A. 
Published: September 2024

AbstractBackgroundThe radiation sensitivity index (RSI) and 12-chemokine gene expression signature (12CK GES) are two gene expression signatures (GES) that were previously developed to predict tumor radiation sensitivity or identify the presence of tertiary lymphoid structures in tumors, respectively. To advance the use of these GES into clinical trial evaluation, their assays must be assessed within the context of the Clinical Laboratory Improvement Amendments (CLIA) process.MethodsUsing HG-U133Plus 2.0 arrays, we first established CLIA laboratory proficiency. Then the accuracy (limit of detection and macrodissection impact), precision (variability by time and operator), sample type (surgery vs. biopsy), and concordance with reference laboratory were evaluated.ResultsRSI and 12CK GES were reproducible (RSI: 0.01 mean difference, 12CK GES 0.17 mean difference) and precise with respect to time and operator. Taken together, the reproducibility analysis of the scores indicated a median RSI difference of 0.06 (6.47% of range) across samples and a median 12CK GES difference of 0.92 (12.29% of range). Experiments indicated that the lower limit of input RNA is 5 ng. Reproducibility with a second CLIA laboratory demonstrated reliability with the median RSI score difference of 0.065 (6% of full range) and 12CK GES difference of 0.93 (12 % of observed range).ConclusionsOverall, under CLIA, RSI and 12CK GES were demonstrated by the Moffitt Cancer Center Advanced Diagnostic Laboratory to be reproducible GES for clinical usage.
AUGMENT: a framework for robust assessment of the clinical utility of segmentation algorithms
Authors: McCague Cathal , Buddenkotte Thomas , Escudero Sanchez Lorena , Hulse David , Pintican Roxana , Rundo Leonardo ,   AUGMENT study team, Brenton James D. , Couturier Dominique-Laurent , Öktem Ozan , Woitek Ramona , Schönlieb Carola-Bibiane , Sala Evis , Crispin Ortuzar Mireia 
Published: September 2024

AbstractBackgroundEvaluating AI-based segmentation models primarily relies on quantitative metrics, but it remains unclear if this approach leads to practical, clinically applicable tools.PurposeTo create a systematic framework for evaluating the performance of segmentation models using clinically relevant criteria.Materials and MethodsWe developed the AUGMENT framework (Assessing Utility of seGMENtation Tools), based on a structured classification of main categories of error in segmentation tasks. To evaluate the framework, we assembled a team of 20 clinicians covering a broad range of radiological expertise and analysed the challenging task of segmenting metastatic ovarian cancer using AI. We used three evaluation methods: (i) Dice Similarity Coefficient (DSC), (ii) visual Turing test, assessing 429 segmented disease-sites on 80 CT scans from the Cancer Imaging Atlas), and (iii) AUGMENT framework, where 3 radiologists and the AI-model created segmentations of 784 separate disease sites on 27 CT scans from a multi-institution dataset.ResultsThe AI model had modest technical performance (DSC=72±19 for the pelvic and ovarian disease, and 64±24 for omental disease), and it failed the visual Turing test. However, the AUGMENT framework revealed that (i) the AI model produced segmentations of the same quality as radiologists (p=.46), and (ii) it enabled radiologists to produce human+AI collaborative segmentations of significantly higher quality (p=<.001) and in significantly less time (p=<.001).ConclusionQuantitative performance metrics of segmentation algorithms can mask their clinical utility. The AUGMENT framework enables the systematic identification of clinically usable AI-models and highlights the importance of assessing the interaction between AI tools and radiologists.Summary statementOur framework, called AUGMENT, provides an objective assessment of the clinical utility of segmentation algorithms based on well-established error categories.Key resultsCombining quantitative metrics with qualitative information on performance from domain experts whose work is impacted by an algorithm’s use is a more accurate, transparent and trustworthy way of appraising an algorithm than using quantitative metrics alone.The AUGMENT framework captures clinical utility in terms of segmentation quality and human+AI complementarity even in algorithms with modest technical segmentation performance.AUGMENT might have utility during the development and validation process, including in segmentation challenges, for those seeking clinical translation, and to audit model performance after integration into clinical practice.
Thiamine pyrophosphokinase-1 deficiency in neurons drives Alzheimer’s multiple pathophysiological alterations
Authors: Sang Shaoming , Zhao Xiangteng , Qian Ting , Xia Yingfeng , Pan Xiaoli , Zhao Qianhua , Cai Fang , Zeng Yeting , Cai Wenwen , Jin Boru , Qiu Hongyan , Xu Yangqi , Huang Qiang , Zhang Yun , Huang Shajin , Jiang Donglang , Wu Yun , Tong Haiyang , Zhang Qing , Wang Changpeng , Cheng Xiaoqin , Zhong Kai , Guan Yihui , Zhu Michael X. , Yu Xiang , Yuan Peng , Song Weihong , Zhong Chunjiu ,   Benfotiamine Phase 2 Clinical Trial Investigators
Published: September 2024

ABSTRACTBackgroundThe mechanism driving multiple pathophysiological alterations in Alzheimer’s disease (AD) remains unclear. Thiamine deficiency, a well-known feature of AD, may contribute to these alterations.MethodsThe expressions of four known genes associated with thiamine metabolism were studied in brain samples from patients with AD and other neurodegenerative disorders. The results were further demonstrated in AD and diabetic mouse and cellular models. The phenotypes of mice with conditionalThiamine pyrophosphokinase-1(Tpk) knockout in brain excitatory neurons were investigated. The therapeutic effects of thiamine diphosphate supplement andTpkdelivery on cellular and mouse models were explored. Phase 2 clinical trial of benfotiamine, a thiamine derivative, plus donepezil was performed.ResultsOnly TPK expression was inhibited in brain samples of AD patients, while none of thiamine-associated genes were significantly changed in other neurodegenerative disorders. TPK inhibition in the brains and neurons was verified in AD and diabetic mouse and cellular models. Mice withTpkdeletion in neurons exhibited all major pathophysiological alterations of AD, including amyloid deposition, Tau hyperphosphorylation, and brain atrophy. TPK expression restoration and thiamine diphosphate supplement ameliorated the pathophysiological and behavioral phenotypes in mouse and cell models withTpkinsufficiency. Benfotiamine delayed cognitive decline in mild-to-moderate AD patients with a dose-effect relationship, particularly with a significant attenuation of the deterioration in moderate AD patients by post hoc analysis.ConclusionsTPK deficiency and hence thiamine diphosphate reduction in neurons are a decisive factor driving multiple pathophysiologic alterations of AD, unveiling a new direction for the disease mechanism and treatment.
Correct deconfounding enables causal machine learning for precision medicine and beyond
Authors: Komeyer Vera , Eickhoff Simon B. , Rathkopf Charles , Grefkes Christian , Patil Kaustubh R. , Raimondo Federico 
Published: September 2024

AbstractArtificial intelligence holds promise for individualized medicine. Yet, predictive models in the neurobiomedical domain suffer from a lack of generalizability and replicability so that transitioning models from prototyping to clinical applications still poses challenges. Key contributors to these challenges are confounding effects; in particular the oftentimes purely statistical perspective on confounding. However, complementing these statistical considerations with causal reasoning from domain knowledge can make predictive models a tool for causal biomedical inference beyond associative insights. Such causal insights give answers to biomedical questions ofhowandwhy, arguably what most biomedical investigations ultimately seek for. Here, we suggest a 5-step approach for targeted, context-informed deconfounding. We exemplify the 5-step approach with a real-world neurobiomedical predictive task using data from the UK Biobank. The core of this approach constitutes a bottom-up causal analysis to identify a correct set of deconfounders and the appropriate deconfounding method for a given causal predictive endeavour. Using the 5-step approach to combine causal with statistical confounder considerations can make predictive models based on observational (big) data a technique comparable to Randomized Control Trials (RCTs). Through causally motivated deconfounding we aim at facilitating the development of reliable and trustworthy AI as a medical tool. In addition, we aim to foster the relevance of low performing or even null result models if they originate from a “skilful interrogation of nature”, i.e. a deconfounding strategy derived from an adequate causal and statistical analysis. Ultimately, causal predictive modelling through appropriate deconfounding can contribute to mutual recursive feedback loops of causal insights across disciplines, scales and species that enable the field to disentangle the cause-effect structure of neurobiomedical mechanisms.
Signals reported to Rucaparib: An Updated Comprehensive Disproportionality Analysis Using FDA Adverse Event Reporting System
Authors: Sharma Anshaj , Adusumilli Pramod Kumar 
Published: September 2024

AbstractOvarian cancer currently ranks as the fourth most common cancer. This cancer is metastatic in nature and making it more challenging to manage. Prostate cancer is the one of the major causes of disease and death among men. Approximately 1.6 million men are diagnosed with prostate cancer. Rucaparib, a poly-ADP ribose polymerase inhibitor has been approved by United States Food and Drug Administration for management of ovarian cancer and prostate cancer. Rucaparib was approved by US Food and Drug Administration in the year 2016 for the treatment of ovarian cancer and 2020 for the treatment of prostate cancer. To assess the potential association between rucaparib and the identified signals, a disproportionality analysis of spontaneous reports is being conducted. Reports were taken from FAERS data base and retrospective case/non case study was conducted. Reporting Odds ratio (ROR), Relative Reporting Ratio (RRR), Chi Squared Value (χ2) and Proportional Reporting Ratio (PRR) and Drug Event (DE) were used to perform disproportionality analysis. 144 signals were considered as positive adverse drug reactions using the criteria χ2>4, PRR >2, ROR >2 and DE ≥ 3. Through disproportionality analysis of the FAERS data, signal was identified between the signals-increased prostate specific antigen, decreased serum magnesium levels, decreased glomerular filtration rates, blood iron decreased and vitamin d decreased and rucaparib. The current investigation indicated that rucaparib may increase the incidence of the identified signals.
Enhancing ADHD Prediction in Adolescents through Fitbit-Derived Wearable Data
Authors: Rahman Muhammad Mahbubur 
Published: September 2024

AbstractAttention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental condition characterized by a complex etiology. The diagnostic process for ADHD is often time-consuming and subjective in nature. Recent advancements in machine learning offer promising avenues for improving ADHD diagnosis automatically using various data sources. In this study, we harness Fitbit-derived physical activity measurements to investigate potential associations with ADHD and evaluate machine learning classifiers for their predictive accuracy in ADHD diagnosis. Our analysis involves a sample of 450 participants from the Adolescent Brain Cognitive Development (ABCD) study data release 5.0. We conduct correlation analyses to explore the connections between ADHD diagnosis and Fitbit-derived measurements, including sedentary time, resting heart rate, and energy expenditure. Subsequently, we employ multivariable logistic regression models to assess the predictive capability of these measurements for ADHD diagnosis. Furthermore, we train machine learning classifiers to achieve a diagnosis by automatically categorizing individuals into ADHD+ and ADHD-groups. Our correlation analysis unveils statistically significant associations between ADHD diagnosis and Fitbit-derived measurements, suggesting a potential link between physical activity patterns and ADHD. Importantly, multivariable logistic regression models demonstrate that some of the Fitbit measurements significantly predict ADHD diagnosis. Notably, our Random Forest machine learning classifier outperforms other classifiers with cross-validation accuracy (0.89), AUC (0.95), precision (0.88), recall (0.90), f1-score (0.89) and test accuracy (0.88), surpassing the performance of previous ADHD classification studies. These findings not only lay the groundwork for further exploration but also offer insights into the clinical integration of wearable data for a deeper understanding and improved identification of ADHD.
Severity of lumbar spinal stenosis does not impact responsiveness to exercise-based rehabilitation
Authors: Shahidi Bahar , Zavareh Armin , Richards Connor , Taitano Lissa , Raiszadeh Kamshad 
Published: September 2024

ABSTRACTSpine pain is a prevalent and costly condition affecting up to 85% of individuals throughout their lifetime, and spinal stenosis is one of the most debilitating sources of spine pain. Although conservative management is the first line of treatment for spinal stenosis, severe cases often are directly referred to surgical intervention due to the belief that conservative strategies delay necessary treatment. However, there are no studies supporting the premise that individuals with more severe stenosis respond poorly to conservative management. The purpose of this study was to compare improvements in pain, disability, strength, medication usage, and patient goals in response to an exercise-based physical therapy program across 1,806 individuals with mild, moderate, or severe lumbar spine stenosis. Participants demonstrated significant improvements in all variables of interest (p<0.001), and 11.5% of participants reported cessation of narcotic use with treatment. There were no significant differences in treatment response across mild, moderate, or severe stenosis groups for any outcome (p>0.546). Exercise-based rehabilitation is as beneficial in the short term for individuals presenting for nonoperative care with severe stenosis compared to their milder counterparts. Future research is needed to evaluate long term durability and cost effectiveness of rehabilitation in this patient population.
Association between Neonatal Necrotizing Enterocolitis and Neurodevelopmental Outcomes: A Comprehensive Systematic Review and Meta-Analysis
Authors: Berken Jonathan A. , Ramanan Priyanka , Akel Mary J. , Miller Corinne , Nunes Denise , Aleynick Daniel N. , Wechsler Joshua B. , Wakschlag Lauren S. , Mithal Leena B. 
Published: September 2024

ABSTRACTPreterm infants are at high risk for systemic inflammatory disorders, including sepsis, meningitis, bronchopulmonary dysplasia, and necrotizing enterocolitis (NEC). The developing brain of the premature newborn is especially susceptible to the cascade of inflammatory mediators elaborated in these conditions that cross the blood-brain barrier. NEC, a severe and potentially fatal condition of the gut that occurs in premature newborns, is a prime example of how an inflammatory reaction, perhaps initially localized, can become generalized and cause systemic harm. One such result is brain injury, especially to the cerebral white matter, which may lead to neurodevelopmental abnormality and dysregulated behavior. Numerous studies have documented an association between necrotizing enterocolitis and neurodevelopmental impairment (NDI), but to date, the brain and behavioral deficits associated with neonatal NEC are not fully understood. We performed a comprehensive systematic review and meta-analysis of existing literature to characterize brain injury and behavioral alterations associated with NEC. 7153 peer-reviewed published manuscripts were screened by two independent reviewers and evidence quality was assessed using GRADE criteria. Of these papers, 62 satisfied the criteria for our review (i.e., no case reports, meta-analyses, systematic reviews, or animal studies). Data from 32 papers using Bayley Scales of Infant and Toddler Development to assess infant outcomes were included in the meta-analysis. Our findings support neonatal necrotizing enterocolitis having deleterious effects on brain and behavioral development and impact on cognitive function, risk of cerebral palsy and motor impairment, educational achievement, behavior, and neuroanatomy. We discuss herein findings of both short-term outcomes (1-3 years) and long-term outcomes (until 13 years). Our meta-analysis also indicates that NEC has a moderate effect size on infant development, with consistent impairment across mental, cognitive, language and motor domains.WHAT DO THE FINDINGS OF THIS REVIEW MEAN?In this review, we describe several short-term and long-term neurodevelopmental outcomes of preterm infants with NEC. These findings suggest that infants with NEC should be monitored with close developmental follow-up so that individuals can receive appropriate testing that may prompt therapies and qualify them to receive early intervention services.HOW UP TO DATE IS THIS REVIEW?The review authors searched for studies published up to 2021. Abstracts with no data available were excluded, so this review does not consider any new findings published from 2022-2024.
Collaborative Large Language Models for Automated Data Extraction in Living Systematic Reviews
Authors: Khan Muhammad Ali , Ayub Umair , Naqvi Syed Arsalan Ahmed , Khakwani Kaneez Zahra Rubab , Sipra Zaryab bin Riaz , Raina Ammad , Zou Sihan , He Huan , Hossein Seyyed Amir , Hasan Bashar , Rumble R. Bryan , Bitterman Danielle S. , Warner Jeremy L. , Zou Jia , Tevaarwerk Amye J. , Leventakos Konstantinos , Kehl Kenneth L. , Palmer Jeanne M. , Murad M. Hassan , Baral Chitta , Riaz Irbaz bin 
Published: September 2024

ABSTRACTObjectiveData extraction from the published literature is the most laborious step in conducting living systematic reviews (LSRs). We aim to build a generalizable, automated data extraction workflow leveraging large language models (LLMs) that mimics the real-world two-reviewer process.Materials and MethodsA dataset of 10 clinical trials (22 publications) from a published LSR was used, focusing on 23 variables related to trial, population, and outcomes data. The dataset was split into prompt development (n=5) and held-out test sets (n=17). GPT-4-turbo and Claude-3-Opus were used for data extraction. Responses from the two LLMs were compared for concordance. In instances with discordance, original responses from each LLM were provided to the other LLM for cross-critique. Evaluation metrics, including accuracy, were used to assess performance against the manually curated gold standard.ResultsIn the prompt development set, 110 (96%) responses were concordant, achieving an accuracy of 0.99 against the gold standard. In the test set, 342 (87%) responses were concordant. The accuracy of the concordant responses was 0.94. The accuracy of the discordant responses was 0.41 for GPT-4-turbo and 0.50 for Claude-3-Opus. Of the 49 discordant responses, 25 (51%) became concordant after cross-critique, with an increase in accuracy to 0.76.DiscussionConcordant responses by the LLMs are likely to be accurate. In instances of discordant responses, cross-critique can further increase the accuracy.ConclusionLarge language models, when simulated in a collaborative, two-reviewer workflow, can extract data with reasonable performance, enabling truly ‘living’ systematic reviews.