bluuebunny/crossref_metadata_2025_split huggingface dataset:
Structured Clinical Reasoning Prompt Enhances LLM’s Diagnostic Capabilities in Diagnosis Please Quiz Cases
Authors: Sonoda Yuki , Kurokawa Ryo , Hagiwara Akifumi , Asari Yusuke , Fukushima Takahiro , Kanzawa Jun , Gonoi Wataru , Abe Osamu 
Published: September 2024

AbstractBackgroundLarge Language Models (LLMs) show promise in medical diagnosis, but their performance varies with prompting. Recent studies suggest that modifying prompts may enhance diagnostic capabilities.ObjectiveThis study aimed to test whether a prompting approach that aligns with general clinical reasoning methodology—specifically, separating processes of summarizing clinical information and making diagnoses based on the summary instead of one-step processing—can enhance LLM’s medical diagnostic capabilitiesMethods322 quiz questions fromRadiology’sDiagnosis Please cases (1998-2023) were used. We employed Claude 3.5 Sonnet, a state-of-the-art LLM, to compare three approaches: 1) Conventional zero-shot chain-of-thought prompt, as a baseline, 2) two-step approach: LLM organizes patient history and imaging findings, then provides diagnoses, and 3) Summary-only approach: Using only the LLM-generated summary for diagnoses.ResultsThe two-step approach significantly outperformed both baseline and summary-only methods in diagnosis accuracy, as determined by McNemar tests. Primary diagnosis accuracy was 60.6% for the two-step approach, compared to 56.5% for baseline (p=0.042) and 56.3% for summary-only (p=0.035). For the top three diagnoses, accuracy was 70.5%, 66.5%, and 65.5% respectively (p=0.005 for baseline, p=0.008 for summary-only). No significant differences were observed between baseline and summary-only approaches.ConclusionOur results indicate that a structured clinical reasoning approach enhances LLM’s diagnostic accuracy. This method shows potential as a valuable tool for deriving diagnoses from free-text clinical information. The approach aligns well with established clinical reasoning processes, suggesting its potential applicability in real-world clinical settings.
Interpretable scRNA-seq Analysis with Intelligent Gene Selection
Authors: Ni Tianhao , Zhang Xinyu , Jin Kaixiu , Pei Guanxiong , Xue Nan , Yan Guanao , Li Taihao , Li Bingjie 
Published: September 2024

AbstractSingle-cell RNA sequencing (scRNA-seq) data analysis faces multiple challenges, including high dimensionality, significant noise, and data loss. To effectively address these issues, we introduce AIGS, a robust and transparent single-cell analysis framework. AIGS utilizes an intelligent gene selection method that systematically identifies the most informative genes for clustering based on the normalized mutual information between pre-learned pseudo-labels and quantified genes. Additionally, AIGS incorporates a scale-invariant distance metric to assess cell-to-cell similarity, enhancing connections between homogenous cells and ensuring more accurate and robust results. Through comprehensive comparisons with state-of-the-art techniques, AIGS demonstrates superior performance in both clustering accuracy and multi-resolution visualization quality. Our in-depth analysis of clustering and visualization results further reveals that AIGS can uncover complex, stage-specific gene expression patterns during the same developmental cell stage.
Urine cf-Nucleosomes: A Non-invasive Window into Human Physiology and Disease
Authors: Lotem Matan , Sharkia Israa , Azria Batia , Falik-Michaeli Tal , Friedman Nir 
Published: September 2024

AbstractUrine contains fragments of cell-free DNA, which hold valuable molecular insights into the processes occurring within the urinary system and the whole body. It is unknown whether these fragments are in the form of chromatin, as in the cell of origin, and whether they maintain the cell of origin chromatin modifications. Here, we examine these questions using cell-free chromatin immunoprecipitation followed by sequencing (cfChIP-seq) on human urine. We demonstrate that we can capture cell-free nucleosomes (cf-nucleosomes) from urine and that these preserve multiple histone post-translational modifications indicative of activation and repression. By analyzing these modifications, we identified the primary tissues contributing to cf-nucleosomes in a cohort of healthy individuals. Notably, we observe distinct populations of circulating cf-nucleosomes in urine samples from healthy donors with a contribution from the kidney, which are not detected in matched urine exfoliated cells or matching plasma samples. We further show that, at most, a negligible amount of urine cf-nucleosomes originates from plasma, suggesting that kidney filtration excludes plasma-circulating nucleosomes from urine. Additionally, we show that urine cf nucleosomes can report pathologically driven changes in the urine of bladder cancer patients, reflecting tumor-associated transcriptional programs and immune responses. Our findings highlight the potential of urine cf nucleosomes as accessible, noninvasive biomarkers for both basic research in renal physiology and monitoring urinary pathologies.Key FindingsUrine cell-free nucleosomes exist and retain multiple histone marks that are informative of gene promoters and enhancers.Urine cfChIP-seq identifies bladder, kidney, and immune cells as the major contributing organs to the pool of urine cell-free nucleosomes.The populations of cell-free nucleosomes in urine and blood are distinct and primarily disjoint, suggesting that few, if any, nucleosomes cross the blood-urine interface.Urine cell-free nucleosomes reflect pathologically driven changes in tumors and immune cells responding to the tumor.
Understanding technology-related prescribing errors for system optimisation: the Technology-Related Error Mechanism (TREM) classification
Authors: Raban Magdalena Z. , Merchant Alison , Fitzpatrick Erin , Baysari Melissa T. , Li Ling , Gates Peter J. , Westbrook Johanna I. 
Published: September 2024

ABSTRACTObjectivesTechnology-related prescribing errors curtail the positive impacts of computerised provider order entry (CPOE) on medication safety. Understanding how technology-related errors occur can inform CPOE optimisation. Previously, we developed a classification of the underlying mechanisms of technology-related errors using prescribing error data from two adult hospitals. Our objective was to update the classification using paediatric prescribing error data, and to assess the reliability with which reviewers could independently apply the classification.Materials and MethodsUsing data on 1696 prescribing errors identified by chart review in 2016 and 2017 at a tertiary paediatric hospital, we identified errors that were technology-related. These errors were investigated to classify their underlying mechanisms using our previously developed classification, and new categories were added based on the data. A two-step process was used to identify and classify technology-related errors involving review of the error in the CPOE and simulating the error in the CPOE testing environment.ResultsThe Technology-Related Error Mechanism (TREM) classification comprises seven categories and 19 subcategories. The seven categories are: 1) errors due to incorrect system configuration or system malfunction, 2) prescribing on the wrong patient record, 3) selection errors, 4) construction errors, 5) editing errors, 6) errors that occur when using workflows that differ from a paper-based system 7) contributing factor: use of hybrid systems.ConclusionTechnology-related errors remain a critical issue for CPOE. The updated TREM classification provides a systematic means of assessing and monitoring technology-related errors to inform and prioritise system improvements, and has now been updated for the paediatric setting.What is already known on this topicTechnology-related errors occur frequently in computerised provider order entry (CPOE).Technology-related errors can be addressed by CPOE modifications; however, an understanding of how the errors occurred is required.What this study addsThis study presents a method for classifying how technology-related errors occur, the Technology-Related Error Mechanism (TREM) classification.How this study might affect research, practice or policyUsing the TREM classification can aid system managers in identifying areas for CPOE optimisation to deliver improved patient safety outcomes.
Comparison of the prevalence and associated factors of chronic kidney disease diagnosed by serum creatinine or cystatin C among young people living with HIV in Uganda
Authors: Nasuuna Esther M , Tomlinson Laurie A , Kalyesubula Robert , Chikwari Chido Dziva , Castelnuovo Barbara , Manabe Yukari C , Nakanjako Damalie , Weiss Helen A. 
Published: September 2024

AbstractIntroductionYoung people living with HIV (YPLHIV) are at increased risk of developing chronic kidney disease (CKD) which is associated with high mortality and morbidity. Early diagnosis is important to halt progression. We aimed to estimate the prevalence and factors associated with CKD among YPLHIV in Kampala, Uganda, and to compare serum creatinine and cystatin C for early diagnosis of CKD in this population.MethodsA cross-sectional study with YPLHIV aged 10 to 24 years was conducted in seven HIV clinics. Participants provided a urine and blood sample to measure urinary albumin, proteinuria, serum creatinine and cystatin C levels at baseline and after three months. The estimated glomerular filtration rate (eGFR) was calculated using CKDEPI 2021, Cockroft-Gault and bedside Schwartz equations using creatinine or cystatin C. The albumin creatinine ratio (ACR) and proteinuria were measured. CKD was defined as either eGFR <60ml/min/1.73m2or <90ml/min/1.73m2or ACR above 30mg/g on two separate occasions. Univariable and multivariable logistic regression were used to estimate adjusted odds ratios (aOR) and 95% confidence intervals (CI) for factors associated with CKD.ResultsA total of 500 participants were enrolled. Most were female (56%; n=280) and aged 10 to 17 years (66.9%; n=335). CKD prevalence ranged from 0-23% depending on the criteria, equation and biomarker used. Cystatin C-based equations estimated higher prevalence of CKD compared to creatinine-based ones. Prevalence of ACR above 30mg/g was 10.1% and of proteinuria 29%. Factors independently associated with CKD were age (aOR=1.42; 95% CI:1.30-1.51) and male sex (aOR=3.02; 95% CI:1.68-5.43).ConclusionCKD prevalence among YPLHIV varied substantially depending on definitions used and the current definition would likely lead to missed cases of CKD among YPLHIV. Estimating equations should be validated against measured GFR in YPLHIV and the optimal definition of CKD in this vulnerable population should be revised to optimise detection and opportunities for reducing disease progression.
Dengue nowcasting in Brazil by combining official surveillance data and Google Trends information
Authors: Xiao Yang , Soares Guilherme , Bastos Leonardo , Izbicki Rafael , Moraga Paula 
Published: September 2024

AbstractDengue is a mosquito-borne viral disease that poses significant public health challenges in tropical and sub-tropical regions worldwide. Surveillance systems are essential for dengue prevention and control. However, traditional systems often rely on delayed data, limiting their effectiveness. To address this, nowcasting methods are needed to estimate underreported cases, enabling more timely decision-making. This study evaluates the value of using Google Trends indices of dengue-related keywords to complement official dengue data for nowcasting dengue in Brazil, a country frequently affected by this disease. We compare various nowcasting approaches that incorporate autoregressive features from official dengue cases, Google Trends data, and a combination of both, using a naive approach as a baseline. The performance of these methods is evaluated by nowcasting weekly dengue cases from March to June 2024 across Brazilian states. Error measures and 95% coverage probabilities reveal that models incorporating Google Trends data enhance the accuracy of weekly nowcasts across states and offer valuable insights into dengue activity levels. To support real-time decision-making, we also present Dengue Tracker, a website that displays weekly dengue nowcasts and trends to inform both decision-makers and the public, improving situational awareness of dengue activity. In conclusion, the study demonstrates the value of digital data sources in enhancing dengue nowcasting, and emphasizes the value of integrating alternative data streams into traditional surveillance systems for better-informed decision-making.Author summaryDengue is a mosquito-borne viral disease that poses significant public health challenges in tropical and sub-tropical regions worldwide. Surveillance systems are crucial for dengue prevention and control. Unfortunately, traditional systems often rely on delayed data, limiting their effectiveness. To address this, nowcasting methods are needed to estimate underreported cases, enabling more timely decision-making. This study evaluates how Google Trends indices of dengue-related keywords can complement official dengue data to improve nowcasting of dengue in Brazil, a country frequently affected by this disease. We compare the performance of various nowcasting approaches that incorporate Google Trends data with other approaches that rely solely on official reported cases data, assessing their accuracy and uncertainty in nowcasting weekly dengue cases from March to June 2024 across Brazilian states. To support real-time decision-making, we also present Dengue Tracker, a website that displays weekly dengue nowcasts offering valuable insights into dengue activity levels. The study demonstrates the potential of digital data sources in enhancing traditional surveillance systems for better-informed decision-making.
Beyond Words: Non-verbal auditory cognitive impairments in Alzheimer’s Disease dementia
Authors: Lad Meher , Deasy Charlotte , Plack Christopher J , Taylor John-Paul , Griffiths Timothy D 
Published: September 2024

ABSTRACTBackgroundVerbal speech-in-noise (SIN) measures are impaired in early Alzheimer’s disease (AD) but may be confounded by linguistic and cultural factors. We investigated whether non-verbal auditory memory could predict cognitive impairment in AD.MethodsWe evaluated 158 cognitively healthy individuals, 26 with mild cognitive impairment (MCI), and 28 with AD dementia using the Addenbrooke’s Cognitive Examination (ACE-III), pure-tone audiometry, verbal SIN tests, and non-verbal auditory memory tests for basic sound features. Group differences were assessed adjusting for age, sex, and education. Logistic regression and receiver operating characteristic (ROC) analyses compared model fit of verbal and non-verbal auditory variables.ResultsAll auditory cognition measures were significantly associated with cognition. Non-verbal measures provided a better fit to diagnosis than verbal measures (AIC difference >10), although ROC analyses showed no significant differences between models.ConclusionsNon-verbal auditory measures are effective measures in distinguishing between cognitively healthy, MCI, and AD dementia individuals.
Childhood Adversity and Adolescent Epigenetic Age Acceleration: The Role of Adolescent Sleep Health
Authors: DiMarzio Karissa , Rojo-Wissar Darlynn M. , Hernandez Valencia Evelyn , Ver Pault Mikayla , Denherder Shane , Lopez Adamari , Lerch Jena , Metrailer Georgette , Merrill Sarah M. , Highlander April , Parent Justin 
Published: September 2024

AbstractStudy ObjectivesWe investigated how a dimension of early life adversity (ELA) capturing threat in the home relates to later epigenetic age acceleration in adolescence through sleep (duration, efficiency, and timing), to empirically test theoretical models suggesting the importance of sleep as a key mechanism linking ELA with poor health outcomes, and to expand the limited literature on sleep and epigenetic aging among youth.MethodsWe utilized data from 861 participants from the Future of Families and Child Wellbeing Study (FFCWS) who participated in the actigraphy sub study at age 15. Sleep variables used were average total sleep time (TST), sleep efficiency (SE), and sleep onset timing. Home threat was determined at ages 3, 5, and 9 from parent reports on the Child Conflict Tactics Scale (CTS-PC), and epigenetic aging was measured through DNA methylation analyses of saliva samples collected at age 15.ResultsHigher levels of childhood home threat exposure were associated with less adolescent TST, lower SE, and later sleep onset timing. Adolescent SE and timing were associated with a faster pace of aging and epigenetic age acceleration. Sleep efficiency and timing mediated the link between childhood home threat exposure and adolescent epigenetic aging.ConclusionsEpigenetic embedding of childhood threat exposure in the home may occur through adversity-related sleep disturbances in adolescence. Findings warrant greater attention to pediatric sleep health in theoretical models of biological embedding of adversity and point to the examination of improving sleep health as a potential way to prevent adversity-related epigenetic age acceleration.Statement of SignificanceThis study brings together two largely disparate bodies of literature on early life adversity and epigenetics and sleep and epigenetics. Findings advance knowledge on how adversity impacts specific aspects of sleep (duration, efficiency, and timing), and biological aging, offering potential pathways for mitigating long-term health risks associated with childhood adversity.
CV.eDNA: A hybrid approach to invertebrate biomonitoring using computer vision and DNA metabarcoding
Authors: Blair Jarrett D. , Weiser Michael D. , Siler Cameron , Kaspari Michael , Smith Sierra N. , McLaughlin Jessica F. , Marshall Katie E. 
Published: September 2024

AbstractAutomated invertebrate classification using computer vision has shown significant potential to improve specimen processing efficiency. However, challenges such as invertebrate diversity and morphological similarity among taxa can make it difficult to infer fine-scale taxonomic classifications using computer vision. As a result, many invertebrate computer vision models are forced to make classifications at coarser levels, such as at family or order.Here we propose a novel modular method to combine computer vision and bulk DNA metabarcoding specimen processing pipelines to improve the accuracy and taxonomic granularity of individual specimen classifications. To improve specimen classification accuracy, our methods use multimodal fusion models that combine image data with DNA-based assemblage data. To refine the taxonomic granularity of the model’s classifications, our methods cross-references the classifications with DNA metabarcoding detections from bulk samples. We demonstrated these methods using a continental-scale, invertebrate bycatch dataset collected by the National Ecological Observatory Network. We also introduce the CV.eDNA R package, which aims to assist practitioners looking to implement our methods.Using our methods, we reached a classification accuracy of 79.6% across the 17 taxa using real DNA assemblage data, and 83.6% when the assemblage data was “error-free”, resulting in a 2.2% and 6.2% increase in accuracy when compared to a model trained using only images. After cross-referencing with the DNA metabarcoding detections, we improved taxonomic granularity in up to 72.2% of classifications, with up to 5.7% reaching species-level.By providing computer vision models with coincident DNA assemblage data, and refining individual classifications using DNA metabarcoding detections, our methods the potential to greatly expand the capabilities of biological computer vision classifiers. Our methods allow computer vision classifiers to infer taxonomically fine-grained classifications when it would otherwise be difficult or impossible due to challenges of morphologic similarity or data scarcity. These methods are not limited to terrestrial invertebrates and could be applied in any instance where image and DNA metabarcoding data are concurrently collected.
Spatiotemporal structure and substates in facial expressions and speech movements
Authors: Cuve Hélio Clemente José , Sowden-Carvalho Sophie , Cook Jennifer 
Published: September 2024

AbstractRanging from overt emotion displays to the understated eyebrow raise accompanying speech, facial expressions are indispensable cues in social interaction. Yet, how these inherently dynamic cues contribute to emotion signalling remains only partially understood. One overlooked factor is how facial actions combine and recombine over time to produce nuanced expressions. Moreover, although most face-to-face interactions naturally involve speech, its contribution to facial emotion signalling has been largely neglected. Building on motor control principles and employing a novel data-driven pipeline - combining spatiotemporal dimensionality reduction, dynamic feature extraction, classification and clustering - we investigated the structure and function of facial dynamics in bothExpression onlyandEmotive speechsignals. We show that a few fundamental spatiotemporal patterns reliably differentiated between specific emotion dynamics such as happy, sad and angry as well as betweenExpression onlyandEmotive speechsignals. Furthermore, these spatiotemporal components exhibit meaningful substates, which reflect transient dynamics that are diagnostic of emotion content. This underlying spatiotemporal structure represents an efficient encoding strategy, optimising the transmission and perception of emotion and non-emotion facial signals. These findings have implications for understanding normative and “atypical” face-to-face signalling and can inform the design of expressive emotive capabilities in artificial social agents. Moreover, our data-driven pipeline can be extended to study broader aspects of multimodal social signalling.Significance StatementIn face-to-face interactions, facial movements convey both emotional and speech-related information, yet their complex dynamic signalling remains only partly understood. This study explores how facial movements evolve over time, integrate emotional and speech cues. Using advanced techniques to measure and analyse moment-to-moment changes in dozens of facial muscles we identified fundamental movement patterns that differentiate emotional signals. These dynamic patterns enable both isolated emotional signalling and simultaneous emotion cues during speech. These findings can inform understanding of typical and atypical face-to-face interactions abilities and enhance the design of human-like facial expressions in social robots and virtual agents. We also contribute a novel data-driven pipeline for analysing facial dynamics, applicable to broader facets of multimodal social communication.