bluuebunny/crossref_metadata_2025_split huggingface dataset:
ODIASP: An Open User-Friendly Software for Automated SMI Determination—Application to an Inpatient Population
Authors: Charrière Katia , Ragusa Antoine , Genoux Béatrice , Vilotitch Antoine , Artemova Svetlana , Dumont Charlène , Beaudoin Paul-Antoine , Madiot Pierre-Ephren , Ferretti Gilbert R. , Bricault Ivan , Fontaine Eric , Bosson Jean-Luc , Moreau-Gaudry Alexandre , Giai Joris , Bétry Cécile 
Published: October 2024

AbstractBackgroundThe diagnosis of malnutrition has evolved with the GLIM recommendations, which advocate for integrating phenotypic criteria, including muscle mass measurement. The GLIM framework specifically suggests using skeletal muscle index (SMI) assessed via CT scan at the third lumbar level (L3) as a first-line approach. However, manual segmentation of muscle from CT images is often time-consuming and infrequently performed in clinical practice. This study aims to develop and validate an open-access, user-friendly software tool called ODIASP for automated SMI determination.MethodsData were retrospectively collected from a clinical data warehouse at Grenoble Alpes University Hospital, including epidemiological and imaging data from CT scans. All consecutive adult patients admitted in 2018 to our tertiary center who underwent at least one CT scan capturing images at the L3 vertebral level and had a recorded height were included. The ODIASP tool combines two algorithms to automatically perform L3 slice selection and skeletal muscle segmentation, ensuring a seamless process. Agreement between cross-sectional muscle area (CSMA) values obtained via ODIASP and reference methodology was evaluated using the intraclass correlation coefficient (ICC). The prevalence of reduced SMI was also assessed.ResultsSMI values were available for 2,503 participants, 53.3% male, with a median age of 66 years [51-78] and a median BMI of 24.8 kg/m2[21.7-28.7]. There was substantial agreement between the reference method and ODIASP (ICC: 0.971; 95% CI: 0.825 to 0.989) in a validation subset of 674 CT scans. After correcting for systematic errors (a 5.8 cm2[5.4-6.3] overestimation of the CSMA), the agreement improved to 0.984 (95% CI: 0.982 to 0.986), indicating excellent agreement. The prevalence of reduced SMI was estimated at 9.1% overall (11.0% in men and 6.6% in women). To facilitate usage, the ODIASP software is encapsulated in a user-friendly interface.ConclusionsThis study demonstrates that ODIASP is a reliable tool for automated muscle segmentation at the L3 vertebra level from CT scans. The integration of validated AI algorithms into a user-friendly platform enhances the ability to assess SMI in diverse patient cohorts, ultimately contributing to improved patient outcomes through more accurate assessments of malnutrition and sarcopenia.
Relationship between Body Mass Index, Gray Matter Volume and peripheral inflammation in patients with post-COVID condition
Authors: Claaß Luise V. , Schick Franziska , Rocktäschel Tonia , Garza Alejandra P. , Gaser Christian , Reuken Philipp A. , Stallmach Andreas , Finke Kathrin , Thanarajah Sharmili Edwin , Walter Martin , Dunay Ildiko Rita , Besteher Bianca , Opel Nils 
Published: October 2024

AbstractBackgroundObesity is linked to low-grade peripheral inflammation and is recognized as an independent risk factor for severe COVID-19. Obesity and overweight have furthermore been shown to relate to structural brain alterations. Post-COVID condition (PCC) has in turn been associated with structural brain alterations and lingering immunological changes. Therefore, in this study, we aimed to assess whether obesity contributes to structural brain alterations and differences in immunological markers in PCC patients.MethodsWe investigated a previously established cohort of PCC patients (n = 61). Whole-brain comparison of gray matter volume (GMV) was conducted by voxel-based morphometry (VBM). Obesity, as measured by body mass index (BMI), as well as age, gender and total intracranial volume (TIV) were included as regressors in a linear regression model.ResultsA significant negative association was found between higher BMI and lower GMV in the right thalamus (p(FWE) = 0.039, k = 209, TFCE = 1037.97, x = 18, y = −21, z = 8). Moreover, BMI, GMV and immunological markers were linked in PCC. Specifically, BMI was primarily positively associated with Interleukin-6 and negatively with Interleukin-7, while GMV showed strong positive associations with Interleukin-8.LimitationsA small cohort size and no available data on BMI changes before and after SARS-CoV2 infection limit the interpretation of our findings.ConclusionThe results suggest that BMI contributes to GMV alterations in PCC patients, with both BMI and GMV being associated with peripheral immunological markers. These findings indicate that converging mechanisms, such as inflammation and structural brain alterations, may play a role in obesity and PCC.
Design and implementation of Electronic Health Record Computerised Decision Support System (CDSS) trials: A Systematic Review
Authors: Chen Yang , Wilson Matthew G. , Nagendran Myura , Collard Didier , Wijayarathne Jayathri , Dilan Matiwalakumbura , Wickramaratne Ravi , Dahele Karan , Samways Jack , Jani Yogini , Shah Anoop , Lumbers Tom , Harris Steve , Asselbergs Folkert W 
Published: October 2024

ABSTRACTObjectivesConduct a systematic review of the existing evidence base pertaining to the conduct of randomised controlled trials of clinical decision support systems embedded within electronic health record systems. Further, to describe whether key features of trial design and implementation were consistently reported.Materials and MethodsA systematic search of MEDLINE was conducted in April 2022. Three independent reviewers screened the search results. A 27-item checklist was used to extract data from the screened studies. A subgroup analysis was conducted to classify trials of clinical decision support systems based on whether they encouraged guideline adherence or represented new knowledge generating mechanisms.Results5,213 records were retrieved. Following screening, 106 studies were included in the review. The majority of studies evaluated active alerts seeking to improve adherence to clinical guidelines rather than generate new knowledge. Few studies quantified the existing ecosystem of decision support at the study site, or explored phenomena like alert fatigue.DiscussionThis systematic review provides a detailed analysis of the characteristics of trials evaluating clinical decision support systems. It highlights significant under-reporting of key factors which may affect the reproducibility and generalisability of trial results - particularly with respect to measurement of alert fatigue, description of the underlying digital ecosystem and additional co-interventions used within trials.ConclusionAs clinical workflows undergo digital transformation, randomised controlled trials of clinical decision support systems require greater standardisation, in both conduct and reporting. This represents an area of expanding interest given the increasing use of artificial intelligence-enabled decision support.STRENGTHS & LIMITATIONS-This study presents the results of an updated systematic review of studies evaluating the effectiveness clinical decision support systems.-It used a comprehensive checklist to extract detail pertaining to five information domains on trial quality and description.-Studies were evaluated to determine whether the clinical decision support system was knowledge generating or designed to improve guideline adherence.-The review was limited to randomised trials and excluded quasi-experimental and observational studies of clinical decision support systems.
Towards a Universal Map of EEG: A semantic, low-dimensional manifold for EEG Classification, Clustering and Prognostication
Authors: Krumm Laura , Kranz Dominik D. , Halimeh Mustafa , Nelde Alexander , Amorim Edilberto , Zafar Sahar , Jing Jin , Thomas Robert J. , Westover M. Brandon , Meisel Christian 
Published: October 2024

AbstractDespite its complexity, whole-brain neural activity spontaneously organizes into a limited range of states, indicating that a low-dimensional representation or embedding might be sufficient to capture much of its macroscale dynamics. Clinical practice makes use of this notion by classifying EEG into discrete states, for example in the wake-sleep cycle or along the ictal-interictal continuum. Such classification into distinct classes has clinical value and simplifies EEG interpretation (by humans and, more recently, also by artificial intelligence [AI]) but misses subtle differences, mixtures and transitions between states. In other words, it does not adequately reflect the true underlying, low-dimensional continuum of states. The lack of a continuum EEG map limits more realistic characterizations of EEG states and challenges prognostication in patients with disorders of consciousness due to their diverse patterns, etiologies and underlying pathophysiologies. We here use data across the health-disease continuum (wake [W], sleep [REM, N1, N2, N3], ictal-interictal-continuum [lateralized and generalized periodic discharges (LPD, GPD), lateralized and generalized rhythmic delta activity (LRDA, GRDA)], seizures [SZ], burst suppression [BS]; 20,043 patients, 288,986 EEG segments) and a deep neural network that optimises a low-dimensional embedding space. This universal map of EEG (UM-EEG) is highly semantic, confined and captures meaningful distances as well as transitions between EEG states. Classification and clustering of unseen EEG are easily implemented using standard techniques on the map that match performance of current AI algorithms while extending it to the currently largest set of classes across the full health-disease continuum (mean AUROCs and 95% CI one-vs-all classification: W 0.94 [0.94, 0.95], N1 0.85 [0.85, 0.86], REM 0.92 [0.91, 0.92], N2 0.91 [0.90, 0.91], N3 0.98 [0.97, 0.98], GRDA [0.97 [0.96, 0.97], LRDA 0.97 [0.96, 0.97], SZ 0.87 [0.83, 0.91], GPD 0.99 [0.98, 0.90], LPD 0.97 [0.97, 0.97)], BS 0.94 [0.93, 0.94]). Long-term EEGs are represented as trajectories in this continuous embedding space which predict outcome (recovery or death) after cardiac arrest with an AUROC of 0.86 (N= 575 patients). For these patients, the map identifies interpretable factors governing prognosis: the distance to healthy states, the variability of state transitions, and time spent in burst suppression. These factors allow personalized monitoring and comparison of patients to themselves over time. In conclusion, UM-EEG presents a novel, comprehensive and physiologically meaningful representation to characterize and classify brain states along the health-disease continuum. It demonstrates immediate clinical utility by enhancing current diagnostic and prognostic capabilities.
Summer and SERT: Effect of daily sunshine hours onSLC6A4promoter methylation in seasonal affective disorder
Authors: Handschuh P. A. , Murgaš M. , Winkler D. , Winkler-Pjrek E. , Hartmann A. M. , Domschke K. , Baldinger-Melich P. , Rujescu D. , Lanzenberger R. , Spies M. 
Published: October 2024

ABSTRACTMeteorological factors affect the serotonergic system, potentially influencingSLC6A4promoter methylation in seasonal affective disorder (SAD). However, studies on how sunlight impacts methylation and modulates SERT activity in this context remain limited. This study aims to investigate the effect of average daily sunshine duration onSLC6A4promoter methylation in a cohort consisting of both patients with SAD as well as healthy controls (HC). Methylation data were collected from 28 patients with SAD and 40 healthy controls (HC). Average methylation from fourSLC6A4promoter CpG sites was assessed. Daily sunlight data for Vienna, Austria (mean of 28 days before blood sampling), were obtained from ©GeoSphere Austria. A general linear model (GLM) analyzedSLC6A4promoter methylation as the dependent variable, with sunlight hours as the independent variable, and group (SAD, HC), age, sex, and 5-HTTLPR/rs25531 as covariates. Exploratory analyses examined sunlight hours and methylation effects on Beck Depression Inventory (BDI) scores. Sunlight had a significant effect onSLC6A4promoter methylation (p = 0.03), with more sunlight hours resulting in lower methylation (r = −0.25). However, the interaction between sunlight and group was non-significant, suggesting a rather general effect across both groups. Sunlight also influenced BDI scores (p < 0.01), with fewer sunlight hours leading to higher BDI scores (r = −0.25), which aligns with previous research.SLC6A4promoter methylation had no significant effect on BDI scores. Our findings suggest that sunlight impactsSLC6A4promoter methylation, but this effect appears general, not specific to SAD pathophysiology.
Molecular Profiling of a Triple-negative Breast Cancer Cohort in India for EGFR and AR expression analyzed for patient outcomes showed a distinct subset of cellular co-expression
Authors: Vaid Pooja , Puntambekar Anirudha , Kanse Pranali , Kadu Shweta , Agrawal Piyush , Khatpe Aditi , Banale Rituja , Reddy Ruhi , Kelkar Devaki A. , Hannenhalli Sridhar , Shashidhara L S , Koppiker Chaitanyanand , Kulkarni Madhura 
Published: October 2024

AbstractTNBC is the most aggressive breast cancer subtype, with a higher recurrence rate. TNBC proportion within breast cancer incidence in the Indian population is 22-30%. Despite the high incidence rate, the heterogeneity within TNBC subtype in Indian cohorts is not studied at scale.Here, leveraging an Indian cohort of 93 TNBC patients, we evaluated the basal and LAR subtypes in terms of the expression of known markers such as EGFR and AR and further assessed the association of marker gene expression with patient outcome and treatment response.In our cohort, 65% of the patients were EGFR-positive, 38% had positive AR expression, where both the subsets showed shorter disease-free survival outcomes. Additionally, 25% of the cohort showed AR and EGFR co-expression. Upon closer observation, using IHC and duplex staining, we noted that 15% of the tumors, in fact, had double-positive cancer cells, i.e., cellular co-expression of AR and EGFR. Patients with double-positive cells had poorer disease-free survival compared to the ones with the tissue-level co-expression of EGFR and AR but without cellular co-expression. The presence of EGFR+AR+ double-positive cells was further validated in publicly available single-cell data sets for TNBC patients from other ethnic backgrounds, albeit to a lesser extent than what was observed in our Indian cohort. Overall, our results highlight the heterogeneous nature of Indian TNBC tumors and provide further insight into ethnic variation in TNBC presentation that can be further exploited for precision and personalized targeted therapy.
Effect of the Choosing Wisely Canada campaign on prescription of nonsteroidal anti-inflammatory drugs in people with hypertension, heart failure and chronic kidney disease in Canada
Authors: Hategeka Celestin , Cheng Lucy , Worthington Heather C , Laba Tracey-Lea , Law Michael R 
Published: October 2024

ABSTRACTThe Choosing Wisely Canada (CWC) campaign focuses on helping physicians and patients engage in conversations about unnecessary tests and treatments. In 2014, a CWC recommendation advised against prescribing nonsteroidal anti-inflammatory drugs (NSAIDs) in individuals with hypertension, heart failure or chronic kidney disease (CKD). We evaluated the impact of this recommendation on prescription of NSAIDs in British Columbia (BC), Canada. We identified all persons continuously registered with BC’s Medical Service Plan at any point between October 1, 2011 and December 31, 2017 with hypertension, heart failure or CKD. Using prescription claims data and an interrupted time series analysis, we estimated the quarterly number of people initiating NSAIDs and the proportion of days covered (PDC). We also conducted sub-group analyses to study the impact of the recommendation by patients’ demographics (sex and age). We analyzed 1,479,704 NSAID claims from 903,732 patients with a diagnosis of hypertension, heart failure and/or CKD. Overall, we found no statistically significant change either immediately or over time in initiation and PDC of NSAIDs. However, we did observe a decrease in both initiation and PDC of NSAIDs over time among male patients and patients aged ≤65 years. We found that the CWC recommendation had a mixed impact, with initiation and PDC of NSAIDs significantly declining only among male and younger patients.HighlightsReducing low-value care can help to counter increasing healthcare spending.CWC can contribute to reducing low-value care, however, evidence on its effectiveness remains elusive.CWC led to NSAIDs prescription decline for patients ≤65 years and male patients.Overall, NSAID prescribing has been declining over time irrespective of CWC.
Enhanced Prediction of Seafloor Ecological State Using 16S Nanopore Sequencing
Authors: Philip Melcy , Nilsen Tonje , Majaneva Sanna , Pettersen Ragnhild , Stokkan Morten , Ray Jessica Louise , Keeley Nigel , Rudi Knut , Snipen Lars-Gustav 
Published: October 2024

AbstractAnthropogenic stress on benthic habitats, particularly from aquaculture, calls for accurate and efficient monitoring of the macrofauna ecological state. Recent advancements in Oxford Nanopore Technology (ONT) together with environmental DNA offers cost-effective and rapid, on-site monitoring of such ecosystems. Previous studies have demonstrated that Nanopore sequencing provides sufficient precision for predicting ecological state, despite reported challenges with sequencing accuracy. In this study, we aim to predict the seafloor ecological state with both Illumina and Nanopore 16S rRNA gene sequencing data and using a combination of machine learning and feature selection. We analyzed 88 seafloor samples from aquaculture sites located on a north-south gradient along the Norwegian coast. Both sequencing methods were evaluated in combination with various bioinformatic approaches in the context of predicting the normalized EQR index (nEQR, standard ecological index based on macroinvertebrate counting) as a metric of seafloor ecosystem status. Our results show that the predictive performance of Illumina and Nanopore sequencing platforms are comparable, establishing Nanopore as a feasible alternative to illumina sequencing. By employing a stabilized LASSO regression, the feature set (potential taxa) was efficiently optimized from thousands to 40-60 OTUs. The feature selection reduced prediction errors to less than half of what was obtained through full feature modeling. This feature set demonstrated strong predictive accuracy across both sequencing technologies, with a high correlation between observed and predicted nEQR values. The Pearson correlation coefficient of 0.98 for Illumina and 0.95 (mean prediction error: ±0.04) for Nanopore data (mean prediction error: ±0.06). This study demonstrates that continual improvements in Nanopore sequencing accuracy, in combination with optimized feature selection on a broader set of samples, provides a precise and cost-effective monitoring method for marine benthic environments.
Seeing isn’t believing? Mixed effects of a perspective-getting intervention to improve mentoring relationships for science doctoral students
Authors: Tuma Trevor T. , Fedesco Heather N. , Rosenzweig Emily Q. , Chen Xiao-Yin , Dolan Erin L. 
Published: October 2024

AbstractScience doctoral students can experience negative interactions with faculty mentors and internalize these experiences, potentially leading to self-blame and undermining their research self-efficacy. Helping students perceive these interactions adaptively may protect their research self-efficacy and maintain functional mentoring relationships. We conducted a pre-registered, longitudinal field experiment of a novel perspective-getting intervention combined with attribution retraining to help students avoid self-blame and preserve research self-efficacy. Science doctoral students (n= 155) were randomly assigned to read about mentor perspectives on negative interactions (i.e., Perspective-getting Condition) or about mentoring with no mentor perspective (i.e., control condition). Contrary to our hypotheses, we found no main effects of the intervention on students’ self-blame or research self-efficacy. However, for students with lower pre-intervention mentorship relationship satisfaction, the intervention preserved research self-efficacy six months later. This study provides evidence that perspective getting may be protective for students who are most in need of relationship intervention.Educational Relevance and Implications StatementEffective mentoring relationships are fundamental for promoting the success of doctoral students in science, yet not all mentoring relationships are high quality. This study assessed the effectiveness of a brief perspective-getting intervention (where students are given the perspective of what it is like to be a research mentor) that aimed to protect science doctoral students from blaming themselves for negative interactions with faculty mentors and maintain their research self-efficacy. Results showed that on average across all students, the intervention did not affect students’ self-blame for negative interactions or their research self-efficacy. However, the intervention did help students with less satisfying mentoring relationships maintain their self-efficacy. Thus, perspective-getting shows some promise for protecting science doctoral students from harm that can be caused by negative interactions with faculty mentors.
Developing a Multiscale Neural Connectivity Knowledgebase of the Autonomic Nervous System
Authors: Imam Fahim T. , Gillespie Thomas H. , Ziogas Ilias , Surles-Zeigler Monique C. , Tappan Susan , Ozyurt Burak I. , Boline Jyl , de Bono Bernard , Grethe Jeffrey S. , Martone Maryann E. 
Published: October 2024

ABSTRACTThe Stimulating Peripheral Activity to Relieve Conditions (SPARC) program is a U.S. National Institutes of Health (NIH) funded effort to enhance our understanding of the neural circuitry responsible for visceral control. SPARC’s mission is to identify, extract, and compile our overall existing knowledge and understanding of the autonomic nervous system (ANS) connectivity between the central nervous system and end organs. A major goal of SPARC is to use this knowledge to promote the development of the next generation of neuromodulation devices and bioelectronic medicine for nervous system diseases. As part of the SPARC program, we have been developing SCKAN, a dynamic knowledge base of ANS connectivity that contains information about the origins, terminations, and routing of ANS projections. The distillation of SPARC’s connectivity knowledge into this knowledge base involves a rigorous curation process to capture connectivity information provided by experts, published literature, textbooks, and SPARC scientific data. SCKAN is used to automatically generate anatomical and functional connectivity maps on the SPARC portal.In this article, we present the design and functionality of SCKAN, including the detailed knowledge engineering process developed to populate the resource with high quality and accurate data. We discuss the process from both the perspective of SCKAN’s ontological representation as well as its practical applications in developing information systems. We share our techniques, strategies, tools and insights for developing a practical knowledgebase of ANS connectivity that supports continual enhancement.