bluuebunny/crossref_metadata_2025_split huggingface dataset:
Combining AI and human support in mental health: a digital intervention with comparable effectiveness to human-delivered care
Authors: Palmer Clare E , Marshall Emily , Millgate Edward , Warren Graham , Ewbank Michael P. , Cooper Elisa , Lawes Samantha , Bouazzaoui Malika , Smith Alastair , Hutchins-Joss Chris , Young Jessica , Margoum Morad , Healey Sandra , Marshall Louise , Mehew Shaun , Cummins Ronan , Tablan Valentin , Catarino Ana , Welchman Andrew E , Blackwell Andrew D 
Published: July 2024

AbstractBackgroundEscalating global mental health demand exceeds existing clinical capacity. Scalable digital solutions will be essential to expand access to high-quality mental healthcare for everyone. This study evaluated a structured, evidence-based digital program for mild, moderate and severe anxiety that combined an Artificial Intelligence (AI) driven conversational agent to deliver content with human clinical oversight and user support to maximize outcomes.ObjectiveThis study aimed to measure engagement, clinical effectiveness, acceptability and safety of this digital intervention in comparison to externally generated comparator groups.MethodsAll prospective participants (N=299) were given the digital intervention to use for up to 9 weeks. Endpoints for effectiveness, engagement, acceptability, and safety were collected before, during and after the intervention, and at one-month follow-up. Adherence and effectiveness were compared to three propensity-matched real-world patient comparator groups: i) waiting control; ii) face-to-face cognitive behavioral therapy (CBT); and iii) remote typed-CBT.ResultsParticipants used the program for a median of 6 hours over 53 days. There was a large clinically meaningful reduction in anxiety symptoms for the intervention group (per-protocol (PP; n=169): change on GAD-7 = –7.4,d= 1.6; intention-to-treat (ITT; n=299): change on GAD-7 = –5.4,d= 1.1) that was statistically superior to the waiting control (PP:d= 1.3; ITT:d= 0.8), non-inferior to human-delivered care, and was sustained at one-month follow-up.ConclusionsBy combining AI and human support, the digital intervention achieved clinical outcomes comparable to human-delivered care while significantly reducing the required clinician time by up to 8 times. These findings highlight the potential of technology to scale effective evidence-based mental healthcare, address unmet need, and ultimately impact quality of life and economic burden globally.
Pre-Diagnosis Observational and Prescription History Associated with Alzheimer’s Disease Incidence
Authors: Williams Gareth 
Published: July 2024

AbstractBackgroundAlzheimer’s disease (AD) has so far proved refractory to intervention. However, disease incidence is variable across prior medication and observational measures. The present study adopted a data-driven approach to inform possible drug repurposing strategies in the light of concurrent prescription and biometric data.Methods and ResultsA real-world dataset was harnessed to compare observational and prescription data for 250,000 individuals prior to AD diagnosis against an age-, sex-, and clinical practice-matched control cohort with no AD history. Observation data was shown to both explain the associations of classes of drug prescription with AD incidence and to bolster the repurposing potential of the GLP-1 agonist class of anti-diabetic drugs.ConclusionThis study provides insights into how drug repurposing based on prescription histories can be informed by concurrent observational data. These findings offer novel insights to be explored in future research on causal models for AD progression.
“It does help but there’s a limit…”: Young people’s perspectives on policies that restrict hot food takeaways opening near schools
Authors: Savory B , Thompson C , Adams J , Amies-Cull B , Chang M , Hassan S , Derbyshire D , Keeble M , Liu B , Medina-Lara A , Mytton OT , Rahilly J , Rogers N , Smith R , White M , Burgoine T , Cummins S 
Published: July 2024

AbstractBackgroundLocal authorities (LAs) in England are increasingly using the planning system to control the proliferation of hot food takeaway outlets (‘takeaways’) near schools as part of a range of policies to promote healthy weight in children. These ‘takeaway management zones’ (TMZs) include restrictions on planning permission to open new takeaways within a certain distance of a school. In this qualitative study we explore young people’s perspectives of TMZs.MethodsWe purposively recruited 46 young people (aged 11-18 years old) attending secondary school across two contrasting London LAs with operating TMZs. We conducted semi-structured, walking group interviews (“go-alongs”) in January-February 2023 in the local food environment close to participants’ schools. We analysed data using framework analysis.ResultsParticipants generally viewed TMZs as reasonable and uncontroversial but were not always aware that TMZs were in operation. Although participants understood that TMZs prevented new outlets from opening, they observed they did not seem to reduce existing provision. This was viewed positively as it did not result in the closure of local takeaways perceived as important components of the social fabric of school life. Participants believed that the potential health impact of TMZs is limited by their exclusive focus on takeaways as other food retail commonly patronised by young people, such as convenience stores, are important sources of unhealthy food. Participants also identified inadequacies in the wider food environment, including the school dining environment and access to food delivery apps.ConclusionsOur findings suggest that although young people find TMZs acceptable and believe they have some positive impact on diet, they did not perceive TMZs as effective as they could be. Participants articulated that the management of takeaways on their own is unlikely to reduce exposure to unhealthy foods. Widening the remit of planning policy to include outlets selling convenience foods may be important for policy optimisation.HighlightsYoung people’s perspectives of takeaway management zones are exploredAcceptability of zones was high as the existing food environment remains unchangedThe impact of takeaway management zones was deemed limited by its focus and scopeConsidering wider aspects of the food environment is key for policy developmentFuture policy should also consider young people’s social and emotional needs
Altered model-based prediction error signalling in the medial prefrontal cortex in patients with obsessive-compulsive disorder: An fMRI study
Authors: Sen Pritha , Koch Kathrin , Schmitz-Koep Benita , Gürsel Deniz , Knolle Franziska 
Published: July 2024

AbstractBackgroundCompared to healthy individuals, patients with obsessive-compulsive disorder (OCD) are found to rely more on model-free decision-making strategies which may underlie symptom expression. It is, however, unclear whether these behavioural differences are represented in neural alterations of model-free and model-based decision-making when tested simultaneously.MethodsWe investigated the neural signatures of 22 OCD patients and 22 matched controls who completed a two-step Markov decision-making task during functional MRI scanning. We used hierarchical Bayesian modelling and Bayesian statistics to examine model-based and model-free decision-making behaviours. Parametric regressors were employed for model-free and model-based reward prediction errors to inform neural reward presentation, which we analysed using a Bayesian Multilevel Modeling (BML) approach. Associations between significant activations and symptoms as well as cognitive scores were explored using Bayesian linear regression.ResultsWhile controls received significantly more rewards and were significantly less stochastic compared to patients, both groups similarly relied on model-free decision-making strategies. Importantly, our group comparison of neural reward prediction error responses showed greater activation for model-based reward prediction error in the medial prefrontal cortex (mPFC) in OCD patients compared to controls, but no differences for model-free reward prediction error processing. Increased mPFC activity was associated with lower depressive symptoms but not with other clinical or cognitive scores.ConclusionThese findings support the notion that OCD is associated with a bias towards habitual behaviour, which may be expressed through altered mPFC activity underlying goal-directed behaviour. Tackling the hyperactivity on the mPFC may provide a new target for optimised treatment and interventions.
Examining the independent roles of cannabis use and tobacco use in depression risk: a multivariable Mendelian randomisation study
Authors: Burke Chloe , Freeman Tom P , Sallis Hannah , Wootton Robyn E , Taylor Gemma MJ 
Published: July 2024

ABSTRACTBackgroundCannabis and tobacco use are consistently associated with major depressive disorder (MDD) in conventional observational studies. However, these substances are often co-used, and the independent causal role of cannabis use in risk of MDD remains unclear.MethodsUnivariable and multivariable MR (MVMR) were used to explore the total and independent causal effects of genetic liability to tobacco use and cannabis use on MDD. Our primary estimator was the inverse-variance weighted (IVW) method, with other methods as sensitivity analyses. For the exposures, we used genome-wide association study (GWAS) summary statistics among European ancestry individuals for several tobacco use (i.e., smoking initiation, smoking continuation, smoking heaviness) and cannabis use (i.e., cannabis initiation, cannabis use disorder [CUD]) phenotypes. For the outcome, a GWAS of MDD was conducted using individual-level data from UK Biobank.ResultsUnivariable MR indicated a causal effect of smoking initiation on MDD (odds ratio [OR]IVW= 1.34, 95% confidence interval [CI] = 1.27 – 1.42), with consistent but weaker evidence for smoking continuation (ORIVW= 1.13, 95% CI = 0.93 – 1.37) and smoking heaviness (ORIVW= 1.15, 95% CI = 0.99 – 1.33). There was no clear evidence for a causal effect of cannabis initiation on MDD (ORIVW= 1.00, 95% CI = 0.91– 1.11). Univariable MR indicated some evidence for a causal effect of CUD on MDD (ORIVW= 1.14, 95% CI = 1.04 – 1.25), which attenuated to the null when adjusting for liability to smoking initiation (ORMVMR-IVW= 1.03, 95% CI = 0.97 – 1.08).ConclusionsThis study provides limited evidence for an independent causal effect of cannabis use on MDD, and stronger evidence for an independent causal effect of tobacco use on MDD. Analyses were limited by low power, and future research should triangulate these findings with results from high-quality observational studies.
Channel Capacity of Genome-Wide Cell-Free DNA Fragment Length Distribution in Colorectal Cancer
Authors: Matov Alexandre 
Published: July 2024

ABSTRACTEach piece of cell-free DNA (cfDNA) has a length determined by the exact metabolic conditions in the cell it belonged to at the time of cell death. The changes in cellular regulation leading to a variety of patterns, which are based on the different number of fragments with lengths up to several hundred base pairs (bp) at each of the almost three billion genomic positions, allow for the detection of disease and also the precise identification of the tissue of their origin.A Kullback-Leibler (KL) divergence computation identifies different fragment lengths and areas of the human genome, depending on the stage, for which disease samples, starting from pre-clinical disease stages, diverge from healthy donor samples. We provide examples of genes related to colorectal cancer (CRC), which our algorithm detected to belong to divergent genomic bins. The staging of CRC can be viewed as a Markov chain and that provides a framework for studying disease progression and the types of epigenetic changes occurring longitudinally at each stage, which might aid the correct classification of a new hospital sample.In a new look to treat such data as grayscale value images, pattern recognition using artificial intelligence (AI) could be one approach to classification. In CRC, Stage I disease does not, for the most part, shed any tumor in circulation, making detection difficult for established machine learning (ML) methods. This leads to the deduction that early detection, where we can only rely on changes in the metabolic patterns, can be accomplished when the information is considered in its entirety, for example by applying computer vision methods.
Asymptomatic and mildly symptomatic influenza virus infections by season -- Case-ascertained household transmission studies, United States, 2017-2023
Authors: Biddle Jessica E. , Nguyen Huong Q. , Talbot H. Keipp , Rolfes Melissa A. , Biggerstaff Matthew , Johnson Sheroi , Reed Carrie , Belongia Edward A. , Grijalva Carlos G. , Mellis Alexandra M. 
Published: July 2024

AbstractAsymptomatic influenza virus infection occurs but may vary by factors such as age, influenza vaccination status, or influenza season. We examined the frequency of influenza virus infection and associated symptoms using data from two case-ascertained household transmission studies (conducted from 2017—2023) with prospective, systematic collection of respiratory specimens and symptoms. From the 426 influenza virus infected household contacts that met our inclusion criteria, 8% were asymptomatic, 6% had non-respiratory symptoms, 23% had acute respiratory symptoms, and 62% had influenza-like illness symptoms. Understanding the prevalence of asymptomatic and mildly symptomatic influenza cases is important for implementing effective influenza prevention strategies and enhancing the effectiveness of symptom-based surveillance systems.
Towards predicting PTSD symptom severity using portable EEG-derived biomarkers
Authors: Peddi Ashritha , Sendi Mohammad S. E. , Minton Sean T. , Hinojosa Cecilia A. , West Emma , Langhinrichsen-Rohling Ryan , Ressler Kerry J. , Calhoun Vince D. , van Rooij Sanne J.H. 
Published: July 2024

AbstractPosttraumatic Stress Disorder (PTSD) is a heterogeneous mental health disorder that occurs following traumatic experience. Understanding its neurobiological basis is crucial to advance early diagnosis and treatment. Electroencephalography (EEG) can be used to explore the neurobiological basis of PTSD. However, only limited research has explored mobile EEG, which is important for scalability. This proof-of-concept study delves into mobile EEG-derived biomarkers for PTSD and their potential implications. Over four weeks, we measured PTSD symptoms using the PTSD checklist for DSM-5 (PCL-5) at multiple timepoints, and we recorded multiple EEG sessions from 21 individuals using a mobile EEG device. In total, we captured 38 EEG sessions, each comprising two recordings that lasted approximately 180 seconds, to evaluate reproducibility. Next, we extracted Shannon entropy, as a measure of the randomness or unpredictability of the signal and spectral power for the fronto-temporal regions of interest, including electrodes at AF3, AF4, T7, and T8 for each EEG recording session. We calculated the partial correlation between the EEG variables and PCL-5 measured closest to the EEG session, using age, sex, and the grouping variable ‘batch’ as covariates. We observed a significant negative correlation between Shannon entropy in fronto-temporal regions and PCL-5 scores. Specifically, this association was evident in the AF3 (r= -0.456, FDR-correctedp= 0.01), AF4 (r= -0.362, FDR-correctedp= 0.04), and T7 (r= -0.472, FDR-correctedp= 0.01) regions. Additionally, we found a significant negative association between the alpha power estimated from AF4 and PCL-5 (r=-0.429, FDR-correctedp=0.04). Our findings suggest that EEG data acquired using a mobile EEG device is associated with PTSD symptom severity, offering valuable insights into the neurobiological mechanisms underlying PTSD.
Predicting Early-Onset Colorectal Cancer in Individuals Below Screening Age Using Machine Learning and Real-World Data
Authors: Sun Chengkun , Mobley Erin M. , Quillen Michael B. , Parker Max , Daly Meghan , Wang Rui , Visintin Isabela , Awad Ziad , Fishe Jennifer , Parker Alexander , George Thomas J. , Bian Jiang , Xu Jie 
Published: July 2024

AbstractBackgroundColorectal cancer (CRC) is now the leading cause of cancer-related deaths among young Americans. Our study aims to predict early-onset CRC (EOCRC) using machine learning (ML) and structured electronic health record (EHR) data for individuals under the screening age of 45.MethodsWe identified a cohort of patients under 45 from the OneFlorida+ Clinical Research Consortium. Given the distinct pathology of colon cancer (CC) and rectal cancer (RC), we created separate prediction models for each cancer type with various ML algorithms. We assessed multiple prediction time windows (0, 1, 3, and 5 years) and ensured robustness through propensity score matching (PSM) to account for confounding variables. Model performance was assessed using established metrics. Additionally, we employed the Shapley Additive exPlanations (SHAP) to identify risk factors for EOCRC.ResultsOur study yielded results, with Area Under the Curve (AUC) scores of 0.811, 0.748, 0.689, and 0.686 for CC prediction, and 0.829, 0.771, 0.727, and 0.721 for RC prediction at 0, 1, 3, and 5 years, respectively. Notably, predictors included immune and digestive system disorders, along with secondary cancers and underweight, prevalent in both CC and RC groups. Blood diseases emerged as prominent indicators of CC.ConclusionThis study highlights the potential of ML techniques in leveraging EHR data to predict EOCRC, offering valuable insights for potential early diagnosis in patients who are below the recommended screening age.
Using GenAI to train mental health professionals in suicide risk assessment: Preliminary findings
Authors: Elyoseph Zohar , Levkovitch Inbar , Haber Yuval , Levi-Belz Yossi 
Published: July 2024

BackgroundSuicide risk assessment is a critical skill for mental health professionals (MHPs), yet traditional training in this area is often limited. This study examined the potential of generative artificial intelligence (GenAI)-based simulator to enhance self- efficacy in suicide risk assessment among MHPs.MethodA quasi-experimental study was conducted with 43 MHPs from Israel. Participants attended an online seminar and interacted with a GenAI-powered suicide risk assessment simulator. They completed pre- and post-intervention questionnaires measuring suicide risk assessment self-efficacy and willingness to treat suicidal patients. Qualitative data on user experience were collected.ResultsWe found a significant increase in self-efficacy scores following the intervention. Willingness to treat patients presenting suicide risk increased slightly but did not reach significance. Qualitative feedback indicated that participants found the simulator engaging and valuable for professional development. However, participants raised concerns about over-reliance on AI and the need for human supervision during training.ConclusionThis preliminary study suggests that GenAI-based simulators hold promise as a tool to enhance MHPs’ competence in suicide risk assessment. However, further research with larger samples and control groups is needed to confirm these findings and address ethical considerations surrounding AI use in suicide risk assessment training. AI-powered simulation tools have the potential to democratize access to high-quality training in mental health, potentially contributing to global suicide prevention efforts. However, their implementation should be carefully considered to ensure they complement rather than replace human expertise.
Cognitive and Cholinergic Systems Trajectories in Parkinson Disease
Authors: Brown Taylor , Kanel Prabesh , Carli Giulia , Barr Jaimie , Bohnen Nicolaas I. , Albin Roger L. 
Published: July 2024

ABSTRACTObjectiveCognitive decline in Parkinson disease (PD) is a disabling and highly variable non-motor feature. While cholinergic systems degeneration is linked to cognitive impairments in PD, most prior research reported cross-sectional associations. We aimed to fill this gap by investigating whether baseline regional cerebral vesicular acetylcholine transporter ligand [18F]-fluoroethoxybenzovesamicol ([18F]-FEOBV) binding predicts longitudinal cognitive changes in mild to moderate, non-demented PD subjects.MethodsSeventy-five non-demented, mild-moderate PD subjects received baseline standardized cognitive evaluations and [18F]-FEOBV PET imaging with repeat cognitive evaluations 2 years later. Participants were classified into four cognitive classes based on stability or change in cognition: Persistent normal (no MCI at baseline and follow-up), Persistent MCI, MCI conversion, and MCI reversion. Whole-brain voxel comparisons with normal controls, and voxel-based and cluster volume-of-interest correlation analyses with longitudinal cognitive changes were performed.ResultsWhole-brain voxel comparisons of each class with a matched control group revealed unique bi-directional differences in baseline regional [18F]-FEOBV binding. Increased regional [18F]-FEOBV binding in predominantly anterior cortical and sub-cortical regions was found in the persistent normal and MCI reversion groups. Whole-brain voxel correlation analysis between baseline [18F]-FEOBV binding and two-year longitudinal percent changes in cognition identified a specific regional pattern of reduced posterior cortical, limbic and paralimbic [18F]-FEOBV binding predictive of global cognitive declines and across five cognitive domains at two-year follow-ups.InterpretationCholinergic system changes correlate with varying cognitive trajectories in mild-moderate PD. Upregulation of cholinergic neurotransmission may be an important compensatory process in mild-moderate PD.
Vaccine-related misinformation and attitude toward COVID-19 vaccination in Japan
Authors: Furuse Yuki , Tabuchi Takahiro 
Published: July 2024

AbstractWe have struggled with vaccine hesitancy for vaccination rollout during the COVID-19 pandemic. Although the spread of misinformation seems to play a role in vaccine hesitancy, the extent to which it affects attitudes toward COVID-19 vaccination is unknown. Here, we investigated the prevalence of beliefs about misinformation regarding COVID-19 vaccines in vaccinated and unvaccinated populations in Japan and analyzed associated risk factors. An online survey of 31,000 participants in 2021 found that 8.1% of vaccine-accepted individuals believed vaccine-related misinformation, whereas 36.6% of those who refused vaccination believed misinformation. Most factors associated with beliefs about misinformation and vaccine hesitancy overlapped, including young age, unmarried status, low income, particular information sources, and history of COVID-19 infection. Interestingly, some factors, such as age and sources of information, had different effects on vaccine acceptance between individuals who did not believe misinformation and those who did. Advanced age was associated with vaccine acceptance among non-misinformation believers. In contrast, misinformation believers in their 10s and 20s were more willing to be vaccinated than older adults. The effects of television and Internet information were stronger in individuals who believed misinformation on their attitude toward vaccination than non-misinformation believers. This study highlights the importance of understanding the relationship between beliefs about misinformation and vaccine hesitancy for ongoing and future pandemics.