bluuebunny/crossref_metadata_2025 huggingface dataset:
Bibliometric analysis of emerging trend and top 100 most cited articles in primary central nervous system lymphoma
Authors: Geng Yibo , Liu Yiqi , Wang Yang , Li Xiong
Published: July 2024

Background

Over the past thirty years, numerous studies on primary central nervous system lymphoma (PCNSL) have been conducted, encompassing epidemiology, diagnosis, treatment, and mechanisms. However, no bibliometric analysis has been performed in this field. Therefore, we conducted a bibliometric analysis to elucidate the evolution and current status of PCNSL research and identify the most-cited articles across various disciplines.

Material and Methods

Literature published between January 1992 and June 2024 in the Web of Science Core Collection (WoSCC) was extracted and classified. Three platforms (R-bibliometrix, VOSviewer, and Citespace) were used for the bibliometric analysis. Negative binomial regression was employed to establish a model for the risk ratio of citation.

Results

A total of 1,798 publications were included in the analysis. The annual number of publications has shown a steady increase over time. The United States emerged as the leading country in terms of both the number of publications and total citations. Among individual researchers, Hoang-Xuan Khe from Sorbonne University was the most prolific in terms of publication count, while Lisa M. DeAngelis from Memorial Sloan Kettering Cancer Center received the highest number of citations. The study titled “Report of an International Workshop to Standardize Baseline Evaluation and Response Criteria for Primary CNS Lymphoma” was identified as the most cited work in this field. Keyword analysis indicated that immune therapy and non-invasive diagnosis are the primary focal points of current research. Among the top 100 most-cited articles, interventional studies demonstrated a higher citation ratio (RR = 1.357, 95% CI 1.044-1.764, P=0.023), whereas chemoradiotherapy studies exhibited a lower citation ratio (RR = 0.763, 95% CI 0.603-0.965, P=0.024).

Conclusion

Research hotspots and trends in PCNSL were identified and explored using bibliometric and visual methods. The highly cited articles across multiple disciplines related to PCNSL may help generate high-quality research ideas, particularly in the fields of immunotherapy and non-invasive diagnosis.
Seed banking alters native seed microbiome composition and function relative to natural populations
Authors: Russell Dylan , Rajabal Vaheesan , Alfonzetti Matthew , Van der Merwe Marlien M. , Gallagher Rachael V. , Tetu Sasha G.
Published: July 2024

Seed banks are a vital resource for preserving plant species diversity globally. However, seedling establishment and survival rates from banked seeds can be poor. Despite a growing appreciation for the role of seed microbiota in supporting seed quality and plant health, our understanding of the effects of conventional seed banking processes on seed microbiomes remains limited.

We investigated the composition and functional potential of the epiphytic seed microbiome of a native plant species using both 16S rRNA gene sequencing and culture-based approaches.

Comparing the bacterial community composition of freshly collected seeds and those sourced from seed banking organisations, we found stored seeds hosted significantly less diverse bacterial populations, with substantial reductions in both low-abundance taxa and some core community members identified in unstored seeds. Bacteria with key plant growth promoting traits including IAA production, ACC deaminase activity, phosphate solubilisation, siderophore activity, and nitrogen fixation were identified in seed epiphytic communities, but these beneficial traits were less prevalent in stored seed compared to fresh seeds.

Overall, these results suggest that epiphytic seed microbiomes may undergo significant changes during the storage process, selecting for bacteria tolerant to storage conditions, and potentially reducing the population of plant-growth promoting bacteria on seeds.
Prefrontal metabolite alterations in individuals with posttraumatic stress disorder: a 7T magnetic resonance spectroscopy study
Authors: Reid Meredith A. , Whiteman Sarah E. , Camden Abigail A. , Jeffirs Stephanie M. , Weathers Frank W.
Published: July 2024

Background

Evidence from animal and human studies suggests glutamatergic dysfunction in posttraumatic stress disorder (PTSD). The purpose of this study was to investigate glutamate abnormalities in the dorsolateral prefrontal cortex (DLFPC) of individuals with PTSD using 7T MRS, which has better spectral resolution and signal-to- noise ratio than lower field strengths, thus allowing for better spectral quality and higher sensitivity. We hypothesized that individuals with PTSD would have lower glutamate levels compared to trauma-exposed individuals without PTSD and individuals without trauma exposure. Additionally, we explored potential alterations in other neurometabolites and the relationship between glutamate and psychiatric symptoms.

Methods

Individuals with PTSD (n=27), trauma-exposed individuals without PTSD (n=27), and individuals without trauma exposure (n=26) underwent 7T MRS to measure glutamate and other neurometabolites in the left DLPFC. The severities of PTSD, depression, anxiety, and dissociation symptoms were assessed.

Results

We found that glutamate was lower in the PTSD and trauma-exposed groups compared to the group without trauma exposure. Furthermore,N-acetylaspartate (NAA) was lower and lactate was higher in the PTSD group compared to the group without trauma exposure. Glutamate was negatively correlated with depression symptom severity in the PTSD group. Glutamate was not correlated with PTSD symptom severity.

Conclusion

In this first 7T MRS study of PTSD, we observed altered concentrations of glutamate, NAA, and lactate. Our findings provide evidence for multiple possible pathological processes in individuals with PTSD. High-field MRS offers insight into the neurometabolic alterations associated with PTSD and is a powerful tool to probe trauma- and stress-related neurotransmission and metabolismin vivo.
Acid cleavable biotin-alkyne improves sensitivity for direct detection of biotin labeled peptides in BONCAT analysis
Authors: McClatchy Daniel B. , Yates John R.
Published: July 2024

BONCAT (Biorthogonal noncanonical amino acid tagging) is a labeling strategy that covalently adds a biotin-alkyne (BA) to methionine analogs via a click reaction. When methionine analogs are incorporated into a proteome, enrichment of the BA-labeled proteins allows the detection of newly synthesized proteins (NSP) by mass spectrometry. We previously reported that using our Direct Detection of Biotin-containing Tags (DidBIT) strategy, protein identifications and confidence are increased by enriching for BA-peptides instead of BA-proteins. We compared cleavable BA (DADPS) and uncleavable BA in the identification and TMT quantification of NSP. More than fifty percent more proteins were identified and quantified using DADPS than with uncleavable BA. Interrogation of the data revealed that multiple factors are responsible for the superior performance of DADPS.
A novel c.515delCHSPB8-multisystem proteinopathy associated with inclusion body myopathy with cardiomyopathy
Authors: Tan Jiayan , Duong Han , Milone Margherita , Gibbs Liliane , Tedesco Barbara , Poletti Angelo , Kimonis Virginia
Published: July 2024

Mutations in theHSPB8, or heat shock protein family B (small) member 8, is a member of the chaperone-assisted selective autophagy complex and has recently been associated with rimmed vacuolar myopathy. We report a family with aHSPB8rimmed vacuolar myopathy caused by a novel c.515delC variant with progressive muscle weakness, pathological findings of rimmed vacuoles of muscle biopsy, and one individual who died of cardiomyopathy. Whole exome sequencing analyses revealed a c.515delC, resulting in a translational frameshift expected to elongate the protein by an additional 49 amino acid tail. We reviewed the clinical characteristics of all reported patients (N= 26, 15 males and 11 females) in the literature, and performed a genotype/phenotype analysis. Males in the families appeared to present with an earlier age of onset, more severe muscle weakness compared to females, and a higher prevalence of other organ system complications including cardiomyopathy in two males. In conclusion, this family expands the phenotype/genotype correlations inHSPB8-associated myopathy with rimmed vacuoles and c.515 delC. We note that c.515 appears to be a hot spot, and the phenotype appears to be more severe in males in this disease, with an earlier onset of the myopathy.
A Development Framework for Trustworthy Artificial Intelligence in Health with Example Code Pipelines
Authors: de-Manuel-Vicente Carlos , Fernández-Narro David , Blanes-Selva Vicent , García-Gómez Juan M , Sáez Carlos
Published: July 2024

Technological trends point to Artificial Intelligence (AI) as a crucial tool in healthcare, but its development must respect human rights and ethical standards to ensure robustness and safety. Despite general good practices are available, health AI developers lack a practical guide to address the construction of trustworthy AI. We introduce a development framework to serve as a reference guideline for the creation of trustworthy AI systems in health. The framework provides an extensible Trustworthy AI matrix that classifies technical methods addressing the EU guideline for Trustworthy AI requirements (privacy and data governance; diversity, non-discrimination and fairness; transparency; and technical robustness and safety) across the different AI lifecycle stages (data preparation; model development, deployment and use, and model management). The matrix is complemented with generic but customizable example code pipelines for the different requirements with state-of-the-art AI techniques using Python. A related checklist is provided to help validate the application of different methods on new problems. The framework is validated using two representative open datasets, and it is provided as Open Source to the scientific and development community. The presented framework provides health AI developers with a theoretical development guideline with practical examples, aiming to ensure the development of robust and safe health AI and Clinical Decision Support Systems. GitHub repository:https://github.com/bdslab-upv/trustworthy-ai
Democratizing Infectious Disease Modeling: An AI Assistant for Generating, Simulating, and Analyzing Dynamic Models
Authors: Proctor Joshua L. , Chabot-Couture Guillaume
Published: July 2024

Understanding and forecasting infectious disease spread is pivotal for effective public health management. Traditional dynamic disease modeling is an essential tool for characterization and prediction, but often requires extensive expertise and specialized software, which may not be readily available in low-resource environments. To address these challenges, we introduce an AI-powered modeling assistant that utilizes advanced capabilities from OpenAI’s latest models and functionality. This tool enhances the accessibility and usability of infectious disease models and simulation frameworks by allowing users to generate or modify model configurations through intuitive natural language inputs or by importing explicit model descriptions. Our prototype integrates with an established open-source disease simulation framework called the Compartmental Modeling Software (CMS) to provide a seamless modeling experience from setup to analysis. The AI assistant efficiently interprets disease model parameters, constructs accurate model files, executes simulations in a controlled environment, and assists in result interpretation using advanced analytics tools. It encapsulates expert knowledge and adheres to best practices to support users ranging from novices to expert modelers. Furthermore, we discuss the limitations of this AI assistant, particularly its performance in complex scenarios where it might generate inaccurate specifications. By enhancing the ease of disease modeling and supporting ongoing capacity-building initiatives, we believe that AI assistants like this one could significantly contribute to global health efforts by empowering researchers, especially in regions with limited resources, to develop and refine their disease models independently. This innovative approach has the potential to democratize disease modeling in global health, offering a scalable solution that adapts to diverse needs across a wide-range of geographies, languages, and populations.
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

Background

Escalating 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.

Objective

This study aimed to measure engagement, clinical effectiveness, acceptability and safety of this digital intervention in comparison to externally generated comparator groups.

Methods

All 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.

Results

Participants 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.

Conclusions

By 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

Background

Alzheimer’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 Results

A 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.

Conclusion

This 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

Background

Local 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.

Methods

We 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.

Results

Participants 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.

Conclusions

Our 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.

Highlights

Young people’s perspectives of takeaway management zones are explored

Acceptability of zones was high as the existing food environment remains unchanged

The impact of takeaway management zones was deemed limited by its focus and scope

Considering wider aspects of the food environment is key for policy development

Future 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

Background

Compared 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.

Methods

We 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.

Results

While 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.

Conclusion

These 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

Background

Cannabis 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.

Methods

Univariable 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.

Results

Univariable 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).

Conclusions

This 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

Each 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

Asymptomatic 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

Posttraumatic 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

Background

Colorectal 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.

Methods

We 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.

Results

Our 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.

Conclusion

This 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

Background

Suicide 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.

Method

A 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.

Results

We 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.

Conclusion

This 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

Objective

Cognitive 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.

Methods

Seventy-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.

Results

Whole-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.

Interpretation

Cholinergic 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

We 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.
F-CPI: Prediction of activity changes induced by fluorine substitution using multimodal deep learning
Authors: Zhang Qian , Yin Wenhai , Chen Xinyao , Zhou Aimin , Zhang Guixu , Zhao Zhi , Li Zhiqiang , Zhang Yan , Shen Jingshan , Zhu Weiliang , Jiang Xiangrui , Xu Zhijian
Published: July 2024

There are a large number of fluorine (F)-containing compounds in approved drugs, and F substitution is a common method in drug discovery and development. However, F is difficult to form traditional hydrogen bonds and typical halogen bonds. As a result, accurate prediction of the activity after F substitution is still impossible using traditional drug design methods, whereas artificial intelligence driven activity prediction might offer a solution. Although more and more machine learning and deep learning models are being applied, there is currently no model specifically designed to study the effect of F on bioactivities. In this study, we developed a specialized deep learning model, F-CPI, to predict the effect of introducing F on drug activity, and tested its performance on a carefully constructed dataset. Comparison with traditional machine learning models and popular CPI task models demonstrated the superiority and necessity of F-CPI, achieving an accuracy of approximately 89% and a precision of approximately 67%. In the end, we utilized F-CPI for the structural optimization of hit compounds against SARS-CoV-2 3CLpro. Impressively, in one case, the introduction of only one F atom resulted in a more than 100-fold increase in activity (IC50: 22.99 nM vs. 28190 nM). Therefore, we believe that F-CPI is a helpful and effective tool in the context of drug discovery and design.
Head-to-head comparison of aptamer- and antibody-based proteomic platforms in human cerebrospinal fluid samples from a real-world memory clinic cohort
Authors: Puerta Raquel , Cano Amanda , García-González Pablo , García-Gutiérrez Fernando , Capdevila María , de Rojas Itziar , Olivé Clàudia , Blázquez-Folch Josep , Sotolongo-Grau Oscar , Miguel Andrea , Montrreal Laura , Martino-Adami Pamela , Khan Asif , Orellana Adelina , Sung Yun Ju , Frikke-Schmidt Ruth , Marchant Natalie , Lambert Jean Charles , Rosende-Roca Maitée , Alegret Montserrat , Fernández Maria Victoria , Marquié Marta , Valero Sergi , Tárraga Lluís , Cruchaga Carlos , Ramírez Alfredo , Boada Mercè , Smets Bart , Cabrera-Socorro Alfredo , Ruiz Agustín
Published: July 2024

High-throughput proteomic platforms have a crucial role in identifying novel Alzheimer’s disease (AD) biomarkers and pathways. In this study, we evaluated the reproducibility and reliability of aptamer-based (SomaScan® 7k) and antibody-based (Olink® Explore 3k) proteomic platforms in cerebrospinal fluid (CSF) samples from the Ace Alzheimer Center Barcelona real-world cohort. Intra- and interplatform reproducibility was evaluated through correlations between two independent SomaScan® assays analyzing the same samples and between SomaScan® and Olink® results. Our 12-category metric of reproducibility combining both correlation analyses identified 2,428 highly reproducible SomaScan CSF measures, with over 600 proteins well reproduced on another proteomic platform. The association analyses among AD clinical phenotypes revealed that the significant associations mainly involved reproducible proteins. The validation of reproducibility in these novel proteomics platforms, measured using this scarce biomaterial, is essential for accurate analysis and proper interpretation of innovative results. This classification metric could enhance confidence in multiplexed proteomic platforms and improve the design of future panels.
Autoencoder Imputation of Missing Heterogeneous Data for Alzheimer's Disease Classification
Authors: Haridas Namitha Thalekkara , Sanchez-Bornot Jose M , McClean Paula L , Wong-Lin KongFatt
Published: July 2024

Accurate diagnosis of Alzheimer's disease (AD) relies heavily on the availability of complete and reliable data. Yet, missingness of heterogeneous medical and clinical data are prevalent and pose significant challenges. Previous studies have explored various data imputation strategies and methods on heterogeneous data, but the evaluation of deep learning algorithms for imputing heterogeneous AD data is limited. In this study, we addressed this by investigating the efficacy of denoising autoencoder-based imputation of missing key features of a heterogeneous data that comprised tau-PET, MRI, cognitive and functional assessments, genotype, sociodemographic, and medical history. We focused on extreme (40-70%) missing at random of key features which depend on AD progression; we identified them as history of mother having AD, APoE ε4 alleles, and clinical dementia rating. Along with features selected using traditional feature selection methods, we included latent features extracted from the denoising autoencoder for subsequent classification. Using random forest classification with 10-fold cross-validation, we evaluated the AD predictive performance of imputed datasets and found robust classification performance, with accuracy of 79-85% and precision of 71-85% across different levels of missingness. Additionally, our results demonstrated high recall values for identifying individuals with AD, particularly in datasets with 40% missingness in key features. Further, our feature-selected dataset using feature selection methods, including autoencoder, demonstrated higher classification score than that of the original complete dataset. These results highlight the effectiveness and robustness of autoencoder in imputing crucial information for reliable AD prediction in AI-based clinical decision support systems.
Comparison of Pericapsular Nerve Group and Lateral Quadratus Lumborum Blocks for Analgesia after Primary Total Hip Arthroplasty: A Randomized Controlled Trial
Authors: Johnson Ellen L. H. , Kelly Tara L. , Wolf Bethany J. , Hansen Erik , Brown Andrew , Lautenschlager Carla , Wilson Sylvia H.
Published: July 2024

Introduction

The quadratus lumborum block (QLB) and the pericapsular nerve group (PENG) block both provide effective postoperative analgesia after hip surgery while minimizing impact on motor function. This study aimed to compare QLB and PENG in patients undergoing primary total hip arthroplasty.

Methods

This superiority trial randomized patients scheduled for elective total hip arthroplasty to receive a lateral QLB or PENG with lateral femoral cutaneous nerve blocks for postoperative analgesia. Perioperative analgesic protocols were standardized. The primary outcome was postoperative cumulative opioid consumption at 72 hours. Secondary outcome was postoperative pain scores. Additional outcomes of interest included time to first ambulation, length of stay, patient reported outcome measures, and opioid-related side effects.

Results

This trial consented and randomized 106 subjects and 101 were included in analysis: PENG (n=50), QLB (n=51). Mean (95% CI) opioid consumption (IV MME) in the first 72 hours did not differ between PENG [109.6 (93.6, 125.6)] and QL [92.3 (76.6, 107.9)] groups (p=0.129) There were no significant differences between treatment arms in average pain score, time to ambulation, distance ambulated, rate of same day discharge, or hospital length of stay. There were also no differences in patient reported outcomes using HOOS-JR and PROMIS-10 scores.

Conclusion

Patients undergoing primary THA receiving preoperative PENG vs QLB had similar opioid consumption, pain scores, time to ambulation, and hospital length of stay. Both QL and PENG blocks are analgesic options in patients undergoing primary THA.

Clinical Trials Registration

NCT05710107;www.ClinicalTrial.gov

IRB Protocol ID: Pro00124880

Key message

Pericapsular nerve group (PENG) block may provide analgesia after hip arthroplasty and improve early functional recovery. This study evaluated postoperative opioid consumption in patients randomized to PENG or lateral quadratus lumborum block (QLB).

Opioid consumption, pain scores, motor recovery, and functional outcome measures did not differ in patients randomized to PENG vs lateral QLB.

PENG and lateral QLBs are analgesic options following total hip arthroplasty with similar rates of same day discharge.
Chromosome X-Wide Common Variant Association Study (XWAS) in Autism Spectrum Disorder
Authors: Mendes Marla , Chen Desmond Zeya , Engchuan Worrawat , Leal Thiago Peixoto , Thiruvahindrapuram Bhooma , Trost Brett , Howe Jennifer L. , Pellecchia Giovanna , Nalpathamkalam Thomas , Alexandrova Roumiana , Salazar Nelson Bautista , McKee Ethan Alexander , Alfaro Natalia Rivera , Lai Meng-Chuan , Bandres-Ciga Sara , Roshandel Delnaz , Bradley Clarrisa A. , Anagnostou Evdokia , Sun Lei , Scherer Stephen W.
Published: July 2024

Autism Spectrum Disorder (ASD) displays a notable male bias in prevalence. Research into rare (<0.1) genetic variants on the X chromosome has implicated over 20 genes in ASD pathogenesis, such asMECP2,DDX3X, andDMD. The "female protective effect" in ASD suggests that females may require a higher genetic burden to manifest similar symptoms as males, yet the mechanisms remain unclear. Despite technological advances in genomics, the complexity of the biological nature of sex chromosomes leave them underrepresented in genome-wide studies. Here, we conducted an X chromosome-wide association study (XWAS) using whole-genome sequencing data from 6,873 individuals with ASD (82% males) across Autism Speaks MSSNG, Simons Simplex Cohort SSC, and Simons Foundation Powering Autism Research SPARK, alongside 8,981 population controls (43% males). We analyzed 418,652 X-chromosome variants, identifying 59 associated with ASD (p-values 7.9×10⁻⁶ to 1.51×10⁻⁵), surpassing Bonferroni-corrected thresholds. Key findings include significant regions on chrXp22.2 (lead SNP=rs12687599, p=3.57×10⁻⁷) harboringASB9/ASB11, and another encompassingDDX53/PTCHD1-ASlong non-coding RNA (lead SNP=rs5926125, p=9.47×10⁻⁶). When mapping genes within 10kb of the 59 most significantly associated SNPs, 91 genes were found, 17 of which yielded association with ASD (GRPR,AP1S2,DDX53,HDAC8,PCDH19,PTCHD1,PCDH11X,PTCHD1-AS,DMD,SYAP1,CNKSR2,GLRA2,OFD1,CDKL5,GPRASP2,NXF5,SH3KBP1).FGF13emerged as a novel X-linked ASD candidate gene, highlighted by sex-specific differences in minor allele frequencies. These results reveal significant new insights into X chromosome biology in ASD, confirming and nominating genes and pathways for further investigation.
Chiral-vacuum excited replicae in QCD modeling
Authors: Garnacho-Velasco Eduardo , Bicudo Pedro J. de A. , Ribeiro J. Emilio F. T. , Llanes-Estrada Felipe J. , Molina Lucas Pérez , Herreros Victor Serrano , Fernández Jorge Vallejo
Published: July 2024

We present a detailed study of the Bardeen-Cooper-Schrieffer (BCS) gap equation “replicae” or excited vacuum states, orthogonal to the ground-state one, in the chiral-quark sector of the Hamiltonian Coulomb-gauge model of chromodynamics. Analyzing the number of negative eigenmodes of the energy density’s Hessian we believe that we have identified all of the (negative energy-density) vacua of this nonlinear system, namely the ground BCS state and one (or two) replicae for slightly massive (or massless) quarks, given the interaction strength typical of the strong interactions. The meson spectrum over each of the replicae looks similar, so the differences are not significant enough given model uncertainties, but matrix elements are more sensitive and allow to distinguish them. We propose to look for such excited vacua in lattice gauge theory by trying to identify excitations with scalar quantum numbers which have energies proportional to the lattice volume (unlike conventional mesons for which the mass stabilizes to a constant upon taking the infinite volume limit).

Published by the American Physical Society
2024
Novel parton density determination code
Authors: Capel Francesca , Aggarwal Ritu , Botje Michiel , Caldwell Allen , Schulz Oliver , Verbytskyi Andrii
Published: July 2024

We introduce our novel Bayesian parton density determination code, artonensity.jl. The motivation for this new code, the framework, and its validation are described. As we show, artonensity.jl provides both a flexible environment for the determination of parton densities and a wealth of information concerning the knowledge update provided by the analyzed dataset.

Published by the American Physical Society
2024