bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Conserved Inhibitory Interdomain Interaction Regulates DNA-binding Activities of Hybrid Two-component Systems inBacteroides
Authors: Gao Rong , Wu Ti , Stock Ann M. 
Published: April 2024

AbstractHybrid two-component systems (HTCSs) comprise a major class of transcription regulators of polysaccharide utilization genes inBacteroides. Distinct from classical two-component systems in which signal transduction is carried out by intermolecular phosphotransfer between a histidine kinase (HK) and a cognate response regulator (RR), HTCSs contain the membrane sensor HK and the RR transcriptional regulator within a single polypeptide chain. Tethering the DNA-binding domain (DBD) of the RR with the dimeric HK domain in an HTCS could potentially promote dimerization of the DBDs and would thus require a mechanism to suppress DNA-binding activity in the absence of stimulus. Analysis of phosphorylation and DNA-binding activities of several HTCSs fromBacteroides thetaiotaomicronrevealed a DBD-suppression mechanism in which an inhibitory interaction between the DBD and the phosphoryl group-accepting receiver domain (REC) decreases autophosphorylation rates of HTCS-RECs as well as represses DNA-binding activities in the absence of phosphorylation. Sequence analyses and structure predictions identified a highly conserved sequence motif correlated with a conserved inhibitory domain arrangement of REC and DBD. Presence of the motif, as in most HTCSs, or its absence, in a small subset of HTCSs, is likely predictive of two distinct regulatory mechanisms evolved for different glycans. Substitutions within the conserved motif relieve the inhibitory interaction and result in elevated DNA-binding activities in the absence of phosphorylation. Our data suggest a fundamental regulatory mechanism shared by most HTCSs to suppress DBD activities using a conserved inhibitory interdomain arrangement to overcome the challenge of the fused HK and RR components.ImportanceDifferent dietary and host-derived complex carbohydrates shape the gut microbial community and impact human health. InBacteroides, the prevalent gut bacteria genus, utilization of these diverse carbohydrates relies on different gene clusters that are under sophisticated control by various signaling systems, including the hybrid two-component systems (HTCSs). We have uncovered a highly conserved regulatory mechanism in which the output DNA-binding activity of HTCSs is suppressed by interdomain interactions in the absence of stimulating phosphorylation. A consensus amino acid motif is found to correlate with the inhibitory interaction surface while deviations from the consensus can lead to constitutive activation. Understanding of such conserved HTCS features will be important to make regulatory predictions for individual systems as well as to engineer novel systems with substitutions in the consensus to explore the glycan regulation landscape inBacteroides.
Modification of Azithromycin to Mitigate its Arrest of Autophagy
Authors: Quarrington Ryan D , Sapula Sylvia A , Lester Susan E , Miller Matthew M , Munic Kos Vesna , Kopp Benjamin T , Jersmann Hubertus PA , Blencowe Anton , Roscioli Eugene 
Published: April 2024

AbstractAims/hypothesiAutophagy plays a critical role in the survival and microbial clearance functions of airway epithelial cells (AECs). Contrary to the known roles of azithromycin (AZM) in promoting microbial clearance, our preliminary evidence suggests that AZM inhibits autophagy in mammalian cells ‒ an effect that could potentially promote bacterial residency in chronic respiratory diseases. Here we investigate the structure-activity relationship of AZMvsother common macrolides and AZM-3’-N-oxide (AZM-[O]), to mitigate the off-target arrest of autophagy.MethodParticipant-derived chronic obstructive pulmonary disease (COPD)vscontrol airway biopsies were assessed for intrinsic defects in autophagy gene activity via qRT-PCR array. Forin vitrostudies, the 16HBE14o-AEC line and Western blot were used to assess macrolidevsautophagy structure-activity relationships, and autophagic flux by quantifying the protein abundance of LC3B-IIvsSequestosome-1. Subsequent assessments of antimicrobial activity were conducted using anE. colimodel and the micro-broth dilution method. Immunomodulatory outcomes were assessed by quantifying the secretion of IL-6 in a lipopolysaccharide LPS-stimulated THP-1 macrophage model.ResultsSignatures of transcript-abundance showed an overall reduction in autophagy gene activity in COPDvscontrol biopsies. AZM significantly inhibited autophagic flux, evidenced by the accumulation of LC3B-II and Sequestosome-1, compared to its precursors and other macrolides including roxithromycin and clarithromycin. Notably, oxidation of AZM to produce AZM-[O] significantly alleviated this inhibitory effect on autophagy without completely preserving its antimicrobial and immunomodulatory functions.ConclusionThe potent off-target block of autophagic flux demonstrated by AZM can be negated by manipulation of its ionisation state. We are currently investigating the AZM-protein interactome and further AZM derivatives that preserve the immunomodulatory function of the parent molecule, to provide a new therapeutic option for chronic respiratory disease and to safeguard the efficacy of AZM.
Predicting Risk of Alzheimer’s Diseases and Related Dementias with AI Foundation Model on Electronic Health Records
Authors: Zhu Weicheng , Tang Huanze , Zhang Hao , Rajamohan Haresh Rengaraj , Huang Shih-Lun , Ma Xinyue , Chaudhari Ankush , Madaan Divyam , Almahmoud Elaf , Chopra Sumit , Dodson John A. , Brody Abraham A. , Masurkar Arjun V. , Razavian Narges 
Published: April 2024

ABSTRACTEarly identification of Alzheimer’s disease (AD) and AD-related dementias (ADRD) has high clinical significance, both because of the potential to slow decline through initiating FDA-approved therapies and managing modifiable risk factors, and to help persons living with dementia and their families to plan before cognitive loss makes doing so challenging. However, substantial racial and ethnic disparities in early diagnosis currently lead to additional inequities in care, urging accurate and inclusive risk assessment programs. In this study, we trained an artificial intelligence foundation model to represent the electronic health records (EHR) data with a vast cohort of 1.2 million patients within a large health system. Building upon this foundation EHR model, we developed a predictive Transformer model, namedTRADE, capable of identifying risks for AD/ADRD and mild cognitive impairment (MCI), by analyzing the past sequential visit records. Amongst individuals 65 and older, our model was able to generate risk predictions for various future timeframes. On the held-out validation set, our model achieved an area under the receiver operating characteristic (AUROC) of 0.772 (95% CI: 0.770, 0.773) for identifying the AD/ADRD/MCI risks in 1 year, and AUROC of 0.735 (95% CI: 0.734, 0.736) in 5 years. The positive predictive values (PPV) in 5 years among individuals with top 1% and 5% highest estimated risks were 39.2% and 27.8%, respectively. These results demonstrate significant improvements upon the current EHR-based AD/ADRD/MCI risk assessment models, paving the way for better prognosis and management of AD/ADRD/MCI at scale.
Increased Resting Brain Entropy in Mild to Moderate Depression was Decreased by Nonpharmacological Treatment
Authors: Song Dong-Hui , Wang Yin , Wang Ze 
Published: April 2024

AbstractEntropy indicates systematic irregularity and information capacity. Recent years have seen increasing interest in assessing regional brain entropy (BEN) using fMRI in healthy controls (HCs) and patients with various brain diseases. Depression and anti-depressant related BEN alterations have been reported in several initial studies. Re-examining these effects using independent cohort is crucial given the high complexity of depression. In this study, we used open data from OpenNeuro from 46 mild to moderate depression patients and 20 HCs to examine regional BEN and its changes due to nonpharmacological treatment (14 patients underwent nonpharmacological treatment). Functional connectivity (FC) analysis was performed to assess the inter-regional relationship between the brain regions showing BEN effects and the rest of the brain. Compared to HCs, depression patients showed increased BEN in left DLPFC, precuneus, and limbic system, including the amygdala, parahippocampal gyrus and hippocampus. Increased BEN in DLPFC, precuneus, and amygdala were suppressed by a nonpharmacological treatment in each individual patient. HCs had positive FC between left and right DLPFC and negative FC between left DLPFC and limbic areas, while patients had abnormally lower or negative FC between left and right DLPFC and positive FC between left DLPFC and the limbic area. These left DLPFC seeded FC changes in patients were reverted after nonpharmacological treatment. The findings highlight the left DLPFC and limbic system in depression and the treatment effects, and patients with depression exhibit significant emotion dysregulation, which is effectively addressed by nonpharmacological treatment targeting the top-down emotion regulation functions mediated by the DLPFC-limbic system. Different from existing literature, these results suggest the entropy/irregularity of DLPFC and limbic system as a potential mechanism underlying depression and suggest BEN in left DLPFC as a potential personalized marker for assessing depression and the corresponding nonpharmacological treatment effects.
Measuring the Impact of Medical Cannabis Law Adoption on Employer-sponsored Health Insurance Costs: A Difference-in-Difference analysis, 2003-2022
Authors: Doucette Mitchell L. , Hemraj Dipak , Fisher Emily , Macfarlan D. Luke 
Published: April 2024

AbstractIntroductionRecent studies suggest that medical cannabis laws might contribute to a reduction in health insurance costs within the individual health insurance markets at the state level. We investigated the effects of adopting a medical cannabis law on the cost of employer-sponsored health insurance.MethodsWe analyzed state-level data from the Medical Expenditure Panel Survey - Insurance Component (MEPS-IC) Private Sector spanning from 2003 to 2022. The outcomes included log transformed average total premiums per employee for single, employee-plus-one, and family coverage plans. We utilized the Sun and Abraham (2021) difference-in-difference (DiD) method, looking at the overall DiD and event-study DiD. Models were adjusted for various state-level demographics and dichotomous policy variables including whether a state later adopted recreational cannabis as well as time and unit fixed effects and population weights.ResultsFor states that adopted a medical cannabis law, there was a significant decrease in the log average total premium per employee for single (-0.034, standard error [SE] = 0.009) and employee plus one (-0.025, SE = 0.009) coverage plans considering the first 10 years of policy change compared to states without such laws. Looking at the last five years of policy change, we saw increases in effect size and statistical significance. Sensitivity analyses suggest findings are robust to our model specifications.DiscussionAdoption of a medical cannabis law may contribute to decreases in healthcare costs. This phenomenon is likely a secondary effect and suggests positive externalities outside of medical cannabis patients.Key PointsStates that passed medical cannabis laws, compared to states that did not pass such laws, saw decreases in the average total costs for employer-sponsored health insurance premiums.The difference in average total costs for employer-sponsored health insurance premiums, comparing states with and without medical cannabis laws, grew over time.This paper contributes to the idea that medical cannabis laws may contain positive externalities.
A Systematic Review of ChatGPT and Other Conversational Large Language Models in Healthcare
Authors: Wang Leyao , Wan Zhiyu , Ni Congning , Song Qingyuan , Li Yang , Clayton Ellen Wright , Malin Bradley A. , Yin Zhijun 
Published: April 2024

AbstractBackgroundThe launch of the Chat Generative Pre-trained Transformer (ChatGPT) in November 2022 has attracted public attention and academic interest to large language models (LLMs), facilitating the emergence of many other innovative LLMs. These LLMs have been applied in various fields, including healthcare. Numerous studies have since been conducted regarding how to employ state-of-the-art LLMs in health-related scenarios to assist patients, doctors, and public health administrators.ObjectiveThis review aims to summarize the applications and concerns of applying conversational LLMs in healthcare and provide an agenda for future research on LLMs in healthcare.MethodsWe utilized PubMed, ACM, and IEEE digital libraries as primary sources for this review. We followed the guidance of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRIMSA) to screen and select peer-reviewed research articles that (1) were related to both healthcare applications and conversational LLMs and (2) were published before September 1st, 2023, the date when we started paper collection and screening. We investigated these papers and classified them according to their applications and concerns.ResultsOur search initially identified 820 papers according to targeted keywords, out of which 65 papers met our criteria and were included in the review. The most popular conversational LLM was ChatGPT from OpenAI (60), followed by Bard from Google (1), Large Language Model Meta AI (LLaMA) from Meta (1), and other LLMs (5). These papers were classified into four categories in terms of their applications: 1) summarization, 2) medical knowledge inquiry, 3) prediction, and 4) administration, and four categories of concerns: 1) reliability, 2) bias, 3) privacy, and 4) public acceptability. There are 49 (75%) research papers using LLMs for summarization and/or medical knowledge inquiry, and 58 (89%) research papers expressing concerns about reliability and/or bias. We found that conversational LLMs exhibit promising results in summarization and providing medical knowledge to patients with a relatively high accuracy. However, conversational LLMs like ChatGPT are not able to provide reliable answers to complex health-related tasks that require specialized domain expertise. Additionally, no experiments in our reviewed papers have been conducted to thoughtfully examine how conversational LLMs lead to bias or privacy issues in healthcare research.ConclusionsFuture studies should focus on improving the reliability of LLM applications in complex health-related tasks, as well as investigating the mechanisms of how LLM applications brought bias and privacy issues. Considering the vast accessibility of LLMs, legal, social, and technical efforts are all needed to address concerns about LLMs to promote, improve, and regularize the application of LLMs in healthcare.
Safety, Pharmacokinetics, Biomarker Response, and Efficacy of E6742, a Dual Antagonist of Toll-Like Receptors 7 and 8, in a First-in-Patient, Randomized, Double-Blind, Phase 1/2 Study in Systemic Lupus Erythematosus
Authors: Tanaka Yoshiya , Kumanogoh Atsushi , Atsumi Tatsuya , Ishii Tomonori , Tago Fumitoshi , Aoki Mari , Yamamuro Shintaro , Akira Shizuo 
Published: April 2024

ABSTRACTObjectivesTo evaluate the safety, tolerability, pharmacokinetics (PK), biomarker response, and efficacy of E6742 in a phase 1/2 study in patients with systemic lupus erythematosus (SLE).MethodsTwo sequential cohorts of SLE patients were enrolled and randomized to 12 weeks of twice-daily treatment with E6742 (100 or 200 mg; n = 8 or 9) or placebo (n = 9).ResultsThe proportion of patients with any treatment-emergent adverse events (TEAEs) was 58.8% in the E6742 group (37.5% for 100 mg; 77.8% for 200 mg) and 66.7% in the placebo group. No Common Terminology Criteria for Adverse Events ≥ Grade 3 TEAEs occurred. PK parameter levels were similar between SLE patients and healthy adults in previous phase 1 studies. The interferon gene signature (IGS) and levels of proinflammatory cytokines (interleukin-1β, interleukin-6, tumor necrosis factor-α) after ex-vivo challenge with a Toll-like receptor 7/8 agonist were immediately decreased by E6742 treatment. Dose-dependent improvements in the British Isles Lupus Assessment Group-based Composite Lupus Assessment response were observed at Week 12 in the E6742 (37.5% for 100 mg; 57.1% for 200 mg) and placebo (33.3%) groups. E6742 also had therapeutic effects on other symptoms, including skin inflammation, arthritis, and levels of anti-double-stranded DNA antibodies and complements.ConclusionsE6742 had a favorable safety profile and was well tolerated, with marked IGS responses and sufficient efficacy signals in patients with SLE. These results provide the first clinical evidence to support E6742 in the treatment of SLE, and support larger, longer-term clinical trials.Trial registration numberNCT05278663.KEY MESSAGESWhat is already known on this topicBecause of the limited efficacy and safety concerns of current drug therapies, unmet medical needs remain for many patients with systemic lupus erythematosus (SLE), necessitating new, more efficacious drugs.There is strong evidence for the relationship between Toll-like receptor (TLR)7/8 and SLE pathophysiology, and two phase 1 clinical studies of E6742, a small molecular selective dual antagonist of TLR7/8, in healthy adults showed good tolerance without safety issues.What this study addsE6742 was well tolerated in this phase 1/2 clinical trial of patients with SLE, demonstrating a favorable safety profile and providing a markedly improved interferon gene signature and sufficient efficacy signals.How this study might affect research, practice or policyThis study provides the first clinical evidence to suggest that E6742, as a first-in-class TLR7/8 inhibitor, may be beneficial for SLE.The study outcomes also support larger, longer-term clinical trials of E6742.
Dopa responsiveness in Parkinson’s disease
Authors: Gandhi Sacha E , Nodehi Anahita , Lawton Michael A , Grosset Katherine A , Marshall Vicky , Ben-Shlomo Yoav , Grosset Donald G 
Published: April 2024

AbstractBackgroundDopaminergic responsiveness is a defining feature of Parkinson’s disease (PD). However, there is limited information on how this evolves over time.ObjectivesTo examine serial dopaminergic responses, if there are distinct patterns, and which factors predict these.MethodsWe analyzed data from theParkinson’s Progression Markers Initiativeon repeated dopaminergic challenge tests (≥ 24.5% defined as a definite response). Growth-mixture modeling evaluated for different response patterns and multinomial logistic regression tested for predictors of these clusters.ResultsThere were 1,525 dopaminergic challenge tests from 336 patients. At enrolment, mean age was 61.2 years (SD 9.6), 66.4% were male and disease duration was 0.5 years (SD 0.5). 1 to 2 years after diagnosis, 48.0% of tests showed a definite response, but this proportion increased with longer duration (51.1-74.3%). We identified 3 response groups: ‘Striking’ (n = 29, 8.7%); ‘Excellent’ (n = 110; 32.7%) and ‘Modest’ (n = 197, 58.6%). Significant differences were as follows: striking responders commenced treatment earlier (P = 0.02), were less likely to be on dopamine agonist monotherapy (P = 0.01), and had better cognition (P < 0.01) and activities of daily living (P = 0.01). Excellent responders had higher challenge doses (P = 0.03) and were more likely to be on combination therapy (P < 0.01).ConclusionThree distinct patterns of the dopaminergic response were observed. The proportion of PD cases with definite dopa responsiveness increased over time, so the initial treatment response may be an unreliable diagnostic aid.
Rationale and design of the Cancer Immunotherapy Evidence Living (CIEL) Library: A continuously updated clinical trial database of cancer immunotherapies
Authors: Boesen K , Hirt J , Düblin P , Läubli H , Kassenda B , Hemkens LG , Janiaud P 
Published: April 2024

AbstractBackgroundImmunotherapies for cancers are being tested in large numbers of clinical trials. It is nearly impossible for clinicians and researchers to stay current with the evidence, and traditional systematic reviews and clinical guidelines are not suited to ensure a continued overview of all trials and their results. To address this problem, we have designed a free-to-use, and publicly available database of clinical trials that aims to be continuously updated, the Cancer Immunotherapy Evidence Living (CIEL) Library.MethodsWe aimed to include planned, ongoing, and completed interventional trials of immunotherapies for cancer, regardless of trial design (e.g., randomization, blinding, and type of comparator). We systematically searched PubMed (for published reports) andClinicalTrials.gov(for registered clinical trials). PubMed retrieved records were screened using the AI-assisted software ASReview and manually extracted and curated. We imported data fromClinicalTrials.govusing the Clinical Trials Transformation Initiative database which then requires further curation. The CIEL-Library is available and searchable via a web application (https://app.ciel-library.org/#/). It also contains the ‘Match My Patient’ feature, a patient-centered clinical decision support system, which can filter planned, ongoing or completed trials based on four patient characteristics (disease staging, previous treatments, performance status, and location). We piloted our database with one type of cancer immunotherapy, the tumor-infiltrating lymphocytes (TIL) transfer.ConclusionThe CIEL-Library offers a blueprint for a dynamic evidence synthesis infrastructure providing an exhaustive collection of clinical trials with trial characteristics and results, which can be applied across different fields, specialties, and topics. The Match My Patient search function may be very useful to implement trial research into patient-centered care by helping to find a trial for enrolment of a patient or to find results for making decisions, for example, in tumor boards.The main challenges to making a continuously updated database of clinical trials are the time and resources needed to populate it with curated and updated data. The CIEL-Library project illustrates the potential and the main limitations to designing such continuously updated trial databases that intent to be directly used in routine care.