bluuebunny/crossref_metadata_2025_split huggingface dataset:
New IPECAD open-source model framework for the health technology assessment of early Alzheimer’s disease treatment: development and use cases
Authors: Handels Ron , Herring William L. , Grimm Sabine , Sköldunger Anders , Winblad Bengt , Wimo Anders , Jönsson Linus 
Published: April 2024

ABSTRACTObjectivesMarket access and reimbursement decisions for new Alzheimer’s disease (AD) treatments are informed by economic evaluations. An open-source model with intuitive structure for model cross-validation can support the transparency and credibility of such evaluations. We describe the new IPECAD open-source model framework (version 2) for the health-economic evaluation of early AD treatment and use it for cross-validation and addressing uncertainty.MethodsA cohort state transition model using a categorized composite domain (cognition and function) was developed by replicating an existing reference model and testing it for internal validity. Then, features of existing “ICER” and “AD-ACE” models assessing lecanemab treatment were implemented for model cross-validation. Additional uncertainty scenarios were performed on choice of efficacy outcome from trial, natural disease progression, treatment effect waning and stopping rules, and other methodological choices. The model is available open-source as R code, spreadsheet and web-based version viahttps://github.com/ronhandels/IPECAD.ResultsIn the IPECAD model incremental life years, QALY gains and cost savings were 21-31% smaller compared to the ICER model and 36-56% smaller compared to the AD-ACE model. IPECAD model results were particularly sensitive to assumptions on treatment effect waning and stopping rules and choice of efficacy outcome from trial.ConclusionsWe demonstrated the ability of a new IPECAD opens-source model framework for researchers and decision-makers to cross-validate other (HTA submission) models and perform additional uncertainty analyses, setting an example for open science in AD decision modeling and supporting important reimbursement decisions.
An atlas of causal and mechanistic drivers of interpatient heterogeneity in glioma
Authors: Turkarslan Serdar , He Yu , Hothi Parvinder , Murie Carl , Nicolas Abran , Kannan Kavya , Park James H , Pan Min , Awawda Alaa , Cole Zachariah D , Shapiro Mark A , Stuhlmiller Timothy J , Lee Hwahyung , Patel Anoop P , Cobbs Charles , Baliga Nitin S 
Published: April 2024

ABSTRACTGrade IV glioma, formerly known as glioblastoma multiforme (GBM) is the most aggressive and lethal type of brain tumor, and its treatment remains challenging in part due to extensive interpatient heterogeneity in disease driving mechanisms and lack of prognostic and predictive biomarkers. Using mechanistic inference of node-edge relationship (MINER), we have analyzed multiomics profiles from 516 patients and constructed an atlas of causal and mechanistic drivers of interpatient heterogeneity in GBM (gbmMINER). The atlas has delineated how 30 driver mutations act in a combinatorial scheme to causally influence a network of regulators (306 transcription factors and 73 miRNAs) of 179 transcriptional “programs”, influencing disease progression in patients across 23 disease states. Through extensive testing on independent patient cohorts, we share evidence that a machine learning model trained on activity profiles of programs within gbmMINER significantly augments risk stratification, identifying patients who are super-responders to standard of care and those that would benefit from 2ndline treatments. In addition to providing mechanistic hypotheses regarding disease prognosis, the activity of programs containing targets of 2ndline treatments accurately predicted efficacy of 28 drugs in killing glioma stem-like cells from 43 patients. Our findings demonstrate that interpatient heterogeneity manifests from differential activities of transcriptional programs, providing actionable strategies for mechanistically characterizing GBM from a systems perspective and developing better prognostic and predictive biomarkers for personalized medicine.
Laboratory validation of a simplified DNA extraction protocol followed by a portable qPCR detection ofM. tuberculosisDNA suitable for point of care settings
Authors: dos Santos Soares Tainá , Bello Graziele Lima , dos Santos Petry Ianca Moraes , Nicola Maria Rita Castilho , Vitoria da Silva Larissa , Barcellos Regina Bones , Silvestri Joana Morez , Rossetti Maria Lucia , Costa Alexandre Dias Tavares 
Published: April 2024

AbstractTuberculosis, caused byMycobacterium tuberculosis, is a treatable and curable disease, and yet remains one of the leading causes of death worldwide. Diagnosis is essential to reducing the number of cases and starting treatment, but costly tests and equipments that require complex infrastructure hamper their widespread use as a tool to contain the disease in vulnerable populations as well countries lacking resources. Therefore, it becomes necessary to develop new technological approaches to molecular methods as well as screening tests that can be rapidly conducted among people presenting to a health facility to differentiate those who should have further diagnostic evaluation for TB from those who should undergo further investigation for non-TB diagnoses. The present study aimed to evaluate two experimental DNA extraction methods from clinical samples (FTA card versus sonication) followed by analysis in a portable qPCR instrument (the Q3-plus). The FTA card-based protocol showed 100% sensitivity and specificity, while the sonication protocol showed 80% sensitivity and 89% specificity when compared to the traditional gold standard culture. The portable protocol, comprised by the FTA card method and the portable instrument Q3-Plus, showed sensitivity and specificity of 92% and 61%, respectively, when compared to culture, and 75% and 81%, respectively, when compared to the standard TB case classification. The ROC curve showed an AUC of 0.78 (p<0.001) for the portable protocol and 0.93 (p<0.001) for the GeneXpert MTB/RIF. The limit of detection (LOD) forMycobacterium tuberculosis(H37Rv strain) detection in spiked samples obtained using the portable protocol (FTA card and Q3-Plus) was 19.3 CFU/mL. As an added benefit, using the FTA card facilitates sample handling, transport, and storage. It is concluded that the use of the FTA card protocol and the Q3-Plus yields similar sensitivity and specificity as the gold standard diagnostic tests and case classification. We suggest that the platform is suitable to use as a point of care tool, assisting in the screening of tuberculosis in hard-to-reach or resource-limited areas.
Classification of Schizophrenic Traits in Transcriptions of Audio Spectra from Patient Literature: Artificial Intelligence Models Enhanced by Geometric Properties
Authors: Marques Paulo César F. , Soares Lucas Rafael F. , Araujo André Victor de A. , Monteiro Arthur Ribeiro , Batista Arthur Almeida Leitão , Pimentel Túlio Farias , Calheiros Lis de Lima , Padilla Maria Helena N. S. , Pacheco André , Queda Fabio , Oliveira João Ricardo M. , Filho José Luiz de Lima , Bocanegra Silvana , Albuquerque Jones 
Published: April 2024

ABSTRACTSchizophrenia is a severe mental illness that affects approximately 1% of the global population and presents significant challenges for patients, families, and healthcare professionals. Characterized by symptoms such as delusions, hallucinations, disorganized speech or behavior, and cognitive impairment, this condition has an early onset and chronic trajectory, making it a debilitating challenge. Schizophrenia also imposes a substantial burden on society, exacerbated by the stigma associated with mental disorders. Technological advancements, such as computerized semantic, linguistic, and acoustic analyses, are revolutionizing the understanding and assessment of communication alterations, a significant aspect in various severe mental illnesses. Early and accurate diagnosis is crucial for improving prognosis and implementing appropriate treatments. In this context, the advancement of Artificial Intelligence (AI) has provided new perspectives for the treatment of schizophrenia, with machine learning techniques and natural language processing allowing a more detailed analysis of clinical, neurological, and behavioral data sets. The present article aims to present a proposal for computational models for the identification of schizophrenic traits in texts. The database used in this article was created with 139 excerpts of patients’ speeches reported in the book “Memories of My Nervous Disease” by German judge Daniel Paul Schreber, classifying them into three categories: 1 - schizophrenic, 2 - with schizophrenic traits and 3 - without any relation to the disorder. Of these speeches, 104 were used for training the models and the others 35 for validation.Three classification models were implemented using features based on geometric properties of graphs (number of vertices, number of cycles, girth, vertex of maximum degree, maximum clique size) and text entropy. Promising results were observed in the classification, with the Decision Tree-based model [1] achieving 100% accuracy, the KNN-k-Nearest Neighbor model observed with 62.8% accuracy, and the ‘centrality-based’ model with 59% precision. The high precision rates, observed when geometric properties are incorporated into Artificial Intelligence Models, suggest that the models can be improved to the point of capturing the language deviation traits that are indicative of schizophrenic disorders. In summary, this study paves the way for significant advances in the use of geometric properties in the field of psychiatry, offering a new data-based approach to the understanding and therapy of schizophrenia.
Quantifying the Regional Disproportionality of COVID-19 Spread
Authors: Sasaki Kenji , Ikeda Yoichi , Nakano Takashi 
Published: April 2024

AbstractBackgroundThe COVID-19 pandemic has caused serious health problems and has had major economic and social consequences worldwide. Understanding how infectious diseases spread can help mitigating the social and economic impact.ObjectiveThe study focuses to capture the degrees of disproportionality in prevalence rates of infectious disease across different regions over time.MethodsWe analyze the numbers of daily COVID-19 confirmed cases in the United States collected by Johns Hopkins University over 1100 days since the first reported case in January 2020 in order to assess quantitatively the disproportionality of the confirmed cases using the Theil index, a measure of imbalance used in economics. Results:Our results reveal a dynamic pattern of interregional disproportionality in the confirmed cases by monitoring variations in regional contributions to the Theil index as the pandemic progresses.ConclusionsThe combined monitoring of this indicator and the confirmed cases is crucial for understanding regional differences in infectious diseases and for effective planning of response and resource allocation.
Functional divergence between the two cerebral hemispheres contributes to human fluid intelligence
Authors: Liang Xinyu , Luo Junhao , Yang Liyuan , Vatansever Deniz , Jefferies Elizabeth , Gong Gaolang 
Published: April 2024

AbstractHemispheric lateralization is linked to potential cognitive advantages. It is considered a driving force behind the generation of human intelligence. However, establishing quantitative links between the degree of lateralization and intelligence in humans remains elusive. In this study, we propose a framework that utilizes the functional aligned multidimensional representation space derived from hemispheric functional gradients to compute between-hemisphere distances within this space. Applying this framework to a large cohort (N=777 from the Human Connectome Project), we identified high functional divergence across the two hemispheres within the frontoparietal control network. We found that both global divergence between the cerebral hemispheres and regional divergence within the multiple demand network were positively associated with fluid composite score and partially mediated the influence of brain size on individual differences in fluid intelligence. Together, these findings illuminate the profound significance of brain lateralization as a fundamental organizational principle of the human brain, providing direct evidence that hemispheric lateralization supports human fluid intelligence.
A blueprint for national assessments of the blue carbon capacity of kelp forests applied to Canada’s coastline
Authors: McHenry Jennifer , Okamoto Daniel K. , Filbee-Dexter Karen , Krumhansl Kira , MacGregor Kathleen A. , Hessing-Lewis Margot , Timmer Brian , Archambault Philippe , Attridge Claire M. , Cottier Delphine , Costa Maycira , Csordas Matt , Johnson Ladd E. , Lessard Joanne , Mora-Soto Alejandra , Metaxas Anna , Neufeld Chris , Pontier Ondine , Reshitnyk Luba , Starko Samuel , Yakimishyn Jennifer , Baum Julia K. 
Published: April 2024

AbstractKelp forests offer substantial carbon fixation, with the potential to contribute to natural climate solutions (NCS). However, to be included in national NCS inventories, governments must first quantify the kelp-derived carbon stocks and fluxes leading to carbon sequestration. Here, we present a blueprint for assessing the national carbon sequestration capacity of kelp forests in which data synthesis and Bayesian hierarchical modelling enable estimates of kelp forest carbon production, storage, and export capacity from limited data. Applying this blueprint to Canada’s extensive coastline, we find kelp forests store an estimated 1.4 Tg C in short-term biomass and produce 3.1 Tg C yr-1with modest carbon fluxes to the deep ocean. Arctic kelps had the highest carbon stocks and production capacity, while Pacific kelps had greater carbon fluxes overall due to their higher productivity and export rates. Our transparent, reproducible blueprint represents an important step towards accurate carbon accounting for kelp forests.
wwLearning the language of proteins and predicting the impact of mutations
Authors: Hu Bin , Babinski Michal , Gibson Kaetlyn , Li Po-E , Li Valerie , Chain Patrick 
Published: April 2024

AbstractMutations in proteins directly impact their structure and function. Understanding the “language” of proteins, or the sequence to function (genotype-phenotype) relationship has many real-world applications. One set of applications includes those in biodefense, such as biological threat detection and biosurveillance, antibody engineering, and medical countermeasure development. In this study, we present a novel language model-based approach that can rapidly analyze vast collections of sequences, and make near real-time functional predictions that compare favorably to those made using conventional bioinformatic and experimental methods. Our findings reveal that tailored protein language models can predict protein mutation phenotypes, such as binding affinity or level of expression, when they are trained with high-throughput functional data. Protein language models applied to viral genomes can also discern the lineage within a family (e.g., sarbecovirus sequences). Coupled with sequenced-based biosurveillance, this type of model may provide early warning signals of potential zoonotic spillovers (i.e. host jumping) or “escape” from existing medical countermeasures posed by novel mutations. This research not only underscores the potential of ML and language models in addressing pressing challenges in understanding the mapping of sequence to function, but further elucidates their potential application in accelerating the response to biological threats as they evolve.
SPARROW reveals cell states and functions influenced by microenvironment zones in complex tissues
Authors: Zhao Peiyao A , Garber Jessica , Gustafson Claire , Kim June , Malone Jocelin , Savage Adam , Skene Peter , Li Xiao-jun 
Published: April 2024

AbstractSpatially resolved transcriptomics technologies have significantly enhanced our ability to understand cellular characteristics within tissue contexts. However, they present a trade-off between spatial resolution and transcriptome coverage. This limitation, compounded with analytical tools treating cell type inference and cellular neighbourhood identification as separate processes, hinders a unified understanding of tissue features across scales. Our computational framework, SPARROW, infers cell types and delineates cellular organization patterns as microenvironment zones using an interconnected architecture. SPARROW algorithmically achieves single cell spatial resolution and whole transcriptome coverage by integrating spatially resolved transcriptomics and scRNA-seq data. Using SPARROW, we identified established and novel microenvironment zone-specific ligand-receptor mediated interactions in human tonsils, discoveries that would not be possible using either modality alone. Moreover, SPARROW uncovered novel cell states in the mouse hypothalamus, underscoring the influence of microenvironment zones on cell identities. Lastly, through its common latent spaces that facilitate cross-tissue comparisons, SPARROW revealed distinct inflammation states between different lymph node tissues. Overall, SPARROW integrates cellular gene expression with spatial organization, providing a comprehensive characterization of tissue features across scales and samples.
Coordinated regulation by lncRNAs results in tight lncRNA–target couplings
Authors: Chiu Hua-Sheng , Somvanshi Sonal , de Lavergne Eric de Bony , Wei Zhaowen , Trypsteen Wim , Scorsone Kathleen A. , Patel Ektaben , Tang Tien T. , Flint David B. , Panah Mohammad Javad Najaf , Kim Hyunjae Ryan , Rathi Purva , Lee Yan-Hwa Wu , Woodfield Sarah , Vasudevan Sanjeev A. , Heczey Andras Attila , Chen Ting-Wen , Gaber M. Waleed , Sawakuchi Gabriel Oliveira , Mestdagh Pieter , Yang Xuerui , Sumazin Pavel 
Published: April 2024

SUMMARYThe determination of long non-coding RNA (lncRNA) function is a major challenge in RNA biology with applications to basic, translational, and medical research [1–7]. Our efforts to improve the accuracy of lncRNA-target inference identified lncRNAs that coordinately regulate both the transcriptional and post-transcriptional processing of their targets. Namely, these lncRNAs may regulate the transcription of their target and chaperone the resulting message until its translation, leading to tightly coupled lncRNA and target abundance. Our analysis suggested that hundreds of cancer genes are coordinately and tightly regulated by lncRNAs and that this unexplored regulatory paradigm may propagate the effects of non-coding alterations to effectively dysregulate gene expression programs. As a proof-of-principle we studied the regulation of DICER1 [8, 9]—a cancer gene that controls microRNA biogenesis—by the lncRNAZFAS1, showing thatZFAS1activatesDICER1transcription and blocks its post-transcriptional repression to phenomimic and regulate DICER1 and its target microRNAs.
Apoptotic proteins inLeishmania donovani:In silicoscreening, modelling, and validation by knock-out and gene expression analysis
Authors: Kumar Ketan , Crobu Lucien , Sterkers Yvon , Prajapati Vijay Kumar 
Published: April 2024

AbstractVisceral leishmaniasis (VL), a life-threatening vector-borne illness that disproportionately affects children and elderly immunocompromised people, is a primary tropical neglected disease. No apoptotic partner proteins inL. donovanihave been reported yet, which might contribute to the knowledge of parasite cell death and the establishment of alternative therapeutics. We used the Orthologues algorithm to search for the mammalian Bcl-2 family proteins orthologs, one anti-apoptotic and two pro-apoptotic, inL. donovani. We also included a pro-death aquaporin (AQP) protein due to its characteristic BH3 domain, which is known to interact with pro-apoptotic proteins in mammals. Molecular docking and molecular dynamics simulation studies were conducted to assess the protein-protein interaction between the identified apoptotic proteins and mimic mammalian intrinsic apoptotic pathways. The results showed that the pro-apoptotic protein interacted with the hydrophobic pocket of the anti-apoptotic ortholog, forming a stable complex, which may represent a critical event in the apoptotic pathways of leishmaniasis. To further establish an apoptotic pathway inL. donovani, we used several CRISPR-Cas9 approaches to target the identified proteins. The pure knocked population mutants, and episomal over-expressing mutant cells were exposed to apoptotic stimuli. TUNEL assay and quantitative expression profiling suggested that these proteins are needed during the parasite’s apoptosis and could play a role in the parasite’s survival.Author SummaryVisceral leishmaniasis, a fatal systemic infection affecting internal organs, is one of three types of leishmaniasis in mammals alongside cutaneous and mucocutaneous leishmaniasis. It predominantly occurs in tropical and subtropical climatic zones,Leishmania donovanipredominant in the Indian subcontinent andLeishmania infantumin the Mediterranean basin, the Middle East, Central Asia, South America, and Central America. This disease primarily affects children, immunocompromised adults, and the elderly.L donovani,transmitted by the infected sandflies complete its life cycle in humans, serving as reservoir. During its life cycle, at a particular stage, the parasite undergoes apoptotic-like events, yet underlying proteins or key factors remain unidentified. Using computational methods, we screened theL. donovanigenome for potential candidate genes of the Bcl-2 family apoptotic proteins. We biologically/experimentally validated ourin-silicofindings using molecular editing tools like CRISPR-Cas9, advancing our understanding of the parasite’s apoptotic pathway. Targeting this pathway could lead to more effective therapeutics against visceral leishmaniasis.
Cyr61 delivery promotes angiogenesis during bone fracture repair
Authors: Lang Annemarie , Eastburn Emily A. , Younesi Mousa , Nijsure Madhura , Siciliano Carly , Pranatharthi Haran Annapurna , Panebianco Christopher J. , Seidl Elizabeth , Tang Rui , Alsberg Eben , Willett Nick J. , Gottardi Riccardo , Huh Dongeun , Boerckel Joel D. 
Published: April 2024

AbstractCompromised vascular supply and insufficient neovascularization impede bone repair, increasing risk of non-union. Cyr61, Cysteine-rich angiogenic inducer of 61kD (also known as CCN1), is a matricellular growth factor that is regulated by mechanical cues during fracture repair. Here, we map the distribution of endogenous Cyr61 during bone repair and evaluate the effects of recombinant Cyr61 delivery on vascularized bone regeneration. In vitro, Cyr61 treatment did not alter chondrogenesis or osteogenic gene expression, but significantly enhanced angiogenesis. In a mouse femoral fracture model, Cyr61 delivery did not alter cartilage or bone formation, but accelerated neovascularization during fracture repair. Early initiation of ambulatory mechanical loading disrupted Cyr61-induced neovascularization. Together, these data indicate that Cyr61 delivery can enhance angiogenesis during bone repair, particularly for fractures with stable fixation, and may have therapeutic potential for fractures with limited blood vessel supply.