bluuebunny/crossref_metadata_2025_split huggingface dataset:
Quantum-classical hybrid approach for codon optimization and its practical applications
Authors: Chung You Kyoung , Lee Dongkeun , Lee Junho , Kim Jaehee , Park Daniel K , Huh Joonsuk 
Published: June 2024

AbstractCodon optimization is crucial for gene expression in heterologous hosts with varying genetic codes and codon usage, potentially resulting in enhanced protein expression and stability. Traditionally, the codon optimization problem has been solved using classical numerical techniques; however, with recent advancements, quantum algorithms deployed on quantum computers have been adopted for this purpose. This study proposes a codon sequence search protocol tailored to host preferences. Specifically, codon optimization is formulated as a constrained quadratic binary problem and solved using a quantum-classical hybrid approach, integrating quantum annealing with the Lagrange multiplier method. The proposed methodology is then applied to two real-world scenarios: optimizing the codon sequence of the severe respiratory syndrome coronavirus 2 spike protein in human hosts and insulin inEscherichia coli (E. coli)hosts. Finally, evaluations of several biological metrics demonstrate the effectiveness of our protocol, offering insights into the codon usage patterns governing translational efficiency and adaptation to the genetic code preferences of the host organisms.
PROPOSED SURVEY FOR PATIENTS PRIOR TO MAMMOGRAPHY AS A TOOL TO IMPROVE RADIOLOGICAL REPORTING
Authors: Villaseca Fernando Muñoz , Toro Valeria Pardo , Zulueta Elisabeth Cañipa , Herrera Marco Jiménez , Miranda Jeannette Soto 
Published: June 2024

AbstractBackgroundBreast cancer is one of the leading causes of death among women worldwide. Therefore, early detection through quality mammography, along with information collected from the mammographic survey, is crucial.The objective of this research is to propose a mammographic survey for patients prior to mammography to gather important information for radiologists specializing in mammography when preparing reports.MethodologyThis research was conducted using a semi-structured survey with both closed and open-ended questions, which was completed by 31 radiologists specializing in mammography.ResultsRelevant patient history and risk factors were collected, which are most useful for radiological reporting concerning patient history and potential risk factors.ConclusionBased on the responses collected from radiologists, we propose a final mammographic survey as a tool to aid radiologists in correlating clinical images with patient history. This survey emphasizes family history, gynecological-obstetric history, surgical history, among other factors.
Harnessing generative AI to annotate the severity of all phenotypic abnormalities within the Human Phenotype Ontology
Authors: Murphy Kitty B , Schilder Brian M , Skene Nathan G 
Published: June 2024

0.1AbstractThere are thousands of human phenotypes which are linked to genetic variation. These range from the benign (white eyelashes) to the deadly (respiratory failure). The Human Phenotype Ontology has categorised all human phenotypic variation into a unified framework that defines the relationships between them (e.g. missing arms and missing legs are both abnormalities of the limb). This has made it possible to perform phenome-wide analyses, e.g. to prioritise which make the best candidates for gene therapies. However, there is currently limited metadata describing the clinical characteristics / severity of these phenotypes. With >17500 phenotypic abnormalities across >8600 rare diseases, manual curation of such phenotypic annotations by experts would be exceedingly labour-intensive and time-consuming. Leveraging advances in artificial intelligence, we employed the OpenAI GPT-4 large language model (LLM) to systematically annotate the severity of all phenotypic abnormalities in the HPO. Phenotypic severity was defined using a set of clinical characteristics and their frequency of occurrence. First, we benchmarked the generative LLM clinical characteristic annotations against ground-truth labels within the HPO (e.g. phenotypes in the ‘Cancer’ HPO branch were annotated as causing cancer by GPT-4). True positive recall rates across different clinical characteristics ranged from 89-100% (mean=96%), clearly demonstrating the ability of GPT-4 to automate the curation process with a high degree of fidelity. Using a novel approach, we developed a severity scoring system that incorporates both the nature of the clinical characteristic and the frequency of its occurrence. These clinical characteristic severity metrics will enable efforts to systematically prioritise which human phenotypes are most detrimental to human health, and best targets for therapeutic intervention.
Annotation-free multi-organ anomaly detection in abdominal CT using free-text radiology reports: A multi-center retrospective study
Authors: Sato Junya , Sugimoto Kento , Suzuki Yuki , Wataya Tomohiro , Kita Kosuke , Nishigaki Daiki , Tomiyama Miyuki , Hiraoka Yu , Hori Masatoshi , Takeda Toshihiro , Kido Shoji , Tomiyama Noriyuki 
Published: June 2024

SUMMARYBackgroundArtificial intelligence (AI) systems designed to detect abnormalities in abdominal computed tomography (CT) could reduce radiologists’ workload and improve diagnostic processes. However, development of such models has been hampered by the shortage of large expert-annotated datasets. Here, we used information from free-text radiology reports, rather than manual annotations, to develop a deep-learning-based pipeline for comprehensive detection of abdominal CT abnormalities.MethodsIn this multicenter retrospective study, we developed a deep-learning-based pipeline to detect abnormalities in the liver, gallbladder, pancreas, spleen, and kidneys. Abdominal CT exams and related free-text reports obtained during routine clinical practice collected from three institutions were used for training and internal testing, while data collected from six institutions were used for external testing. A multi-organ segmentation model and an information extraction schema were used to extract specific organ images and disease information, CT images and radiology reports, respectively, which were used to train a multiple-instance learning model for anomaly detection. Its performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and F1 score against radiologists’ ground-truth labels.FindingsWe trained the model for each organ on images selected from 66,684 exams (39,255 patients) and tested it on 300 (295 patients) and 600 (596 patients) exams for internal and external validation, respectively. In the external test cohort, the overall AUC for detecting organ abnormalities was 0·886. Whereas models trained on human-annotated labels performed better with the same number of exams, those trained on larger datasets with labels auto-extracted via the information extraction schema significantly outperformed human-annotated label-derived models.InterpretationUsing disease information from routine clinical free-text radiology reports allows development of accurate anomaly detection models without requiring manual annotations. This approach is applicable to various anatomical sites and could streamline diagnostic processes.FundingJapan Science and Technology Agency.
Routine data collection in home care: A national survey of home care providers in England
Authors: Davey V , Healey J , Liddle J , Beresford B , Rand S , Goodman C , Spilsbury K , Hanratty B 
Published: June 2024

AbstractPurposeMandatory digital social care records and a standardised schedule for collecting information on home care clients are proposed for regulated adult social care providers in England. This is akin to a minimum dataset (MDS). This study aimed to understand current data collection practices in home care, and identify where support for implementation of an MDS is needed.DesignAn online survey of English home care providers was conducted in 2023, asking about the information they collect, store, and share about their clients. Data were analysed using descriptive statistics and logistic regression.FindingsOne hundred and fifty-five responses were received from home care providers in all regions of England, a majority were for-profit organisations (89%). All collected a range of data on client characteristics and observations about care delivered. Monitoring of changes in client wellbeing and use of standardised measurement tools (e.g. functioning, mood or quality of life) were uncommon. Over two-thirds (71%) reported that they reviewed the content of care packages at least every six months. Providers with a majority of self-funding clients were more likely to regularly update information on care needs and client/ family preferences.OriginalityTo the best of our knowledge, this is the first national survey of home care providers on their routine data collection practices.Practical implicationsData collection in UK home care will require expansion, to implement an MDS, which has resource implications for providers. Home care staff will need the skills to collect and use data to enhance client care.
The impact of heat stress on the human plasma lipidome
Authors: Estevao Igor L. , Kazman Josh B. , Bramer Lisa M. , Nicora Carrie , Ren Ming Qiang , Sambuughin Nyamkhishig , Munoz Nathalie , Kim Young-Mo , Bloodsworth Kent , Richert Maile , Teeguarden Justin , Burnum-Johnson Kristin , Deuster Patricia A. , Nakayasu Ernesto S. , Many Gina 
Published: June 2024

AbstractThe year of 2023 displayed the highest average global temperatures since it has been recorded—the duration and severity of extreme heat are projected to increase. Rising global temperatures represent a major public health threat, especially to occupations exposed to hot environments, such as construction and agricultural workers, and first responders. Despite efforts of the scientific community, there is still a need to characterize the pathophysiological processes leading to heat related illness and develop biomarkers that can predict its onset. Here, we performed a plasma lipidomic analysis on male and female subjects who underwent heat tolerance testing (HTT), consisting of a 2-h treadmill walk at 5 km/h with 2% inclination at a controlled temperature of 40°C. We identified 995 lipids from 27 classes, with nearly half of all detected lipids being responsive to HTT. Lipid classes related to substrate utilization were predominantly affected by HTT, with a downregulation of triacylglycerols and upregulation of free fatty acids and acyl-carnitines (CARs). We additionally examined correlations between changes in plasma lipids by using the physiological strain index (PSI). Here, even chain CAR 4:0, 14:0 and 16:1, suggested by-products of incomplete beta oxidation, and diacylglycerols displayed the highest correlation to PSI. PSI did not correlate with plasma lactate levels, suggesting that correlations between even chain CARs and PSI is related to metabolic efficiency versus physical exertion. Overall, our results show that HTT has a strong impact on the plasma lipidome and that metabolic inefficiencies may underlie heat intolerance.
Comparison of Glomerular Filtration Rate Equations in a Rural New Mexico Cohort: Results from the COMPASS Study
Authors: Balasch Monica Moya , Roumelioti Maria-Eleni , Argyropoulos Christos P 
Published: June 2024

AbstractRationale and ObjectiveThe NKF-ASN Task Force recommends accurate kidney function estimation avoiding biases through racial adjustments. We explored the use of multiple kidney function biomarkers and hence estimated glomerular filtration rate (eGFR) equations to improve kidney function calculations in an ethnically diverse patient population.Study designProspective community cohort study.Setting and ParticipantsRural New Mexico clinic with patients > 18 yo.MethodsMarkers of kidney function, IDMS-Creatinine (SCr), chemiluminescence Beta-2 Microglobulin (B2M), Nephelometry-calibrated ELISA Cystatin C (CysC), inflammation, glucose tolerance, demographics, BUN/UACR from the baseline visit of the COMPASS cohort, were analyzed by Kernel-based Virtual Machine learning methods.ResultsAmong 205 participants, the mean age was 50.1, 62% were female, 54.1% Hispanic American and 30.2% Native American. Average kidney function biomarkers were: SCr 0.9 mg/dl, B2M 1.8 mg/L, and CysC 0.7 mg/dl. The highest agreement was observed between SCr and B2M-based eGFR equations [mean difference in eGFRs: (4.48 ml/min/1.73m2], and the lowest agreement between B2M and CysC-based eGFR equations (−24.75 ml/min/1.73m2). There was no pattern of association between the differences in eGFR measures and gender. In the continuous analyses, the absolute eGFR value (p<2 × 10-16) and serum albumin (p =6.4 × 10-5) predicted the difference between B2M- and SCr-based e-GFR. The absolute eGFR value (p<2 × 10-16) and age (p =7.6 x 10-5) predicted the difference between CysC- and SCr-based e-GFR.LimitationsRelatively small sample size, elevated inflammatory state in majority of study participants and no inulin excretion rate measurements.ConclusionB2M should be strongly considered as a kidney function biomarker fulfilling the criteria for the NKF-ASN. B2M’s eGFR equation does not need adjustment for gender or race and showed the highest agreement with SCr-based eGFR equations.
Integrating immunophenotypes reveals causal relationship between tuberculosis and lung cancer: Mendelian randomization study
Authors: Li Xianwen , Huang Dayin , Liu Chang , Yu Wei , Bi Lijun 
Published: June 2024

AbstractBackgroundTuberculosis (TB) and lung cancer (LC) are major respiratory diseases causing significant deaths worldwide. Both primarily affect the lungs and share similar clinical symptoms. The comorbidity rate between TB and LC is high, and epidemiological data suggest a potential association between the two diseases. However, the detailed causal relationships and potential mediators remain underexplored due to limited evidence from observational studies. This study aims to elucidate the relationship between TB and LC, including the pathological subtypes of LC, and to investigate the potential mediating effects of immune cells.Materials and MethodsThe genetic variants for TB and LC were sourced from the IEU Open GWAS Project, while the data for the 731 immunocyte phenotypes were obtained from their genome-wide association studies (GWAS). The inverse variance-weighted (IVW), MR-Egger, weighted median, simple mode and weighted mode were used to evaluate the causal relationship between TB and LC. We then applied a two-step Mendelian randomization (MR) analysis to examine the roles of immunocyte phenotypes as mediators. Additionally, multiple sensitivity analyses were conducted to ensure the reliability of the MR results.ResultsThe results of the IVW methods of the MR analysis indicated a causal relationship between TB and LC, demonstrating a positive correlation (OR = 1.072, 95% CI: 1.010–1.137,P< 0.05). The immune phenotype CD4 on CD39+ resting regulatory T cell is implicated in the linkage between TB and LC as well (mediation proportion: 9.09%). Additionally, no evidence was found to support a reverse causal relationship between TB and LC, nor a causal relationship between TB and the pathological subtypes of LC. There was no observed heterogeneity or horizontal pleiotropy in our MR results.ConclusionOur study suggests the causal effect of TB on increased LC risk, with immune phenotype playing a mediating role. These insights provide a significant basis for the prevention and concurrent management of LC and TB.
Impact of Language Models on Healthcare in Thailand: Benefits, Challenges, and Future Opportunities
Authors: Moongthin Chanakan 
Published: June 2024

AbstractThis study explores the use of Artificial Intelligence (AI), specifically Large Language Models (LLMs) in the Thai healthcare sector, focusing on applications such as diagnosis, patient monitoring, and automated question-and-answer systems. While AI has the potential to improve diagnosis accuracy, reduce the time required for appointment, and enhance patient care, several challenges prevent widespread adoption of LLMs in healthcare, including significant computational resources required for deployment, data privacy and security concerns, and Thai language being a low-resource language. Through a comprehensive analysis of publicly available online data and literature, this study examines the current state of AI adoption in Thai healthcare, identifying key barriers to adoption and providing recommendations for overcoming these challenges, including targeted training and education for healthcare professionals, strategic government initiatives, and investments in infrastructure. By addressing these issues, Thailand can harness the full potential of AI technologies to enhance its healthcare system, ensuring better patient outcomes and operational efficiencies.
Predictive coding model can detect novelty on different levels of representation hierarchy
Authors: Li T. Ed , Tang Mufeng , Bogacz Rafal 
Published: June 2024

AbstractNovelty detection, also known as familiarity discrimination or recognition memory, refers to the ability to distinguish whether a stimulus has been seen before. It has been hypothesized that novelty detection can naturally arise within networks that store memory or learn efficient neural representation, because these networks already store information on familiar stimuli. However, computational models instantiating this hypothesis have not been shown to reproduce high capacity of human recognition memory, so it is unclear if this hypothesis is feasible. This paper demonstrates that predictive coding, which is an established model previously shown to effectively support representation learning and memory, can also naturally discriminate novelty with high capacity. Predictive coding model includes neurons encoding prediction errors, and we show that these neurons produce higher activity for novel stimuli, so that the novelty can be decoded from their activity. Moreover, the hierarchical predictive coding networks uniquely perform novelty detection at varying abstraction levels across the hierarchy, i.e., they can detect both novel low-level features, and novel higher-level objects. Overall, we unify novelty detection, associative memory, and representation learning within a single computational framework.
Artificial intelligence and species distribution ensemble models inform resource interactions with offshore wind development
Authors: Ingram Evan C. , Dunton Keith J. , Frisk Michael G. , Butler Liam 
Published: June 2024

AbstractDevelopment of offshore wind energy resources has led to growing concerns for marine wildlife. However, significant uncertainty remains regarding the technology’s potential to impact species of interest that may occupy planned development sites. This is further compounded by the difficulty of monitoring highly migratory or data-poor species in marine waters, making practical assessment of site- or species-specific threats that could require additional management intervention particularly problematic. Here, I identify a highly generalizable framework to inform species interactions in marine habitats allocated for offshore resource exploitation, using telemetry-derived artificial intelligence species distribution models. Results from a case study of the federally protected Atlantic Sturgeon (Acipenser oxyrinchus) demonstrate excellent discriminatory capacity (i.e., AUC ≥ 0.9) at a relatively fine scale (raster resolution = 1 km2), while providing critical information on predicted occurrence over a broad swath of unmonitored marine habitats (i.e., the Atlantic OCS region of the US; area > 620,000 km2). Furthermore, ensemble map products developed from these models are readily scalable to ongoing management needs and, when overlaid with offshore wind energy lease areas, can feed directly into management strategies to inform best practices for potential habitat influences on Atlantic Sturgeon, as well as other species of commercial or conservation interest.
Social cues on stone tools outweigh raw material properties in wild primates
Authors: Henke-von der Malsburg J , Reeves J , Proffitt T , Falótico T , Rufo HP , Luncz LV 
Published: June 2024

1.AbstractThe ability to select appropriate tool material enabled early hominins access to new resources and environments. The underlying mechanisms driving tool selection effectively remain unknown. Observations of extant primates have demonstrated strong selectivity for specific tools, offering analogous insight into technological decision-making. However, whether tool selection is determined by individual experience alone or social information plays a role remained difficult to disentangle. Here, we used an experimental approach to investigate decision-making factors for tool selection in non-human primates. We provided naturalistic nut-cracking opportunities to wild capuchin monkeys, one of the most prolific extant tool users. We offered standardized stones varying in asocial (material properties) and social cues (evidence of previous use) to two populations, differing in their previous experience of natural materials. Our results show that both populations persistently selected tools based on their material properties when only asocial cues were provided. However, when provided with both asocial and social cues combined, they consistently selected previously used material regardless of material properties. These findings suggest that wild capuchin monkeys discriminate between raw material properties; however, prioritize social cues when present. Tool selection behaviors are therefore shaped by indirect social processes and highlight the importance of culturally transmitted information for skill acquisition in technological primates.
Cholesterol inhibits assembly and activation of the EphA2 receptor
Authors: Schuck Ryan J , Ward Alyssa E , Sahoo Amita R , Rybak Jennifer A , Pyron Robert J , Trybala Thomas N , Simmons Timothy B , Baccile Joshua A , Sgouralis Ioannis , Buck Matthias , Lamichhane Rajan , Barrera Francisco N 
Published: June 2024

AbstractThe receptor tyrosine kinase EphA2 drives cancer malignancy by facilitating metastasis. EphA2 can be found in different self-assembly states: as a monomer, dimer, and oligomer. However, our understanding remains limited regarding which EphA2 state is responsible for driving pro-metastatic signaling. To address this limitation, we have developed SiMPull-POP, a single-molecule method for accurate quantification of membrane protein self-assembly. Our experiments revealed that a reduction of plasma membrane cholesterol strongly promoted EphA2 self-assembly. Indeed, low cholesterol caused a similar effect to the EphA2 ligand ephrinA1-Fc. These results indicate that cholesterol inhibits EphA2 assembly. Phosphorylation studies in different cell lines revealed that low cholesterol increased phospho-serine levels, the signature of oncogenic signaling. Investigation of the mechanism that cholesterol uses to inhibit the assembly and activity of EphA2 indicate an in-trans effect, where EphA2 is phosphorylated by protein kinase A downstream of beta-adrenergic receptor activity, which cholesterol also inhibits. Our study not only provides new mechanistic insights on EphA2 oncogenic function, but also suggests that cholesterol acts as a molecular safeguard mechanism that prevents uncontrolled self-assembly and activation of EphA2.