bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Machine Learning-based approach for Simultaneous Detection of Interfering Analytes in Electrochemical Nanobiosensors
Authors: Jain Ritwik , Verma Srishti , Dutta Gorachand 
Published: March 2024

AbstractElectrochemical biosensors can be used to detect analytes of importance precisely. These sensors generate rapid and accurate electrical signals that reveal the presence and concentration of the targeted analyte. Detecting multiple analytes simultaneously with an electrochemical biosensor is advantageous. It provides cost and time efficiency, multiplexing capability, and flexibility, making it valuable in diverse applications such as medical diagnostics, environmental monitoring, and industrial processes. However, simultaneous detection of analytes may suffer from the problem of interference. The interference effect causes the signal of an analyte at a particular concentration to deviate from the expected one. We observe a similar effect in the simultaneous detection of Folic Acid and Uric Acid using a nanomaterial-based electrochemical sensor. To address this effect, we propose a machine learning (ML) approach. ML algorithms handle complex interactions by autonomously identifying patterns, dependencies, and nonlinear relationships within data, enabling it to make predictions and decisions in intricate and dynamic scenarios. Our approach can be generalised to any two analytes showing interference and would scale well to interference between multiple analytes. We test several regression algorithms and compare their performance to the standard calibration plot method. As compared to the standard method, our approach shows a 4.49 µM decrease in concentration prediction error.
Type I interferon alters invasive extravillous trophoblast function
Authors: Simoni Michael K. , Negatu Seble G. , Park Ju Young , Mani Sneha , Arreguin Montserrat C. , Amses Kevin , Huh Dan Dongeun , Mainigi Monica , Jurado Kellie A. 
Published: March 2024

ABSTRACTInappropriate type I interferon (IFN) signaling during embryo implantation and placentation is linked to poor pregnancy outcomes. Here, we evaluated the consequence of elevated type I IFN exposure on implantation using a biomimetic model of human implantation in an organ-on-a-chip device. We found that type I IFN reduced extravillous trophoblast (EVT) invasion capacity. Analyzing single-cell transcriptomes, we uncovered that IFN truncated endovascular EVT emergence in the implantation-on-a-chip device by stunting EVT epithelial-to-mesenchymal transition. Disruptions to the epithelial-to-mesenchymal transition is associated with the pathogenesis of preeclampsia, a life-threatening hypertensive disorder of pregnancy. Strikingly, unwarranted IFN stimulation induced genes associated with increased preeclampsia risk and a preeclamptic gene-like signature in EVTs. These dysregulated EVT phenotypes ultimately reduced EVT-mediated endothelial cell vascular remodeling in the implantation-on-a-chip device. Overall, our work indicates IFN signaling can alter EVT epithelial-to-mesenchymal transition progression which results in diminished EVT-mediated spiral artery remodeling and a preeclampsia gene signature upon sustained stimulation. Our work implicates unwarranted type I IFN as a maternal disturbance that can result in abnormal EVT function that could trigger preeclampsia.
Incidence, risk factors, and clinical symptom profile of reinfection during Omicron-dominated COVID-19 outbreak in Hong Kong: A retrospective cohort study
Authors: Huang Ziyang , Luo Jingyuan , Li Haoran , Cheng Xingye , Zhang Jialing , Tang Hiu To , Wong Hoi Ki , Cheung Chun Hoi , Bian Zhaoxiang , Lyu Aiping , Tian Liang 
Published: March 2024

AbstractBackgroundDespite the World Health Organization’s declaration of the end of the COVID-19 pandemic, reinfection persists and continues to strain the global healthcare system. With the emergence of the most recent variant of SARS-CoV-2 named JN.1, retrospective analysis of epidemiological characteristics of previous cases involving the Omicron variant is essential to provide references for preventing reinfection caused by the ongoing new SARS-Cov-2 variants.MethodsThis retrospective cohort study included 6325 patients infected with SARS-CoV-2 during the Omicron-dominated outbreak (from December 2021 to May 2022) in Hong Kong. Statistical analysis was conducted to demonstrate the epidemiological characteristics and a logistic regression model was utilized to identify risk factors associated with reinfection.ResultsThe Omicron reinfection incidence was 5.18% (n = 353). No significant difference was observed in receiving mRNA (BNT162b2) vaccine and inactivated (CoronaVac) vaccine between reinfection and non-reinfection groups (p>0.05). Risk factors were identified as female gender (p<0.001), longer infection duration (p<0.05), comorbidity of eyes, ear, nose, throat disease (p<0.01), and severe post-infection impact on daily life and work (p<0.05), while ≥70 years old (p<0.05) and vaccination after primary infection (p<0.01) were associated with a lower risk of reinfection. The prevalence of most symptoms after reinfection was lower than the first infection, except for fatigue.ConclusionNo significant difference in mRNA (BNT162b2) vaccine and inactivated (CoronaVac) vaccine against reinfection. Post-infection vaccination could lower the risk of reinfection, which potentially inform the development of preventive measures including vaccination policies against potential new SARS-Cov-2 variants.
Artificial intelligence-enhanced risk stratification of cancer therapeutics-related cardiac dysfunction using electrocardiographic images
Authors: Oikonomou Evangelos K. , Sangha Veer , Dhingra Lovedeep S. , Aminorroaya Arya , Coppi Andreas , Krumholz Harlan M. , Baldassarre Lauren A. , Khera Rohan 
Published: March 2024

ABSTRACTBackgroundRisk stratification strategies for cancer therapeutics-related cardiac dysfunction (CTRCD) rely on serial monitoring by specialized imaging, limiting their scalability.ObjectivesTo examine an artificial intelligence (AI)-enhanced electrocardiographic (AI-ECG) surrogate for imaging risk biomarkers, and its association with CTRCD.MethodsAcross a five-hospital U.S.-based health system (2013-2023), we identified patients with breast cancer or non-Hodgkin lymphoma (NHL) who received anthracyclines (AC) and/or trastuzumab (TZM), and a control cohort receiving immune checkpoint inhibitors (ICI). We deployed a validated AI model of left ventricular systolic dysfunction (LVSD) to ECG images (≥0.1, positive screen) and explored its association with i) global longitudinal strain (GLS) measured within 15 days (n=7,271 pairs); ii) future CTRCD (new cardiomyopathy, heart failure, or left ventricular ejection fraction [LVEF]<50%), and LVEF<40%. In the ICI cohort we correlated baseline AI-ECG-LVSD predictions with downstream myocarditis.ResultsHigher AI-ECG LVSD predictions were associated with worse GLS (−18% [IQR:-20 to −17%] for predictions<0.1, to −12% [IQR:-15 to −9%] for ≥0.5 (p<0.001)). In 1,308 patients receiving AC/TZM (age 59 [IQR:49-67] years, 999 [76.4%] women, 80 [IQR:42-115] follow-up months) a positive baseline AI-ECG LVSD screen was associated with ∼2-fold and ∼4.8-fold increase in the incidence of the composite CTRCD endpoint (adj.HR 2.22 [95%CI:1.63-3.02]), and LVEF<40% (adj.HR 4.76 [95%CI:2.62-8.66]), respectively. Among 2,056 patients receiving ICI (age 65 [IQR:57-73] years, 913 [44.4%] women, follow-up 63 [IQR:28-99] months) AI-ECG predictions were not associated with ICI myocarditis (adj.HR 1.36 [95%CI:0.47-3.93]).ConclusionAI applied to baseline ECG images can stratify the risk of CTRCD associated with anthracycline or trastuzumab exposure.CONDENSED ABSTRACTThere is an unmet need for scalable and affordable biomarkers to stratify the risk of cancer therapeutics-related cardiac dysfunction (CTRCD). In this hospital system-based, decade-long cohort of patients without cardiomyopathy receiving anthracyclines or trastuzumab, a validated artificial intelligence algorithm applied to baseline electrocardiographic (AI-ECG) images identified individuals with a 2-fold and 4.8-fold risk of developing any cardiomyopathy or left ventricular ejection fraction <40%, respectively. This supports a role for AI-ECG interpretation of images as a scalable approach for the baseline risk stratification of patients initiating cardiotoxic chemotherapy.
Local, calcium- and reward-based synaptic learning rule that enhances dendritic nonlinearities can solve the nonlinear feature binding problem
Authors: Khodadadi Zahra , Trpevski Daniel , Lindroos Robert , Hellgren Kotaleski Jeanette 
Published: March 2024

AbstractThis study investigates the computational potential of single striatal projection neurons (SPN), emphasizing dendritic nonlinearities and their crucial role in solving complex integration problems. Utilizing a biophysically detailed multicompartmental model of an SPN, we introduce a calcium-based, local synaptic learning rule dependent on dendritic plateau potentials. According to what is known about excitatory corticostriatal synapses, the learning rule is governed by local calcium dynamics from NMDA and L-type calcium channels and dopaminergic reward signals. In order to devise a self-adjusting learning rule, which ensures stability for individual synaptic weights, metaplasticity is also used. We demonstrate that this rule allows single neurons to solve the nonlinear feature binding problem, a task traditionally attributed to neuronal networks. We also detail an inhibitory plasticity mechanism that contributes to dendritic compartmentalization, further enhancing computational efficiency in dendrites. Thisin silicostudy highlights the computational potential of single neurons, providing deeper insights into neuronal information processing and the mechanisms by which the brain executes complex computations.
NeuroFold: A Multimodal Approach to Generating Novel Protein Variantsin silico
Authors: Amani Keaun , Fish Michael , Smith Matthew D. , Castroverde Christian Danve M. 
Published: March 2024

AbstractThe generation of high-performance enzyme variants with desired physicochemical and functional properties presents a formidable challenge in the field of protein engineering. Existingin silicodesign methods are limited by inadequate training data, insufficient diversity within datasets, and suboptimal sampling techniques. Here, we introduce a novel approach that addresses these limitations and significantly improves the efficiency of generating functional enzyme variants. Using a multimodal approach, NeuroFold can leverage sequence, structural, and homology data during both sampling and discrimination phases, thereby enabling more diverse and informed sampling of the sequence space. Our model demonstrated a 40-fold increase in Spearman rank correlation as compared to large language models (LLMs) such as ESM-1v and empowers the rapid creation of high-quality enzyme variants, such as the β-lactamase variants generated by NeuroFold in this study, which demonstrated increased thermostability and varying levels of activity. This pipeline represents a promising advancement in the field of enzyme engineering, offering a valuable tool for the development of novel enzymes with enhanced performance and desired chemical properties.
The best of two worlds: using stacked generalization for integrating expert range maps in species distribution models
Authors: Oeser Julian , Zurell Damaris , Mayer Frieder , Çoraman Emrah , Toshkova Nia , Deleva Stanimira , Natradze Ioseb , Benda Petr , Ghazaryan Astghik , Irmak Sercan , Hasanov Nijat Alisafa , Guliyeva Gulnar Gahraman , Gritsina Mariya , Kuemmerle Tobias 
Published: March 2024

AbstractSpecies distribution models (SDMs) are powerful tools for assessing suitable habitat across large areas and at fine spatial resolution. Yet, the usefulness of SDMs for mapping species’ realized distributions is often limited, since data biases or missing information on dispersal barriers or biotic interactions hinder them from accurately delineating species’ range limits. One way to overcome this limitation is to integrate SDMs with expert range maps, which provide coarse-scale information on the extent of species’ ranges that is complementary to information offered by SDMs. Here, we propose a new approach for integrating expert range maps in SDMs based on an ensemble method called stacked generalization. Specifically, our approach relies on training a meta-learner regression model using predictions from one or more SDM algorithms alongside the distance of training points to expert-defined ranges as predictor variables. We demonstrate our approach with an occurrence dataset for 49 bat species covering four biodiversity hotspots in the Eastern Mediterranean, Western Asia, and Central Asia. Our approach offers a flexible method to integrate expert range maps with any combination of SDM modeling algorithms, thus facilitating the use of algorithm ensembles. In addition, it provides a novel, data-driven way to account for uncertainty in expert-defined ranges not requiring prior knowledge about their accuracy, which is often lacking. Our approach holds considerable promise for better understanding species distributions, and thus for biogeographical research and conservation planning. In addition, our work highlights the overlooked potential of stacked generalization as an ensemble method in species distribution modeling.
Natural soil suppressiveness against soilborne phytopathogens extends to the control of insect pest
Authors: Harmsen Nadine , Vesga Pilar , Glauser Gaétan , Klötzli Françoise , Heiman Clara M. , Altenried Aline , Vacheron Jordan , Muller Daniel , Moënne-Loccoz Yvan , Steinger Thomas , Keel Christoph , Garrido-Sanz Daniel 
Published: March 2024

AbstractSince the 1980s, soils in a 22-km2area near Lake Neuchâtel in Switzerland have been recognized for their innate ability to suppress the black root rot plant disease. Their efficacy against insect pests has not been studied. We demonstrate that natural soil suppressiveness also protects plants from the leaf-feeding pest insectOulema melanopus. Plants grown in the most suppressive soil have a reduced stress response toOulemafeeding, reflected by dampened levels of herbivore defense-related phytohormones and benzoxazinoids, and enhanced salicylate levels in plants without the insect indicate defense-priming. The rhizosphere microbiome network of the suppressive soils was highly tolerant to the destabilizing impact of insect exposure. The presence of plant-beneficial bacteria in the suppressive soils along with priming conferred plant resistance to the insect pest, manifesting also in the onset of insect microbiome dysbiosis. This intricate soil-plant-insect feedback extends natural soil suppressiveness from soilborne diseases to insect pests.
ACLASP1variant suggests a phenotypic relation with lissencephaly in humans
Authors: Alsafeh Rawan , AlHashem Amal , Elsayed Aly , Yüksel Zafer , Graies-Tlili Kalthoum , Thabet Farah , Tabarki Brahim 
Published: March 2024

AbstractLissencephaly is a severe brain developmental disorder; characterized by reduced brain folding due to defective neuronal migration. This study investigates the genetic basis of lissencephaly in a consanguineous family, focusing on theCLASP1gene. Whole-exome sequencing identified a novel homozygous variant (c.4442G>A p.(Arg1481His)) inCLASP1. Clinical evaluation revealed severe developmental delays, microcephaly, seizures, and lissencephaly in the affected siblings. The variant was heterozygous in the healthy parents and a heterozygous carrier in an unaffected sibling. This study underscores the role of CLASP1 in brain development and suggests that the identified variant disrupts CLASP1 interaction with the microtubule cytoskeleton, contributing to lissencephaly pathogenesis.
The β2-adrenergic receptor (ADRB2) entrains circadian gene oscillation and diurnal responses to virus infection in CD8+T cells
Authors: Sharma Drashya , Kohlbach Kira A. , Maples Robert , Farrar J. David 
Published: March 2024

AbstractAdaptive immune cells are regulated by circadian rhythms (CR) under both steady state conditions and during responses to infection. Cytolytic CD8+T cells display variable responses to infection depending upon the time of day of exposure. However, the neuronal signals that entrain these cyclic behaviors remain unknown. Immune cells express a variety of neurotransmitter receptors including nicotinic, glucocorticoid, and adrenergic receptors. Here, we demonstrate that the β2-adrenergic receptor (ADRB2) regulates the periodic oscillation of select core clock genes, such asPer2andBmal1, and selective loss of theAdrb2gene dramatically perturbs the normal diurnal oscillation of clock gene expression in CD8+T cells. Consequently, their circadian-regulated anti-viral response is dysregulated, and the diurnal development of CD8+T cells into variegated populations of cytolytic T cell (CTL) effectors is dramatically altered in the absence of ADRB2 signaling. Thus, theAdrb2directly entrains core clock gene oscillation and regulates CR-dependent T cell responses to virus infection as a function of time-of-day of pathogen exposure.One Sentence SummaryThe β2-adrenergic receptor regulates circadian gene oscillation and downstream daily timing of cytolytic T cell responses to virus infection.
Emodin: an alveolar macrophage protector in acute pancreatitis induced lung injury
Authors: Chen Zhe , Dong Xuanchi , Song Yongwei , Lan Bowen , Luo Yalan , Wen Haiyun , Li Hailong , Chen Hailong 
Published: March 2024

AbstractEmodin (EMO), an anthraquinone derivative from roots and leaves of various plants, has been widely used in many inflammatory diseases. Alveolar macrophages (AMs) play a critical role in maintaining alveolar homeostasis in the lung. To investigate the pathophysiological mechanism of AMs in acute pancreatitis-associated lung injury (AP-ALI) and the potential protective therapeutic of EMO for AP-ALI, AMs isolated from AP-ALI mice, murine cell line MH-S and RAW264.7 were pre-treated with EMO to assess its protective roles on regulating inflammation, pyroptosis, and mitophagy of macrophages in AP-ALI. The results showed that 1) in vivo, the relative quantity of AMs was significantly decreased across the time in API-ALI; however, the mitochondria flux presented earlier changes than the resident AMs alteration in our experimental system. EMO pretreatment significantly alleviated the severity of lung injury and improved the damaged alveolar structure, and further reversed the reduction of mitochondria impairment in AMs in mice. 2) In vitro, EMO significantly suppressed LPS/ATP associated NLRP3 inflammasome activation, enhanced mitophagy, and protected mitochondria damage. Furthermore, the data of mitophagy inhibition by 3-MA demonstrated that EMO protective effects were partially via manipulating mitophagy-mitochondria-alveolar macrophage axis. Our data shed light on the comprehensive understanding on EMO therapeutics in AP-ALI.
Hearing loss at 6-monthly assessments from age 12 to 36 months: secondary outcomes from randomised controlled trials of novel pneumococcal conjugate vaccine schedules
Authors: Leach A.J. , Wilson N. , Arrowsmith B. , Beissbarth J. , Mulholland E.K. , Santosham M. , Torzillo P.J. , McIntyre P. , Smith-Vaughan H. , Skull S.A. , Oguoma V.M. , Chatfield M.D. , Lehmann D. , Brennan-Jones C. G. , Binks M. J. , Licciardi P.V. , Andrews R. , Snelling T. , Krause V. , Carapetis J. , Chang A.B. , Morris P.S. 
Published: March 2024

AbstractIntroductionIn remote communities, Australian First Nations children with hearing loss are disproportionately at risk of poor school readiness and performance, compared to those with normal hearing. Our objective was to compare two pneumococcal conjugate vaccine (PCV) formulations and mixed schedules (the PREVIX trials) designed to broaden protection and reduce conductive hearing loss to age 36 months.MethodsIn two sequential parallel, open-label, randomised controlled trials, eligible infants were first allocated 1:1:1 at age 28-38 days to standard or mixed PCV primary schedules, then at age 12 months to a booster dose (1:1) of PCV13 (13-valent pneumococcal conjugate vaccine, +P) or PHiD-CV10 (10-valent pneumococcalHaemophilus influenzaeprotein D conjugate vaccine, +S). Here we report secondary hearing outcomes in the +P and +S groups at 6-monthly scheduled assessments from age 12 to 36 months.FindingsFrom March 2013 to September 2018, 461 hearing assessments were performed. Prevalence of mild-moderate hearing loss declined in both groups from ∼75% at age 12 months to ∼53% at 36 months. At primary endpoint age 18 months, prevalence of moderate (disabling) hearing loss was 21% and 41% in the +P and +S groups, respectively (difference −19% [95% confidence interval −38, −1], p=0.07) and prevalence of normal hearing was 36% and 16%, respectively (difference 19% [95%CI 2, 37], p=0.05). At subsequent timepoints prevalence of moderate hearing loss remained lower in the +P group at −3% [95% CI −23, 18] at age 24 months, −12% [95%CI −30, 6] at 30 months, and −9% [95%CI −23, 5] at 36 months.InterpretationThis study provides first evidence of the high prevalence and persistence of mild and moderate hearing loss throughout early childhood. A lower prevalence of moderate (disabling) hearing loss in the +P group may have substantial benefits for high-risk children and warrants further investigation.Trial registrationClinicalTrials.govNCT01735084andNCT01174849https://clinicaltrials.gov/study/NCT01735084https://clinicaltrials.gov/study/NCT01174849FundingNational Health and Medical Research Council of Australia (GNT605810, GNT1046999, GNT1120353)
Sparse haplotype-based fine-scale local ancestry inference at scale reveals recent selection on immune responses
Authors: Yang Yaoling , Durbin Richard , Iversen Astrid K. N. , Lawson Daniel J. 
Published: March 2024

AbstractIncreasingly efficient methods for inferring the ancestral origin of genome regions are needed to gain new insights into genetic function and history as biobanks grow in scale. Here we describe two near-linear time algorithms to learn ancestry harnessing the strengths of a Positional Burrows-Wheeler Transform (PBWT). SparsePainter is a faster, sparse replacement of previous model-based ‘chromosome painting’ algorithms to identify recently shared haplotypes, whilst PBWTpaint uses further approximations to obtain lightning-fast estimation optimized for genome-wide relatedness estimation. The computational efficiency gains of these tools for fine-scale local ancestry inference offer the possibility to analyse large-scale genomic datasets in completely novel ways. Application to the UK Biobank shows that haplotypes better represent ancestries than principal components, whilst linkage-disequilibrium of ancestry identifies signals of recent changes to population-specific selection for many genomic regions associated with immune responses, suggesting new avenues for understanding the pathogen-immune system interplay on a historical timescale.