bluuebunny/crossref_metadata_2025_split huggingface dataset:
Identification and validation of aging-related genes in heart failure based on multiple machine learning algorithms
Authors: Yu Yiding , Wang Lin , Hou Wangjun , Xue Yitao , Liu Xiujuan , Li Yan 
Published: December 2023

BackgroundIn the face of continued growth in the elderly population, the need to understand and combat age-related cardiac decline becomes even more urgent, requiring us to uncover new pathological and cardioprotective pathways.MethodsWe obtained the aging-related genes of heart failure through WGCNA and CellAge database. We elucidated the biological functions and signaling pathways involved in heart failure and aging through GO and KEGG enrichment analysis. We used three machine learning algorithms: LASSO, RF and SVM-RFE to further screen the aging-related genes of heart failure, and fitted and verified them through a variety of machine learning algorithms. Finally, we searched for drugs to treat age-related heart failure through the DSigDB database.ResultsWe obtained 57 up-regulated and 195 down-regulated aging-related genes in heart failure through WGCNA and CellAge databases. GO and KEGG enrichment analysis showed that aging-related genes are mainly involved in mechanisms such as Cellular senescence and Cell cycle. We further screened aging-related genes through machine learning and obtained 14 key genes. We verified the results on the test set and 2 external validation sets using 15 machine learning algorithm models and 207 combinations, and the highest accuracy was 0.911. Through screening of the DSigDB database, we believe that rimonabant and lovastatin have the potential to delay aging and protect the heart.ConclusionsWe identified aging signature genes and potential therapeutic drugs for heart failure through bioinformatics and multiple machine learning algorithms, providing new ideas for studying the mechanism and treatment of age-related cardiac decline.
AhR ligands from LGG metabolites promote piglet intestinal ILC3 activation and IL-22 secretion to inhibit PEDV infection
Authors: Wang Junhong , Zhao Yibo , Cui Tong , Bao Hongyu , Gao Ming , Cheng Mingyang , Sun Yu , Lu Yiyuan , Guan Jiayao , Zhang Di , Jiang Yanlong , Huang Haibin , Shi Chunwei , Wang Jianzhong , Wang Nan , Hu Jingtao , Yang Wentao , Yang Guilian , Zeng Yan , Wang Chunfeng , Cao Xin 
Published: December 2023

AbstractIn maintaining organismal homeostasis, gut immunity plays a crucial role. The coordination between the microbiota and the immune system through bidirectional interactions regulates the impact of microorganisms on the host. Our research focused on understanding the relationship between substantial changes in jejunal intestinal flora and metabolites and intestinal immunity during porcine epidemic diarrhea virus (PEDV) infection in piglets. We discovered thatLactobacillus rhamnosus GG(LGG) could effectively prevent PEDV infection in piglets. Further investigation revealed thatLGGmetabolites interact with type 3 innate lymphoid cells (ILC3) in the jejunum of piglets through the aryl hydrocarbon receptor (AhR). This interaction promotes the activation of ILC3 cells and the production of interleukin-22 (IL-22). Subsequently, IL-22 facilitates the proliferation of IPEC-J2 cells and activates the STAT3 signaling pathway, thereby preventing PEDV infection. Moreover, the AhR receptor exerts its influence on various cell types within organoids, including intestinal stem cells (ISCs), Paneth cells, and enterocytes, fostering their growth and development, suggesting a broad impact of AhR on intestinal health. In conclusion, our study demonstrates the ability ofLGGto modulate intestinal immunity and effectively prevent PEDV infection in piglets. These findings highlight the potential application ofLGGas a preventive measure against viral infections in livestock.
Automation of Experimental Workflows for High Throughput Robotic Cultivations
Authors: Kaspersetz L. , Schröder-Kleeberg F. , Mione F. M. , Martinez E. C. , Neubauer P. , Cruz-Bournazou M. N. 
Published: December 2023

ABSTRACTProcess systems engineering methods and tools have been difficult to apply in bioprocess engineering, mainly due to the high complexity of biological systems and the low reproducibility of the experiments. High throughput robotic cultivation platforms in combination with computational tools for experimental design, resource scheduling, and operation, are rapidly gaining popularity. One important contribution being the generation of data in high throughput needed to overcome this lack of data with high information content and the worrying reproducibility crisis in life sciences. In this work, directed acyclic graphs are used to represent, manage and track all experimental workflows in a robotic platform. They support data provenance and enable traceability and reproducibility of workflows in robotic facilities. The experimental workflows are automated using Apache Airflow enabling to manage all necessary steps for fed-batch cultivations, including sampling, sample transport by a mobile robot, feed additions, data collection, storage in a SQL database and model fitting. The added value of this system is demonstrated in scale-down experiments, whereE. coliBL21 (DE3), producing elastin like proteins, exhibits robustness towards glucose oscillations that mimic industrial cultivation conditions.
Make or break: The influence of expected challenges and rewards on the motivation and experience associated with cognitive effort exertion
Authors: Zhang Yue , Leng Xiamin , Shenhav Amitai 
Published: December 2023

AbstractChallenging goals can induce harder work but also greater stress, in turn potentially undermining goal achievement. We sought to examine how mental effort and subjective experiences thereof interact as a function of challenge level and the size of the incentives at stake. Participants performed a task that rewarded individual units of effort investment (correctly performed Stroop trials) but only if they met a threshold number of correct trials within a fixed time interval (challenge level). We varied this challenge level (Study 1, N = 40), and the rewards at stake (Study 2, N = 79), and measured variability in task performance and self-reported affect across task intervals. Greater challenge and higher rewards facilitated greater effort investment but also induced greater stress, while higher rewards (and lower challenge) simultaneously induced greater positive affect. Within intervals, we observed an initial speed up then slowdown in performance, which could reflect dynamic reconfiguration of control. Collectively, these findings further our understanding of the influence of task demands and incentives on mental effort exertion and wellbeing.
Low-intensity pulsed ultrasound stimulation (LIPUS) modulates microglial activation following intracortical microelectrode implantation
Authors: Li Fan , Gallego Jazlyn , Tirko Natasha N , Greaser Jenna , Bashe Derek , Patel Rudra , Shaker Eric , Van Valkenburg Grace E , Alsubhi Alanoud S , Wellman Steven , Singh Vanshika , Padill Camila Garcia , Gheres Kyle W. , Bagwell Roger , Mulvihill Maureen , Kozai Takashi D.Y. 
Published: December 2023

AbstractMicroglia are important players in surveillance and repair of the brain. Their activation mediates neuroinflammation caused by intracortical microelectrode implantation, which impedes the application of intracortical brain-computer interfaces (BCIs). While low-intensity pulsed ultrasound stimulation (LIPUS) can attenuate microglial activation, its potential to modulate the microglia-mediated neuroinflammation and enhance the bio-integration of microelectrodes remains insufficiently explored. We found that LIPUS increased microglia migration speed from 0.59±0.04 to 1.35±0.07 µm/hr on day 1 and enhanced microglia expansion area from 44.50±6.86 to 93.15±8.77 µm2/min on day 7, indicating improved tissue healing and surveillance. Furthermore, LIPUS reduced microglial activation by 17% on day 6, vessel-associated microglia ratio from 70.67±6.15 to 40.43±3.87% on day 7, and vessel diameter by 20% on day 28. Additionally, microglial coverage of the microelectrode was reduced by 50% in week 1, indicating better tissue-microelectrode integration. These data reveal that LIPUS helps resolve neuroinflammation around chronic intracortical microelectrodes.
Predicting the Purity of Multispecific Antibodies From Sequence Using Machine Learning: Methods and Applications
Authors: Mazurek Aviva , Davis Athena , Comeau Stephen R. , Tsang Kenny , Rivera Javier , Huang Zhong-Fu , Holt John , Kumar Sandeep , Kasturirangan Srinath 
Published: December 2023

AbstractMultispecific antibodies are prominent therapeutic agents, but many molecular formats and drug candidates that show promise during molecular discovery stages cannot be scaled up and developed into drugs due to inadequate developability. During the discovery stages, the selection of molecule format(s), molecule design, purity, and initial physiochemical stability testing criteria largely rely on scientists’ experience. Machine learning, however, can identify hidden trends in large datasets, aiding in the selection of drug candidates with improved developability. In this study, we present a machine learning approach to predict antibody purity, measured by the percentage of monomer after protein A purification. Using the amino acid sequences of variable regions, molecular formats, germlines and germline pairings, and calculated physiochemical properties as inputs, machine learning models were trained to predict the percentage of monomer for a given multispecific antibody (Figure 1). The dataset employed in this study consists of ∼500 multi-specific antibodies generated during BI’s internal drug discovery programs. Our results indicate that machine learning, when applied to sequence, germline, and format data, can effectively predict antibody percentage of monomer. Incorporating this approach into high-throughput multispecific antibody screening processes can save time and resources by reducing the need to test a large subset of potentially unstable antibodies. While this study focused on percentage of monomer as a test case, similar approaches can be employed to predict other antibody properties, such as melting temperature (Tm), hydrophobicity (aHIC), and solution stability properties (AC-SINS).
blik: an extensible napari plugin for cryo-ET data visualisation, annotation and analysis
Authors: Gaifas Lorenzo , Timmins Joanna , Gutsche Irina 
Published: December 2023

AbstractPowerful, workflow-agnostic and interactive visualisation is essential for the ad-hoc, human-in-the-loop workflows typical of cryo-electron tomography (cryo-ET). While several tools exist for visualisation and annotation of cryo-ET data, they are often integrated as part of monolithic processing pipelines, or focused on a specific task and offering limited reusability and extensibility. With each software suite presenting its own pros and cons and often tools tailored to address specific challenges, seamless integration between available pipelines is often a difficult task. As part of the effort to enable such flexibility and move the software ecosystem towards a more collaborative and modular approach, we developedblik, an open-sourcenapariplugin for visualisation and annotation of cryo-ET data (source code:https://github.com/brisvag/blik).blikoffers fast, interactive, and user-friendly 3D visualisation thanks tonapari, and is built with extensibility and modularity at the core. Data is handled and exposed through well-established scientific Python libraries such asnumpyarrays andpandasdataframes. Reusable components (such as data structures, file read/write, and annotation tools) are developed as independent Python libraries to encourage reuse and community contribution. By easily integrating with established image analysis tools – even outside of the cryo-ET world –blikprovides a versatile platform for interacting with cryo-ET data. On top of core visualisation features – interactive and simultaneous visualisation of tomograms, particle picks and segmentations –blikprovides an interface for interactive tools such as manual particle picking, surface-based and filament-based particle picking and image segmentation, as well as simple filtering tools. Additional self-containednapariplugins developed as part of this work also implement interactive plotting and selection based on particle features, and label interpolation for easier segmentation. Finally, we highlight the differences with existing software and showcaseblik’s applicability in biological research.
The Effects of Physical and Mental Fatigue on Time Perception
Authors: Goudini Reza , Zahiri Ali , Alizaheh Shahab , Drury Benjamin , Anvar Saman Hadjizadeh , Daneshjoo Abdolhamid , Behm David G 
Published: December 2023

AbstractThe subjective perception of time holds a foundational significance within the realm of human psychology and our conceptualizations of reality and how we elucidate the chronological progression of events within our lives. While there have been some studies examining the effects of exercise on time perception during the exercise period, there are no studies investigating the effects of fatiguing exercise on time perception after the exercise intervention. This study aimed to investigate the effects of physical and mental fatigue on time estimates over 30-seconds (5-, 10-, 20-, and 30-seconds) immediately after exercise and 6-minutes after the post-test. Seventeen healthy and recreationally active volunteers (14 males, 3 females) were subjected to three conditions: physical fatigue, mental fatigue, and control. All participants completed a familiarization and three 30-minute experimental conditions (control, physical fatigue (cycling at 65% peak power output), and mental fatigue (Stroop task for 1100 trials) on separate days. Heart rate and body temperature were recorded at the pre-test, start, 5-, 10-, 20-, 30-seconds of the interventions, post-test, and 6-min follow-up. Rating of perceived exertion (RPE) was recorded four times during the intervention. Time perception was measured prospectively (at 5-, 10-, 20-, and 30-seconds) at the pre-test, post-test, and 6-minute follow-up. Physical fatigue significantly (p=0.001) underestimated time compared to mental fatigue and control conditions at the post-test and follow-up, with no significant differences between mental fatigue and control conditions. Heart rate, body temperature, and RPE were significantly (all p=0.001) higher with physical fatigue compared to mental fatigue and control conditions during the intervention and at the post-test. This study demonstrated that cycling-induced fatigue led to time underestimation compared to mental fatigue and control conditions. It is crucial to consider that physical fatigue has the potential to lengthen an individual’s perception of time estimates in sports or work environments.
A Meta-model for ADMET Property Prediction Analysis
Authors: Padi Sarala , Cardone Antonio , Sriram Ram D. 
Published: December 2023

AbstractIn drug discovery analysis chemical absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties play a critical role. These properties allow the quantitative evaluation of a designed drug’s efficacy. Several machine learning models have been designed for the prediction of ADMET properties. However, no single method seems to enable the accurate prediction of these properties. In this paper, we build a meta-model that learns the best possible way to combine the scores from multiple heterogeneous machine learning models to effectively predict the ADMET properties. We evaluate the performance of our proposed model against the Therapeutics Data Commons (TDC) ADMET benchmark dataset. The proposed meta-model outperforms state-of-the-art methods such as XGBoost in the TDC leaderboard, and it ranks first in five and in the top three positions for fifteen out of twenty-two prediction tasks.
Crucial role for Sodium Hydrogen Exchangers in SGLT2 inhibitor-induced arterial relaxations
Authors: Forrester Elizabeth A , Benítez-Angeles Miguel , Redford Kaitlyn E. , Rosenbaum Tamara , Abbott Geoffrey W , Barrese Vincenzo , Dora Kim , Albert Anthony P , Dannesboe Johs , Salles-Crawley Isabelle , Jepps Thomas A , Greenwood Iain A 
Published: December 2023

1AbstractIntroductionSodium dependent glucose transporter 2 (SGLT2 or SLC5A2) inhibitors effectively lower blood glucose and are also approved treatments for heart failure independent of raised glucose. One component of the cardioprotective effect is reduced cardiac afterload but the mechanisms underlying peripheral relaxation are ill defined and variable. We speculated that SGLT2 inhibitors promoted arterial relaxation via the release of the potent vasodilator calcitonin gene-related peptide (CGRP) from sensory nerves independent of glucose transport.Experimental approachThe functional effects of SGLT2 inhibitors (dapagliflozin, empagliflozin, ertugliflozin) and the sodium/hydrogen exchanger 1 (NHE1) blocker cariporide were determined on pre-contracted mesenteric and renal arteries from male Wistar rats using Wire-Myography. SGLT2, NHE1, CGRP and TRPV1 expression in both arteries was determined by Western blot and immunohistochemistry. Kv7.4/5/KCNE4 and TRPV1 currents were measured in the presence and absence of dapagliflozin and empagliflozin.ResultsAll SGLT2 inhibitors produced a concentration dependent relaxation (1µM-100µM) of mesenteric arteries that was considerably greater than in renal arteries. Cariporide relaxed mesenteric arteries but not renal arteries. Immunohistochemistry with TRPV1 and CGRP antibodies revealed a dense innervation of sensory nerves in mesenteric arteries that was absent in renal arteries. Consistent with a greater sensory nerve component, the TRPV1 agonist capsaicin produced significantly greater relaxations in mesenteric arteries compared to renal arteries. Relaxations to dapagliflozin, empagliflozin and cariporide were attenuated by incubation with the CGRP receptor antagonist BIBN-4096, the Kv7 blocker linopirdine and the TRPV1 antagonist AMG-517 as well as by depletion of neuronal CGRP. Neither dapagliflozin nor empagliflozin directly activated heterologously expressed TRPV1 channels or Kv7 channels. Strikingly, only NHE1 colocalised with TRPV1 in sensory nerves, and cariporide pre-application prevented the relaxant response to SGLT2 inhibitors.ConclusionsSGLT2 inhibitors relax mesenteric arteries by a novel mechanism involving the release of CGRP from sensory nerves following inhibition of the Na+/H+exchanger.
Exploring the neuronal and systemic physiological contributions to spontaneous cerebral fluctuations – Insights from functional MRI in The Maastricht Study
Authors: Vergoossen L.W.M. , Jansen J.F.A. , Schram M.T. , Jong J.J.A. de , Boxtel M.P.J. van , Reulen J.P.H. , Houben A.J.H.M. , Greevenbroek M.M.J. , Huybrechs D. , Backes W.H. 
Published: December 2023

AbstractObjectiveFunctional MRI (fMRI) is sensitive to changes in the blood oxygen level-dependent (BOLD) signal, which originates from neurovascular coupling, the mechanism that links neuronal activity to changes in cerebral blood flow. Isolating the native spontaneous neuronal fluctuations from the BOLD signal of the resting-state is challenging, as the signal induced by neuronal activity represents only a small part of this signal. Furthermore, many other non-neuronal (systemic) oscillations contribute, such as from the cardiovascular and respiratory system with frequencies partly overlapping those of the spontaneous fluctuations. The objective of this study is to investigate to what extent various systemic physiological signals are associated with the measured BOLD signal, in particular the frequency interval pertaining to the spontaneous cerebral fluctuations (10-100 mHz). Additionally, we investigate whether these associations were independent of cardiometabolic risk factors.MethodsWithin the population-based Maastricht Study, 3T resting-state functional MRI and physiological measures, covering cardiac, respiratory, myogenic, neurogenic, and endothelial activity, were acquired (n=1,651, 48% woman, aged 59±8 years). As both neuronal and non-neuronal physiological signals contain frequencies that vary over time, a wavelet transformation (WT) was used. Time-series were decomposed into seven wavelet subbands, and for each subband, the energy of the BOLD signal was calculated. Multivariable linear regression analysis was used to investigate the association of the physiological measures, in particular cognitive function, with the wavelet energy per subband, independent of cardiometabolic risk factors.ResultsWe found that physiological measures were associated with the energy of certain frequency subbands of the spectrum of the measured fMRI signal. Both cognitive performance and blood pressure variations, as measures of neurogenic and myogenic activity respectively, were associated with the energy of the frequency subband 3 (31.2-62.5 mHz). Furthermore, cardiac and respiratory activity were associated with the energy of the high frequency subband 1 (>125 mHz), and endothelial activity with the energy of low frequency subbands 6 and 7 (<10 mHz). Part of these associations were dependent on cardiometabolic risk factors.ConclusionWe found an association between myogenic and neurogenic activity and the frequency specific BOLD signal. Our findings highlight the strong intertwining of neuronal, vascular, and cardiometabolic activity and emphasize the importance of a proper selection of the resting-state frequency range in fMRI studies on cognitive function.
RCT-Twin-GAN Generates Digital Twins of Randomized Control Trials Adapted to Real-world Patients to Enhance their Inference and Application
Authors: Thangaraj Phyllis M. , Shankar Sumukh Vasisht , Oikonomou Evangelos K. , Khera Rohan 
Published: December 2023

AbstractBackgroundRandomized clinical trials (RCTs) are designed to produce evidence in selected populations. Assessing their effects in the real-world is essential to change medical practice, however, key populations are historically underrepresented in the RCTs. We define an approach to simulate RCT-based effects in real-world settings using RCT digital twins reflecting the covariate patterns in an electronic health record (EHR).MethodsWe developed a Generative Adversarial Network (GAN) model,RCT-Twin-GAN, which generates a digital twin of an RCT (RCT-Twin) conditioned on covariate distributions from an EHR cohort. We improved upon a traditional tabular conditional GAN,CTGAN, with a loss function adapted for data distributions and by conditioning on multiple discrete and continuous covariates simultaneously. We assessed the similarity between a Heart Failure with preserved Ejection Fraction (HFpEF) RCT (TOPCAT), a Yale HFpEF EHR cohort, and RCT-Twin. We also evaluated cardiovascular event-free survival stratified by Spironolactone (treatment) use.ResultsBy applyingRCT-Twin-GANto 3445 TOPCAT participants and conditioning on 3445 Yale EHR HFpEF patients, we generated RCT-Twin datasets between 1141-3445 patients in size, depending on covariate conditioning and model parameters. RCT-Twin randomly allocated spironolactone (S)/placebo (P) arms like an RCT, was similar to RCT by a multi-dimensional distance metric, and balanced covariates (median absolute standardized mean difference (MASMD) 0.017, IQR 0.0034-0.030). The 5 EHR-conditioned covariates in RCT-Twin were closer to the EHR compared with the RCT (MASMD 0.008 vs 0.63, IQR 0.005-0.018 vs 0.59-1.11). RCT-Twin reproduced the overall effect size seen in TOPCAT (5-year cardiovascular composite outcome odds ratio (95% confidence interval) of 0.89 (0.75-1.06) in RCT vs 0.85 (0.69-1.04) in RCT-Twin).ConclusionsRCT-Twin-GANsimulates RCT-derived effects in real-world patients by translating these effects to the covariate distributions of EHR patients. This key methodological advance may enable the direct translation of RCT-derived effects into real-world patient populations and may enable causal inference in real-world settings.