bluuebunny/crossref_metadata_2025 huggingface dataset:
Automated mood disorder symptoms monitoring from multivariate time-series sensory data: Getting the full picture beyond a single number
Authors: Corponi Filippo , Li Bryan M. , Anmella Gerard , Mas Ariadna , Sanabra Miriam , Vieta Eduard ,   INTREPIBD Group, Lawrie Stephen M. , Whalley Heather C. , Hidalgo-Mazzei Diego , Vergari Antonio
Published: March 2023

Mood disorders are among the leading causes of disease burden worldwide. They manifest with changes in mood, sleep, and motor-activity, observable with physiological data. Despite effective treatments being available, limited specialized care availability is a major bottleneck, hindering preemptive interventions. Nearcontinuous and passive collection of physiological data from wearables in daily life, analyzable with machine learning, could mitigate this problem, bringing mood disorders monitoring outside the doctor’s office. Previous works attempted predicting a single label, e.g. disease state or a psychometric scale total score. However, clinical practice suggests that the same label can underlie different symptom profiles, requiring personalized treatment. In this work we address this limitation by proposing a new task: inferring all items from the Hamilton Depression Rating Scale (HDRS) and the Young Mania Rating Scale (YMRS), the most-widely used standardized questionnaires for assessing depression and mania symptoms respectively, the two polarities of mood disorders. Using a naturalistic, single-center cohort of patients with a mood disorder (N=75), we develop an artificial neural network (ANN) that inputs physiological data from a wearable device and scores patients on HDRS and YMRS in moderate agreement (quadratic Cohen’sκ= 0.609) with assessments by a clinician. We also show that, when using as input physiological data recorded further away from when HDRS and YMRS were collected by the clinician, the ANN performance deteriorates, pointing to a distribution shift, likely across both psychometric scales and physiological data. This suggests the task is challenging and research into domain-adaptation should be prioritized towards real-world implementations.
Efficacy of Endovascular Thrombectomy in the Oldest Old (Stroke 90+ Study): A Community Center Experience
Authors: Brant-Zawadzki Michael , Mastrolia Deborah , Hara Junko , Baker Christopher , Mesipam Avinash , Peck Wallace , Brown David
Published: March 2023

Background

While endovascular thrombectomy (EVT) is considered as the most effective treatment for a select group of patients with acute ischemic stroke and large vessel occlusion, its safety and efficacy in older patients are still debated especially in “read-world” settings. This study reports outcomes of EVT in acute ischemic stroke patients aged 90 and older in our community hospital setting.

Methods

Data between January 2018 and December 2022 were aggregated for all acute ischemic stroke patients with aged 90 and older at the time of EVT. Thirty-one patients met the criteria and were included in this report. The data valuables included, but are not limited to, demographics, stroke risk factors, thrombolysis in cerebral infarction (TICI), modified Rankin Scale (mRS), and NIH Stroke Scale (NIHSS).

Results

All 31 patients had improvement in TICI scale. One had symptomatic intracranial hemorrhage after EVT not related to the procedure, but likely on the basis of reperfusion breakthrough. Three patients expired prior to their discharge from non-stroke related causes. Of remaining 28, four expired, six went into hospice care, and four lost to follow-up by 30-days post DC. Of six hospice cases, one expired by 90-day post DC, and additional three were lost to follow-up. Given this data, 20/27 (74%) survived to 30 days and 16/24 (67%) to 90 days. For their NIHSS symptomatic categories, 15/28 (54%) patients improved, 10/28 (36%) remained the same, and 3/28 (11%) declined. For mRS, at 30-days post DC, 7/24 (30%) patients showed improvement, 7/24 (30%) remained the same, and 10/24 (40%) declined. At 90-days post DC, 7/21 (33%) showed improvement from DC, 4/21 (19%) remained the same, and 10/21 (48%) declined.

Conclusions

While a larger cohort study is necessary, our report supports the safety and efficacy of EVT in this patient aged 90 and older in a real-world setting.
Impact of opinion dynamics on the public health damage inflicted by COVID-19 in the presence of societal heterogeneities
Authors: Ali Rex N. , Sarkar Saswati
Published: March 2023

Certain behavioral practices such as wearing surgical masks, observing social distancing, and accepting vaccines impede the spread of COVID-19 and contain the severity of symptoms in the infected individuals. Opinions regarding whether to observe such behavioral practices evolve over time through interactions via networks that overlap with but are not identical to the physical interaction networks over which the disease progresses. This necessitates the joint study of COVID-19 evolution and opinion dynamics. We develop a mathematical model that can be easily adapted to a wide range of behavioral practices and captures in a computationally tractable manner the joint evolution of the disease and relevant opinions in populations of large sizes. Populations of large sizes are typically heterogeneous in that they comprise individuals of different age groups, genders, races, and underlying health conditions. Such groups have different propensities to imbibe severe forms of the disease, different physical contact, and social interaction patterns and rates. These lead to different disease and opinion dynamics in them. Our model is able to capture such diversities. Computations using our model reveal that opinion dynamics have a strong impact on fatality and hospitalization counts and the number of man-days lost due to symptoms both in the regular form of the disease and the extended forms, more commonly known as long COVID. We show that opinion dynamics in certain groups have a disproportionate impact on the overall public health attributes because they have high physical interaction rates, even when they have the lowest propensity to imbibe severe forms of the disease. This identifies a social vulnerability that mal-actors can utilize to inflict heavy public health damages through opinion campaigns targeting specific segments. Once such vulnerabilities are identified, which we accomplish, adequate precautions may be designed to enhance resilience to such targeted attacks and better protect public health.
Transient loss and recovery of oral chemesthesis, taste and smell with COVID-19: a small case-control series
Authors: Weir Elisabeth M. , Exten Cara , Gerkin Richard C. , Munger Steven D. , Hayes John E.
Published: March 2023

Anosmia is common with respiratory virus infections, but loss of taste or chemesthesis is rare. Reports of true taste loss with COVID-19 were viewed skeptically until confirmed by multiple studies. Nasal menthol thresholds are elevated in some with prior COVID-19 infections, but data on oral chemesthesis are lacking. Many patients recover quickly, but precise timing and synchrony of recovery are unclear. Here, we collected broad sensory measures over 28 days, recruiting adults (18-45 years) who were COVID-19 positive or recently exposed (close contacts per U.S. CDC criteria at the time of the study) in the first half of 2021. Participants received nose clips, red commercial jellybeans (Sour Cherry and Cinnamon), and scratch-n-sniff cards (ScentCheckPro). Among COVID-19 cases who entered the study on or before Day 10 of infection, Gaussian Process Regression showed odor identification and odor intensity (two distinct measures of function) each declined relative to controls (close contacts who never developed COVID-19), but effects were larger for intensity than identification. To assess changes during early onset, we identified four COVID-19 cases who enrolled on or prior to Day 1 of their illness – this allowed for visualization of baseline ratings, loss, and recovery of function over time. Four controls were matched for age, gender, and race. Variables included sourness and sweetness (Sour Cherry jellybeans), oral burn (Cinnamon jellybeans), mean orthonasal intensity of four odors (ScentCheckPro), and perceived nasal blockage. Data were plotted over 28 days, creating panel plots for the eight cases and controls. Controls exhibited stable ratings over time. By contrast, COVID-19 cases showed sharp deviations over time. No single pattern of taste loss or recovery was apparent, implying different taste qualities might recover at different rates. Oral burn was transiently reduced for some before recovering quickly, suggesting acute loss may be missed in data collected after acute illness ends. Changes in odor intensity or odor identification were not explained by nasal blockage. Collectively, intensive daily testing shows orthonasal smell, oral chemesthesis and taste were each altered by acute COVID-19 infection, and this disruption was dyssynchronous for different modalities, with variable loss and recovery rates across modalities and individuals.
Optimized ITS/CITS models for intervention evaluation considering the nonlinear impact of covariates
Authors: Zhang Xiangliang , Yin Rong , Pan Yan , Zhong Wenfang , Kong Di , Chen Wen
Published: March 2023

There is a lack of approaches to evaluate the effectiveness of interventions when there are nonlinear impacts of covariates to the outcome series.

Based on the classic framework of ITS/CITS segmented regression, while considering autocorrelation of time series, we adopted a nonlinear dynamic modeling strategy (Hammerstein) to measure the nonlinear effects of covariates, and proposed four optimized models: ITS-A, CITS-A, ITS-HA, and CITS-HA. To compare the accuracy and precision in estimating the long-term impact of an intervention between the optimized and classic segmented models, we constructed a sequence generator to simulate the outcome series with actual characteristics. The relative error with respect to the true value was the accuracy indicator, and the width of the 95% CI and the truth value coverage rate of the corresponding 95% CI are the precision indicator for model assessments.

The relative error of impact evaluation in the four optimized models was 4.49 percentage points lower than that in the classic models, specifically ITS-A (14.34%) and ITS-HA (21.47%) relative to ITS (26.66%), CITS-A (16.57%), and CITS-HA (17.94%) relative to CITS (21.59%). The width of the 95% CI of point estimate of long-term impacts in the optimized models was 0.1261, which was expanded by 58.71% compared with 0.0875 for the classic model. However, the optimized models covered the true value in all test scenarios, whereas the coverage rates of the classic ITS and CITS models were 73.33% and 83.33%, respectively.

The optimized models are useful tools as they can assess the long-term impact of interventions with additional considerations for the nonlinear effects of covariates and allow for modeling of time-series autocorrelation and lag of intervention effects.
A scalable variational approach to characterize pleiotropic components across thousands of human diseases and complex traits using GWAS summary statistics
Authors: Zhang Zixuan , Jung Junghyun , Kim Artem , Suboc Noah , Gazal Steven , Mancuso Nicholas
Published: March 2023

Genome-wide association studies (GWAS) across thousands of traits have revealed the pervasive pleiotropy of trait-associated genetic variants. While methods have been proposed to characterize pleiotropic components across groups of phenotypes, scaling these approaches to ultra large-scale biobanks has been challenging. Here, we propose FactorGo, a scalable variational factor analysis model to identify and characterize pleiotropic components using biobank GWAS summary data. In extensive simulations, we observe that FactorGo outperforms the state-of-the-art (model-free) approach tSVD in capturing latent pleiotropic factors across phenotypes, while maintaining a similar computational cost. We apply FactorGo to estimate 100 latent pleiotropic factors from GWAS summary data of 2,483 phenotypes measured in European-ancestry Pan-UK BioBank individuals (N=420,531). Next, we find that factors from FactorGo are more enriched with relevant tissue-specific annotations than those identified by tSVD (P=2.58E-10), and validate our approach by recapitulating brain-specific enrichment for BMI and the height-related connection between reproductive system and muscular-skeletal growth. Finally, our analyses suggest novel shared etiologies between rheumatoid arthritis and periodontal condition, in addition to alkaline phosphatase as a candidate prognostic biomarker for prostate cancer. Overall, FactorGo improves our biological understanding of shared etiologies across thousands of GWAS.
Interactive association between gut microbiota and thyroid cancer: a Mendelian randomization and systematic review
Authors: Hou Tianzhichao , Wang Qi , Dai Huajie , Hou Yanan , Zheng Jie , Wang Tiange , Lin Hong , Wang Shuangyuan , Li Mian , Zhao Zhiyun , Chen Yuhong , Xu Yu , Lu Jieli , Liu Ruixin , Ning Guang , Wang Weiqing , Xu Min , Bi Yufang
Published: March 2023

Context

The association between gut microbiota and thyroid cancer remains controversial.

Objective

We aimed to systematically investigate the interactive causal relationships between the abundance and metabolism pathways of gut microbiota, and thyroid cancer.

Methods

We leveraged the genome wide association studies for the abundance of 211 microbiota taxa from the MiBioGen study (N=18,340); 205 microbiota metabolism pathways from the Dutch Microbiome Project (N=7738); and thyroid cancer from the largest meta-analysis of Global Biobank Meta-analysis Initiative (N cases=6699 and N participants=1,620,354). We performed a bidirectional Mendelian randomization (MR) to investigate the causality from microbiota taxa, metabolism pathways to thyroid cancer, and vice versa. We did a systematic review of the previous observational studies and compared MR results with observational findings.

Results

Eight taxa and twelve metabolism pathways had causal effects on thyroid cancer, whereRuminococcaceaeUCG004genus (P=0.001),Streptococcaceae family(P=0.016),Olsenellagenus (P=0.029), ketogluconate metabolism pathway (P=0.003), pentose phosphate pathway (P=0.016), and L-arginine degradation II in AST pathway (P=0.0007) were supported by sensitivity analyses. Conversely, thyroid cancer had causal effects on three taxa and two metabolism pathways, whereHoldemanellagenus (P=0.015) was supported by sensitivity analyses. TheProteobacteriaphylum,Streptococcaceaefamily,Ruminococcus2genus, andHoldemanellagenus were significantly associated with thyroid cancer in both systematic review and MR, while other 121 significant taxa in observational results were not supported by MR.

Discussions

These findings implicated the potential role of host-microbiota crosstalk in thyroid cancer, while the discrepancy among observational studies called for further investigations.
Assessment and Improvement of Elixhauser Comorbidity Index for Predicting In-hospital Mortality in Heart Transplant Patients
Authors: Li Renxi
Published: March 2023

Background

Heart transplant (HT) has a high in-hospital mortality of around 5%. Risk prediction in-hospital mortality can be informative for transplant candidacy and post-HT prognosis. Elixhauser Comorbidity Index (ECI) is an ICD diagnostic code-based comorbidity measurement tool that can predict in-hospital mortality. While it has been validated in the large in-patient population, the accuracy of the mortality prediction has not been assessed in HT.

Methods

This study assessed the in-hospital mortality risk prediction by ECI as well as demographic variables in HT patients in the National Inpatient Sample (NIS) database. Demographic information was included in the multivariable ECI with demographics (ECID) model to assess in-hospital mortality. Moreover, ECI and age were used to develop a single index adjusted ECI (aECI) for mortality prediction.

Results

Age best predicts (c-statistic = 0.673, 95% CI = 0.638-0.709) in-hospital mortality, followed by ECI (c-statistic = 0.638, 95% CI = 0.598-0.678), race (c-statistic = 0.571, 95% CI = 0.533-0.609). Sex did not have predictive power (c-statistic = 0.501, 95% CI = 0.467-0.535) for in-hospital mortality. The predictive power of ECI was improved (c-statistic = 0.753, 95% CI = 0.720-0.785) in the ECID model. The single measure aECI had comparable discriminative power (c-statistic = 0.763, 95% CI = 0.731-0.794) to ECID in predicting in-hospital mortality.

Conclusion

This study showed that ECI was an effective measure to predict post-HT in-hospital mortality. The improved measure aECI can be easily derived from ECI as a quick reference to assess post-HT in-hospital mortality in both the clinic and health administration.
PyTorch-FEA: Autograd-enabled Finite Element Analysis Methods with Applications for Biomechanical Analysis of Human Aorta
Authors: Liang Liang , Liu Minliang , Elefteriades John , Sun Wei
Published: March 2023

Motivation

Finite-element analysis (FEA) is widely used as a standard tool for stress and deformation analysis of solid structures, including human tissues and organs. For instance, FEA can be applied at a patient-specific level to assist in medical diagnosis and treatment planning, such as risk assessment of thoracic aortic aneurysm rupture/dissection. These FEA-based biomechanical assessments often involve both forward and inverse mechanics problems. Current commercial FEA software packages (e.g., Abaqus) and inverse methods exhibit performance issues in either accuracy or speed.

Methods

In this study, we propose and develop a new library of FEA code and methods, named PyTorch-FEA, by taking advantage of autograd, an automatic differentiation mechanism in PyTorch. We develop a class of PyTorch-FEA functionalities to solve forward and inverse problems with improved loss functions, and we demonstrate the capability of PyTorch-FEA in a series of applications related to human aorta biomechanics. In one of the inverse methods, we combine PyTorch-FEA with deep neural networks (DNNs) to further improve performance.

Results

We applied PyTorch-FEA in four fundamental applications for biomechanical analysis of human aorta. In the forward analysis, PyTorch-FEA achieved a significant reduction in computational time without compromising accuracy compared with Abaqus, a commercial FEA package. Compared to other inverse methods, inverse analysis with PyTorch-FEA achieves better performance in either accuracy or speed, or both if combined with DNNs.
Scaling deep phylogenetic embedding to ultra-large reference trees: a tree-aware ensemble approach
Authors: Jiang Yueyu , McDonald Daniel , Knight Rob , Mirarab Siavash
Published: March 2023

Phylogenetic placement of a query sequence on a backbone tree is increasingly used across biomedical sciences to identify the content of a sample from its DNA content. The accuracy of such analyses depends on the density of the backbone tree, making it crucial that placement methods scale to very large trees. Moreover, a new paradigm has been recently proposed to place sequences on the species tree using single-gene data. The goal is to better characterize the samples and to enable combined analyses of marker-gene (e.g., 16S rRNA gene amplicon) and genome-wide data. The recent method DEPP enables performing such analyses using metric learning. However, metric learning is hampered by a need to compute and save a quadratically growing matrix of pairwise distances during training. Thus, DEPP (or any distance-based method) does not scale to more than roughly ten thousand species, a problem that we faced when trying to use our recently released Greengenes2 (GG2) reference tree containing 331,270 species. Scalability problems can be addressed in phylogenetics using divide- and-conquer. However, applying divide- and-conquer to data-hungry machine learning methods needs nuance. This paper explores divide- and-conquer for training ensembles of DEPP models, culminating in a method called C-DEPP that uses carefully crafted techniques to enable quasi-linear scaling while maintaining accuracy. C-DEPP enables placing twenty million 16S fragments on the GG2 reference tree in 41 hours of computation.
i-stLearn: An interactive platform for spatial transcriptomics analysis
Authors: Pham Duy , Balderson Brad , Nguyen Quan
Published: March 2023

Emerging spatial transcriptomics technologies (e.g. Visium, Slide-seq, or MERFISH) have made it possible to keep the spatial information while profiling gene expression of every cell/spatial-spot. Integrating expression values, spatial coordinates, and imaging data type promises to bring more biological insights but is still technically challenging. A user-friendly software tool to enable interactive analysis of spatial transcriptomic data by the broader community is lacking. We present i-stLearn, an all-in-on web application with an analysis pipeline and interactive visualization for studying spatial heterogeneity using spatial transcriptomics data. i-stLearn can be used to gain biological insights from tissue through key analysis types cell-cell interaction analysis, clustering, and trajectory inference. Using functions, users can interactively segment the tissue and identify cellular state transition or cellular communications in a heterogeneous biological sample.
Hyaluronic acid turnover controls the severity of Cerebral Cavernous Malformations in bioengineered human micro-vessels
Authors: Yordanov Teodor E. , Martinez Marco A. E. , Esposito Tyron , Tefft Juliann B. , Labzin Larisa I. , Stehbens Samantha J. , Rowan Alan E. , Hogan Benjamin M. , Chen Christopher S. , Lauko Jan , Lagendijk Anne K.
Published: March 2023

Cerebral Cavernous Malformations (CCMs) are vascular lesions that predominantly form in blood vessels of the central nervous system (CNS) upon loss of the CCM multimeric protein complex. The endothelial cells (ECs) within CCM lesions are characterised by overactive MEKK3 kinase and KLF2/4 transcription factor signalling, leading to pathological changes such as increased EC spreading and reduced junctional integrity. Concomitant to aberrant EC signalling, non-autonomous signals from the extracellular matrix (ECM) have also been implicated in CCM lesion growth and these factors might explain why CCM lesions mainly develop in the CNS. Here, we adapted a three dimensional (3D) microfluidic system to examine CCM1 deficient human micro-vessels in distinctive ECMs. We validate that EC pathological hallmarks are maintained in this 3D model. We further show that key genes responsible for homeostasis of Hyaluronic Acid (HA), a major ECM component of the CNS, are dysregulated in CCM. Supplementing the ECM in our model with forms of HA that are predicted to be reduced, inhibits CCM cellular phenotypes, independent of KLF2/4. This study thereby provides a proof-of-principle that ECM embedded 3D microfluidic models are ideally suited to identify how changes in ECM structure and signalling impact vascular malformations.
Novel role of bone morphogenetic protein 9 (BMP9) in innate host responses to HCMV infection
Authors: Stempel Markus , Drakesmith Hal , Brinkmann Melanie M.
Published: March 2023

Herpesviruses modulate immune control to secure lifelong infection. The diverse mechanisms Human Cytomegalovirus (HCMV) employs in this regard can reveal unanticipated aspects of cellular signalling involved in antiviral immunity. Here, we describe a novel relationship between the transforming growth factor-beta (TGF-β) family cytokine bone morphogenetic protein 9 (BMP9) and HCMV infection. We identified cross-talk between BMP9-induced and type I interferon (IFN) receptor-mediated signaling, showing that BMP9 boosts the transcriptional response to and antiviral activity of IFNβ, and thereby enhances restriction of HCMV replication. We also show that BMP9 is secreted by human fibroblasts upon HCMV infection. However, HCMV infection impairs BMP9-induced enhancement of the response to IFNβ, indicating that this innate immune signalling role of BMP9 is actively targeted by HCMV. Indeed, we show that transmembrane proteins US18 and US20, which downregulate type I BMP receptors, are necessary and sufficient to cause inhibition of BMP9-mediated boosting of the antiviral response to IFNβ, so that HCMV lacking US18 and US20 is more sensitive to IFNβ. These results demonstrate that HCMV has a mutually antagonistic relationship with BMP9 and substantially extend the growing body of evidence that BMP signalling is an underappreciated modulator of innate immunity to viral infection.
Generating an efficient arginase variant for medical and industrial uses: In Silico engineering
Authors: Al-Madhagi Haitham Ahmed , Alsugoor Mahdi H.
Published: March 2023

Human arginase is a multifaceted enzyme that can be utilised for various medical and industrial applications, including as a replacement therapy for enzyme-deficient patients and for the industrial production of ornithine. However, no report has explored the in-silico engineering of this novel enzyme. The crystal structure of human arginase 1 was downloaded from the protein databank, and its quality was checked prior to further analysis. CUPSAT and DeepDDG webservers were then employed to nominate the most stable variants, which were prepared by the UCSF Chimera v1.16 modelling system and refined by the GalaxyRefine tool. Docking (i.e., to reference substrate and inhibitor), stability confirmation and dynamics simulations were conducted for all proposed variants, compared to the wild-type version of the enzyme. G119L was the best mutant in all the mentioned aspects, which was afterwards cloned in silico as a final step for the experimental testing thereof. Accordingly, G119L is found to be a valuable arginase mutant that deserves experimental validation to be employed for medical and industrial purposes.
Lysyl oxidase regulates epithelial differentiation and barrier integrity in eosinophilic esophagitis
Authors: Sasaki Masaru , Hara Takeo , Wang Joshua X. , Zhou Yusen , Kennedy Kanak V. , Umeweni Nicole N. , Alston Maiya A. , Spergel Zachary C. , Nakagawa Ritsu , Mcmillan Emily A. , Whelan Kelly A. , Karakasheva Tatiana A. , Hamilton Kathryn E. , Ruffner Melanie A. , Muir Amanda B.
Published: March 2023

Background & Aims

Epithelial disruption in eosinophilic esophagitis (EoE) encompasses both impaired differentiation and diminished barrier integrity. We have shown that lysyl oxidase (LOX), a collagen cross-linking enzyme, is upregulated in the esophageal epithelium in EoE. However, the functional roles of LOX in the esophageal epithelium remains unknown.

Methods

We investigated roles for LOX in the human esophageal epithelium using 3-dimensional organoid and air-liquid interface cultures stimulated with interleukin (IL)-13 to recapitulate the EoE inflammatory milieu, followed by single-cell RNA sequencing, quantitative reverse transcription-polymerase chain reaction, western blot, histology, and functional analyses of barrier integrity.

Results

Single-cell RNA sequencing analysis on patient-derived organoids revealed that LOX was induced by IL-13 in differentiated cells. LOX-overexpressing organoids demonstrated suppressed basal and upregulated differentiation markers. Additionally, LOX overexpression enhanced junctional protein genes and transepithelial electrical resistance. LOX overexpression restored the impaired differentiation and barrier function, including in the setting of IL-13 stimulation. Transcriptome analyses on LOX-overexpressing organoids identified enriched bone morphogenetic protein (BMP) signaling pathway compared to wild type organoids. Particularly, LOX overexpression increased BMP2 and decreased BMP antagonist follistatin. Finally, we found that BMP2 treatment restored the balance of basal and differentiated cells.

Conclusions

Our data support a model whereby LOX exhibits non-canonical roles as a signaling molecule important for epithelial homeostasis in the setting of inflammation via activation of BMP pathway in esophagus. The LOX/BMP axis may be integral in esophageal epithelial differentiation and a promising target for future therapies.
Menstrual cycle associated alteration of vastus lateralis motor unit function
Authors: Piasecki Jessica , Guo Yuxiao , Jones Eleanor J. , Phillips Bethan E. , Stashuk Daniel W. , Atherton Philip J. , Piasecki Mathew
Published: March 2023

Estrogen and progesterone are the primary female sex hormones and have net excitatory and inhibitory effects, respectively, on neuronal function. Fluctuating concentrations across the menstrual cycle has led to several lines of research in relation to neuromuscular function, yet evidence from animal and cell culture models have yet to be demonstrated in human motor units (MU) coupled with quantification of circulating hormones.

Intramuscular electromyography (iEMG) was applied to record MU potentials (MUP) and corresponding MUP trains (MUPT) from the vastus lateralis of eumenorrheic females during the early follicular, ovulation and mid luteal phases of the menstrual cycle, alongside assessments of neuromuscular performance. Multi-level regression models were applied to explore effects of time and of contraction level. Statistical significance was accepted as p<0.05.

Knee extensor maximum voluntary contraction (MVC), jump power, force steadiness, and balance did not differ across the menstrual phases (all p>0.4). Firing rate of low threshold MU (10% MVC) was reduced during phases of high progesterone (β=-0.82Hz, p<0.001), with no difference in MUPs analysed from 25% MVC contractions. MUPs were more complex during ovulation and mid luteal phase (p<0.03), with no change in neuromuscular junction transmission instability (p>0.3).

Assessments of neuromuscular performance did not differ across the menstrual cycle. The suppression of low threshold MU firing rate during periods of increased progesterone may suggest a potential inhibitory effect and an alteration of recruitment strategy, however this had no discernible effect on performance. These findings highlight contraction level dependent modulation of VL MU function over the eumenorrheic cycle.
Exploring the Spatiotemporal Influence of Climate on American Avian Migration with Random Forests
Authors: Bick I. Avery , Bakkestuen Vegar , Pedersen Marius , Raja Kiran , Sethi Sarab
Published: March 2023

North and South American Birds have adapted to climatic and ecological patterns to inform their Spring and Fall migration timings. Temperature and precipitation patterns are shifting under anthropogenic climate change, causing downstream effects on plant flowering cycles, insect populations, and habitat availability. Understanding how these cues trigger migration could improve the effectiveness and timing of bird surveys, as well as informing habitat protection and creation efforts to lessen biodiversity loss due to climate change. Here, we employ a modeling approach to explore how climate spatiotemporally affects accuracy of predicting bird occurrence. Specifically, we train an ensemble of random forests on subsets of North and South American climate data to predict distributions of historical bird occurrence probability for passerine bird species in a North American forested region on eBird citizen science surveys from 2008-2018. We further investigate the relevance of each feature, region, and temporal lag for predicting the observed bird occurrence in a forested region in Northeast America, finding that both temperature and precipitation facilitate accurate prediction. For predicting species in October, when many passerines have begun their southward winter migration, we achieve more accurate predictions of bird occurrence using lagged, rather than current, climate features alone to predict communities in October. We also note significant higher random forest feature importance for some lagged North American climate features than South American features. These results suggest that machine learning models may be useful for identifying spatiotemporal climatic cues that affect migratory behavior. Lastly, we explore the application and limitations of random forests for prediction of future bird occurrence using 2021-2040 climate projections.
Reducing squalene epoxidase by the aging-dependent intra-tissue cholesterol accumulation is associated with increased colorectal cancer patient severity in high-risk populations
Authors: Jun Soo Young , Yoon Hyang Ran , Yoon Ji-Yong , Lee Jeong-Ju , Kim Jin-Man , Kim Nam-Soon
Published: March 2023

Objective

Recently, we demonstrated cholesterol accelerating colorectal cancer (CRC) progression via squalene epoxidase (SQLE) reduction, activating the β-catenin oncogenic pathway while downregulating the p53 pathway, mediated by the inhibition of GSK3β activity (GSK3βpS9). However, the interrelationship between cholesterol increase and CRC progression with aging has never been determined.

Design

We utilized case data from public databases and human specimens to assess the relationship between cholesterol accumulation and CRC progression with aging. Digital image analysis-machine learning with multiplex fluorescence-immunohistochemistry evaluated the effects of SQLE, p53WT, p53MT, and GSK3βpS9(hereafter candidates) on the survival of CRC patients. Also, the prognostic and diagnostic abilities were assessed by a time-dependent receiver operating characteristic (timeROC) and a ROC curve with and without the discriminant score for the candidates as a single or whole, respectively.

Results

We found an accumulation of cholesterol and cholesteryl ester in tissues with aging, which led to the acceleration of CRC progression through substantial decreases of SQLE, p53WT, p53MTexpressions and inhibition of GSK3β activity in advanced CRCs. Retrospective studies demonstrated that SQLE significantly impacted the shortened progression-free survival of the population with progressive pathological severity and high CRC risk beyond the age of 50. Clinical assays further showed the excellent prognostic and diagnostic abilities of SQLE and GSK3βpS9but also the substantial diagnostic potential of the combined candidate for the aged high-risk CRC population.

Conclusion

We provide new insights into the relationship between cholesterol increase and CRC progression with aging and identify valuable biomarkers for aged populations with high-risk CRC.
Rapid and scalable preclinical evaluation of personalized antisense oligonucleotide therapeutics using organoids derived from rare disease patients
Authors: Means John C. , Louiselle Daniel A. , Farrow Emily G. , Pastinen Tomi , Younger Scott T.
Published: March 2023

Personalized antisense oligonucleotides (ASOs) have achieved positive results in the treatment of rare genetic disease. As clinical sequencing technologies continue to advance, the ability to identify rare disease patients harboring pathogenic genetic variants amenable to this therapeutic strategy will likely improve. Here, we describe a scalable platform for generating patient-derived cellular models and demonstrate that these personalized models can be used for preclinical evaluation of patient-specific ASOs. We establish robust protocols for delivery of ASOs to patient-derived organoid models and confirm reversal of disease-associated phenotypes in cardiac organoids derived from a Duchenne muscular dystrophy (DMD) patient harboring a structural deletion in the dystrophin gene amenable to treatment with existing ASO therapeutics. Furthermore, we design novel patient-specific ASOs for two additional DMD patients (siblings) harboring a deep intronic variant in the dystrophin gene that gives rise to a novel splice acceptor site, incorporation of a cryptic exon, and premature transcript termination. We show that treatment of patient-derived cardiac organoids with patient-specific ASOs results in restoration of DMD expression and reversal of disease-associated phenotypes. The approach outlined here provides the foundation for an expedited path towards the design and preclinical evaluation of personalized ASO therapeutics for a broad range of rare diseases.
Long-term stress shapes dynamic reconfiguration of functional brain networks across multi-task demands
Authors: Gao Hongyao , Zeng Yimeng , Tian Ting , Liu Chao , Wu Jianhui , Wu Haitao , Qin Shaozheng
Published: March 2023

Exposure to sustained stress can have a profound impact on the brain, emotion and cognition, with either adaptive or maladaptive effects. Human functional brain networks are dynamically organized to enable rapid and flexible adaptation to meet ever-changing task demands. Yet, little is known about how long-term stress alters the dynamic reconfiguration of functional brain networks across multi-task demands. Here we show prominent changes in the dynamic reconfiguration of large-scale brain networks during resting-state, emotional and working-memory processing under long-term stress. Hidden Markov Model analysis detected several latent brain states and switching processes involving the default mode, emotional salience and executive-control networks that are dominant to rest, emotion and working memory, respectively. Critically, long-term stress increased persistent time on brain states relevant to goal-directed demands and cognitive control, with more frequent transitions to these brain states when compared to controls. Furthermore, long-term stress led to higher correlations of the occupancy and persistency of brain states linked to psychological distress and behavioral performance. Our findings provide a neurocognitive framework whereby long-term stress shapes the way the brain adapts to varying task demands and increases the sensitivity of functional brain networks to psychological and behavioral responses. These changes can be both adaptive and maladaptive, reflecting the complex effects of long-term stress on brain function.
A visual paired associate learning (vPAL) paradigm to study memory consolidation during sleep
Authors: Schmidig J.F. , Geva-Sagiv M. , Falach R. , Yakim S. , Gat Y. , Sharon O. , Fried I. , Nir Y.
Published: March 2023

The hippocampus helps transform an experience into an enduring memory by associating its multiple aspects. Sleep improves the consolidation of the newly formed associations, leading to stable long-term memory. Most research on human declarative memory and its consolidation during sleep uses word-pair associations requiring exhaustive learning. Here we present the visual paired association learning (vPAL) paradigm, in which participants learn new associations between images of celebrities and animals. vPAL associations are based on a one-shot exposure that resembles learning in natural conditions. We tested if vPAL can reveal a role for sleep in memory consolidation by assessing the specificity of memory recognition, and the cued recall performance, before and after sleep. We found that a daytime nap improved the stability of recognition memory and discrimination abilities compared to identical intervals of wakefulness. By contrast, cued recall of associations did not exhibit significant sleep-dependent effects. High-density EEG during naps further revealed an association between sleep spindle density and stability of recognition memory. Thus, the vPAL paradigm opens new avenues for future research on sleep and memory consolidation across ages and heterogeneous populations in health and disease.
Antibacterial activity of solid surfaces is critically dependent on relative humidity, inoculum volume and organic soiling
Authors: Kaur Harleen , Rosenberg Merilin , Kook Mati , Danilian Dmytro , Kisand Vambola , Ivask Angela
Published: March 2023

Antimicrobial surface materials potentially prevent pathogen transfer from contaminated surfaces. Efficacy of such surfaces is assessed by standard methods using wet exposure conditions known to overestimate antimicrobial activity compared to dry exposure. Some dry test formats have been proposed but semi-dry exposure scenariose.g.,oral spray or water droplets exposed to ambient environment, are less studied. We aimed to determine the impact of environmental test conditions on antibacterial activity against the model speciesEscherichia coliandStaphylococcus aureus. Surfaces based on copper, silver, and quaternary ammonium with known or claimed antimicrobial properties were tested in conditions mimicking microdroplet spray or larger water droplets exposed to variable relative air humidity in the presence or absence of organic soiling. All the environmental parameters critically affected antibacterial activity of the tested surfaces from no effect in high-organic dry conditions to higher effect in low-organic humid conditions but not reaching the effect size demonstrated in the ISO 22169 wet format. Copper was the most efficient antibacterial surface followed by silver and quaternary ammonium based coating. Antimicrobial testing of surfaces using small droplet contamination in application-relevant conditions could therefore be considered as one of the worst-case exposure scenarios relevant to dry use surfaces.

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Antibacterial activity of copper and silver surfaces is highly dependent on environmental testing conditions with maximum efficiency in low-organic wet conditions to no antibacterial activity in high-organic dry conditions indicating the need to test antimicrobial surface materials in application-relevant test formats as opposed to current standards.
Wnt-Ror-Dvl signalling and the dystrophin complex organize planar-polarized membrane compartments inC. elegansmuscles
Authors: Peysson Alice , Zariohi Noura , Gendrel Marie , Chambert-Loir Amandine , Frébault Noémie , Cheynet Elise , Andrini Olga , Boulin Thomas
Published: March 2023

Cell polarity mechanisms allow the formation of specialized membrane domains with unique protein compositions, signalling properties, and functional characteristics. By analysing the localization of potassium channels and proteins belonging to the dystrophin-associated protein complex, we reveal the existence of distinct planar-polarized membrane compartments at the surface ofC. elegansmuscle cells. We find that muscle polarity is controlled by a non-canonical Wnt signalling cascade involving the ligand EGL-20/Wnt, the receptor CAM-1/Ror, and the intracellular effector DSH-1/Dishevelled. Interestingly, classical planar cell polarity proteins are not required for this process. Using time-resolved protein degradation, we demonstrate that –while it is essentially in place by the end of embryogenesis– muscle polarity is a dynamic state, requiring continued presence of DSH-1 throughout post-embryonic life. Our results reveal the unsuspected complexity of theC. elegansmuscle membrane and establish a novel genetically tractable model system to study cellular polarity and membrane compartmentalizationin vivo.
A parasitoid serpin gene that disrupts host immunity shows adaptive evolution of alternative splicing
Authors: Yan Zhichao , Fang Qi , Yang Lei , Xiao Shan , Wang Jiale , Ye Gongyin
Published: March 2023

Alternative splicing (AS) is a major source of protein diversity in eukaryotes, but less is known about its evolution compared to gene duplication (GD). How AS and GD interact is also largely understudied. By constructing the evolutionary trajectory of a serpin gene PpSerpin-1 (Pteromalus puparumserpin 1) in parasitoids and other insects, we found that both AS and GD jointly contribute to serpin protein diversity. These two processes are negatively correlated and show divergent features in both protein and regulatory sequences. Furthermore, parasitoid wasps exhibit higher numbers of serpin protein/domains than nonparasitoids, resulting from more GD but less AS in parasitoids. Nevertheless, PpSerpin-1 shows an exon expansion of AS compared to other parasitoids. We find that several isoforms of PpSerpin-1 are involved in the wasp immune response, have been recruited to both wasp venom and larval saliva, and suppress host immunity. In summary, we report the differential features of AS and GD in the evolution of insect serpins and their associations with the parasitic life strategy, and we provide an example of how a parasitoid serpin gene adapts to parasitism through AS.
Implantation of engineered adipocytes that outcompete tumors for resources suppresses cancer progression
Authors: Nguyen Hai P. , Sheng Rory , Murray Elizabeth , Ito Yusuke , Bruck Michael , Biellak Cassidy , An Kelly , Lynce Filipa , Dillon Deborah A. , Magbanua Mark Jesus M. , Huppert Laura A. , Hammerlindl Heinz , Esserman Laura , Rosenbluth Jennifer M. , Ahituv Nadav
Published: March 2023

Tumors acquire an increased ability to obtain and metabolize nutrients. Here, we engineered and implanted adipocytes to outcompete tumors for nutrients and show that they can substantially reduce cancer progression. Growing cells or xenografts from several cancers (breast, colon, pancreas, prostate) alongside engineered human adipocytes or adipose organoids significantly suppresses cancer progression and reduces hypoxia and angiogenesis. Transplanting modulated adipocyte organoids in pancreatic or breast cancer mouse models nearby or distal from the tumor significantly suppresses its growth. To further showcase therapeutic potential, we demonstrate that co-culturing tumor organoids derived from human breast cancers with engineered patient-derived adipocytes significantly reduces cancer growth. Combined, our results introduce a novel cancer therapeutic approach, termed adipose modulation transplantation (AMT), that can be utilized for a broad range of cancers.
A Retina-Inspired Computational Model for Stimulation Efficacy Characterization and Implementation Optimization of Implantable Optogenetic Epi-Retinal Neuro-Stimulators
Authors: Yousefi Tayebeh , Kassiri Hossein
Published: March 2023

In this paper, a biologically-informed computational framework is developed to model the efficacy and to optimize the implementation of an implantable epi-retinal prosthesis that performs optogenetic stimulation through aμLED array. The developed model is capable of translating visual stimulus inputs into corresponding signals evoked in the transfected retinal cells through optogenetic stimulation, calculating the subsequent neuronal activities of the following retinal layers, and estimating the resulted brain’s visual perception. As such, it can model and quantitatively analyze the impact of optical stimulation parameters (intensity, frequency, directivity, wavelength, etc.) and theμLED array’s physical specifications (array size, density, pitch, implantation location, etc.) on the efficacy of the stimulation.

Using this model, we compared optical and electrical stimulations in terms of the structural similarity between their induced visual perception in the brain and the visual stimulus input. We showed that thanks to the cell-type specificity of optogenetic stimulation, it can induce more relevant visual perception qualities than electrical stimulation. We also showed that its resulted visual perception substantially improves with scaling the stimulator’s array size. The model was also used to qualitatively and quantitatively analyze the impact of parameters such as implantation location, light intensity, single- and dual-wavelength stimulation, and illumination divergence angle on the quality of the optical-stimulation-induced visual perception. In each case, the simulation results were followed by our interpretation from a biological point of view. More importantly, in each case, we discussed how the results could be used for optimizing different parameters of an implantable optogenetic stimulator to achieve maximum efficacy and energy efficiency. Keywords: Retinal prosthesis, optogenetics, visual perception, optical stimulation,μLED array, computational model, spatial resolution, pathway-specific stimulation.