bluuebunny/crossref_metadata_2025 huggingface dataset:
Bronchoalveolar lavage metabolome dynamics reflect underlying disease and chronic lung allograft dysfunction
Authors: Martin Christian , Mahan Kathleen S. , Wiggen Talia D. , Gilbertsen Adam J. , Hertz Marshall I. , Hunter Ryan C. , Quinn Robert A.
Published: November 2022

Background

Progression of chronic lung disease often leads to the requirement for a lung transplant (LTX). Despite improvements in short-term survival after LTX, chronic lung allograft dysfunction (CLAD) remains a critical challenge for long-term survival. This study investigates the relationship between the metabolome of bronchoalveolar lavage fluid (BALF) from subjects post-LTX with underlying lung disease and CLAD severity.

Methods

Untargeted LC-MS/MS metabolomics was performed on 960 BALF samples collected over 10 years from LTX recipients with alpha-1-antitrypsin disease (AATD, n=22), cystic fibrosis (CF, n=46), chronic obstructive pulmonary disease (COPD, n = 79) or pulmonary fibrosis (PF, n=47). Datasets were analyzed using machine learning and multivariate statistics for associations with underlying disease and final CLAD severity.

Results

BALF metabolomes varied by underlying disease state, with AATD LT recipients being particularly distinctive (PERMANOVA,p=0.001). We also found a significant association with the final CLAD severity score (PERMANOVA,p=0.001), especially those with underlying CF. Association with CLAD severity was driven by changes in phosphoethanolamine (PE) and phosphocholine lipids that increased and decreased, respectively, and metabolites from the bacterial pathogenPseudomonas aeruginosa. P. aeruginosasiderophores, quorum-sensing quinolones, and phenazines were detected in BALF, and 4-hydroxy-2-heptylquinoline (HHQ) was predictive of the final CLAD stage in samples from CF patients (R=0.34;p≤0.01). Relationships between CLAD stage andP. aeruginosametabolites were especially strong in those with CF, where 61% of subjects had at least one of these metabolites in their first BALF sample after transplant.

Conclusions

BALF metabolomes after LTX are distinctive based on the underlying disease and reflect final CLAD stage. In those with more severe outcomes, there is a lipid transition from PC to predominantly PE phospholipids. The association ofP. aeruginosametabolites with CLAD stages in LTX recipients with CF indicates this bacterium and its metabolites may be drivers of allograft dysfunction.

Key messages

Despite the high prevalence of CLAD among LTX recipients, its pathology is not well understood, and no single molecular indicator is known to predict disease onset. Our machine learning metabolomic-based approach allowed us to identify patterns associated with a shift in the lipid metabolism and bacterial metabolites predicting CLAD onset in CF. This study provides a better understanding about the progression of allograft dysfunction through the molecular transitions within the transplanted lung from the host and bacterial pathogens.
Projected Cancer Burden, Challenges, and Barriers to Cancer Prevention and Control Activities in the State of Telangana
Authors: Mahajan Hemant , Neha Reddy M , Devi NG Marina , Poli Usha Rani , Jayaram M , Tetali Shailaja , Murthy GVS
Published: November 2022

Background and Aim

The Telangana cancer care program is a proactive, comprehensive initiative encompassing infrastructure development, human resource skilling and ensuring financial protection to those below poverty line. The broad aim of this exercise was to identify modalities to augment the Telangana State Cancer control Plan to implement a sustainable comprehensive cancer care model for Telangana.

Methods

We conducted in-depth interviews of stakeholders (17 patients and 25 providers) to identify barriers and challenges to access existing cancer care system; calculated the estimated magnitude of cancer and commensurate workload (in terms of visits to tertiary cancer care system for chemotherapy, radiotherapy, and surgery and human and equipment requirement) for the next 15 years (from 2021 to 2036). Using the anecdotal evidence and information from stakeholders’ interviews, we developed patient-journey funnels for cancer patients, which helped us to appreciate at what levels of care leakages occur.

Results

We estimated a 28% increase in the number of new cancer cases per year and the resultant workload: number of visits, chemotherapy, radiotherapy, surgeries, specialized human resources and equipment, from 2021 to 2036. Stakeholders mentioned delayed access to healthcare system as the main reason for the poor prognosis of patients. The common reasons cited for delayed access were: poor cancer-literacy including prevailing myths and misconception, financial barriers, and rural residence. Patient journey funnel for cancer care revealed major leakage from screened positive to diagnosis confirmation step. Patient leakage varies from ∼70% to 90% from screened positive till treatment completion.

Conclusion

Govt. of Telangana has initiated several measures to strengthen the healthcare system and to promote the uptake of cancer care services to manage the rising burden of cancer and resultant increasing workload. However, there is ample scope for further improvement (such as improved healthcare access, reduced patient leakage, commensurate human skills and infrastructure development etc.) to deliver comprehensive cancer care services in the state.

Highlights

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Cancer cases are rising in Telangana, which is experiencing high epidemiological transition

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The healthcare infrastructure for cancer care is predominantly urban-centric

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Overall, 28% increase in new cancer cases and resultant workload per year from 2021 to 2036 was estimated

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Delayed access to healthcare system due to barriers at community and health system level

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Major patient leakage across cancer care continuum, which ranges from 70 to 90%
Inter-Rater Agreement for the Annotation of Neurologic Concepts in Electronic Health Records
Authors: Oommen Chelsea , Howlett-Prieto Quentin , Carrithers Michael D. , Hier Daniel B.
Published: November 2022

The extraction of patient signs and symptoms recorded as free text in electronic health records is critical for precision medicine. Once extracted, signs and symptoms can be made computable by mapping to clinical concepts in an ontology. Extracting clinical concepts from free text is tedious and time-consuming. Prior studies have suggested that inter-rater agreement for clinical concept extraction is low. We have examined inter-rater agreement for annotating neurologic concepts in clinical notes from electronic health records. After training on the annotation process, the annotation tool, and the supporting neuro-ontology, three raters annotated 15 clinical notes in three rounds. Inter-rater agreement between the three annotators was high for text span and category label. A machine annotator based on a convolutional neural network had a high level of agreement with the human annotators, but one that was lower than human inter-rater agreement. We conclude that high levels of agreement between human annotators are possible with appropriate training and annotation tools. Furthermore, more training examples combined with improvements in neural networks and natural language processing should make machine annotators capable of high throughput automated clinical concept extraction with high levels of agreement with human annotators.
Effect of energy shortages on institutional delivery in India
Authors: Amporfu Eugenia , Irwin Bridget R. , Trakinsky Benjamin Sas , Grépin Karen A.
Published: November 2022

Introduction

Energy shortages are a common challenge in many low- and middle-income countries and can disrupt the operation of healthcare facilities, which can compromise patient outcomes and affect health service utilization. Maternal healthcare use, in particular, has been found to be negatively correlated with power outages in other contexts. The following study investigates the association between state-level energy shortages and institutional delivery rates in India and how the association varies according to women’s socio-economic status.

Methods

Using data from the 1998-99 and 2005-06 India Demographic and Health Surveys, along with information on power outages from India’s Central Electricity Authority, we estimate the association between energy shortages and institutional delivery rates using both logistic and multinomial regressions.

Results

Energy shortages were associated with reduced rates of institutional delivery: a 10% increase in the shortage level corresponded to a 1.1% decline in the percentage of women giving birth in a healthcare facility. Deliveries in public health facilities were more likely to be disrupted by energy shortages than deliveries in private facilities.

Conclusion

Energy shortages are an important determinant of institutional delivery in India. Given that increasing institutional delivery rates is likely important to reduce maternal mortality, policymakers should work to mitigate the impact of energy disruptions on healthcare seeking behaviours.
The Clinical Utility of the BMD-related comprehensive Genome-wide polygenic score in identifying individuals with a high risk of osteoporotic fractures
Authors: Xiao Xiangxue , Wu Qing
Published: November 2022

Purpose

This study sought to construct genome-wide polygenic scores for femoral neck and total body BMD and to estimate their potential in identifying individuals with a high risk of osteoporotic fractures.

Methods

Genome-wide polygenic scores were developed and validated for femoral neck and total body BMD. We externally tested the PGSs, both by themselves and in combination with available clinical risk factors, in 455,663 European ancestry individuals from the UK Biobank. The predictive accuracy of the developed genome-wide PGS was also compared with previously published restricted PGS employed in fracture risk assessment.

Results

For each unit decrease in PGSs, the genome-wide PGSs were associated with up to a 1.17-fold increased fracture risk. Out of four studied PGSs,PGS_TBBMD81(HR: 1.03; 95%CI 1.01-1.05, p=0.001) had the weakest and thePGS_TBBMDldpred(HR: 1.17; 95%CI 1.15-1.19, p<0.0001) had the strongest association with an incident fracture. In the reclassification analysis, compared to the FRAX base model, the models with,PGS_FNBMD63,PGS_TBBMD81,PGS_FNBMDldpred, andPGS_TBBMDldpredimproved the reclassification of fracture by 2% (95% CI, 1.5% to 2.4%), 0.2% (95% CI, 0.1% to 0.3%), 1.4% (95% CI, 1.3% to 1.5%), and 2.2% (95% CI, 2.1% to 2.4%), respectively.

Conclusions

Our findings suggested that an efficient PGS estimate enables the identification of strata with up to 1.5-fold difference in fracture incidence. Incorporating PGS information into clinical diagnosis is anticipated to increase the benefits of screening programs in the population level.
Intrathecal oxytocin for neuropathic pain: A randomized, controlled, cross-over trial
Authors: Eisenach James C. , Curry Regina S. , Houle Timothy T.
Published: November 2022

Objective

To investigate the effect of intrathecal oxytocin compared to placebo on pain and hypersensitivity in individuals with chronic neuropathic pain.

Study design

Randomized, controlled, double-blind cross-over study

Setting

Outpatient clinical research unit.

Subjects

Individuals between ages of 18 and 70 years with neuropathic pain caudal to the umbilicus for at least 6 months.

Methods

Individuals received two blinded intrathecal injections of either oxytocin or saline, separated by at least 7 days, and ongoing neuropathic pain (VAS: visual analog scale) and areas of hypersensitivity were measured at intervals for 4 hours. The primary outcome was VAS pain, analyzed by linear mixed effects model. Secondary outcomes were verbal pain intensity scores at intervals for 7 days and areas of hypersensitivity and elicited pain for 4 hr after injections.

Results

The study was stopped early after completion of 5 of 40 subjects planned due to slow recruitment and funding limitations. Pain intensity prior to injection was 4.75 ± 0.99 and modeled pain intensity decreased more after oxytocin than placebo to 1.61 ± 0.87.and 2.49 ± 0.87, respectively (p=0.003). Daily pain scores were lower in the week following injection of oxytocin than saline (2.53 ± 0.89 vs 3.66 ±0.89; p=0.001). Hypersensitivity differed between oxytocin and placebo by small amounts in opposite directions depending on modality tested. There were no study drug related adverse effects.

Discussion

Although limited by the small number of subjects studied, oxytocin reduced pain more than placebo in all subjects. Further study of spinal oxytocin in this population is warranted.
The Impact of State-Level Prenatal Substance Use Policies on Rates of Maternal and Infant Mortality in the United States: A Legal Epidemiology Study
Authors: Thomas Kathryn A. , Struble Cara A. , Stenersen Madeline R. , Moore Kelly
Published: November 2022

Rates of maternal and infant mortality remain significantly higher in the United States than other similarly developed countries. Prior research has shown that substance use during pregnancy leads to increased rates of maternal and infant mortality, yet no studies have examined the impact of state-level policies regarding substance use during pregnancy on rates of maternal and infant mortality across all 50 states. The current study utilized publicly available data to examine state-level impact of punitive prenatal substance use laws on maternal and infant mortality. Results revealed that mandated testing laws significantly predicted maternal mortality after controlling for race, poverty, neonatal abstinence syndrome, prenatal care, and substance use in pregnant women, with the entire model accounting for 59.6% of the variance in maternal mortality. Child abuse laws significantly predicted rates of infant mortality, when controlling for race, health insurance, neonatal abstinence syndrome, prenatal care, and substance use in pregnant women, with the entire model accounting for 70.8% of variance in infant mortality. In both regression models, lack of prenatal care increased rates of maternal and infant mortality. Results provide support for the position that laws punishing prenatal substance use may lead to higher rates of maternal and infant mortality. Clinical and policy implications are discussed.
GMRC: Gene Median Ratio Clustering Algorithm to Rank Clusters and Identify Driver Genes in Intrahepatic Cholangiocarcinoma
Authors: Puri Nandita , Arora Ridhi , Tanwar Vishesh Kumar , Das Sajal K.
Published: November 2022

Intrahepatic Cholangiocarcinoma (iCCA) is an asymptomatic malignancy of the bile ducts in the liver. The research is predicated on the assumption that a disease is a cluster of genes interacting in a regulatory network and a few driver genes regulating the network. Keeping this in mind, the paper proposes a Gene Median Ratio Clustering (GMRC) algorithm to independently rank the clusters and identify driver genes by hybridizinggene-median ratio(GMR) andgene cluster network(GCN). GMR then employs the Poisson distribution to translate right-skewed data into the Normal distribution and the Anscombe transformation to eliminate data noise and determine the median difference in gene concentration between distinct disease stages. Additionally, hierarchical clustering is separately applied to raw data for gene clustering based on the RNA sequence count. In the process, GMRC is evaluated over the GSE32225 dataset of 155 patients to extract 12 clusters network for proliferation patients.
Coalescent RNA-localizing and transcriptional activities of SAM68 modulate adhesion and subendothelial basement membrane assembly
Authors: Rekad Zeinab , Ruff Michaël , Radwanska Agata , Grall Dominique , Ciais Delphine , Van Obberghen-Schilling Ellen
Published: November 2022

Endothelial cell interactions with their extracellular matrix are essential for vascular homeostasis and expansion. Large-scale proteomic analyses aimed at identifying components of integrin adhesion complexes have revealed the presence of several RNA Binding Proteins (RBPs) of which the functions at these sites remain poorly understood. Here, we explored the role of the RBP SAM68 (Src associated in mitosis, of 68 kDa) in endothelial cells. We found that SAM68 is transiently localized at the edge of spreading cells where it participates in membrane protrusive activity and the conversion of nascent adhesions to mechanically-loaded focal adhesions by modulation of integrin signaling and local delivery of β-actin mRNA. Furthermore, SAM68 depletion impacts cell-matrix interactions and motility through induction of key matrix genes involved in vascular matrix assembly. In a 3D environment SAM68-dependent functions in both tip and stalk cells contribute to the process of sprouting angiogenesis. Altogether, our results identify the RBP SAM68 as a novel actor in the dynamic regulation of blood vessel networks.
Arabidopsis pollen Prolyl-hydroxylases P4H4/6 are required for correct hydroxylation and secretion of LRX11 in pollen tubes
Authors: Sede Ana R. , Wengier Diego L. , Borassi Cecilia , Ricardi Martiniano , Somoza Sofía C. , Aguiló Rafael , Estevez José M. , Muschietti Jorge P.
Published: November 2022

Major constituents of the plant cell walls are structural proteins that belong to the Hydroxyproline-rich glycoprotein family. Leucine-rich repeat extensis are contain a leucine-rich domain and a C-terminal domain with repetitive Ser-Pro(3-5) motifs plausible to be glycosylated. We have demonstrated that pollen-specific LRX8-11 fromArabidopsis thalianaare necessary to maintain the integrity of the pollen tube cell wall during polarized growth. In classical EXTs and likely in LRXs, proline residues are converted to hydroxyproline by Prolyl-4-hydroxylases, thus defining novelO-glycosylation sites. In this context, we aimed to determine whether hydroxylation and subsequentO-glycosylation of Arabidopsis pollen LRXs are necessary for their proper function and cell wall localization in pollen tubes. We hypothesized that pollen-expressed P4H4 and P4H6 catalyze the hydroxylation of the proline units present in Ser-Pro(3-5) motifs of LRX8-LRX11. Here, we show thep4h4-1 p4h6-1double mutant exhibits a significant reduction in pollen germination rates and a slight reduction in pollen tube length. Pollen germination is also inhibited by specific P4Hs inhibitors, suggesting that prolyl hydroxylation is required for pollen tube development. Plants expressingpLRX11::LRX11-GFPin thep4h4-1 p4h6-1background show partial relocalization of LRX11-GFP from the pollen tube tip apoplast to the cytoplasm. Finally, IP-MS- MS analysis revealed a decrease in oxidized prolines in LRX11-GFP in thep4h4-1 p4h6-1background when compared tolrx11plants expressingpLRX11::LRX11-GFP. Together, these results suggest that P4H4 and P4H6 are required for pollen germination and are also involved in LRX11 hydroxylation necessary for its localization at the cell wall of pollen tubes.

One Sentence Summary

Pollen-expressed P4H4 and P4H6 are required for pollen germination and for proper hydroxylation and secretion of LRX11 in pollen tubes.
CT perfusion stroke lesion threshold calibration between deconvolution algorithms
Authors: Chung Kevin J. , De Sarno Danny , Lee Ting-Yim
Published: November 2022

Structured Abstract

Objective

CTP is an important diagnostic tool in managing patients with acute ischemic stroke, but challenges persist in the reliability of stroke lesion volumes determined with different software. We investigated a systematic method to calibrate CTP lesion thresholds between deconvolution algorithms using a digital perfusion phantom.

Approach

The accuracy of one model-independent and two model-based deconvolution algorithms in estimating ground truth cerebral blood flow (CBF) and Tmax in the phantom was quantified. Reference thresholds for ischemic core and penumbra were model-independent CBF<30% and Tmax>6 s, respectively, which is the current clinical standard. The equivalent model-based CBF and Tmax thresholds were determined by comparing linear regressions of phantom ground truth and deconvolution-estimated perfusion between algorithms. Calibrated thresholds were then validated in 63 patients with large vessel stroke by comparing admission CTP ischemic core and <3-hour diffusion-weighted imaging (DWI) lesion volume by Bland-Altman analysis. Agreement in target mismatch (core < 70 ml, penumbra ≥ 15 ml, mismatch ratio ≥ 1.8) determined by the three methods was assessed by Cohen’s kappa (κ) and concordance.

Main Results

The calibrated thresholds were CBF<15% and Tmax>6 s for both model-based methods. DWI minus CTP lesion mean volume differences (95% limits of agreement) were +16.2 (−30.9 to 63.3) ml, +10.9 (−32.9 to 54.7) ml, and +13.8 (−48.1 to 75.7) ml for model-independent and the two calibrated model-based approaches, respectively. Agreement in mismatch profiles with the two model-based deconvolution methods versus model-independent assessment was κ = 0.87 (95% confidence interval [CI]: 0.72 to 1.00) and κ = 0.86 (95% CI: 0.70 to 1.00), and both achieved 95% concordance.

Significance

We reported a systematic method of calibrating perfusion thresholds between deconvolution algorithms based on their quantitative accuracy. This may harmonize ischemic lesion volumes determined by different CTP software.
Real-world usage diminishes validity of Artificial Intelligence tools
Authors: Vaid Akhil , Sawant Ashwin , Suarez-Farinas Mayte , Lee Juhee , Kaul Sanjeev , Kovatch Patricia , Freeman Robert , Jiang Joy , Jayaraman Pushkala , Fayad Zahi , Argulian Edgar , Lerakis Stamatios , Charney Alexander , Wang Fei , Levin Matthew , Glicksberg Benjamin , Narula Jagat , Hofer Ira , Singh Karandeep , Nadkarni Girish N
Published: November 2022

Background

Substantial effort has been directed towards demonstrating use cases of Artificial Intelligence in healthcare, yet limited evidence exists about the long-term viability and consequences of machine learning model deployment.

Methods

We use data from 130,000 patients spread across two large hospital systems to create a simulation framework for emulating real-world deployment of machine learning models. We consider interactions resulting from models being re-trained to improve performance or correct degradation, model deployment with respect to future model development, and simultaneous deployment of multiple models. We simulate possible combinations of deployment conditions, degree of physician adherence to model predictions, and the effectiveness of these predictions.

Results

Model performance shows a severe decline following re-training even when overall model use and effectiveness is relatively low. Further, the deployment of any model erodes the validity of labels for outcomes linked on a pathophysiological basis, thereby resulting in loss of performance for future models. In either case, mitigations applied to offset loss of performance are not fully corrective. Finally, the randomness inherent to a system with multiple deployed models increases exponentially with adherence to model predictions.

Conclusions

Our results indicate that model use precipitates interactions that damage the validity of deployed models, and of models developed in the future. Without mechanisms which track the implementation of model predictions, the true effect of model deployment on clinical care may be unmeasurable, and lead to patient data tainted by model use being permanently archived within the Electronic Healthcare Record.
Blind validation of MSIntuit, an AI-based pre-screening tool for MSI detection from histology slides of colorectal cancer
Authors: Saillard Charlie , Dubois Rémy , Tchita Oussama , Loiseau Nicolas , Garcia Thierry , Adriansen Aurélie , Carpentier Séverine , Reyre Joelle , Enea Diana , Kamoun Aurélie , Rossat Stéphane , Sefta Meriem , Auffret Michael , Guillou Lionel , Fouillet Arnaud , Kather Jakob Nikolas , Svrcek Magali
Published: November 2022

Objective

Mismatch Repair Deficiency (dMMR) / Microsatellite Instability (MSI) is a key biomarker in colorectal cancer (CRC). Universal screening of CRC patients for dMMR/MSI status is now recommended, but contributes to increased workload for pathologists and delayed therapeutic decisions. Deep learning has the potential to ease dMMR/MSI testing in clinical practice, yet no comprehensive validation of a clinically approved tool has been conducted.

Design

We developed an MSI pre-screening tool, MSIntuit, that uses deep learning to identify MSI status from H&E slides. For training, we used 859 slides from the TCGA database. A blind validation was subsequently performed on an independent dataset of 600 consecutive CRC patients. Each slide was digitised using Phillips-UFS and Ventana-DP200 scanners. Thirty dMMR/MSI slides were used for calibration on each scanner. Prediction was then performed on the remaining 570 patients following an automated quality check step. The inter and intra-scanner reliability was studied to assess MSIntuit’s robustness.

Results

MSIntuit reached a sensitivity and specificity of 97% (95% CI: 93-100%) / 46% (42-50%) on DP200 and of 95% (90-98%) / 47% (43-51%) on UFS scanner. MSIntuit reached excellent agreement on the two scanners (Cohen’s κ: 0.82) and was repeatable across multiple rescanning of the same slide (Fleiss’ κ: 0.82).

Conclusion

We performed a successful blind validation of the first clinically approved AI-based tool for MSI detection from H&E slides. MSIntuit reaches sensitivity comparable to gold standard methods (92-95%) while ruling out almost half of the non-MSI population, paving the way for its use in clinical practice.
Hospital length of stay throughout bed pathways and factors affecting this time: a non-concurrent cohort study of Colombia COVID-19 patients and an unCoVer network project
Authors: Ruiz Galvis Lina Marcela , Perez Aguirre Carlos Andres , Pérez Bedoya Juan Pablo , Mendoza Cardozo Oscar Ignacio , Christopher Barengo Noël , Sanchez Escudero Juan Pablo , Jimenez Jonathan Cardona , Diaz Valencia Paula Andrea
Published: November 2022

Predictions of hospital beds occupancy depends on hospital admission rates and the length of stay (LoS) according to bed type (hospital and intensive care unit beds). The objective of this study was to describe the LoS of COVID-19 hospital patients in Colombia during 2020-2021. Accelerated failure time models were used to estimate the LoS distribution according to each bed type and throughout each bed pathway. Acceleration factors and 95% confidence intervals were calculated to measure the effect on LoS of the outcome, sex, age, admission period during the epidemic (i.e., epidemic waves, peaks or valleys, and before/after vaccination period), and patients geographic origin. Most of the admitted COVID-19 patients occupied just hospital bed. Recovered patients spent more time in the hospital and intensive care unit than deceased patients. Men had longer LoS than women. In general, the LoS increased with age. Finally, the LoS varied along epidemic waves. It was lower in epidemic valleys than peaks, and became shorter after vaccinations began in Colombia than before. Our study highlights the necessity of analyzing local data on hospital admission rates and LoS to design strategies to prioritize hospital beds resources during the current and future pandemics.
Evolutionarily conserved waves of tooth replacement in the gecko are dependent on local signaling
Authors: Brink Kirstin S. , Cytrynbaum Eric , Grieco Theresa M. , Henriquez Joaquin I. , Zhitnitsky Anna , Richman Joy M.
Published: November 2022

The fossil record contains dinosaur jaws with rows of unerupted successional teeth that are arranged in a variety of elegant patterns. The remnants of these patterns are visible in modern dentate reptiles but the mechanism for generating and maintaining these rows of teeth is unknown. The biology underlying the tooth replacement pattern was hypothesized to either be stimuli transmitted across tooth families in the jaws (Edmund) or secretion of local inhibitory molecules that would stagger development of adjacent tooth families (Osborn). To test these hypotheses and generate new ones, we completed a study on 6 treated adult geckos in which one side of the jaw had teeth removed. Wax bites were used to record the maxillary teeth 2 times a week. Tooth presence or absence was recorded and transformed mathematically. The time between eruption at each tooth position was measured as was the relative phase compared to the immediate adjacent teeth over successive bites. The period between eruption events at each tooth position was approximately 30 days with some lengthening over time. The average relative phase showed there was a tilt in the data that fit the observation that alternating teeth were being shed. This tilt was opposite on the left and right sides of the jaw. The asymmetry of the right and left sides was consistent across the dentition. After plucking, the pattern recovers after 3 periods fitting with the consistent finding that there are 3 teeth in each tooth family. Ablated areas did not recover tooth formation even after 14 months. The plucked animals showed evidence of fixed, local signaling that restores the pattern. Two models based on Osborn’s concept of a “zone of inhibition” deviate from the observed data. The ablated animals show no change in patterns of tooth eruption anterior and posterior to the gap. Thus there is no support for the Wave stimulus theory of Edmund. Finally, we propose a new Phase Inhibition Model. This model assumes fixed initiation sites at which teeth are initiated at some phase within a month-long cycle and that, as a tooth is initiated, the cycles of nearby initiation sites are inhibited in their progress. This inhibition causes nearest neighbours to erupt in anti-synchrony. This model best maintained the tilt, spacing timing of the real biological data. Mathematical modeling was sensitive enough to measure the normal developmental instability and the resilience of the gecko to restore homeostasis after tooth removal.
Linking movement and dive data to prey distribution models: new insights in foraging behaviour and potential pitfalls of movement analyses
Authors: Florko Katie R.N. , Shuert Courtney R. , Cheung William W.L. , Ferguson Steven H. , Jonsen Ian D. , Rosen David A.S. , Sumaila U. Rashid , Tai Travis C. , Yurkowski David J. , Auger-Méthé Marie
Published: November 2022

Background

Animal movement data are regularly used to infer foraging behaviour and relationships to environmental characteristics, often to help identify critical habitat. To characterize foraging, movement models make a set of assumptions rooted in theory, for example, time spent foraging in an area increases with higher prey density.

Methods

We assessed the validity of these assumptions by associating horizontal movement and diving of satellite-telemetered ringed seals (Pusa hispida) – an opportunistic predator – in Hudson Bay, Canada, to modelled prey data and environmental proxies.

Results

Modelled prey biomass data performed better than their environmental proxies (e.g., sea surface temperature) for explaining seal movement, however movement was not related to foraging effort. Counter to theory, seals appeared to forage more in areas with relatively lower prey diversity and biomass, potentially due to reduced foraging efficiency in those areas.

Conclusions

Our study highlights the need to validate movement analyses with prey data to effectively estimate the relationship between prey availability and foraging behaviour.
Structural insights into perilipin 3 membrane association in response to diacylglycerol accumulation
Authors: Choi Yong Mi , Ajjaji Dalila , Fleming Kaelin D. , Borbat Peter P. , Jenkins Meredith L. , Moeller Brandon E. , Fernando Shaveen , Bhatia Surita R. , Freed Jack H. , Burke John E. , Thiam Abdou Rachid , Airola Michael V.
Published: November 2022

Lipid droplets (LDs) are dynamic organelles that contain an oil core mainly composed of triglycerides (TAG) that is surrounded by a phospholipid monolayer and LD-associated proteins called perilipins (PLINs). During LD biogenesis, perilipin 3 (PLIN3) is recruited to nascent LDs as they emerge from the endoplasmic reticulum. Here, we analyzed how lipid composition affects PLIN3 recruitment to membrane bilayers and LDs, and the structural changes that occur upon membrane binding. We found the TAG precursors phosphatidic acid and diacylglycerol (DAG) recruit PLIN3 to membrane bilayers and define an expanded Perilipin-ADRP-Tip47 (PAT) domain that preferentially binds DAG enriched membranes. Membrane binding induces a disorder/order transition of alpha helices within the PAT domain and 11-mer repeats, with intramolecular distance measurements consistent with the expanded PAT domain adopting a folded but dynamic structure upon membrane binding. In cells, PLIN3 is recruited to DAG enriched ER membranes, and this requires both the PAT domain and 11-mer repeats. This provides molecular details of PLIN3 recruitment to nascent LDs and identifies a function of the PAT domain of PLIN3 in DAG binding.
Holistic profiling of the venom from the lethal spiderPhoneutria nigriventerby combining high-throughput ion channel screens with venomics
Authors: Cardoso FC , Walker AA , King GF , Gomez MV
Published: November 2022

Spider venoms are a unique source of bioactive peptides, many of which display remarkable biological stability and neuroactivity.Phoneutria nigriventer, often referred to as the Brazilian wandering spider, banana spider or “armed” spider, is endemic to South America and amongst the most dangerous venomous spiders in the world. There are 4,000 envenomation accidents withP. nigriventereach year in Brazil, which can lead to symptoms including priapism, hypertension, blurred vision, sweating, and vomiting. In addition to its clinical relevance,P. nigriventervenom contains peptides that provide therapeutic effects in a range of disease models. In this study, we explored the neuroactivity and molecular diversityP. nigriventervenom using fractionation-guided high-throughput cellular assays coupled to proteomics and multi-pharmacology activity to broaden the knowledge about this venom and its therapeutic potential and provide a proof-of-concept for an investigative pipeline to study spider-venom derived neuroactive peptides. We coupled proteomics with ion channel assays using a neuroblastoma cell line to identify venom compounds that modulate the activity of voltage-gated sodium and calcium channels, as well as the nicotinic acetylcholine receptor. Our data revealed thatP. nigriventervenom is highly complex compared to other neurotoxin-rich venoms and contains potent modulators of voltage-gated ion channels which were classified into four families of neuroactive peptides based on their activity and structures. In addition to the reportedP. nigriventerneuroactive peptides, we identified at least 27 novel cysteine-rich venom peptides for which their activity and molecular target remains to be determined. Our findings provide a platform for studying the bioactivity of known and novel neuroactive components in the venom ofP. nigriventerand other spiders and suggests that our discovery pipeline can be used to identify ion channel-targeting venom peptides with potential as pharmacological tools and to drug leads.
Effect of surfactants on SARS-CoV-2: Molecular Dynamics Simulations
Authors: Domingo Marc , Faraudo Jordi
Published: November 2022

Surfactants are commonly used as disinfection agents in personal care products against bacteria and viruses, including SARS-CoV-2. However, there is a lack of understanding of the molecular mechanisms of the inactivation of viruses by surfactants. Here, we employ coarse grain (CG) and all-atom (AA) molecular dynamics simulations to investigate the interaction between general families of surfactants and the SARS-CoV-2 virus. To this end, we considered a CG model of a full virion. Overall, we found that surfactants have only a small impact over the virus envelope, being inserted into the envelope without dissolving it or generating pores, at the conditions considered here. However, we found that surfactants may induce a deep impact on the spike protein of the virus (responsible for its infectivity), easily covering it and inducing its collapse over the envelope surface of the virus. AA simulations confirmed that both negatively and positively charged surfactants are able to extensively adsorb over the spike protein and get inserted into the virus envelope. Our results suggest that the best strategy for the design of surfactants as virucidal agents will be to focus on those strongly interacting with the spike protein.
CLOOME: contrastive learning unlocks bioimaging databases for queries with chemical structures
Authors: Sanchez-Fernandez Ana , Rumetshofer Elisabeth , Hochreiter Sepp , Klambauer Günter
Published: November 2022

Currently, bioimaging databases cannot be queried by chemical structures that induce the phenotypic effects captured by an image. Through the advent of the contrastive learning paradigm, images and text could be embedded into the same space. We build on this contrastive learning paradigm, to present a novel retrieval system that is able to identify the correct bioimage given a chemical structure out of a database of∼2,000 candidate images with a top-1 accuracy>70 times higher than a random baseline. Additionally, the learned embeddings of our method are highly transferable to various relevant downstream tasks in drug discovery, including activity prediction, microscopy image classification and mechanism of action identification.
PlantTribes2: tools for comparative gene family analysis in plant genomics
Authors: Wafula Eric K. , Zhang Huiting , Kuster Gregory Von , Leebens-Mack James H. , Honaas Loren A. , dePamphilis Claude W.
Published: November 2022

Plant genome-scale resources are being generated at an increasing rate as sequencing technologies continue to improve and raw data costs continue to fall; however, the cost of downstream analyses remains large. This has resulted in a considerable range of genome assembly and annotation qualities across plant genomes due to their varying sizes, complexity, and the technology used for the assembly and annotation. To effectively work across genomes, researchers increasingly rely on comparative genomic approaches that integrate across plant community resources and data types. Such efforts have aided the genome annotation process and yielded novel insights into the evolutionary history of genomes and gene families, including complex non-model organisms. The essential tools to achieve these insights rely on gene family analysis at a genome-scale, but they are not well integrated for rapid analysis of new data, and the learning curve can be steep. Here we present PlantTribes2, a scalable, easily accessible, highly customizable, and broadly applicable gene family analysis framework with multiple entry points including user provided data. It uses objective classifications of annotated protein sequences from existing, high-quality plant genomes for comparative and evolutionary studies. PlantTribes2 can improve transcript models and then sort them, either genome-scale annotations or individual gene coding sequences, into pre-computed orthologous gene family clusters with rich functional annotation information. Then, for gene families of interest, PlantTribes2 performs downstream analyses and customizable visualizations including, (1) multiple sequence alignment, (2) gene family phylogeny, (3) estimation of synonymous and non-synonymous substitution rates among homologous sequences, and (4) inference of large-scale duplication events. We give examples of PlantTribes2 applications in functional genomic studies of economically important plant families, namely transcriptomics in the weedy Orobanchaceae and a core orthogroup analysis (CROG) in Rosaceae. PlantTribes2 is freely available for use within the main public Galaxy instance and can be downloaded from GitHub or Bioconda. Importantly, PlantTribes2 can be readily adapted for use with genomic and transcriptomic data from any kind of organism.
SBMLKinetics: A Tool for Annotation-Independent Classification of Reaction Kinetics for SBML Models
Authors: Xu Jin
Published: November 2022

Background

Reaction networks are widely used as mechanistic models in systems biology to reveal principles of biological systems. Reactions are governed by kinetic laws that describe reaction rates. Selecting the appropriate kinetic laws is difficult for many modelers. There exist tools that attempt to find the correct kinetic laws based on annotations. Here, I developed annotation-independent technologies that assist modelers by focusing on finding kinetic laws commonly used for similar reactions.

Results

Recommending kinetic laws and other analyses of reaction networks can be viewed as a classification problem. Existing approaches to determining similar reactions rely heavily on having good annotations, a condition that is often unsatisfied in model repositories such as BioModels. I developed an annotation-independent approach to find similar reactions via reaction classifications. I proposed a two-dimensional kinetics classification scheme (2DK) that analyzed reactions along the dimensions of kinetics type (K type) and reaction type (R type). I identified approximately ten mutually exclusive K types, including zeroth order, mass action, Michaelis-Menten, Hill kinetics, and others. R types were organized by the number of distinct reactants and the number of distinct products in reactions. I constructed a tool, SBMLKinetics, that inputted a collection of SBML models and then calculated reaction classifications as the probability of each 2DK class. The effectiveness of 2DK was evaluated on BioModels, and the scheme classified over 95% of the reactions.

Conclusions

2DK had many applications. It provided adata-drivenannotation-independent approach to recommending kinetic laws by using type common for the kind of models in combination with the R type of the reactions. Alternatively, 2DK could also be used to alert users that a kinetic law was unusual for the K type and R type. Last, 2DK provided a way to analyze groups of models to compare their kinetic laws. I applied 2DK to BioModels to compare the kinetics of signaling networks with the kinetics of metabolic networks and found significant differences in K type distributions.
DeepBryo: a web app for AI-assisted morphometric characterization of cheilostome bryozoans
Authors: Di Martino Emanuela , Berning Björn , Gordon Dennis P , Kuklinski Piotr , Liow Lee Hsiang , Ramsfjell Mali H , Ribeiro Henrique L , Smith Abigail M , Taylor Paul D , Voje Kjetil L , Waeschenbach Andrea , Porto Arthur
Published: November 2022

Bryozoans are becoming an increasingly popular study system in macroevolutionary, ecological, and paleobiological research. Members of this colonial invertebrate phylum are notable for displaying an exceptional degree of division of labor in the form of specialized modules (polymorphs), which allow for the inference of individual allocation of resources to reproduction, defense, and growth using simple morphometric tools. However, morphometric characterizations of bryozoans are notoriously labored, due to the high number of structures often captured per image, as well as the need for specialized knowledge necessary for classifying individual skeletal structures within those images.

We here introduce DeepBryo, a web application for deep learning-based morphometric characterization of cheilostome bryozoans. DeepBryo requires a single image as input and performs measurements automatically using instance segmentation algorithms. DeepBryo is capable of detecting objects belonging to six classes and outputting fourteen morphological shape measurements for each object based on the inferred segmentation maps. The users can visualize the predictions, check for errors, and directly filter model outputs on the web browser. Measurements can then be downloaded as a comma-separated values file.

DeepBryo has been trained and validated on a total of 72,412 structures, belonging to six different object classes in 935 SEM images of cheilostome bryozoans belonging to 109 different families. The model shows high (>0.8) recall and precision for zooid-level structures. Its misclassification rate is low (~4%) and largely concentrated in a single object class (opesia). The model’s estimated structure-level area, height, and width measurements are statistically indistinguishable from those obtained via manual annotation (r2varying from 0.89 to 0.98) and show no detectable bias. DeepBryo reduces the person-hours required for characterizing the zooids in individual colonies to less than 1% of the time required for manual annotation at no significant loss of measurement accuracy.

Our results indicate that DeepBryo enables cost-, labor,- and time-efficient morphometric characterization of cheilostome bryozoans. DeepBryo can greatly increase the scale of macroevolutionary, ecological, taxonomic, and paleobiological analyses, as well as the accessibility of deep learning tools for this emerging model system. Finally, DeepBryo provides the building blocks necessary for adapting the current application to other study groups.
Toxicity impacts on human adipose MSCs acutely exposed to Aroclor and non-Aroclor mixtures of PCBs
Authors: Behan-Bush Riley M. , Liszewski Jesse N. , Schrodt Michael V. , Vats Bhavya , Li Xueshu , Lehmler Hans-Joachim , Klingelhutz Aloysius J. , Ankrum James A.
Published: November 2022

PCBs accumulate in adipose where they may impact the growth and function of cells within the tissue. This is particularly concerning during adolescence when adipocytes expand rapidly. Herein we sought to understand how exposure to PCB mixtures found in U.S. schools affects human adipose mesenchymal stem/stromal cell (MSC) health and function. We investigated how exposure to Aroclor 1016 and Aroclor 1254, as well as a newly characterized non-Aroclor mixture that resembles the PCB profile found in cabinets, Cabinet Mixture, affects adipose MSC growth, viability, and function in vitro. We found that exposure to all three mixtures resulted in two distinct types of toxicity. At PCB concentrations >20 μM, the majority of MSCs die, while at 1-10 μM MSCs remained viable but display numerous alterations to their phenotype. At these sublethal concentrations, MSC rate of expansion slowed, and morphology changed. Further assessment revealed PCB-exposed MSCs had impaired adipogenesis and a modest decrease in immunosuppressive capabilities. Thus, exposure to PCB mixtures found in schools negatively impacts the health and function of adipose MSCs. This work has implications for human health due to MSCs’ role in supporting the growth and maintenance of adipose tissue.

SYNOPSIS

PCB mixtures found in schools are toxic to human adipose mesenchymal stem/stromal cells, stunting their growth and altering their function in ways that could contribute to metabolic diseases.