bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparison of methods for identifying the optimal treatment duration in trials for antibiotics
Authors: Dufault Suzanne M. , Aldana Brian H. , Phillips Patrick P.J.
Published: October 2024

Background

The optimal duration of antibiotic treatmentmust strike a delicate balance: it must be long enough to achieve desirable efficacy yet short enough to prevent the development of toxicities, adverse events, and mitigate other arduous aspects related to patient burden. Historically, the approach used to determine duration of antibiotic treatment has been inefficient, severely impacting the refinement of therapeutics for tuberculosis where treatment duration, and its complications, can be extensive. Meeting the WHO’s End TB target of developing shorter, more tolerable TB therapeutics will require rigorously re-evaluating best practices in duration-ranging trial designs and statistical methodologies. Many of the challenges in duration-ranging have parallels and proposed solutions in the field of dose-ranging where the literature is substantially more established and where the traditions of qualitative, pairwise comparison studies have been replaced with model-based approaches. Such methods are more efficient and allow for interpolation between the doses observed. Research on efficient study designs and methods for duration-ranging, while similarly attempting to capture a monotonic response relationship, has only just accelerated in earnest over the last two decades.

Methods

This work examines the utility of cutting-edge dose-finding methods (such as MCP-Mod) for duration-ranging of TB treatments. We compare the operating characteristics of the adapted model-based duration-ranging methodologies against standard qualitative methods for the purposes of estimating optimal duration and describing the duration-response relationship, using a simulation study motivated by a Multi-Arm Multi-Stage Response Over Continuous Intervention (MAMS-ROCI) clinical trial design. We explore three specific targets: 1) power to detect a duration-response relationship, 2) ability to accurately reproduce the duration-response curve, and 3) ability to estimate the optimal duration within an acceptable margin of error.

Results

We find that model-based methods outperform standard qualitative comparisons on every target examined, particularly when the sample size is constrained to that of a typical Phase II trial.

Conclusions

We conclude that the success of the next era in TB therapeutics duration evaluation trials, and antibiotics duration-ranging more broadly, will meaningfully rely on the ability to simultaneous pair innovative model-based statistical methods with re-imagined study designs such as MAMS-ROCI.
Biases in Race and Ethnicity Introduced by Filtering Electronic Health Records for ‘Complete Data’
Authors: Acitores Cortina Jose M. , Fatapour Yasaman , Zietz Michael , Brown Kathleen LaRow , Gisladottir Undina , Peter Danner , Bear Don’t Walk Oliver John , Kuchi Aditi , Srinivasan Apoorva , Liu Hongyu , Berkowitz Jacob , Tsang Kevin , Friedrich Nadine , Kievelson Sophia , Tatonetti Nicholas P.
Published: October 2024

Objective

Integrated clinical databases from national biobanks have advanced the capacity for disease research. Data quality and completeness filters are used when building clinical cohorts to address limitations of data missingness. However, these filters may unintentionally introduce systemic biases when they are correlated with race and ethnicity. In this study, we examined the race/ethnicity biases introduced by applying common filters to four clinical records databases.

Materials and Methods

We used 19 filters commonly used in electronic health records research on the availability of demographics, medication records, visit details, observation periods, and other data types. We evaluated the effect of applying these filters on self-reported race and ethnicity. This assessment was performed across four databases comprising approximately 12 million patients.

Results

Applying the observation period filter led to a substantial reduction in data availability across all races and ethnicities in all four datasets. However, among those examined, the availability of data in the white group remained consistently higher compared to other racial groups after applying each filter. Conversely, the Black/African American group was the most impacted by each filter on these three datasets, Cedars-Sinai dataset, UK-Biobank, and Columbia University Dataset.

Discussion and Conclusion

Our findings underscore the importance of using only necessary filters as they might disproportionally affect data availability of minoritized racial and ethnic populations. Researchers must consider these unintentional biases when performing data-driven research and explore techniques to minimize the impact of these filters, such as probabilistic methods or the use of machine learning and artificial intelligence.
The Development and Validation of the Global Post Trauma Symptom Scale-Uganda among Trauma-Affected Adults
Authors: Murphy Michalopoulos Lynn , Meinhart Melissa , Walton Erin , Robertson David , Thompson Autumn , Northrup Thomas , Kim Jong Sung , Conway Anne , Aggarwal Nikita
Published: October 2024

The purpose of this study was to adapt and validate the Global Post-Traumatic Stress Scale (GPTSS) among adult caregivers of youth living with HIV in Uganda. This is the first study to adapt and validate a non-western instrument measuring post-traumatic symptoms in Uganda, which is critical in the accurate assessment of caregiver trauma on psychosocial functioning. The study utilized qualitative (i.e., cognitive interviews) and quantitative (e.g., classical test and item response theory) methods to establish content, criterion, and construct validity. The results indicated that the GPTSS was a valid and reliable assessment tool for the population. Specifically, cognitive interview results (n=31) indicated that participants broadly understood items of the GPTSS, but revisions in wording were needed to capture the intended meaning of some items. A reduced version of the GPTSS, GPTSS-U, was created through item response theory analyses based on discrimination criteria and difficulty parameters. A positive relationship between higher GPTSS-U and the PHQ-9 scores indicated acceptable criterion validity with a moderate effect size (r = 0.547;p< 0.000). Results from the graded response model indicated high discrimination parameters (rangeb= -.88, 3.14). Test information function curve findings indicated that the GPTSS is most precise at moderate to severe levels of post-traumatic symptoms. Using nested linear regression models, we found incremental validity as the total scores on the GPTSS-U significantly predicted functional impairment (p< 0.05) beyond the impact of the Post Traumatic Stress Checklist-5. Overall findings indicate that post-traumatic symptom measures must be adapted and developed for cross-cultural use to maintain validity and contextual relevance.
No maternal health without mental health: suggested indicators to monitor perinatal mental health globally
Authors: Palestra Francesca , Moran Allisyn C. , Chowdhary Neerja , Salisbury Tatiana Taylor , Dua Tarun , McNab Shanon , Layton Elly , Kennedy Elissa C , Homer Caroline SE , Fisher Jane , Honickman Simone , Hill Zelee
Published: October 2024

Perinatal mental health refers to the psychological wellbeing of women during pregnancy and up to one year postpartum. Perinatal mental health conditions as depression significantly affect maternal, newborn health, and child development worldwide. However, the absence of standardized indicators limits effective monitoring and evaluation. This paper introduces a framework with indicators for global perinatal mental health monitoring.

The framework development involved a scoping review, expert consultations, and stakeholder surveys. A global expert group, guided by evidence and the World Health Organization (WHO), identified an initial set of indicators. Two rounds of online surveys were conducted, allowing stakeholders to rank these indicators and suggest additional ones. Indicators were chosen based on their validity, reliability, relevance, feasibility, and potential impact for advocacy purposes on perinatal mental health. The WHO expert working group finalized the set of indicators.

Six perinatal mental health indicators were identified for future testing. These include three primary indicators: policy presence, screening coverage, and prevalence of perinatal mental health conditions. Three secondary indicators cover mental health expenditure, healthcare professional training, and provision of care.

Challenges to implementing standardized monitoring include resource limitations and data collection methods. Strengthening health workers and facility capacity to measure, report, and interpret perinatal mental health data, including the use of screening tools, is crucial. The integration of these indicators into existing systems, such as health information management systems (HIMS) and national surveys, will be key.

Monitoring perinatal mental health is essential for improving maternal and newborn outcomes. The proposed framework offers a means to enhance global monitoring and guide policy interventions. Global stakeholders are encouraged to integrate perinatal mental health into their policy, program, and monitoring agendas.
Charting the liver and lung metastatic niche in breast cancer
Authors: Sznurkowska Magdalena K , Castro-Giner Francesc , Krol Ilona , Zhang Yongzhan , Ozimski Lauren L. , D'Anna Irene , Ring Alexander , Schwab Fabienne Dominique , Zhang Yu Wei , Zhou Jianwen , Saini Massimo , Strittmatter Karin , Budinjas Selina , Vujanovic Marko , Kontarakis Zacharias , Pontecorvi Giada , Albrecht Francesca , Mertz Kirsten D. , Auray Gael , Giachino Claudio , Taylor Verdon , Gvozdenovic Ana , Kovacs Werner , Malanchi Ilaria , Frauchiger-Heuer Heike , Wicki Andreas , Vetter Marcus , Ligorio Matteo , Aceto Nicola
Published: October 2024

Breast cancer progression to visceral organs such as lung and liver is regarded as a dreadful event, unequivocally associated with a poor prognosis. Yet, these vital sites are characterized by highly diverse cellular microenvironments and physiological functions, suggesting that they may influence cancer cells behavior in divergent ways. Unexpectedly, we find that while the liver microenvironment fosters metastasis-promoting properties and boosts secondary spread, the lungs impose a roadblock to the same processes. Using patient data and tissues from rapid autopsy, as well as mouse models with barcode-mediated metastasis tracing, niche labeling technology and single cell analysis of both tumor cells and their direct microenvironment, we dissect cellular and molecular microenvironmental factors that impose this differential behavior. Among these, we identify BMP2-producing endothelial cells as critical players within the liver metastatic niche, capable to enhance metastasis-to-metastasis dissemination. Targeting BMP2 receptor on breast cancer cells suppresses their metastasis-forming ability. Altogether, we reveal a contrast in the site-specific behavior of lung and liver metastases in breast cancer, highlighting microenvironmental factors that contribute to this diversity, as well as organ-specific opportunities for intervention.
Unveiling the Origins and Genetic Makeup of the ‘Forgotten People’: A Study of the Sarmatian-Period Population in the Carpathian Basin
Authors: Schütz Oszkár , Maróti Zoltán , Tihanyi Balázs , Kiss Attila P. , Nyerki Emil , Gînguță Alexandra , Kiss Petra , Varga Gergely I. B. , Kovács Bence , Maár Kitti , Kovacsóczy Bernadett Ny. , Lukács Nikoletta , Major István , Marcsik Antónia , Patyi Eszter , Szigeti Anna , Tóth Zoltán , Walter Dorottya , Wilhelm Gábor , Andrási Réka Cs. , Bernert Zsolt , Kis Luca , Oța Liana , Pálfi György , Pintye Gábor , Pópity Dániel , Simalcsik Angela , Soficaru Andrei Dorian , Spekker Olga , Varga Sándor , Neparáczki Endre , Török Tibor
Published: October 2024

Summary

The nomadic Sarmatians dominated the Pontic Steppe from 3rd century BCE and the Great Hungarian Plain from 50 CE until the Huns’ 4th-century expansion. In this study, we present the first large-scale genetic analysis of 156 genomes from 1st- to 5th-century Hungary and the Carpathian foothills. Our findings reveal minor East Asian ancestry in the Carpathian Basin (CB) Sarmatians, distinguishing them from other regional populations. Using F4-statistics, qpAdm, and IBD analysis, we show that CB Sarmatians descended from Steppe Sarmatians originating in the Ural and Kazakhstan regions, with Romanian Sarmatians serving as a genetic bridge between the two groups. We also identify two previously unknown migration waves during the Sarmatian era and a notable continuity of the Sarmatian population into the Hunnic period, despite a smaller influx of Asian-origin individuals. These results shed new light on Sarmatian migrations and the genetic history of a key population neighbouring the Roman Empire.
Contrastive functional connectivity defines neurophysiology-informed symptom dimensions in major depression
Authors: Zhu Hao , Tong Xiaoyu , Carlisle Nancy B , Xie Hua , Keller Corey J , Oathes Desmond J , Nemeroff Charles B , Fonzo Gregory A , Zhang Yu
Published: October 2024

Background: Major depressive disorder (MDD) is a prevalent psychiatric disorder characterized by substantial clinical and neurobiological heterogeneity. Conventional studies that solely focus on clinical symptoms or neuroimaging metrics often fail to capture the intricate relationship between these modalities, limiting their ability to disentangle the complexity in MDD. Moreover, patient neuroimaging data typically contains normal sources of variance shared with healthy controls, which can obscure disorder-specific variance and complicate the delineation of disease heterogeneity. Methods: We employed contrastive principal component analysis to extract disorder-specific variations in fMRI-based resting-state functional connectivity (RSFC) by contrasting MDD patients (N=233) with age-matched healthy controls (N=285). We then applied sparse canonical correlation analysis to identify latent dimensions in the disorder variations by linking the extracted contrastive connectivity features to clinical symptoms in MDD patients. Results: Two significant and generalizable dimensions linking distinct brain circuits and clinical profiles were discovered. The first dimension, associated with an apparent internalizing-externalizing symptom dimension, was characterized by self-connections within the visual network and also associated with choice reaction times of cognitive tasks. The second dimension, associated with personality facets such as extraversion and conscientiousness typically inversely associated with depression symptoms, is primarily driven by self-connections within the dorsal attention network. This depression-protective personality dimension is also associated with multiple cognitive task performances related to psychomotor slowing and cognitive control. Conclusions: Our contrastive RSFC-based dimensional approach offers a new avenue to dissect clinical heterogeneity underlying MDD. By identifying two stable, neurophysiology-informed symptom dimensions in MDD patients, our findings may enhance disease mechanism insights and facilitate precision phenotyping, thus advancing the development of targeted therapeutics for precision mental health.
Insights into Heart Failure Metabolite Markers through Explainable Machine Learning
Authors: Baron Cantin , Mehanna Pamela , Daneault Caroline , Hausermann Leslie , Busseuil David , Tardif Jean-Claude , Dupuis Jocelyn , Des Rosiers Christine , Ruiz Matthieu , Hussin Julie
Published: October 2024

Understanding molecular traits through metabolomics offers an avenue to tailor cardiovascular prevention, diagnosis and treatment strategies more effectively. This study focuses on the application of machine learning (ML) and explainable artificial intelligence (XAI) algorithms to detect discriminant molecular signatures in heart failure (HF). In this study, we aim to uncover metabolites with significant predictive value by analyzing targeted metabolomics data through ML models and XAI methodologies. After robust quality control procedures, we analyzed 55 metabolites from 124 plasma samples, including 53 HF patients and 71 controls, comparing Logistic Regression (Logit) models with Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGB), all achieving high accuracy in predicting group labels: 84.20% (σ=5.46), 85.73% (σ=6.25), and 84.80% (σ=7.84), respectively. Permutation-based variable importance and Local Interpretable Model-agnostic Explanations (LIME) were used for group-level and individual-level explainability, respectively, complemented by H-Friedman statistics for variable interactions, yielding reliable, explainable insights of the ML models. Metabolites well-known for their association with heart failure, such as glucose and cholesterol, but also more recently described association such C18:1 carnitine, were reaffirmed in our analysis. The novel discovery of lignoceric acid (C24:0 fatty acid) as a critical discriminator, was confirmed in a replication cohort, underscoring its potential as a metabolite marker. Furthermore, our study highlights the utility of 2-way variable interaction analysis in unveiling a network of metabolite interactions essential for accurate disease prediction. The results demonstrate our approach’s efficacy in identifying key metabolites and their interactions, illustrating the power of ML and XAI in advancing personalized healthcare solutions.

SeeGraphical Abstract
Coupled Solvent Dynamics and Protein Dynamics Help Drive Functional Differences in Exon-19 Deletion Mutants in the Epidermal Growth Factor Receptor (EGFR/ErbB1/HER1) Kinase Domain
Authors: Patil Keshav , Ashtekar Kumar D. , Tsutsui Yuko , Dhabal Debdas , Suresh Krishna , Lemmon Mark A. , Radhakrishnan Ravi
Published: October 2024

Deletions in Exon-19 of the epidermal growth factor receptor (EGFR) play a pivotal role in the pathogenesis of non-small cell lung cancer (NSCLC), influencing patient response to tyrosine kinase inhibitors (TKIs). Although these mutations are known to affect treatment efficacy, the precise molecular mechanisms have been unclear. Building upon recent insights from the study [DOI: 10.1038/s41467-022-34398-z], which identified two distinct mutation profiles associated with differential drug sensitivity and clinical outcomes, our research delves into the molecular dynamics that drive these variances.

We employed molecular dynamics simulations, enhanced sampling methods, and machine learning to classify Exon-19 deletion mutations into two profiles based on their conformational dynamics. Profile 1 mutations display only localized motions in key subdomains in their fluctuations about the equilibrium state, and a high affinity for ATP and consequent resistance to TKIs, while profile 2 mutations show reduced ATP binding affinity due to delocalized motion characterized by an increased flexibility between the N– and C-lobes of the EGFR kinase domain. This structural flexibility perturbs the ATP binding site, leading to decreased affinity and, heightened sensitivity to TKIs.

Our use of the INDUS technique has shed light on the collective solvent dynamics, further elucidating the coupling between long timescale solvent fluctuations and protein conformational dynamics, that likely contributes to the observations in HDX-MS studies. Our free energy analysis, covering timescales relevant to both HDX-MS and ligand interaction, provides a deeper understanding of the relationship between protein and solvent dynamics and their collective impact on drug efficacy in NSCLC with EGFR Exon-19 deletions.
Superior colliculus projections drive dopamine neuron activity and movement but not value
Authors: Poisson Carli L. , Wolff Amy R. , Prohofsky Julianna , Herubin Cassandra , Blake Madelyn , Saunders Benjamin T.
Published: October 2024

To navigate complex environments, animals must rapidly integrate sensory information and respond appropriately to gather rewards and avoid threats. It is well established that dopamine neurons in the ventral tegmental area (VTA) and substantia nigra pars compacta (SNc) are key for creating and maintaining associations between environmental stimuli (i.e., cues) and the outcomes they predict, through Pavlovian learning. However, it remains unclear how relevant sensory information is integrated into dopamine (DA) pathways to guide exploration and learning. The superior colliculus (SC) receives direct visual input, and is anatomically positioned as a relay for rapid sensory augmentation of dopamine neurons, which could underlie the formation of Pavlovian associations. Here, we characterize the anatomical organization and functional impact of SC projections to the VTA and SNc in rats. First, using anatomical tracing techniques, we show that neurons in the intermediate and deep layers of SC synapse densely throughout the ventral midbrain, interfacing directly with neurons projecting to the striatum and ventral pallidum. Using fiber photometry, we find that these SC projections excite both dopamine and GABA neurons in the VTA and dopamine neurons in the SNc in vivo. Despite this, cues predicting SC terminal stimulation did not reliably evoke behavior on their own in an optogenetic Pavlovian conditioning paradigm. Further, optogenetic activation of SC terminals in the VTA/SNc did not support primary reinforcement or produce place preference or avoidance. Instead, we find that stimulation of SC terminals in the VTA and SNc reliably evoked head turning behavior. This body reorientation increased in intensity with repeated stimulations, suggesting that strengthening this circuit could underlie sensorimotor learning related to exploration and attentional bias. Together our results show that collicular neurons contribute to cueguided learning by controlling pose adjustments through interaction with dopamine systems.
Gene encoding CC-NBS-LRR protein on rye chromosome 1RS confers wheat stripe rust resistance
Authors: Wang Chunhui , Chang Yanan , Wang Mian , Wang Jing , Liu Chang , Fan Chaolan , Yi Congyang , Zhou Chen , Yuan Jing , Yang Wuyun , Liu Dengcai , Wang Tao , Liu Yang , Ye Xingguo , Han Fangpu
Published: October 2024

Stripe rust, a globally widespread disease, stands as one of the most significant threats to wheat cultivation. The 1BL/1RS translocation, renowned for its robust resistance to both rust and powdery mildew, has historically played an important role in wheat breeding and production. The gene for resistance to stripe rust on the 1RS is known asYr9and plays an important role in the production of wheat, but over the course of long-term breeding had lost its resistance due to the evolution of stripe rust towards greater and greater virulence. In this paper, we cloned the stripe rust resistance gene,Yr9, from triticale by genetic mapping approach. TheYr9encodes a typical nucleotide-binding leucine-rich repeat (NLR) protein. Both transgenic and overexpression ofYr9in highly stripe rust susceptible wheat varieties conferred complete resistance to the stripe rust races CYR17 and partial resistance to the stripe rust races CYR32, CYR33, and CYR34. In addition, theYr9allele in the 1BL/1RS translocation line also showed the same level of resistance to stripe rust. Both two alleles loses resistance when deployed in the field or inoculated with mixed physiological races collected from the field. Our findings provide valuable insights for breeders to strategically incorporate disease resistance genes and provides a foundation for further understanding how pathogenic bacteria might evolve to evade recognition via NLR type proteins.

Significance

The 1BL/1RS translocation between wheat and rye is the most successful case of exogenous gene application in plant genetic improvement and has been used in wheat breeding for over 50 years. Here we report the cloning of a stripe rust resistance geneYr9located on rye chromosome 1RS using a triticale population. TheYr9encodes a coiled-coil nucleotide-binding site leucine-rich repeat (CC-NBS-LRR) protein that show complete resistance to the stripe rust races CYR17 and partial resistance to the stripe rust races CYR32, CYR33, and CYR34, albeit demonstrating susceptibility under field conditions. Our findings positionYr9as an ideal candidate gene to study the mechanism of inactivation of disease resistance genes as a result of pathogen evolution.
Microbiome Data Integration via Shared Dictionary Learning
Authors: Yuan Bo , Wang Shulei
Published: October 2024

Data integration is a powerful tool for facilitating a comprehensive and generalizable understanding of microbial communities and their association with outcomes of interest. However, integrating data sets from different studies remains a challenging problem because of severe batch effects, unobserved confounding variables, and high heterogeneity across data sets. We propose a new data integration method called MetaDICT, which initially estimates the batch effects by weighting methods in causal inference literature and then refines the estimation via a novel shared dictionary learning. Compared with existing methods, MetaDICT can better avoid the overcorrection of batch effects and preserve biological variation when there exist unobserved confounding variables or data sets are highly heterogeneous across studies. Furthermore, MetaDICT can generate comparable embedding at both taxa and sample levels that can be used to unravel the hidden structure of the integrated data and improve the integrative analysis. Applications to synthetic and real microbiome data sets demonstrate the robustness and effectiveness of MetaDICT in integrative analysis. Using MetaDICT, we characterize microbial interaction, identify generalizable microbial signatures, and enhance the accuracy of disease prediction in an integrative analysis of colorectal cancer metagenomics studies.
Reducing Surgeons’ Routine Task Burden, Enhancing Surgical Decision Making, and Ensuring Patient Safety Using SurgeryLLM
Authors: Ong Chin Siang , Obey Nicholas T. , Zheng Yanan , Cohan Arman , Schneider Eric B.
Published: October 2024

SurgeryLLM, a large language model framework using Retrieval Augmented Generation demonstrably incorporated domain-specific knowledge from current evidence-based surgical guidelines when presented with patient-specific data. The successful incorporation of guideline-based information represents a substantial step toward enabling greater surgeon efficiency, improving patient safety, and optimizing surgical outcomes.
Volatiles from low R:FR-treated maize plants increase the emission of herbivore-induced plant volatiles in their neighbors
Authors: Escobar-Bravo Rocío , Schimmel Bernardus C.J. , Erb Matthias
Published: October 2024

Low Red (R) to Far Red (FR) light ratios, a light signal associated with vegetation shade, can prompt intact maize (Zea mays) plants to constitutively emit more volatiles when exposed to herbivory-induced plant volatiles (HIPVs). Here we investigated how simulated shading affects priming responses in the context of volatile-mediated plant-plant interactions. Receiver maize plants were exposed to either constitutive volatile organic compounds (cVOCs) or HIPVs from emitter maize plants, while we manipulated R:FR light conditions of receivers or emitters. Priming responses in the receivers were then assessed by measuring real-time volatile emissions following simulated herbivory. We show that low R:FR light enhances HIPVs emissions in plants previously exposed to HIPVs from neighbours independently of the light conditions of emitters. We also demonstrate that both cVOCs and HIPVs emitted by maize grown under low R:FR amplify HIPVs emissions in their neighbours. This amplified response could not be explained by FR-mediated changes in the release of green leaf volatiles or terpenoids by emitters, thus suggesting the involvement of other VOCs. We conclude that volatile-mediated plant-plant interactions can be expected to become more intense in denser canopies due to light-mediated amplification of volatile emission and responsiveness.
Predicting The Pathway Involvement For All Pathway and Associated Compound Entries Defined in the Kyoto Encyclopedia of Gene and Genomes
Authors: Huckvale Erik D. , Moseley Hunter N.B.
Published: October 2024

Background/Objectives

Predicting the biochemical pathway involvement of a compound could facilitate the interpretation of biological and biomedical research. Prior prediction approaches have largely focused on metabolism, training machine learning models to solely predict on metabolic pathways. However, there are many other types of pathways in cells and organisms which are of interest to biologists;

Methods

While several publications have made use of the metabolites and metabolic pathways available in the Kyoto Encyclopedia of Genes and Genomes (KEGG), we downloaded all the compound entries with pathway annotations available in KEGG. From this data, we constructed a dataset where each entry contained features representing compounds combined with features representing pathways followed by a binary label indicating whether the given compound is associated with the given pathway. We trained multi-layer perceptron binary classifiers on variations of this dataset;

Results

The model trained on all KEGG compounds and pathways scored an overall mean performance of 0.847, median of 0.848, and standard deviation of 0.0098.

Conclusions

The mean performance of 0.847 with a standard deviation of 0.0098 for all KEGG pathways, compared to the performance of 0.800 and standard deviation of 0.021 of metabolic KEGG pathways only, demonstrates the capability to effectively predict biochemical pathways in general in addition to those specifically related to metabolism. Moreover, the improvement in the performance demonstrates additional transfer learning with the inclusion of non-metabolic pathways.
Genetic Adaptation to Amoxicillin inEscherichia coli: The Limited Role ofdinBandkatE
Authors: Teichmann Lisa , Wenne Marcus , Luitwieler Sam , Dugar Gaurav , Bengtsson-Palme Johan , Kuile Benno ter
Published: October 2024

Bacteria can quickly adapt to sub-lethal concentrations of antibiotics. Several stress and DNA repair genes contribute to this adaptation process. However, the pathways leading to adaptation by acquisition ofde novomutations remain poorly understood. This study explored the roles of DNA polymerase IV (dinB) and catalase HP2 (katE) inE. coli’s adaptation to amoxicillin. These genes are thought to play essential roles in beta-lactam resistance -dinBin increasing mutation rates andkatEin managing oxidative stress. By comparing the adaptation rates, transcriptomic profiles, and genetic changes of wild-type and knockout strains, we aimed to clarify the contributions of these genes to beta-lactam resistance. While all strains exhibited similar adaptation rates and mutations in thefrdDgene andampCoperon, several unique mutations were acquired in the ΔkatEand ΔdinBstrains. Overall, this study distinguishes the contributions of general stress-related genes on the one hand, anddinB,andkatEon the other hand, in development of beta-lactam resistance.
The impact of barcode-assisted medication administration on medication administration errors in non-unit-dose settings: a systematic review
Authors: Tan Weicong , Bingham Gordon , Tong Erica , Bonakdeh Erfan Shakibaei , Wang Weiqing
Published: October 2024

Objective

This study aims to investigate how barcode-assisted medication administration systems (BCMAs) can affect medication administration errors (MAEs) in non-unit-dose dispensing settings, since unit dose dispensing system can be a confounding variable affecting MAE rates.

Materials and Methods

We conducted a systematic review of articles on MEDLINE, EMBASE, EMCARE, CINAHL and Scopus. Studies are meticulously examined to exclude those with unit-dose dispensing setting.

Results

We included 4 papers in the review. The categories of MAEs reported among these studies are heterogeneous. 2 studies give weak evidence and 1 study give moderate evidence that BCMA can lower some categories of MAEs. 1 studies gives weak evidence that BCMA increase the wrong administration time error.

Discussion

Studies provide weak to moderate evidence that barcode-assisted medication administration can lower certain categories of medication administration errors. However, some reported findings are minimal.

Conclusion

More multi-ward multi-hospital studies need to be conducted to provide stronger evidence on BCMA’s impact on MAEs, especially on dosage-related MAEs, in settings without unit dose dispensing systems.
Membrane Expression Enhances Folding, Multimeric Structure Formation, and Immunogenicity of Viral Capsid Proteins
Authors: Cui Junru , Yuan Fangfeng , Qin Jane , Jeon Ju Hyeong , Yun Dong Soo , Wang Tianlei , Xu Renhuan , Cao Helen , Chen Jianzhu
Published: October 2024

Viral capsid proteins are widely explored for subunit vaccine development but are often hampered by their complexity of production and low immunogenicity. Here, we report a simple approach to overcome these challenges by combining mRNA vaccine technology with protein engineering. Using African swine fever virus (ASFV) capsid proteins P72 and penton as models, we engineered them into membrane-bound and secreted forms, and compared their immunogenicity to the native intracellular form in mice and pigs through mRNA vaccination. The membrane-bound and secreted P72 and penton folded into their native multimeric structure independent of viral chaperone, therefore preserving their conformational epitopes. The membrane-bound P72 and penton also elicited significantly stronger antibody and T cell responses than their secreted or intracellular counterparts. Our study provides a simple approach to enhance folding, multimeric structure formation, and immunogenicity of viral capsid proteins for ASFV subunit vaccine development and immunogenicity of intracellular proteins in general.
Mapping the PTEN Mutation Landscape: Structural and Functional Drivers of Lung Cancer
Authors: Hossain Mohammad Uzzal , Sakib Mohammad Nazmus , Rahman A.B.Z. Naimur , Hasan SM Sajid , Nisha Nazia Hassan , Bhattacharjee Arittra , Chowdhury Zeshan Mahmud , Ahammad Ishtiaque , Das Keshob Chandra , Rahman Mohammad Shahedur , Salimullah Md.
Published: October 2024

Lung cancer is the predominant form of cancer globally, arising from the dysfunction of genetic mutations. Although PTEN mutation is crucial in the aetiology of lung cancer, the mapping of these major drivers has to be determined. We leverage computational algorithms on 43,855 SNPs of PTEN to discover the mutational impact contributing to lung cancer. Fifteen variations were identified as detrimental, and no pertinent studies have previously addressed their structural and functional aspects. Notably, seven variations were identified as the most significant contributors to lethal effects in functional aberration, as demonstrated by the computational assessment. Subsequently, molecular simulation elucidated the structural instability associated with these alterations. Furthermore, drug binding experiments at the mutational site corroborated the destabilization experiments by demonstrating the conformational alteration of the structure, resulting in varied amino acid interactions. In summary, the present study elucidates the influence of mutations in PTEN structure on its functional architecture.
How to select the best zero-shot model for the viral proteins?
Authors: Yu Yuanxi , Jiang Fan , Zhong Bozitao , Hong Liang , Li Mingchen
Published: October 2024

Predicting the fitness of viral proteins holds notable implications for understanding viral evolution, advancing fundamental biological research, and informing drug discovery. However, the considerable variability and evolution of viral proteins make predicting mutant fitness a major challenge. This study introduces the ProPEC, a Perplexity-based Ensemble Model, aimed at improving the performance of zero-shot predictions for protein fitness across diverse viral datasets. We selected five representative pretrained language models (PLMs) as base models. ProPEC, which integrates perplexity-weighted scores from these PLMs with GEMME, demonstrates superior performance compared to individual models. Through parameter sensitivity analysis, we highlight the robustness of perplexity-based model selection in ProPEC. Additionally, a case study on T7 RNA polymerase activity dataset underscores ProPEC’s predictive capabilities. These findings suggest that ProPEC offers an effective approach for advancing viral protein fitness prediction, providing valuable insights for virology research and therapeutic development.

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Investigating Mechanically Activated Currents from Trigeminal Neurons of Non-Human Primates
Authors: Lindquist Karen A , Mecklenburg Jennifer , Hovhannisyan Anahit H. , Ruparel Shivani , Akopian Armen N.
Published: October 2024

Introduction

Pain sensation has predominantly mechanical modalities in many pain conditions. Mechanically activated (MA) ion channels on sensory neurons underly responsiveness to mechanical stimuli. The study aimed to address gaps in knowledge regarding MA current properties in higher order species such as non-human primates (NHP; common marmosets), and characterization of MA currents in trigeminal (TG) neuronal subtypes.

Methods

We employed patch clamp electrophysiology and immunohistochemistry (IHC) to associate MA current types to different marmoset TG neuronal groups. TG neurons were grouped according to presumed marker expression, action potential (AP) width, characteristic AP features, after-hyperpolarization parameters, presence/absence of AP trains and transient outward currents, and responses to mechanical stimuli.

Results

Marmoset TG were clustered into 5 C-fiber and 5 A-fiber neuronal groups. The C1 group likely represent non-peptidergic C-nociceptors, the C2-C4 groups resembles peptidergic C-nociceptors, while the C5 group could be either cold-nociceptors or C-low-threshold-mechanoreceptors (C-LTMR). Among C-fiber neurons only C4 were mechanically responsive. The A1 and A2 groups are likely A-nociceptors, while the A3-A5 groups probably denote different subtypes of A-low-threshold-mechanoreceptors (A-LTMRs). Among A-fiber neurons only A1 was mechanically unresponsive. IHC data was correlated with electrophysiology results and estimates that NHP TG has ∼25% peptidergic C-nociceptors, ∼20% non-peptidergic C-nociceptors, ∼30% A-nociceptors, ∼5% C-LTMR, and ∼20% A-LTMR.

Conclusion

Overall, marmoset TG neuronal subtypes and their associated MA currents have common and unique properties compared to previously reported data. Findings from this study could be the basis for investigation on MA current sensitizations and mechanical hypersensitivity during head and neck pain conditions.
A Method for Electrical Stimulus Artifact Removal Exploiting Neural Refractoriness: Validation by Contrasting Cathodic and Anodic Stimulation
Authors: Nakhmani A. , Block J. , Awad M. , Olson J. , Smith R. , Bentley J.N. , Holland M. , Brinkerhoff S.A. , Gonzalez C. , Moffitt M. , Walker H.
Published: October 2024

Objective

To present a novel method for removing stimulus transient that exploits the absolute refractory period of electrically excitable neural tissues.

Background

Electrical stimulation often generates significant signal artifacts that can obscure important physiological signals. Removal of the artifact and understanding latent information from these signals could provide objective measures of circuit engagement, potentially driving advancements in neuromodulation research and therapies.

Methods

We conducted intracranial physiology studies on five consecutive patients with Parkinson’s disease who underwent deep brain stimulation (DBS) surgery as part of their routine care. Monopolar stimuli (either cathodic or anodic) were delivered in pairs through the DBS electrode across a range of inter-stimulus intervals. Recordings from adjacent unused electrode contacts used broadband sampling and precise synchronization to generate a robust template for the stimulus transient during the absolute refractory period. These templates of stimulus transient were then subtracted from recordings at different intervals to extract and analyze the residual neural potentials.

Results

After artifact removal, the residual signals exhibited absolute and relative refractory periods with timing indicative of neural activity. Cathodic and anodic DBS pulses generated distinct patterns of local tissue activation, showing phase independence from the prior stimulus. The earliest detectable neural responses occurred at short peak latencies (ranging from 0.19 to 0.38 ms post-stimulus) and were completely or partially obscured by the stimulus artifact prior to removal. Cathodic stimuli produced stronger local tissue responses than anodic stimuli, aligning with clinical observations of lower activation thresholds for cathodic stimulation. However, cathodic and anodic pulses induced artifact patterns that were equivalent but opposite.

Interpretation

The proposed artifact removal technique enhances prior approaches by allowing direct measurement of local tissue responses without requirements for stimulus polarity reversal, template scaling, or specialized filters. This approach could be integrated into future neuromodulation systems to visualize stimulus-evoked neural potentials that would otherwise be obscured by stimulus artifacts.
Weakened airway epithelial junctions and enhanced neutrophil elastase release contribute to age-dependent bacteremia risk following pneumococcal pneumonia
Authors: Xu Shuying , Zhu Tianmou , Mou Hongmei , Tan Shumin , Leong John M.
Published: October 2024

Streptococcus pneumoniae(Sp; pneumococcus), the most common agent of community-acquired pneumonia, can spread systemically, particularly in the elderly, highlighting the need for adjunctive therapies. The airway epithelial barrier defends against bacteremia and is dependent upon apical junctional complex (AJC) proteins such as E-cadherin. After mouse lung challenge, pneumolysin (PLY), a keySpvirulence factor, stimulates epithelial secretion of an inflammatory eicosanoid, triggering the infiltration of polymorphonuclear leukocytes (PMNs) that secrete high levels of neutrophil elastase (NE), thus promoting epithelial damage and systemic infection. Here, pulmonary E-cadherin staining of intratracheally inoculated mice revealed PLY-mediated disruption of AJC independently of PMNs. Apical infection of air-liquid interface (ALI) respiratory epithelial monolayers similarly showed that PLY disrupts AJCs. This epithelial damage promoted PMN transmigration and bacterial apical-to-basolateral translocation, and pharmacologically fortifying epithelial barrier function diminished both barrier breachin vitroand bacteremiain vivo. E-cadherin staining afterSpintratracheal inoculation of >20-month-old mice, or apical infection of ALI monolayers derived from these mice, revealed an age-associated vulnerability to PLY-mediated AJC disruption, which in turn enhanced PMN migration and bacteremia. In addition, we found that PMNs from aged mice secrete increased levels of tissue-damaging NE. Simultaneous pharmacological inhibition of tissue-destructive NE and fortification of pulmonary epithelial barrier function was required to reduce the level ofSpbacteremia in aged mice to that of young mice. This work underscores the importance of fully characterizing the multifactorial sources of age-associated susceptibility in devising adjunctive therapies to mitigate invasive pneumococcal disease in the elderly.
Measuring DNA contents of animal and plant genomes with Gnodes, the long and short of it
Authors: Gilbert Donald G.
Published: October 2024

Measurement of DNA contents of genomes is valuable for understanding genome biology, including assessments of genome assemblies, but it is not a trivial problem. Measuring contents of DNA shotgun reads is complicated by several factors: biological contents of genomes at species, individual and tissue or cell levels, laboratory methods, sequencing technology and computational processing for measurement and assembly. This compares, and shares, complications with cytometric (Cym) and related molecular measurements of genome size and contents.

There is an obvious discrepancy between cytometric measurements and current long-read genome assemblies (Asm): genome assemblies average 12% below Cym measured sizes, differing in amounts of duplicated content. This report examines five DNA read types to see if they can be used for more precise and reliable discrimination of major genome contents and sizes. The read types are short, accurate Illumina, long Pacific Biosciences, of low and high accuracy, and long Oxford Nanopore Technology of low and high accuracy. Gnodes is the measurement tool used, which maps DNA to assembly, and measures DNA copy numbers for major genome contents of genes, transposons, repeats, and others, using as a measurement unit the single copies of unique conserved genes. Public data of five well studied genomes, human, corn, zebrafish, sorghum and rice, are used for the primary evidence of this work.

Results of this are mixed and open to interpretations: In broad terms, all DNA types measure about the same genome contents, at or below 90% agreement, which is a level that the other complications can contribute. For precision above a 90% level, long read types differ in supporting larger cytometric sizes (low accuracy reads), or smaller assembly sizes (high accuracy reads), with accurate short-reads roughly between. The weight of evidence suggests that low accuracy long reads are less biased for genome measurement, that high accuracy long reads have a bias of reduced duplications introduced by computational averaging or filtering. The several complicating factors noted can produce discrepancies larger than this average Cym - Asm difference, and are a problem to control.
Fibroblast growth factor-inducible 14 regulates satellite cell self-renewal and expansion during skeletal muscle repair
Authors: Tomaz da Silva Meiricris , Joshi Aniket S. , Kumar Ashok
Published: October 2024

Skeletal muscle regeneration in adults is predominantly driven by satellite cells. Loss of satellite cell pool and function leads to skeletal muscle wasting in many conditions and disease states.

Here, we demonstrate that the levels of fibroblast growth factor-inducible 14 (Fn14) are increased in satellite cells after muscle injury. Conditional ablation of Fn14 in Pax7-expressing satellite cells drastically reduces their expansion and skeletal muscle regeneration following injury. Fn14 is required for satellite cell self-renewal and proliferation as well as to prevent precocious differentiation. Targeted deletion of Fn14 inhibits Notch signaling but leads to the spurious activation of STAT3 signaling in regenerating skeletal muscle and in cultured muscle progenitor cells. Silencing of STAT3 improves proliferation and inhibits premature differentiation of Fn14-deficient satellite cells. Furthermore, conditional ablation of Fn14 in satellite cells exacerbates myopathy in the mdx mouse model of Duchenne muscular dystrophy (DMD) whereas its overexpression improves the engraftment of exogenous muscle progenitor cells into the dystrophic muscle of mdx mice. Altogether, our study highlights the crucial role of Fn14 in the regulation of satellite cell fate and function and suggests that Fn14 can be a potential molecular target to improve muscle regeneration in muscular disorders.