bluuebunny/crossref_metadata_2025 huggingface dataset:
The Fat/Hippo pathway drives photoperiod-induced wing length polyphenism
Authors: Gudmunds Erik , Palahí Aleix , Armisén David , Scofield Douglas G. , Eriksen Freya Darling , Khila Abderrahman , Husby Arild
Published: April 2024

Identifying the genetic mechanisms that translate information from the environment into developmental programs to control size, shape and color are important for gaining insights into adaptation to changing environments. Insect polyphenisms provide good models to study such mechanisms because environmental factors are the main source of trait variation. Here we studied the genetic mechanism that controls photoperiod-induced wing length polyphenism in the water striderGerris buenoi. By sequencing RNA sampled from wing buds across developmental stages under different photoperiodic conditions known to trigger alternative wing developmental trajectories, we found that differences in transcriptional activity arose primarily in the late 5thinstar stage. Among the differentially expressed genes, the Fat/Hippo and ecdysone signaling pathways, both putative growth regulatory mechanisms showed significant enrichment. We used RNA interference against the differentially expressed genes Fat, Dachsous and Yorkie to assess whether they play a causative role in photoperiod induced wing length variation inGerris buenoi. Our results show that the conserved Fat/Hippo pathway is a key regulatory network involved in the control of wing polyphenism in this species. This study provides an important basis for future comparative studies on the evolution of wing polyphenism and significantly deepens our understanding of the genetic regulation of insect polyphenisms.
BiGCN: Leveraging Cell and Gene Similarities for Single-cell Transcriptome Imputation with Bi-Graph Convolutional Networks
Authors: Inoue Yoshitaka , Kulman Ethan , Kuang Rui
Published: April 2024

Motivation

RNA profiling at the single-cell level is essential for characterizing the molecular activities and functions of individual cells. The current technical limitations of single-cell RNA sequencing (scRNA-seq) technologies can lead to a phenomenon known as “dropout”, where a significant portion of gene expression is not captured. Dropout is particularly prominent in genes with low or sparse expression, greatly impacting the reliability and interpretability of scRNA-seq data. Consequently, various techniques have been developed to estimate missing gene expression using imputation, often by either modeling similarities in gene expression among cells or using gene co-expression, but rarely both.

Results

In this study, we introduce a Bi-Graph Convolutional Network (BiGCN), a deep learning method that leverages both cell similarities and gene co-expression to capture cell-type-specific gene co-expression patterns for imputing scRNA-seq data. BiGCN constructs both a cell similarity graph and a gene co-expression graph, and employs them for convolutional smoothing in a dual two-layer Graph Convolutional Networks (GCNs). The embeddings from the two GCNs can subsequently be combined to facilitate the final imputation. BiGCN demonstrates superior performance compared to state-of-the-art imputation methods on both real and simulated scRNA-seq data. Additionally, BiGCN outperforms existing methods when tasked with clustering cells into cell types. We also perform a novel validation using a PBMC scRNA-seq dataset, and this experiment supports that BiGCN’s imputations are more realistic than competing imputation methods. In both the imputation and the cluster tasks, BiGCN consistently outperformed two variants of BiGCN that solely relied on either the gene co-expression graph or cell similarity graph. This indicates that the two graphs offer complimentary information for imputation and cell clustering, underscoring the importance of incorporating both types of information.

Code Availability

https://github.com/inoue0426/scBiGCN.

Contact

kuang@umn.edu
Optimising the flow of mechanical energy in musculoskeletal systems through gearing
Authors: Polet D T , Labonte D
Published: April 2024

Movement is integral to animal life, and most animal movement is actuated by the same engine: skeletal muscle. Muscle input is typically mediated by skeletal elements, resulting in musculoskeletal systems that are “geared”: at any instant, the muscle force and velocity are related to the output force and velocity only via a proportionality constantG, the “mechanical advantage”. The functional analysis of such “simple machines” has traditionally centred around this instantaneous interpretation, such that a small vs largeGis thought to reflect a fast vs forceful system, respectively. But evidence is mounting that a complete analysis ought to also consider the mechanical energy output of a complete contraction. Here, we approach this task systematically, and use the theory of physiological similarity to study how gearing affects the flow of mechanical energy in a minimalist model of a musculoskeletal system. Gearing influences the flow of mechanical energy in two key ways: it can curtail muscle work output, because it determines the ratio between the characteristic muscle work and kinetic energy capacity; and it defines how each unit of muscle work is partitioned into different system energies, i. e. into kinetic vs. “parasitic” energy such as heat. As a consequence of both effects, delivering maximum work in minimum time and with maximum transmission efficiency generally requires a mechanical advantage of intermediate magnitude. This optimality condition can be expressed in terms of two dimensionless numbers, which reflect the key geometric, physiological, and physical properties of the interrogated musculoskeletal system, and the environment in which the contraction takes place. Illustrative application to exemplar musculoskeletal systems predicts plausible mechanical advantages in disparate biomechanical scenarios; yields a speculative explanation for why gearing is typically used to attenuate the instantaneous force output (Gopt<1); and predicts howGneeds to vary systematically with animal size to optimise the delivery of mechanical energy, in superficial agreement with empirical observations. A many-to-one-mapping from musculoskeletal geometry to mechanical performance is identified, such that differences inGalone do not provide a reliable indicator for specialisation for force vs speed—neither instantaneously, nor in terms of mechanical energy output. The energy framework presented here can be used to estimate an optimal mechanical advantage across variable muscle physiology, anatomy, mechanical environment and animal size, and so facilitates investigation of the extent to which selection has made efficient use of gearing as degree of freedom in musculoskeletal “design”.
Inference of chromosome selection parameters and missegregation rate in cancer from DNA-sequencing data
Authors: Xiang Zijin , Liu Zhihan , Dinh Khanh N.
Published: April 2024

Aneuploidy is frequently observed in cancers and has been linked to poor patient outcome. Analysis of aneuploidy in DNA-sequencing (DNA-seq) data necessitates untangling the effects of the Copy Number Aberration (CNA) occurrence rates and the selection coefficients that act upon the resulting karyotypes. We introduce a parameter inference algorithm that takes advantage of both bulk and single-cell DNA-seq cohorts. The method is based on Approximate Bayesian Computation (ABC) and utilizes CINner, our recently introduced simulation algorithm of chromosomal instability in cancer. We examine three groups of statistics to summarize the data in the ABC routine: (A) Copy Number-based measures, (B) phylogeny tip statistics, and (C) phylogeny balance indices. Using these statistics, our method can recover both the CNA probabilities and selection parameters from ground truth data, and performs well even for data cohorts of relatively small sizes. We find that only statistics in groups A and C are well-suited for identifying CNA probabilities, and only group A carries the signals for estimating selection parameters. Moreover, the low number of CNA events at large scale compared to cell counts in single-cell samples means that statistics in group B cannot be estimated accurately using phylogeny reconstruction algorithms at the chromosome level. As data from both bulk and single-cell DNA-sequencing techniques becomes increasingly available, our inference framework promises to facilitate the analysis of distinct cancer types, differentiation between selection and neutral drift, and prediction of cancer clonal dynamics.
Predation by a ciliate community mediates temperature and nutrient effects on a peatland prey prokaryotic community
Authors: DeWitt Katrina , Carrell Alyssa A. , Rocca Jennifer D. , Votzke Samantha , Yammine Andrea , Peralta Ariane L. , Weston David J. , Pelletier Dale A. , Gibert Jean P.
Published: April 2024

Temperature significantly impacts microbial communities’ composition and function, which subsequently plays a vital role in the global carbon cycle that ultimately fuels climate change. Interactions between different microorganisms might be critical in shaping how these communities react to both temperature changes. Additionally, rising temperatures are occurring in the context of increasingly nutrient-rich ecosystems due to human activity. Nonetheless, we lack a comprehensive understanding of how predation influences microbial communities in future climate scenarios and an increasingly nutrient-rich world. Here, we assess whether predation by key bacterial consumers—ciliates—influences a microbial community’s freshwater temperature and nutrient response regarding biomass, diversity, structure, and function. In a three-week microcosm experiment, we exposed mostly prokaryotic microbial communities to a community of ciliate predators at two different temperature scenarios (ambient and +3°C, i.e., a conservative projection of climate change by 2050) and nutrient levels (low and elevated). Nutrients, temperature, and ciliate presence influenced microbial biomass and function separately, but their interaction had the largest explanatory power over the observed changes in microbial community biomass, structure, and function. Our study supports previous findings that temperature and nutrients are essential drivers of microbial community structure and function but also demonstrates that the presence of predators can mediate these effects, indicating that the biotic context is as important as the abiotic context to understand microbial responses to novel climates.

Importance

While the importance of the abiotic environment in microbial communities has long been studied, how prevalent ecological interactions, like predation and the broader abiotic context, may influence these responses is largely unknown. Our study disentangles the complex interplay between temperature, nutrients, and predation and their joint effects on microbial community diversity and function. The findings suggest that while temperature and nutrients are fundamental drivers of microbial community dynamics, the presence of predators significantly mediates these responses. Our study underscores the profound impact of abiotic factors on microbial communities, but how to properly understand, let alone predict, these responses, we need to account for the biotic context in which these are occurring.
Ultrasensitive amplification-free quantification of a methyl CpG-rich cancer biomarker by single-molecule kinetic fingerprinting
Authors: Dai Liuhan , Johnson-Buck Alexander , Laird Peter W. , Tewari Muneesh , Walter Nils G.
Published: April 2024

The most well-studied epigenetic marker in humans is the 5-methyl modification of cytosine in DNA, which has great potential as a disease biomarker in liquid biopsies of cell-free DNA. Currently, quantification of DNA methylation relies heavily on bisulfite conversion followed by PCR amplification and NGS or microarray analysis. PCR is subject to potential bias in differential amplification of bisulfite-converted methylatedversusunmethylated sequences. Here, we combine bisulfite conversion with single-molecule kinetic fingerprinting to develop an amplification-free assay for DNA methylation at the branched-chain amino acid transaminase 1 (BCAT1) promoter. Our assay selectively responds to methylated sequences with a limit of detection below 1 fM and a specificity of 99.9999%. Evaluating complex genomic DNA matrices, we reliably distinguish 2-5% DNA methylation at the BCAT1 promoter in whole blood DNA from completely unmethylated whole-genome amplified DNA. Taken together, these results demonstrate the feasibility and sensitivity of our amplification-free, single-molecule quantification approach to improve the early detection of methylated cancer DNA biomarkers.
ARTEMIS - a method for topology-independent superposition of RNA 3D structures and structure-based sequence alignment
Authors: Bohdan Davyd R. , Bujnicki Janusz M. , Baulin Eugene F.
Published: April 2024

Non-coding RNAs play a major role in diverse processes in living cells with their sequence and spatial structure serving as the principal determinants of their function. Superposition of RNA 3D structures is the most accurate method for comparative analysis of RNA molecules and for inferring sequence alignments. Topology-independent superposition is particularly relevant, as evidenced by structurally similar RNAs with sequence permutations such as tRNA and Y RNA. To date, state-of-the-art methods for RNA 3D structure superposition rely on intricate heuristics, and the potential for topology-independent superposition has not been exhausted. Recently, we introduced the ARTEM method for unrestrained pairwise superposition of RNA 3D modules and now we developed it further to solve the global RNA 3D structure alignment problem. Our new tool ARTEMIS significantly outperforms state-of-the-art tools in both sequentially-ordered and topology-independent RNA 3D structure superposition. Using ARTEMIS we discovered a helical packing motif to be preserved within different backbone topology contexts across various non-coding RNAs, including multiple ribozymes and riboswitches. We anticipate that ARTEMIS will be essential for elucidating the landscape of RNA 3D folds and motifs featuring sequence permutations that thus far remained unexplored due to limitations in previous computational approaches.
Transposable elements identify previously overlooked regions undergoing parallel evolution in response to diverse selective pressures
Authors: Guirao-Rico Sara , Gonzalez Josefa
Published: April 2024

Evolve and Resequence (E&R) studies have contributed to closing the gap between adaptive phenotypic traits and their underlying genomic basis. Unlike other popular approaches, E&R studies provide a unique opportunity to understand the degree of repeatability and constraints of the evolutionary process. Still, one of the major challenges is to accurately identify the genetic variants targeted by selection. Traditionally, E&R studies have only explored part of the variation present in the genomes, SNPs, thus neglecting other sources of adaptive variation such as transposable elements (TEs). In this work, we analyzed the Pool-seq data of five published Drosophila melanogaster E&R studies in the context of TEs. We found some candidate TEs inserted in the same or nearby regions to significant SNPs/genes. As such, these TEs should be added to the set of possible targets of selection. However, we also found a subset of significant TEs that were not associated with previously identified significant SNPs/genes. Most of these TEs are inserted within or flanked by genes related to the trait of interest and thus they are the only potential targets of selection identified in these regions. Functional enrichment analysis of the genes located nearby all candidate adaptive TEs allowed to identify new relevant biological functions. Finally, the replicability of candidate TEs was similar or higher than that reported for SNPs. We argue that TE variants should also be considered as candidate targets of selection in E&R studies and we also provide guidelines to improve the detection of TEs as targets of selection.
GCI: a continuity inspector for complete genome assembly
Authors: Chen Quanyu , Yang Chentao , Zhang Guojie , Wu Dongya
Published: April 2024

Motivation

Recent advances in long-read sequencing technologies have significantly facilitated the production of high-quality genome assembly. The telomere-to-telomere (T2T) gapless assembly has become the new golden standard of genome assembly efforts. Several recent efforts have claimed to produce T2T level reference genomes. However, a universal standard is still missing to qualify a genome assembly to be at T2T standard. Traditional genome assembly assessment metrics (N50 and its derivatives) have no capacity in differentiate between nearly T2T assembly and the truly T2T assembly in continuity either globally and locally. Also these metrics are independent of raw reads, which make them inflated easily by artificial operations. Therefore a gaplessness evaluation tool at single nucleotide resolution to reflect true completeness is urgently needed in the era of complete genomes.

Results

Here, we present a tool called Genome Continuity Inspector (GCI) to assess genome assembly continuity at the single base resolution, that can evaluate how close a genome assembly is close to T2T level. GCI utilized multiple aligners to map long reads from multiple platforms back to the assembly. By incorporating curated mapping coverage of high-confidence read alignments, GCI identifies potential assembly issues. Meanwhile, it also reports GCI scores to quantify the assembly overall continuity in the whole genome or chromosome scale.

Availability and implementation

The open-source GCI code is freely available on Github (https://github.com/yeeus/GCI) under the MIT license.
Adverse outcomes and associations of dengue in pregnancy – a retrospective cohort study
Authors: Nallaperuma Oshadhi Lathmini , Senanayake Hematha Malinath , Godevithana Janaka Chandana
Published: April 2024

Objective

Dengue infection is a significant health issue in tropical areas over the globe. Physiological changes of pregnancy can be confounding the clinical presentation of dengue in pregnancy. This study aimed to investigate the adverse outcomes of antenatal dengue infection and factors associated with maternal, fetal, and neonatal complications.

Methods

A total of 115 pregnant women with laboratory-confirmed dengue infection admitted to two tertiary referral hospitals in Sri Lanka were followed from admission until delivery and discharge. Data on clinical and laboratory outcome variables were collected and analyzed using descriptive and inferential statistics.

Results

Dengue fever accounted for the majority of cases (63.5%), followed by dengue hemorrhagic fever (33.9%) and, dengue expanded syndrome (2.6%). Maternal adverse outcomes were observed in 27.8% of cases with 19.1% of ICU admissions. Dengue category, trimester of infection, platelet count, and AST levels showed a significant association with adverse maternal outcomes. Fetal adverse outcomes affected 8.1% of cases, while neonatal adverse outcomes occurred in 28.7% of live births, with transplacental transmission observed in two cases.

Conclusion

Dengue infection during pregnancy can lead to significant maternal, fetal, and neonatal morbidity and mortality. Identifying risk factors may help predict the severity of these outcomes. Further research is needed to develop robust prediction models and improve management strategies.
The proliferation and differentiation programs are only partially coupled duringC. elegansintestine development
Authors: Dieng Joris , Michaux Grégoire , Pacquelet Anne
Published: April 2024

Cell proliferation and differentiation are essential processes underlying multicellular organism development. Cell proliferation arrest usually precedes terminal differentiation, suggesting that these two processes may be coordinated. Here we took advantage of the very stereotyped development of theC. elegansintestine to address whether the control of the proliferation and differentiation programs are systematically coupled. We show that retarding cell cycle arrest leads to a delay in the recruitment of some, but not all, brush border components. Reciprocally, we find that cell cycle arrest relies on the differentiation factors ELT-2 and ELT-7 only in posterior intestinal cells. The occurrence of supernumerary divisions in the absence of ELT-2 and ELT-7 is associated with changes in the expression pattern of the cell cycle regulators CKI-1 and cyclin B1. Our work thus demonstrates the existence of reciprocal interactions between cell proliferation and cell differentiation. It nevertheless also shows that these two processes are only partially coupled, suggesting the existence of additional mechanisms ensuring their temporal control.
Lateral cell polarization drives organization of epithelia in sea anemone embryos and embryonic cell aggregates
Authors: Atajanova Tavus , Kang Emily Minju , Postnikova Anna , Price Alivia Lee , Doerr Sophia , Du Michael , Ugenti Alicia , Ragkousi Katerina
Published: April 2024

One of the first organizing processes during animal development is the assembly of embryonic cells into epithelia. In certain animals, including Hydra and sea anemones, epithelia also emerge when cells from dissociated tissues are aggregated back together. Although cell adhesion is required to keep cells together, it is not clear whether cell polarization plays a role as epithelia emerge from disordered aggregates. Here, we demonstrate that lateral cell polarization is essential for epithelial organization in both embryos and aggregates of the sea anemoneNematostella vectensis. Specifically, knock down of the lateral polarity protein Lgl disrupts epithelia in developing embryos and impairs the capacity of dissociated cells to epithelialize from aggregates. Cells inlglmutant epithelia lose their columnar shape and have mispositioned mitotic spindles and ciliary basal bodies. Together, our data suggest that inNematostella, Lgl is required to establish lateral cell polarity and position cytoskeletal organelles in cells of embryos and aggregates duringde novoepithelial organization.
Limited Validity of Mayo Endoscopic Subscore in Ulcerative Colitis with Concomitant Primary Sclerosing Cholangitis
Authors: Wohl Pavel , Krausova Alzbeta , Wohl Petr , Fabian Ondrej , Bajer Lukas , Brezina Jan , Drastich Pavel , Hlavaty Mojmír , Novotna Petra , Kahle Michal , Spicak Julius , Gregor Martin
Published: April 2024

Background and Aims

Ulcerative colitis (UC) with concomitant primary sclerosing cholangitis (PSC) represents a distinct disease entity (PSC-UC). Mayo endoscopic subscore (MES) is a standard tool for assessing disease activity in UC but its relevance in PSC-UC remains unclear. Here, we sought to compare MES in a cohort of UC and PSC-UC patients and assess the accuracy using histological activity scoring (Nancy histological index; NHI).

Methods

MES was assessed in 30 PSC-UC and 29 UC adult patients during endoscopy. NHI and inflammation were evaluated in biopsies from the caecum, rectum, and terminal ileum. In addition, perinuclear anti-neutrophil cytoplasmic antibodies, fecal calprotectin, body mass index, and other relevant clinical characteristics were collected.

Results

The median MES and NHI were similar for UC patients (MES grade 2 and NHI grade 2 in the rectum), but were different for PSC-UC patients (MES grade 0 and NHI grade 2 in the caecum). There was a correlation between MES and NHI for UC patients (Spearman’s ρ = 0.40, p = 0.029), but not for PSC-UC patients. Histopathological examination revealed persistent microscopic inflammation in 88% of PSC-UC patients with MES grade 0 (46% of all PSC-UC patients). Moreover, MES overestimated the severity of active inflammation in another 11% of PSC-UC patients.

Conclusion

MES fails to identify microscopic signs of inflammation in the context of PSC-UC. This indicates that histological evaluation should become an integral part of the diagnostic and grading system in both PSC-UC and PSC.
Human metabolic chambers reveal a coordinated metabolic-physiologic response to nutrition
Authors: Perry Andrew S. , Piaggi Paolo , Huang Shi , Nayor Matthew , Freedman Jane , North Kari , Below Jennifer , Clish Clary , Murthy Venkatesh L. , Krakoff Jonathan , Shah Ravi V.
Published: April 2024

The emerging field of precision nutrition is based on the notion that inter-individual responses across diets of different calorie-macronutrient content may contribute to inter-individual differences in metabolism, adiposity, and weight gain. Free-living diet studies have been traditionally challenged by difficulties in controlling adherence to prescribed calories and macronutrient content and rarely allow a period of metabolic stability prior to metabolic measures (to minimize influences of weight changes). In this context, key physiologic measures central to precision nutrition responses may be most precisely quantified via whole room indirect calorimetry over 24-h, in which precise control of activity and nutrition can be achieved. In addition, these studies represent unique “N of 1” human crossover metabolic-physiologic experiments during which specific molecular pathways central to nutrient metabolism may be discerned. Here, we quantified 263 circulating metabolites during a ≈40-day inpatient admission in which up to 94 participants underwent seven monitored 24-h nutritional interventions of differing macronutrient composition in a whole-room indirect calorimeter to capture precision metabolic responses. Broadly, we observed heterogenous responses in metabolites across dietary chambers, with the exception of carnitines which tracked with 24-h respiratory quotient. We identified excursions in shared metabolic species (e.g., carnitines, glycerophospholipids, amino acids) that mapped onto gold-standard calorimetric measures of substrate oxidation preference and lipid availability. These findings support a coordinated metabolic-physiologic response to nutrition, highlighting the relevance of these controlled settings to uncover biological pathways of energy utilization during precision nutrition studies.
DCAE-SR: Design of a Denoising Convolutional Autoencoder for reconstructing Electrocardiograms signals at Super Resolution
Authors: Lomoio Ugo , Veltri Pierangelo , Guzzi Pietro Hiram , Liò Pietro
Published: April 2024

Electrocardiogram (ECG) signals play a pivotal role in cardiovascular diagnostics, providing essential information on the electrical activity of the heart. However, the inherent noise and limited resolution in ECG recordings can hinder accurate interpretation and diagnosis. In this paper, we propose a novel model for ECG super resolution (SR) that uses a DNAE to enhance temporal and frequency information inside ECG signals. Our approach addresses the limitations of traditional ECG signal processing techniques. Our model takes in input 5-second length ECG windows sampled at 50 Hz (very low resolution) and it is able to reconstruct a denoised super-resolution signal with an x10 upsampling rate (sampled at 500 Hz). We trained the proposed DCAE-SR on public available myocardial infraction ECG signals. Our method demonstrates superior performance in reconstructing high-resolution ECG signals from very low-resolution signals with a sampling rate of 50 Hz. We compared our results with the current deep-learning literature approaches for ECG super-resolution and some non-deep learning reproducible methods that can perform both super-resolution and de-noising. We obtained current state-of-the-art performances in super-resolution of very low resolution ECG signals frequently corrupted by ECG artifacts. We were able to obtain a signal-to-noise ratio of 12.20 dB (outperforms previous 4.68 dB), mean squared error of 0.0044 (outperforms previous 0.0154) and root mean squared error of 4.86% (outperforms previous 12.40%). In conclusion, our DCAE-SR model offers a robust (to artefact presence), versatile and explainable solution to enhance the quality of ECG signals. This advancement holds promise in advancing the field of cardiovascular diagnostics, paving the way for improved patient care and high-quality clinical decisions.
Continued underutilization with pronounced geographic variation in clozapine use
Authors: Cavanah Luke R. , Tian Maria Y. , Goldhirsh Jessica L. , Huey Leighton Y. , Piper Brian J.
Published: April 2024

Introduction

Schizophrenia-spectrum disorders are debilitating and contribute to a substantial economic burden. Clinicians have historically underutilized clozapine, an atypical antipsychotic traditionally reserved for use in treatment-resistant schizophrenia, due to the medication’s adverse effect profile and associated management requirements, concerns of poor treatment adherence, and poor training/exposure to the use. In addition to alleviating schizophrenia symptoms when multiple other medications have failed, clozapine has other unique benefits that compel its use such as its use being associated with reduced suicide ideation and action, aggression, substance use, and all-cause mortality.

Methods

This study aimed to characterize clozapine utilization by US Medicare patients from 2015-20. Additionally, we identified the states that prescribed significantly different amounts than the national average.

Results

We observed a steady decrease in clozapine use adjusted for population (−18.0%) and spending (−24.9%) over time. For all years, there was significant geographic heterogeneity (average: nine-fold) in population-corrected clozapine use. Massachusetts (2015-20: 95.4, 82.7, 76.8, 72.2, 71.2, 63.7 prescriptions per thousand enrollees) and South Dakota (2015-20: 78.0, 77.4, 78.4, 75.6, 72.0, 71.6) were the only states that prescribed significantly more than average, and none prescribed significantly less.

Discussion

Clozapine use by US Medicare patients is low, decreasing, and concerning for underutilization—patterns likewise seen for the US Medicaid recipients. Further study of the reasons for the state variation is needed. Education interventions, training reform, and devices that ease required routine blood monitoring are all practical solutions to optimize clozapine use.
Post-stroke Epilepsy and its Association with Stroke Location – Systematic review and Meta-analysis
Authors: Park Sihyeong , Esengul Yasar T. , Gharaibeh Khaled , Saleem Sidra , Sheikh Ajaz
Published: April 2024

Objective

There have been several meta-analytic studies that investigated potential risk factors for post-stroke epilepsy (PSE)1,2. Cortical location is known to be associated with increased risk of PSE. However, relationship between the involvement of specific lobe with stroke and PSE has not been evaluated in a meta-analysis due to unavailability of relevant data. To the best of our knowledge, this is the first meta-analysis on this subject matter.

Methods

The PubMed and Embase databases were comprehensively assessed and searched for studies that evaluated the association between the involvement of different lobes with PSE. Studies that evaluated post-stroke epilepsy or recurrent post-stroke seizure were included, and acute symptomatic seizure was excluded. Following search strategy was used, “post-stroke” OR “stroke” OR “intracerebral hemorrhage” AND “epilepsy” OR “seizure” AND “lobe” OR “territory” OR “location”. Joanna Briggs Institute appraisal tool was utilized to perform quality assessment of the included studies. A random effects model was used to estimate pooled odds ratio of the selected outcomes with 95 percent confidence intervals (CIs), and the study heterogeneity was analyzed.

Results

Nine studies3-11were identified with 1328 stroke patients. 9/9 studies showed association of frontal lobe involvement with higher likelihood of PSE (OR=2.10, 95% CI=1.37-3.21, p-value=0.0007). All studies showed association of parietal stroke with increased likelihood of PSE (OR=2.85, 95% CI=1.93-4.22, p-value<0.00001). Previous temporal lobe stroke was associated with increased risk of PSE (OR=3.55, 95% CI=1.95-6.45, p-value<0.00001). In contrast, association between occipital lobe involvement and PSE was not statistically significant (OR=1.37, 95% CI=0.95-1.96, p-value<0.09), and this remained true when subgroup analysis was performed.

Discussion

Our meta-analysis shows that PSE is associated with frontal, parietal, and temporal involvement of previous stroke. There was no statistically significant association between occipital lobe involvement and PSE. Further high quality studies are needed to investigate if stroke in one lobe/territory can be more epileptogenic than those in others.

Key Points

Cerebrovascular disease is the most common etiology of newly diagnosed epilepsy

Prior stroke involving the frontal, temporal, or parietal lobe is associated with increased risk of post-stroke epilepsy

Occipital lobe stroke is not associated with increased risk of post-stroke epilepsy, regardless of the age group.
Detection of measles virus genotype D8 in wastewater of Brussels capital region, Belgium, March 2024
Authors: Rector Annabel , Bloemen Mandy , Hoorelbeke Bart , Van Ranst Marc , Wollants Elke
Published: April 2024

Following the European Centre for Disease Prevention and Control (ECDC) threat assessment brief regarding increased measles case numbers and outbreaks in Europe, we investigated sewage samples from different wastewater treatment plants in Belgium for the presence of measles virus (MV). Samples taken from Brussels North were found to contain MV, which could be characterized as genotype D8. Twelve measles cases were reported in the Brussels capital region in February/March 2024, but additional infections are likely occurring under the radar.
Tracheal aspirate metagenomics reveals association of antibiotic resistance with non-pulmonary sepsis mortality
Authors: Rodríguez-Pérez Héctor , Ciuffreda Laura , Hernández-Beeftink Tamara , Guillen-Guio Beatriz , Domínguez David , Corrales Almudena , Espinosa Elena , Alcoba-Florez Julia , Lorenzo-Salazar Jose M. , González-Montelongo Rafaela , Villar Jesús , Flores Carlos
Published: April 2024

Background

Previous metabarcoding studies based on 16S rRNA sequencing in patients with extrapulmonary sepsis have found early pulmonary dysbiosis associated with a poor prognosis. To further discern this association, here we aimed to better characterize the pulmonary bacterial communities in these patients by leveraging metagenomics and to evaluate if the presence of antibiotic resistance genes (ARGs) could explain the higher mortality of the patients.

Material and methods

Metagenomic sequencing was performed using the Nextera XT Library Prep Kit and HiSeq 4000 (Illumina Inc.) on tracheal aspirate samples that were obtained within 24 h from diagnosis from patients with extrapulmonary sepsis admitted to the Intensive Care Unit (ICU). Analysis involved MetaSpades for contig assembly, Kraken2 and Metaphlan4 for taxonomic classification, and CARD and GTDB-tk for ARGs annotation and assignment to the bacterial species. The relationship between the presence of antibiotic resistance and ICU mortality was evaluated using the Wilcoxon test and logistic regression models adjusting for clinical and demographic variables.

Results

In total, 127 different ARGs were detected circumscribed only to seven patients. The most common ARGs found were from the antibiotic groups of aminoglycosides and beta-lactams, both present in most of the patients. These ARGs were found, almost entirely linked toKlebsiella pneumoniae, Escherichia coli, andPseudomonas aeruginosa. The results also show a significant enrichment of ARGs among patients who died while admitted in the ICU (57%, 95% confidence interval [CI]: 18-90%) compared to surviving patients (20%, 95% CI: 7-40%) (p=0.022). Analyses adjusting for clinical and demographic variables did not alter this result.

Conclusion

Metagenomic sequencing has allowed an unprecedented characterization of the sepsis lung microbiome showing that antibiotic resistance is common among these patients. The results also suggest a relationship between the early accumulation of ARGs in the lung of patients with extrapulmonary sepsis who die while admitted in the ICU. Studies in independent samples will be needed to validate our findings.
Altered functional connectivity between cortical premotor areas and the spinal cord in chronic stroke
Authors: Braaß Hanna , Wolf Silke , Feldheim Jan , Chu Ying , Tinnermann Alexandra , Finsterbusch Jürgen , Büchel Christian , Gerloff Christian , Schulz Robert
Published: April 2024

Background

Neuroscience research has contributed significantly to understanding alterations in brain structure and function after ischemic stroke. Technical limitations have excluded the spinal cord from imaging-based research. Available data are restricted to a few microstructural analyses, and functional connectivity data are absent. The present study attempted to close this knowledge gap and assess alterations in corticospinal activation and coupling changes in chronic stroke.

Methods

Thirteen well-recovered stroke patients underwent corticospinal functional MRI while performing a simple force generation task. Task-related activation was localized in the ipsilesional primary motor cortex (M1), ventral premotor cortex (PMV), and supplementary motor area (SMA), as well as in the cervical spinal cord. Psycho-physiological interactions and linear modeling were used to infer functional connectivity between cortical motor regions and the cervical spinal cord and their associations with motor deficits.

Results

The main finding was that PMV and SMA showed topographically distinct alterations in their connectivity with the spinal cord. Specifically, we found a reduced coupling between SMA and the ipsilateral ventral spinal cord and an enhanced coupling between PMV and ventral and intermediate central spinal zones. Lower SMA- and higher PMV-related spinal cord couplings were correlated with residual deficits.

Conclusion

This work provides first-in-human functional insights into stroke-related alterations in the functional connectivity between cortical motor areas and the spinal cord, suggesting that different premotor areas and spinal neuronal assemblies might be involved in coupling changes. It adds a novel, promising approach to better understanding stroke recovery and developing innovative models to comprehend treatment strategies with spinal cord stimulation.
Vaccination and collective action under social norms
Authors: Morsky Bryce
Published: April 2024

Social dynamics are an integral part of the spread of disease affecting contact rates as well as the adoption of pharmaceutical and non-pharmaceutical interventions. When vaccines provide waning immunity, efficient and timely uptake of boosters is required to maintain protection and flatten the curve of infections. How then do social dynamics affect the timely up-take of vaccines and thereby the course of an epidemic? To explore this scenario, a behavioural-epidemiological is developed here. It features a tipping-point dynamic for the uptake of vaccines that combines the risk of infection, perceived morbidity risk of the vaccine, and social payoffs for deviating from the vaccination decision making of others. The social payoffs are derived from a social norm of conformity, and they create a collective action problem. A key finding driven by this dilemma is that waves of vaccine uptake and infections can occur due to inefficient and delayed uptake of boosters. This results in a nonlinear response of the infection load to the transmission rate: an intermediate transmission rate can result in greater prevalence of disease relative to more or less transmissible diseases. Further, global information about the prevalence of the disease and vaccine uptake increases the infection load and peak relative to information restricted to individuals’ contact net-works. Thus, decisions driven by local information can mitigate the collective action problem across the population. Finally, the optimal public policy program to promote boosters is shown to be one that focuses on overcoming the social inertia to vaccinate at the start of an epidemic.
Optimized patient-specific immune checkpoint inhibitors therapy for cancer treatment based on tumor immune microenvironment modeling
Authors: Yao Yao , Chen Frank Youhua , Zhang Qingpeng
Published: April 2024

Objective

Enhancing patient response to immune checkpoint inhibitors (ICIs) is crucial in cancer immunotherapy. We aim to create a data-driven mathematical model of the tumor immune microenvironment (TIME) and utilize deep reinforcement learning (DRL) to optimize patient-specific ICI therapy combined with chemotherapy (ICC).

Methods

Using patients’ genomic and transcriptomic data, we develop an ordinary differential equations (ODEs)-based TIME model to characterize interactions among chemotherapy, ICIs, immune cells, and cancer cells. A DRL algorithm is trained to determine the personalized optimal ICC therapy.

Results

Numerical experiments with real-world data demonstrates that the proposed TIME model can predict ICI therapy response. The DRL-derived personalized ICC therapy outperforms predefined fixed schedules. For tumors with extremely low CD8+T cell infiltration (“extremely cold tumors”), DRL recommends high-dosage chemotherapy alone. For tumors with higher CD8+T cell infiltration (“cold” and “hot tumors”), an appropriate chemotherapy dosage induces CD8+T cell proliferation, enhancing ICI therapy outcomes. Specifically, for “hot tumors,” chemotherapy and ICI are administered simultaneously, while for “cold tumors,” a mid-dosage of chemotherapy makes the TIME “hotter” before ICI administration. However, a number of “cold tumors” with rapid resistant cancer cell growth, ICC eventually fails.

Conclusion

This study highlights the potential of utilizing real-world clinical data and DRL to develop personalized optimal ICC by understanding the complex biological dynamics of a patient’s TIME. Our ODE-based TIME model offers a theoretical framework for determining the best use of ICI, and the proposed DRL model may guide personalized ICC schedules.

SIGNIFICANCE STATEMENT

Our research presents a novel data-driven approach to personalized cancer treatment by combining artificial intelligence and mathematical models of the tumor’s surrounding environment, known as the tumor immune microenvironment (TIME). This innovative method allows for the optimization of patient-specific immune checkpoint inhibitors and combined chemotherapy therapy. By utilizing deep reinforcement learning, our approach can adapt and improve treatment strategies for individual patients, ultimately maximizing the effectiveness of cancer therapies. This pioneering work has the potential to significantly enhance clinical decision-making and improve patient outcomes, paving the way for personalized cancer immunotherapy.
The potential of gene drive releases in malaria vector species to reduce the malaria burden in different African environments
Authors: Hancock Penelope A. , North Ace , Leach Adrian W. , Winskill Peter , Ghani Azra , Godfray H. Charles J. , Burt Austin , Mumford John D.
Published: April 2024

Gene drive technologies are a promising means of malaria control with the potential to cause widespread and sustained reductions in transmission. In real environments, however, gene drive impacts will depend on local ecological and epidemiological factors. We develop a data-driven model to investigate the impacts of a gene drive that causes vector population suppression. We apply the model to sixteen ∼12,000km2areas of west Africa that span variation in vector ecology and malaria prevalence. We simulate gene drive releases targeting different vector species combinations, and estimate reductions in vector abundance, malaria prevalence and clinical cases in children. Average reductions in vector abundance ranged from 77.3%-99.1% across areas when the gene drive releases targeted all major vector species (Anopheles gambiae,An. coluzzii,An. arabiensis,andAn. funestus). Impacts on malaria depended strongly on which vector species were targeted. Consistently strong impacts required releases targeting all vector species, which reduced prevalence by over 90% in all areas. When the major vector speciesAn. gambiaeandAn. coluzziiwere targeted, average reductions in prevalence ranged from no reduction to 72% across areas, and reductions in cumulative clinical cases ranged from zero to 57%. When other new interventions including RTS,S vaccination and pyrethroid-PBO bednets were in place, at least 60% more clinical cases were averted when gene drives were added, demonstrating the benefits of integrated interventions. Our results give new insights into the factors determining the impacts of gene drives on malaria across different African settings.
Single Cell Sequencing Provides Clues about the Developmental Genetic Basis of Evolutionary Adaptations in Syngnathid Fishes
Authors: Healey Hope M. , Penn Hayden B. , Small Clayton M. , Bassham Susan , Goyal Vithika , Woods Micah A. , Cresko William A.
Published: April 2024

Seahorses, pipefishes, and seadragons are fishes from the family Syngnathidae that have evolved extraordinary traits including male pregnancy, elongated snouts, loss of teeth, and dermal bony armor. The developmental genetic and cellular changes that led to the evolution of these traits are largely unknown. Recent syngnathid genome assemblies revealed suggestive gene content differences and provide the opportunity for detailed genetic analyses. We created a single cell RNA sequencing atlas of Gulf pipefish embryos to understand the developmental basis of four traits: derived head shape, toothlessness, dermal armor, and male pregnancy. We completed marker gene analyses, built genetic networks, and examined spatial expression of select genes. We identified osteochondrogenic mesenchymal cells in the elongating face that express regulatory genesbmp4, sfrp1a, andprdm16. We found no evidence for tooth primordia cells, and we observed re-deployment of osteoblast genetic networks in developing dermal armor. Finally, we found that epidermal cells expressed nutrient processing and environmental sensing genes, potentially relevant for the brooding environment. The examined pipefish evolutionary innovations are composed of recognizable cell types, suggesting derived features originate from changes within existing gene networks. Future work addressing syngnathid gene networks across multiple stages and species is essential for understanding how their novelties evolved.
Distinct routes to thermotolerance in the fungal pathogenCryptococcus neoformans
Authors: Schwiesow Mara J. W. , Elde Nels C. , Hilbert Zoë A.
Published: April 2024

Increasing temperatures associated with climate change have the potential for far-reaching impact on human health and disease vectors, including fungal pathogens. Pathogenic fungi occupy a wide range of environments across the world, and their ranges have been slowly expanding in recent decades due, in part, to climate change. Despite these links between increasing temperature and higher prevalence of fungal disease, the direct effects of rising environmental temperatures on the evolution of pathogenic fungi remains unclear. In this study, we investigated how increasing temperatures drive adaptive evolution in the human fungal pathogenCryptococcus neoformans. First, we performed serial passages of aC. neoformansenvironmental isolate with gradual changes in temperature over the course of ∼40 days. Through this approach we identified several distinct thermally adapted isolates with competitive growth advantages over the parental strain at high temperatures. We then characterized the phenotypic and genetic changes acquired in several evolved isolates, which include alteration of cell size, colony morphology, and, surprisingly, antifungal resistance. These results highlight the remarkable flexibility of fungi to adapt rapidly to new environments and raise pressing questions about the impacts of rising environmental temperatures on the future of infectious diseases and human health.