Regional variations in HIV diagnosis in Japan before and during the COVID-19 pandemic.

Background: The number of people undergoing voluntary HIV testing has abruptly decreased since 2020. The geographical heterogeneity of HIV infection and the impact of COVID-19 on the diagnosis of HIV at regional level are important to understand. This study aimed to estimate the HIV incidence by geographical region and understand how the COVID-19 pandemic influenced diagnosis of HIV. Methods: We used an extended back-calculation method to reconstruct the epidemiological dynamics of HIV/AIDS by geographical region. We used eight regions: Tokyo, the capital of Japan, Hokkaido plus Tohoku, Kanto plus Koshinetsu (excluding Tokyo), Hokuriku, Tokai, Kinki, Chugoku plus Shikoku, and Kyushu plus Okinawa. Four different epidemiological measurements were evaluated: (i) estimated HIV incidence, (ii) estimated rate of diagnosis, (iii) number of undiagnosed HIV infections, and (iv) proportion of HIV infections that had been diagnosed. Results: The incidence of HIV/AIDS during the COVID-19 pandemic from 2020 to 2022 increased in all regions except Kanto/Koshinetsu (51.3 cases/year), Tokyo (183.9 cases/year), Hokuriku (1.0 cases/year), and Tokai (43.1 cases/year). The proportion of HIV infections that had been diagnosed only exceeded 90% in Tokyo (91.7%, 95% confidence interval (CI): 90.6, 93.3), Kanto/Koshinetsu (91.0%, 95% CI: 87.3, 97.8), and Kinki (92.5%, 95% CI: 90.4, 95.9). The proportion of infections that had been diagnosed was estimated at 83.3% (95% CI: 75.1, 98.7) in Chugoku/Shikoku and 80.5% (95% CI: 73.9, 91.0) in Kyusyu/Okinawa. Conclusions: In urban regions with major metropolitan cities, including Tokyo, Kinki, and Kanto/Koshinetsu, the number of undiagnosed HIV infections is substantial. However, the proportion of undiagnosed infections was estimated to be smaller than in other regions. The diagnosed proportion was the lowest in Kyusyu/Okinawa (80.5%), followed by Chugoku/Shikoku and Hokkaido/Tohoku. The level of diagnosis in those regional prefectures may have been more influenced and damaged by the COVID-19 pandemic than in urban settings.

Advances in predicting breast cancer driver mutations: Tools for precision oncology (Review).

In the modern era of medicine, prognosis and treatment, options for a number of cancer types including breast cancer have been improved by the identification of cancer­specific biomarkers. The availability of high­throughput sequencing and analysis platforms, the growth of publicly available cancer databases and molecular and histological profiling facilitate the development of new drugs through a precision medicine approach. However, only a fraction of patients with breast cancer with few actionable mutations typically benefit from the precision medicine approach. In the present review, the current development in breast cancer driver gene identification, actionable breast cancer mutations, as well as the available therapeutic options, challenges and applications of breast precision oncology are systematically described. Breast cancer driver mutation­based precision oncology helps to screen key drivers involved in disease development and progression, drug sensitivity and the genes responsible for drug resistance. Advances in precision oncology will provide more targeted therapeutic options for patients with breast cancer, improving disease­free survival and potentially leading to significant successes in breast cancer treatment in the near future. Identification of driver mutations has allowed new targeted therapeutic approaches in combination with standard chemo­ and immunotherapies in breast cancer. Developing new driver mutation identification strategies will help to define new therapeutic targets and improve the overall and disease­free survival of patients with breast cancer through efficient medicine.

Dynamics of salt precipitation at pore scale during CO2 subsurface storage in saline aquifer.

CO2 storage in deep saline aquifers is an effective strategy for reducing greenhouse gas emission. However, salt precipitation triggered by evaporation of water into injected dry CO2 causes injectivity reduction. Predicting the distribution of precipitated salts and their impact on near-well permeability remains challenging. Therefore, a detailed investigation of the interactions between salt precipitation and porous domain is essential for of revealing the mechanisms of pore blockage due to salt crystallization. Through series of microfluidic experiments, direct observations, coupled with detailed imaging processing, form the basis for explaining these phenomena and provide a relationship between water and salt saturations, highlighting the critical roles played by local capillary-driven flow and water film along grains in influencing water relocation. The results reveal two distinct types of salt crystallization: occurring inside the brine with smooth edges and at the CO2-brine interface with rough edges. Furthermore, the impact of local heterogeneity and surface wettability on salt precipitation patterns is discussed. The transition region between the porous domains and inlet/outlet channels exhibits brine backflow and a larger amount of salt accumulation. This paper presents a comprehensive analysis of the dynamic process of salt dry-out occurring during CO2 injection at the pore scale.

Perspectives on label-free microscopy of heterogeneous and dynamic biological systems.

Significance: Advancements in label-free microscopy could provide real-time, non-invasive imaging with unique sources of contrast and automated standardized analysis to characterize heterogeneous and dynamic biological processes. These tools would overcome challenges with widely used methods that are destructive (e.g., histology, flow cytometry) or lack cellular resolution (e.g., plate-based assays, whole animal bioluminescence imaging). Aim: This perspective aims to (1) justify the need for label-free microscopy to track heterogeneous cellular functions over time and space within unperturbed systems and (2) recommend improvements regarding instrumentation, image analysis, and image interpretation to address these needs. Approach: Three key research areas (cancer research, autoimmune disease, and tissue and cell engineering) are considered to support the need for label-free microscopy to characterize heterogeneity and dynamics within biological systems. Based on the strengths (e.g., multiple sources of molecular contrast, non-invasive monitoring) and weaknesses (e.g., imaging depth, image interpretation) of several label-free microscopy modalities, improvements for future imaging systems are recommended. Conclusion: Improvements in instrumentation including strategies that increase resolution and imaging speed, standardization and centralization of image analysis tools, and robust data validation and interpretation will expand the applications of label-free microscopy to study heterogeneous and dynamic biological systems.

A Mixed-Effect Kernel Machine Regression Model for Integrative Analysis of Alpha Diversity in Microbiome Studies.

Increasing evidence suggests that human microbiota plays a crucial role in many diseases. Alpha diversity, a commonly used summary statistic that captures the richness and/or evenness of the microbial community, has been associated with many clinical conditions. However, individual studies that assess the association between alpha diversity and clinical conditions often provide inconsistent results due to insufficient sample size, heterogeneous study populations and technical variability. In practice, meta-analysis tools have been applied to integrate data from multiple studies. However, these methods do not consider the heterogeneity caused by sequencing protocols, and the contribution of each study to the final model depends mainly on its sample size (or variance estimate). To combine studies with distinct sequencing protocols, a robust statistical framework for integrative analysis of microbiome datasets is needed. Here, we propose a mixed-effect kernel machine regression model to assess the association of alpha diversity with a phenotype of interest. Our approach readily incorporates the study-specific characteristics (including sequencing protocols) to allow for flexible modeling of microbiome effect via a kernel similarity matrix. Within the proposed framework, we provide three hypothesis testing approaches to answer different questions that are of interest to researchers. We evaluate the model performance through extensive simulations based on two distinct data generation mechanisms. We also apply our framework to data from HIV reanalysis consortium to investigate gut dysbiosis in HIV infection.

Prevalence of strabismus in Pakistan: a systematic review and meta-analysis.

Purpose: Strabismus is an ocular condition characterized by misalignment of the visual axis. The global prevalence of strabismus is about 2-3%, which varies between different countries and ethnicities. The aim of this study was to conduct a meta-analysis of studies, which had previously reported the prevalence of strabismus in Pakistan, in order to obtain the overall prevalence of strabismus in the country. Methods: All community-based studies reporting the prevalence of strabismus from Pakistan were searched using international databases and local ophthalmology journals. Information about sample size, number of individuals with strabismus, and location and duration of the study was recorded. Statistical analysis including heterogeneity testing, pooled prevalence calculation and regression analysis were done using the R software. Results: Heterogeneity tests, Pheterogeneity < .01, suggested high heterogeneity between the different studies. The pooled prevalence of strabismus was 0.7% [95% confidence interval (CI): 0.39%-1.23%] according to the random effects model, with a decreasing trend in prevalence from 1995 to 2020. Esotropia was more frequent than exotropia in both population-based and clinic-based studies. Conclusion: The prevalence of strabismus in Pakistan is comparatively lower than the worldwide prevalence, and it appears to be decreasing over the last three decades, consistent with global trends.

Dissecting the Single-Cell Diversity and Heterogeneity Underlying Cervical Precancerous Lesions and Cancer Tissues.

The underlying cellular diversity and heterogeneity from cervix precancerous lesions to cervical squamous cell carcinoma (CSCC) is investigated. Four single-cell datasets including normal tissues, normal adjacent tissues, precancerous lesions, and cervical tumors were integrated to perform disease stage analysis. Single-cell compositional data analysis (scCODA) was utilized to reveal the compositional changes of each cell type. Differentially expressed genes (DEGs) among cell types were annotated using BioCarta. An assay for transposase-accessible chromatin sequencing (ATAC-seq) analysis was performed to correlate epigenetic alterations with gene expression profiles. Lastly, a logistic regression model was used to assess the similarity between the original and new cohort data (HRA001742). After global annotation, seven distinct cell types were categorized. Eight consensus-upregulated DEGs were identified in B cells among different disease statuses, which could be utilized to predict the overall survival of CSCC patients. Inferred copy number variation (CNV) analysis of epithelial cells guided disease progression classification. Trajectory and ATAC-seq integration analysis identified 95 key transcription factors (TF) and one immunohistochemistry (IHC) testified key-node TF (YY1) involved in epithelial cells from CSCC initiation to progression. The consistency of epithelial cell subpopulation markers was revealed with single-cell sequencing, bulk sequencing, and RT-qPCR detection. KRT8 and KRT15, markers of Epi6, showed progressively higher expression with disease progression as revealed by IHC detection. The logistic regression model testified the robustness of the resemblance of clusters among the various datasets utilized in this study. Valuable insights into CSCC cellular diversity and heterogeneity provide a foundation for future targeted therapy.

Topographic heterogeneity triggers complementary cascades that enhance ecosystem multifunctionality.

Topographic heterogeneity sets the stage for community assembly, but its effects on ecosystem functioning remain poorly understood. Here, we test the hypothesis that topographic heterogeneity underpins multiple cascading species interactions and functional pathways that indirectly control multifunctionality. To do so, we combined experimental manipulation of a form of topographic heterogeneity on rocky shores (holes of various sizes) with a comprehensive assessment of naturally assembled communities and multifunctionality. Structural equation modeling indicated that heterogeneity: (1) enhanced biodiversity by supporting filter feeder richness; (2) triggered a facilitation cascade via reef-forming (polychaete) and biomass-dominant (macroalga) foundation species, which in turn broadly supported functionally diverse epibiotic and understory assemblages; and (3) inhibited a key consumer (limpet). The model supported that these mechanisms exerted complementary positive effects on individual functions (e.g., water filtration, ecosystem metabolism, nutrient uptake) and, in turn, collectively enhanced multifunctionality. Topographic heterogeneity may therefore serve as a cornerstone physical attribute by initiating multiple cascades that propagate through ecological communities via foundation species, ultimately manifesting disproportionate effects on ecosystem multifunctionality.

DrugReSC: targeting disease-critical cell subpopulations with single-cell transcriptomic data for drug repurposing in cancer.

The field of computational drug repurposing aims to uncover novel therapeutic applications for existing drugs through high-throughput data analysis. However, there is a scarcity of drug repurposing methods leveraging the cellular-level information provided by single-cell RNA sequencing data. To address this need, we propose DrugReSC, an innovative approach to drug repurposing utilizing single-cell RNA sequencing data, intending to target specific cell subpopulations critical to disease pathology. DrugReSC constructs a drug-by-cell matrix representing the transcriptional relationships between individual cells and drugs and utilizes permutation-based methods to assess drug contributions to cellular phenotypic changes. We demonstrate DrugReSC's superior performance compared to existing drug repurposing methods based on bulk or single-cell RNA sequencing data across multiple cancer case studies. In summary, DrugReSC offers a novel perspective on the utilization of single-cell sequencing data in drug repurposing methods, contributing to the advancement of precision medicine for cancer.

The Asymmetric Spillover Effects of Retirement on Disability: Evidence From China.

Background and Objectives: Recent research has explored the spillover effects of retirement on spousal well-being, yet limited attention has been given to the short-term impact on spousal disability. This study explored the asymmetric spillover impact of retirement on spouses' disability severity among a national cohort of urban residents in China. Research Design and Methods: Utilizing 4 waves of data (2011-2018) from the China Health and Retirement Longitudinal Survey, we employ a nonparametric regression discontinuity design to estimate the short-term effect of retirement on spousal disability severity. Disability is assessed based on their ability to perform activities of daily living (ADLs) and instrumental activities of daily living (IADLs). Furthermore, we conduct heterogeneity analysis stratified by factors such as the husband's retirement status, health conditions, lifestyle behaviors, and the wife's educational level. Additionally, we explore potential mechanisms including changes in health behaviors, emotions, and disease diagnoses. Results: Our findings indicate that wives' retirement has a significant favorable short-term effect on husbands' ADL scores, with a magnitude of -0.644 points (-9.78% relative to baseline). A significant beneficial effect of wives' retirement on the prevalence of husbands' difficulty in dressing, bathing, and eating was observed with substantial magnitudes of 0.075, 0.201, and 0.051 points, respectively. Various heterogeneity analyses and sensitivity tests confirmed the robustness of our results. The positive spillover effect of wives' retirement likely results from reduced negative emotions in husbands. In contrast, husbands' retirement does not affect the prevalence of ADL/IADL disability in their wives. Discussion and Implications: Underscoring the gender asymmetry in the effects of spousal retirement on disability, this study emphasizes the need for tailored policies considering men's and women's distinct disability experiences.

A Strategy to Compare Single-Cell RNA Sequencing Data Sets Provides Phenotypic Insight into Cellular Heterogeneity Underlying Biological Similarities and Differences Between Samples.

Single-cell RNA sequencing (scRNA-seq) allows for an unbiased assessment of cellular phenotypes by enabling the extraction of transcriptomic data. An important question in downstream analysis is how to evaluate biological similarities and differences between samples in high dimensional space. This becomes especially complex when there is cellular heterogeneity within the samples. Here, we present scCompare, a computational pipeline for comparison of scRNA-seq data sets. Phenotypic identities from a known data set are transferred onto another data set using correlation-based mapping to average transcriptomic signatures from each cluster of cells' annotated phenotype. Statistically derived lower cutoffs for phenotype inclusivity allow for cells to be unmapped if they are distinct from the known phenotypes, facilitating potential novel cell type detection. In a comparison of our tool using scRNA-seq data sets from human peripheral blood mononuclear cells (PBMCs), we show that scCompare outperforms single-cell variational inference (scVI) in higher precision and sensitivity for most of the cell types. scCompare was used on a cardiomyocyte data set where it confirmed the discovery of a distinct cluster of cells that differed between the 2 protocols for differentiation. Further use of scCompare on cell atlas data sets revealed insights into the cellular heterogeneity underpinning biological diversity between samples. In addition, we used a cell atlas to better understand the effect of key parameters used in the scCompare pipeline. We envision that scCompare will be of value to the research community when comparing large scRNA-seq data sets.

Targeting Mitochondria: Influence of Metabolites on Mitochondrial Heterogeneity.

Mitochondria are vital organelles that provide energy for the metabolic processes of cells. These include regulating cellular metabolism, autophagy, apoptosis, calcium ions, and signaling processes. Despite their varying functions, mitochondria are considered semi-independent organelles that possess their own genome, known as mtDNA, which encodes 13 proteins crucial for oxidative phosphorylation. However, their diversity reflects an organism's adaptation to physiological conditions and plays a complex function in cellular metabolism. Mitochondrial heterogeneity exists at the single-cell and tissue levels, impacting cell shape, size, membrane potential, and function. This heterogeneity can contribute to the progression of diseases such as neurodegenerative diseases, metabolic diseases, and cancer. Mitochondrial dynamics enhance the stability of cells and sufficient energy requirement, but these activities are not universal and can lead to uneven mitochondria, resulting in heterogeneity. Factors such as genetics, environmental compounds, and signaling pathways are found to affect these cellular processes and heterogeneity. Additionally, the varying roles of metabolites such as NADH and ATP affect glycolysis's speed and efficiency. An imbalance in metabolites can impair ATP production and redox potential in the mitochondria. Therefore, this review will explore the influence of metabolites in shaping mitochondrial morphology, how these changes contribute to age-related diseases and the therapeutic targets for regulating mitochondrial heterogeneity.

Heterogeneity Analysis on Multi-state Brain Functional Connectivity and Adolescent Neurocognition.

Brain functional connectivity or connectome, a unique measure for brain functional organization, provides a great potential to explain the neurobiological underpinning of behavioral profiles. Existing connectome-based analyses highly concentrate on brain activities under a single cognitive state, and fail to consider heterogeneity when attempting to characterize brain-to-behavior relationships. In this work, we study the complex impact of multi-state functional connectivity on behaviors by analyzing the data from a recent landmark brain development and child health study. We propose a nonparametric, Bayesian supervised heterogeneity analysis to uncover neurodevelopmental subtypes with distinct effect mechanisms. We impose stochastic block structures to identify network-based functional phenotypes and develop a variational expectation-maximization algorithm to facilitate an efficient posterior computation. Through integrating resting-state and task-related functional connectomes, we dissect heterogeneous effect mechanisms on children's fluid intelligence from the functional network phenotypes including Fronto-parietal Network and Default Mode Network under different cognitive states. Based on extensive simulations, we further confirm the superior performance of our method on uncovering brain-to-behavior relationships.

Identifying patient subgroups in MASLD and MASH-associated fibrosis: molecular profiles and implications for drug development.

The incidence of MASLD and MASH-associated fibrosis is rapidly increasing worldwide. Drug therapy is hampered by large patient variability and partial representation of human MASH fibrosis in preclinical models. Here, we investigated the mechanisms underlying patient heterogeneity using a discovery dataset and validated in distinct human transcriptomic datasets, to improve patient stratification and translation into subgroup specific patterns. Patient stratification was performed using weighted gene co-expression network analysis (WGCNA) in a large public transcriptomic discovery dataset (n = 216). Differential expression analysis was performed using DESeq2 to obtain differentially expressed genes (DEGs). Ingenuity Pathway analysis was used for functional annotation. The discovery dataset showed relevant fibrosis-related mechanisms representative of disease heterogeneity. Biological complexity embedded in genes signature was used to stratify discovery dataset into six subgroups of various sizes. Of note, subgroup-specific DEGs show differences in directionality in canonical pathways (e.g. Collagen biosynthesis, cytokine signaling) across subgroups. Finally, a multiclass classification model was trained and validated in two datasets. In summary, our work shows a potential alternative for patient population stratification based on heterogeneity in MASLD-MASH mechanisms. Future research is warranted to further characterize patient subgroups and identify protein targets for virtual screening and/or in vitro validation in preclinical models.

Quantifying the magnitude of the general contextual effect in a multilevel study of SARS-CoV-2 infection in Ontario, Canada: application of the median rate ratio in population health research.

BACKGROUND: Regional variations in SARS-CoV-2 infection were observed in Canada and other countries. Studies have used multilevel analyses to examine how a context, such as a neighbourhood, can affect the SARS-CoV-2 infection rates of the people within it. However, few multilevel studies have quantified the magnitude of the general contextual effect (GCE) in SARS-CoV-2 infection rates and assessed how it may be associated with individual- and area-level characteristics. To address this gap, we will illustrate the application of the median rate ratio (MRR) in a multilevel Poisson analysis for quantifying the GCE in SARS-CoV-2 infection rates in Ontario, Canada. METHODS: We conducted a population-based, two-level multilevel observational study where individuals were nested into regions (i.e., forward sortation areas [FSAs]). The study population included community-dwelling adults in Ontario, Canada, between March 1, 2020, and May 1, 2021. The model included seven individual-level variables (age, sex, asthma, diabetes, hypertension, congestive heart failure, and chronic obstructive pulmonary disease) and four FSA census-based variables (household size, household income, employment, and driving to work). The MRR is a median value of the rate ratios comparing two patients with identical characteristics randomly selected from two different regions ordered by rate. We examined the attenuation of the MRR after including individual-level and FSA census-based variables to assess their role in explaining the variation in rates between regions. RESULTS: Of the 11 789 128 Ontario adult community-dwelling residents, 343 787 had at least one SARS-CoV-2 infection during the study period. After adjusting for individual-level and FSA census-based variables, the MRR was attenuated to 1.67 (39% reduction from unadjusted MRR). The strongest FSA census-based associations were household size (RR = 1.88, 95% CI: 1.71-1.97) and driving to work (RR = 0.68, 95% CI: 0.65-0.71). CONCLUSIONS: The individual- and area-level characteristics in our study accounted for approximately 40% of the between-region variation in SARS-CoV-2 infection rates measured by MRR in Ontario, Canada. These findings suggest that population-based policies to address social determinants of health that attenuate the MRR may reduce the observed between-region heterogeneity in SARS-CoV-2 infection rates.

Exploiting phenotypic heterogeneity to improve production of glutathione by yeast.

BACKGROUND: Gene expression noise (variation in gene expression among individual cells of a genetically uniform cell population) can result in heterogenous metabolite production by industrial microorganisms, with cultures containing both low- and high-producing cells. The presence of low-producing individuals may be a factor limiting the potential for high yields. This study tested the hypothesis that low-producing variants in yeast cell populations can be continuously counter-selected, to increase net production of glutathione (GSH) as an exemplar product. RESULTS: A counter-selection system was engineered in Saccharomyces cerevisiae based on the known feedback inhibition of gamma-glutamylcysteine synthetase (GSH1) gene expression, which is rate limiting for GSH synthesis: the GSH1 ORF and the counter-selectable marker GAP1 were expressed under control of the TEF1 and GSH-regulated GSH1 promoters, respectively. An 18% increase in the mean cellular GSH level was achieved in cultures of the engineered strain supplemented with D-histidine to counter-select cells with high GAP1 expression (i.e. low GSH-producing cells). The phenotype was non-heritable and did not arise from a generic response to D-histidine, unlike that with certain other test-constructs prepared with alternative markers. CONCLUSIONS: The results corroborate that the system developed here improves GSH production by targeting low-producing cells. This supports the potential for exploiting end-product/promoter interactions to enrich high-producing cells in phenotypically heterogeneous populations, in order to improve metabolite production by yeast.

Concomitant BRAF V600E and NRAS Q61R mutations in the same thyroid nodule: a case report.

Background: Papillary thyroid cancer (PTC) is the most common type of well-differentiated endocrine malignancy. Generally, thyroid nodules with multiple oncogenic mutations are uncommon with an occurrence which may be related to more aggressive biological behavior of tumors. RET/PTC rearrangement, RAS, and BRAF mutations are considered to be mutually exclusive in PTC. Concomitant RET/PTC, RAS, or BRAF mutations have been documented, although the impact of these mutations for tumor growth and survival is debated. Case Description: Here we present a rare case of woman 46 years old with a neck mass and thyroid nodule classified as TIR5 on cytological examination. We found contemporary BRAF p.(Val600Glu) [p.(V600E); c.1799T>A] and NRAS p.(Gln61Arg) [p.(Q61R); c.182A>G] mutations in morphologically different areas within the same lobe (the right one); The two lesions show different morphology. The mutated BRAF lesion showed morphological characteristics compatible with classic papillary carcinoma. The mutant NRAS lesion shows morphological features compatible with follicular variant papillary carcinoma. To the best of our knowledges, this is the first time that such mutations, which are normally mutually exclusive, have been detected at the same time. Conclusions: The finding of synchronous mutations is a rare occurrence suggesting for intratumoral heterogeneity (ITH) even in PTC. Patients with multiple mutations have a clinical worse prognosis, generally characterized by an aggressive thyroid cancer, which may influence the surgical treatment, chemotherapy, and BRAF V600E mutation-targeting therapy.

Enhancing the prediction of the invasiveness of pulmonary adenocarcinomas presenting as pure ground-glass nodules: Integrating intratumor heterogeneity score with clinical-radiological features via machine learning in a multicenter study.

Objective: The invasiveness of lung adenocarcinoma significantly impacts clinical decision-making. However, assessing this invasiveness preoperatively, especially when it manifests as pure ground-glass nodules (pGGN) on CT scans, poses challenges. This study aims to quantify intratumor heterogeneity (ITH) and determine whether the ITH score can enhance the accuracy of invasiveness predictions. Methods: A total of 524 patients with lung adenocarcinomas presenting as pGGN were enrolled in the study, with 177 (33.78%) receiving a pathologic diagnosis of invasiveness. Four diagnostic approaches were developed to predict the invasiveness of lung adenocarcinoma presenting as pGGN: (1) conventional lesion size, (2) ITH score, (3) clinical-radiological features (ClinRad), and (4) integration of the ITH score with ClinRad. ClinRad alone or in combination with the ITH score served as the input for 11 machine learning approaches. The trained models were evaluated in an independent validation cohort, and the area under the curve (AUC) was calculated to assess classification performance. Results: The conventional lesion size showed the lowest performance, with an AUC of 0.826 (95% confidence interval [CI]: 0.758-0.894), while the ITH score outperformed it with an AUC of 0.846 (95% CI: 0.787-0.905). The CatBoost model performed best when the ITH score and ClinRad were both used as input features, leading to the development of an ITH-ClinRad-guided CatBoost classifier. CatBoost also excelled with ClinRad alone, resulting in a ClinRad-guided CatBoost classifier with an AUC of 0.830 (95% CI: 0.764-0.896), surpassed by the ITH-ClinRad-guided CatBoost classifier with an AUC of 0.871 (95% CI: 0.818-0.924). Conclusion: The ITH-ClinRad-guided CatBoost classifier emerges as a promising tool with significant potential to revolutionize the management of lung adenocarcinomas presenting as pGGNs.

It's about (taking up) space: Discreteness of individuals and the strength of spatial coexistence mechanisms.

One strand of modern coexistence theory (MCT) partitions invader growth rates (IGR) to quantify how different mechanisms contribute to species coexistence, highlighting fluctuation-dependent mechanisms. A general conclusion from the classical analytic MCT theory is that coexistence mechanisms relying on temporal variation (such as the temporal storage effect) are generally less effective at promoting coexistence than mechanisms relying on spatial or spatiotemporal variation (primarily growth-density covariance). However, the analytic theory assumes continuous population density, and IGRs are calculated for infinitesimally rare invaders that have infinite time to find their preferred habitat and regrow, without ever experiencing intraspecific competition. Here we ask if the disparity between spatial and temporal mechanisms persists when individuals are, instead, discrete and occupy finite amounts of space. We present a simulation-based approach to quantifying IGRs in this situation, building on our previous approach for spatially non-varying habitats. As expected, we found that spatial mechanisms are weakened; unexpectedly, the contribution to IGR from growth-density covariance could even become negative, opposing coexistence. We also found shifts in which demographic parameters had the largest effect on the strength of spatial coexistence mechanisms. Our substantive conclusions are statements about one model, across parameter ranges that we subjectively considered realistic. Using the methods developed here, effects of individual discreteness should be explored theoretically across a broader range of conditions, and in models parameterized from empirical data on real communities.

Symmetry breaking and fate divergence during lateral inhibition in Drosophila.

Lateral inhibition mediates alternative cell fate decision and produces regular cell fate patterns with fate symmetry breaking (SB) relying on the amplification of small stochastic differences in Notch activity via an intercellular negative feedback loop. Here, we used quantitative live imaging of endogenous Scute (Sc), a proneural factor, and of a Notch activity reporter to study the emergence of Sensory Organ Precursor cells (SOPs) in the pupal abdomen of Drosophila. SB was observed at low Sc levels and was not preceded by a phase of intermediate Sc expression and Notch activity. Thus, mutual inhibition may only be transient in this context. In support of the intercellular feedback loop model, cell-to-cell variations in Sc levels promoted fate divergence. The size of the apical area of competing cells did not detectably bias this fate choice. Surprisingly, cells that were in direct contact at the time of SB could adopt the SOP fate, albeit at low frequency (10%). These lateral inhibition defects were corrected by cellular rearrangements, not cell fate change, highlighting the role of cell-cell intercalation in pattern refinement.