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.

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.

Tracking changes in functionality and morphology of repopulated microglia in young and old mice.

BACKGROUND: Microglia (MG) are myeloid cells of the central nervous system that support homeostasis and instigate neuroinflammation in pathologies. Single-cell RNA sequencing (scRNA-seq) revealed the functional heterogeneity of MG in mouse brains. Microglia are self-renewing cells and inhibition of colony-stimulating factor 1 receptor (CSF1R) signaling depletes microglia which rapidly repopulate. The functions of repopulated microglia are poorly known. METHODS: We combined scRNA-seq, bulk RNA-seq, immunofluorescence, and confocal imaging to study the functionalities and morphology of repopulated microglia. RESULTS: A CSRF1R inhibitor (BLZ-945) depleted microglia within 21 days and a number of microglia was fully restored within 7 days, as confirmed by TMEM119 staining and flow cytometry. ScRNA-seq and computational analyses demonstrate that repopulated microglia originated from preexisting progenitors and reconstituted functional clusters but upregulated inflammatory genes. Percentages of proliferating, immature microglia displaying inflammatory gene expression increased in aging mice. Morphometric analysis of MG cell body and branching revealed a distinct morphology of repopulated MG, particularly in brains of old mice. We demonstrate that with aging some repopulated MG fail to reach the homeostatic phenotype. These differences may contribute to the deterioration of MG protective functions with age.

Dynamic alterations and ecological implications of rice rhizosphere bacterial communities induced by an insect-transmitted reovirus across space and time.

BACKGROUND: Cereal diseases caused by insect-transmitted viruses are challenging to forecast and control because of their intermittent outbreak patterns, which are usually attributed to increased population densities of vector insects due to cereal crop rotations and indiscriminate use of pesticides, and lack of resistance in commercial varieties. Root microbiomes are known to significantly affect plant health, but there are significant knowledge gaps concerning epidemics of cereal virus diseases at the microbiome-wide scale under a variety of environmental and biological factors. RESULTS: Here, we characterize the diversity and composition of rice (Oryza sativa) root-associated bacterial communities after infection by an insect-transmitted reovirus, rice black-streaked dwarf virus (RBSDV, genus Fijivirus, family Spinareoviridae), by sequencing the bacterial 16S rRNA gene amplified fragments from 1240 samples collected at a consecutive 3-year field experiment. The disease incidences gradually decreased from 2017 to 2019 in both Langfang (LF) and Kaifeng (KF). BRSDV infection significantly impacted the bacterial community in the rice rhizosphere, but this effect was highly susceptible to both the rice-intrinsic and external conditions. A greater correlation between the bacterial community in the rice rhizosphere and those in the root endosphere was found after virus infection, implying a potential relationship between the rice-intrinsic conditions and the rhizosphere bacterial community. The discrepant metabolites in rhizosphere soil were strongly and significantly correlated with the variation of rhizosphere bacterial communities. Glycerophosphates, amino acids, steroid esters, and triterpenoids were the metabolites most closely associated with the bacterial communities, and they mainly linked to the taxa of Proteobacteria, especially Rhodocyclaceae, Burkholderiaceae, and Xanthomonadales. In addition, the greenhouse pot experiments demonstrated that bulk soil microbiota significantly influenced the rhizosphere and endosphere communities and also regulated the RBSDV-mediated variation of rhizosphere bacterial communities. CONCLUSIONS: Overall, this study reveals unprecedented spatiotemporal dynamics in rhizosphere bacterial communities triggered by RBSDV infection with potential implications for disease intermittent outbreaks. The finding has promising implications for future studies exploring virus-mediated plant-microbiome interactions. Video Abstract.

Treatment-induced stemness and lineage plasticity in driving prostate cancer therapy resistance.

Most human cancers are heterogeneous consisting of cancer cells at different epigenetic and transcriptional states and with distinct phenotypes, functions, and drug sensitivities. This inherent cancer cell heterogeneity contributes to tumor resistance to clinical treatment, especially the molecularly targeted therapies such as tyrosine kinase inhibitors (TKIs) and androgen receptor signaling inhibitors (ARSIs). Therapeutic interventions, in turn, induce lineage plasticity (also called lineage infidelity) in cancer cells that also drives therapy resistance. In this Perspective, we focus our discussions on cancer cell lineage plasticity manifested as treatment-induced switching of epithelial cancer cells to basal/stem-like, mesenchymal, and neural lineages. We employ prostate cancer (PCa) as the prime example to highlight ARSI-induced lineage plasticity during and towards development of castration-resistant PCa (CRPC). We further discuss how the tumor microenvironment (TME) influences therapy-induced lineage plasticity. Finally, we offer an updated summary on the regulators and mechanisms driving cancer cell lineage infidelity, which should be therapeutically targeted to extend the therapeutic window and improve patients' survival.

Spindle oscillations emerge at the critical state of electrically coupled networks in the thalamic reticular nucleus.

Spindle oscillation is a waxing-and-waning neural oscillation observed in the brain, initiated at the thalamic reticular nucleus (TRN) and typically occurring at 7-15 Hz. Experiments have shown that in the adult brain, electrical synapses, rather than chemical synapses, dominate between TRN neurons, suggesting that the traditional view of spindle generation via chemical synapses may need reconsideration. Based on known experimental data, we develop a computational model of the TRN network, where heterogeneous neurons are connected by electrical synapses. The model shows that the interplay between synchronizing electrical synapses and desynchronizing heterogeneity leads to multiple synchronized clusters with slightly different oscillation frequencies whose summed-up activity produces spindle oscillation as seen in local field potentials. Our results suggest that during spindle oscillation, the network operates at the critical state, which is known for facilitating efficient information processing. This study provides insights into the underlying mechanism of spindle oscillation and its functional significance.

Lipid Droplets Big and Small: Basic Mechanisms That Make Them All.

Lipid droplets (LDs) are dynamic storage organelles with central roles in lipid and energy metabolism. They consist of a core of neutral lipids, such as triacylglycerol, which is surrounded by a monolayer of phospholipids and specialized surface proteins. The surface composition determines many of the LD properties, such as size, subcellular distribution, and interaction with partner organelles. Considering the diverse energetic and metabolic demands of various cell types, it is not surprising that LDs are highly heterogeneous within and between cell types. Despite their diversity, all LDs share a common biogenesis mechanism. However, adipocytes have evolved specific adaptations of these basic mechanisms, enabling the regulation of lipid and energy metabolism at both the cellular and organismal levels. Here, we discuss recent advances in the understanding of both the general mechanisms of LD biogenesis and the adipocyte-specific adaptations controlling these fascinating organelles.

Frontoparietal atrophy trajectories in cognitively unimpaired elderly individuals using longitudinal Bayesian clustering.

INTRODUCTION: Frontal and/or parietal atrophy has been reported during aging. To disentangle the heterogeneity previously observed, this study aimed to uncover different clusters of grey matter profiles and trajectories within cognitively unimpaired individuals. METHODS: Structural magnetic resonance imaging (MRI) data of 307 Aß-negative cognitively unimpaired individuals were modelled between ages 60-85 from three cohorts worldwide. We applied unsupervised clustering using a novel longitudinal Bayesian approach and characterized the clusters' cerebrovascular and cognitive profiles. RESULTS: Four clusters were identified with different grey matter profiles and atrophy trajectories. Differences were mainly observed in frontal and parietal brain regions. These distinct frontoparietal grey matter profiles and longitudinal trajectories were differently associated with cerebrovascular burden and cognitive decline. DISCUSSION: Our findings suggest a conciliation of the frontal and parietal theories of aging, uncovering coexisting frontoparietal GM patterns. This could have important future implications for better stratification and identification of at-risk individuals.

Standard chemotherapy impacts on in vitro cellular heterogeneity in spheroids enriched with cancer stem cells (CSCs) derived from triple-negative breast cancer cell line.

Triple-negative breast cancer is a heterogeneous disease with high recurrence and mortality, linked to cancer stem cells (CSCs). Our study characterized distinct cell subpopulations and signaling pathways to explore chemoresistance. We observed cellular heterogeneity among and within the cells regarding phenotyping and drug response. In untreated BT-549 cells, we noted plasticity properties in both CD44+/CD24+/CD146+ hybrid cells and CD44-/CD24+/CD146+ epithelial cells, enabling phenotypic conversion into CD44+/CD24-/CD146- epithelial-mesenchymal transition (EMT)-like like breast CSCs (BCSCs). Additionally, non-BCSCs may give rise to ALDH+ epithelial-like BCSCs. Enriched BCSCs demonstrated the potential to differentiation into CD44-/CD24-/CD146- cells and exhibited self-renewal capabilities. Similar phenotypic plasticity was not observed in untreated Hs 578T and HMT-3522 S1 cells. BT-549 cells were more resistant to paclitaxel/PTX than to doxorubicin/DOX, a phenomenon potentially linked to the presence of CD24+ cells prior to treatment. Under the CSCs-enriched spheroids model, BT-549 demonstrated extreme resistance to DOX, likely due to the enrichment of BCSCs CD44+/CD24-/CD146- and the tumor cells CD44-/CD24-/CD146-. Additionally, DOX treatment induced the enrichment of plastic and chemoresistant cells, further exacerbating resistance mechanisms. BT-549 exhibited high heterogeneity, leading to significant alterations in cell subpopulations under BCSCs enrichment, demonstrating increased phenotypic plasticity during EMT. This phenomenon appears to play a major role in DOX resistance, as indicated by the presence of the refractory cells CD44+/CD24-/CD146- BCSCs EMT-like, CD44-/CD24-/CD146- tumor cells, and elevated STAT3 expression. Gene expression data from BT-549 CSCs-enriched spheroids suggests that ferroptosis may be occurring via autophagic regulation triggered by RAB7A, highlighting this gene as a potential therapeutic target.

The prognostic value of [18F]FDG PET/CT texture analysis prior to transplantation for unresectable colorectal liver metastases.

INTRODUCTION: To determine whether heterogeneity in colorectal liver metastases (CRLM) 18F fluorodeoxyglucose [18F]FDG distribution is predictive of disease-free survival (DFS) and overall survival (OS) following liver transplantation (LT) for unresectable CRLM. METHODS: The preoperative [18F]FDG positron emission tomography/computed tomography examinations of all patients in the secondary cancer 1 and 2 studies were retrospectively assessed. Maximum standardized uptake value (SUVmax), metabolic tumour volume (MTV), and six texture heterogeneity parameters (joint entropyGLCM, dissimilarityGLCM, grey level varianceSZM, size zone varianceSZM, and zone percentageSZM, and morphological feature convex deficiency) were obtained. DFS and OS for patients over and under the median value for each of these parameters were compared by using the Kaplan Meier method and log rank test. RESULTS: Twenty-eight out of 40 patients who underwent LT for unresectable CRLM had liver metastases with uptake above liver background and were eligible for inclusion. Low MTV (p < 0.001) and dissimilarityGLCM (p = 0.016) were correlated to longer DFS. Low MTV (p < 0.001) and low values of the texture parameters dissimilarityGLCM (p = 0.038), joint entropyGLCM (p = 0.015) and zone percentageSZM (p = 0.037) were significantly correlated to longer OS. SUVmax was not correlated to DFS and OS. CONCLUSION: Although some texture parameters were able to significantly predict DFS and OS, MTV seems to be superior to predict both DFS and OS following LT for unresectable CRLM.

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.

The influence of fetal bovine serum concentration on stemness and neuronal differentiation markers in stem cells from human exfoliated deciduous teeth.

Dental Stem Cells (DSCs) from discarded teeth are a non-invasive and ethically favorable source with the potential for neurogenesis due to their ectodermal origin. Stem cells from human exfoliated deciduous teeth (SHED) are particularly promising due to their high differentiation potential and relative immaturity compared to other Mesenchymal Stromal Cells (MSCs). Markers like CD56 and CD271 are critical in identifying MSC subpopulations for therapeutic applications because of their roles in neurodevelopment and maintaining stemness. This study investigates how fetal bovine serum (FBS) concentrations affect the expression of CD56 and CD271 in SHED, influencing their stemness and neuronal differentiation potential. SHEDs were isolated from various donors, cultured, and characterized for MSC traits using markers such as CD14, CD19, CD29, CD34, CD45, CD73, CD90, CD105, CD56, and CD271. Culturing SHED in different FBS conditions (standard 15 %, reduced 1 % and 5 %, and FBS-free) showed that lower FBS concentrations increase CD271 and CD56 expression while maintaining the standard MSC immunophenotype. Importantly, the enhanced expression of these markers can be induced even after SHEDs have been expanded in standard FBS concentrations. These findings suggest that FBS concentration can optimize SHED culture conditions, enhancing their suitability for regenerative medicine and tissue engineering applications.

Mimicking and analyzing the tumor microenvironment.

The tumor microenvironment (TME) is increasingly appreciated to play a decisive role in cancer development and response to therapy in all solid tumors. Hypoxia, acidosis, high interstitial pressure, nutrient-poor conditions, and high cellular heterogeneity of the TME arise from interactions between cancer cells and their environment. These properties, in turn, play key roles in the aggressiveness and therapy resistance of the disease, through complex reciprocal interactions between the cancer cell genotype and phenotype, and the physicochemical and cellular environment. Understanding this complexity requires the combination of sophisticated cancer models and high-resolution analysis tools. Models must allow both control and analysis of cellular and acellular TME properties, and analyses must be able to capture the complexity at high depth and spatial resolution. Here, we review the advantages and limitations of key models and methods in order to guide further TME research and outline future challenges.

Investigating risk factors associated with injury severity in highway crashes: A hybrid approach integrating two-step cluster analysis and latent class ordered regression model with covariates.

Highway crashes are responsible for a significant number of severe and fatal injuries drawing considerable attention from transportation authorities and safety researchers. This paper aims to investigate the unobserved heterogeneous effects of various risk factors, such as pre-crash circumstances, environmental and road conditions, vehicle-involved information, and driver attributes on injury severities. Our methodology uses a hybrid approach that combines two-step cluster analysis and latent class ordered regression model with covariates. The proposed approach extends traditional latent class model by elucidating potential relationships among predictors, covariates, and outcomes. A cross-sectional crash data covering a period of over five years (2011-2016) was obtained via the Dutch crash registration database for modeling injury severity outcomes. The results reveal substantial and statistically significant differences in injury severity between two latent classes. Moreover, we identify road lighting, time of crash, road surface conditions, weather, and season as covariates influencing class membership prediction. Factors such as high speed, alcohol involvement, frontal collision points, and older driver demographics increase the probability of serious injury and facility across all cases analyzed. Additionally, we observe notable heterogeneity effects between the two classes regarding temporal characteristics, the number and type of vehicles involved, as well as driver gender. Our findings provide specific and valuable insights into injury severity outcomes, which can inform the formulation of targeted safety countermeasures and regulatory strategies for traffic policies and relevant agencies.

Pan-cancer characterization of cellular senescence reveals its inter-tumor heterogeneity associated with the tumor microenvironment and prognosis.

Cellular senescence (CS) is characterized by the irreversible cell cycle arrest and plays a key role in aging and diseases, such as cancer. Recent years have witnessed the burgeoning exploration of the intricate relationship between CS and cancer, with CS recognized as either a suppressing or promoting factor and officially acknowledged as one of the 14 cancer hallmarks. However, a comprehensive characterization remains absent from elucidating the divergences of this relationship across different cancer types and its involvement in the multi-facets of tumor development. Here we systematically assessed the cellular senescence of over 10,000 tumor samples from 33 cancer types, starting by defining a set of cancer-associated CS signatures and deriving a quantitative metric representing the CS status, called CS score. We then investigated the CS heterogeneity and its intricate relationship with the prognosis, immune infiltration, and therapeutic responses across different cancers. As a result, cellular senescence demonstrated two distinct prognostic groups: the protective group with eleven cancers, such as LIHC, and the risky group with four cancers, including STAD. Subsequent in-depth investigations between these two groups unveiled the potential molecular and cellular mechanisms underlying the distinct effects of cellular senescence, involving the divergent activation of specific pathways and variances in immune cell infiltrations. These results were further supported by the disparate associations of CS status with the responses to immuno- and chemo-therapies observed between the two groups. Overall, our study offers a deeper understanding of inter-tumor heterogeneity of cellular senescence associated with the tumor microenvironment and cancer prognosis.

Understanding variability: the role of meta-analysis of variance.

Meta-analyses traditionally compare the difference in means between groups for one or more outcomes of interest. However, they do not compare the spread of data (variability), which could mean that important effects and/or subgroups are missed. To address this, methods to compare variability meta-analytically have recently been developed, making it timely to review them and consider their strengths, weaknesses, and implementation. Using published data from trials in major depression, we demonstrate how the spread of data can impact both overall effect size and the frequency of extreme observations within studies, with potentially important implications for conclusions of meta-analyses, such as the clinical significance of findings. We then describe two methods for assessing group differences in variability meta-analytically: the variance ratio (VR) and coefficient of variation ratio (CVR). We consider the reporting and interpretation of these measures and how they differ from the assessment of heterogeneity between studies. We propose general benchmarks as a guideline for interpreting VR and CVR effects as small, medium, or large. Finally, we discuss some important limitations and practical considerations of VR and CVR and consider the value of integrating variability measures into meta-analyses.

Monitoring the Biochemical Activity of Single Anammox Granules with Microbarometers.

Granule-based anaerobic ammonium oxidation (Anammox) is a promising biotechnology for wastewater treatments with extraordinary performance in nitrogen removal. However, traditional analytical methods often delivered an average activity of a bulk sample consisting of millions and even billions of Anammox granules with distinct sizes and components. Here, we developed a novel technique to monitor the biochemical activity of individual Anammox granules in real-time by recording the production rate of nitrogen gas with a microbarometer in a sealed chamber containing only one granule. It was found that the specific activity of a single Anammox granule not only varied by tens of folds among different individuals with similar sizes (activity heterogeneity) but also revealed significant breath-like dynamics over time (temporal fluctuation). Statistical analysis on tens of individuals further revealed two subpopulations with distinct color and specific activity, which were subsequently attributed to the different expression levels of heme c content and hydrazine dehydrogenase activity. This study not only provides a general methodology for various kinds of gas-producing microbial processes but also establishes a bottom-up strategy for exploring the structural-activity relationship at a single sludge granule level, with implications for developing a better Anammox process.

CT-based conventional radiomics and quantification of intratumoral heterogeneity for predicting benign and malignant renal lesions.

BACKGROUND: With the increasing incidence of renal lesions, pretreatment differentiation between benign and malignant lesions is crucial for optimized management. This study aimed to develop a machine learning model utilizing radiomic features extracted from various regions of interest (ROIs), intratumoral ecological diversity features, and clinical factors to classify renal lesions. METHODS: CT images (arterial phase) of 1,795 renal lesions with confirmed pathology from three hospital sites were split into development (1184, 66%) and test (611, 34%) cohorts by surgery date. Conventional radiomic features were extracted from eight ROIs of arterial phase images. Intratumoral ecological diversity features were derived from intratumoral subregions. The combined model incorporating these features with clinical factors was developed, and its performance was compared with radiologists' interpretation. RESULTS: Combining intratumoral and peritumoral radiomic features, along with ecological diversity features yielded the highest AUC of 0.929 among all combinations of features extracted from CT scans. After incorporating clinical factors into the features extracted from CT images, our combined model outperformed the interpretation of radiologists in the whole (AUC = 0.946 vs 0.823, P < 0.001) and small renal lesion (AUC = 0.935 vs 0.745, P < 0.001) test cohorts. Furthermore, the combined model exhibited favorable concordance and provided the highest net benefit across threshold probabilities exceeding 60%. In the whole and small renal lesion test cohorts, the AUCs for subgroups with predicted risk below or above 95% sensitivity and specificity cutoffs were 0.974 and 0.978, respectively. CONCLUSIONS: The combined model, incorporating intratumoral and peritumoral radiomic features, ecological diversity features, and clinical factors showed good performance for distinguishing benign from malignant renal lesions, surpassing radiologists' diagnoses in both whole and small renal lesions. It has the potential to save patients from unnecessary invasive biopsies/surgeries and to enhance clinical decision-making.

Recent progress and applications of single-cell sequencing technology in breast cancer.

Single-cell RNA sequencing (scRNA-seq) technology enables the precise analysis of individual cell transcripts with high sensitivity and throughput. When integrated with multiomics technologies, scRNA-seq significantly enhances the understanding of cellular diversity, particularly within the tumor microenvironment. Similarly, single-cell DNA sequencing has emerged as a powerful tool in cancer research, offering unparalleled insights into the genetic heterogeneity and evolution of tumors. In the context of breast cancer, this technology holds substantial promise for decoding the intricate genomic landscape that drives disease progression, treatment resistance, and metastasis. By unraveling the complexities of tumor biology at a granular level, single-cell DNA sequencing provides a pathway to advancing our comprehension of breast cancer and improving patient outcomes through personalized therapeutic interventions. As single-cell sequencing technology continues to evolve and integrate into clinical practice, its application is poised to revolutionize the diagnosis, prognosis, and treatment strategies for breast cancer. This review explores the potential of single-cell sequencing technology to deepen our understanding of breast cancer, highlighting key approaches, recent advancements, and the role of the tumor microenvironment in disease plasticity. Additionally, the review discusses the impact of single-cell sequencing in paving the way for the development of personalized therapies.