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Experimental evolution reveals contrasting adaptive landscapes in lab and field environments.

Experimental evolution is widely used to infer microbial responses to environmental change, yet most laboratory studies impose constant, well-mixed conditions that differ fundamentally from fluctuating, spatially structured field environments. We compared genomic evolution in the leaf litter-associated bacterium Curtobacterium strain MMLR14_002 under control and warming treatments in laboratory culture and in a complementary field experiment. Laboratory-derived isolates accumulated more mutations per genome and exhibited stronger locus-level parallelism, with mutations recurring in a small number of coding loci. Field-derived isolates accumulated fewer mutations per genome, and these mutations rarely occurred in the same coding loci across replicate populations. Instead, field isolates exhibited a higher proportion of intergenic mutations, with mutations recurring in the same intergenic regions across independent field deployments. When coding mutations were detected in the field, they were distributed across functionally diffuse targets and more often involved metabolic pathways than the core cellular processes repeatedly targeted during laboratory evolution. Warming itself did not consistently influence mutation accumulation or the genomic distribution of mutations; instead, laboratory and field contexts primarily shaped the accumulation, targets, and repeatability of genomic change. These results suggest that laboratory thermal evolution identifies adaptive routes favored under sustained selection but may overestimate coding-level parallelism under heterogeneous field conditions. Bridging laboratory and field evolution will likely require experimental designs that incorporate temporal variability and spatial heterogeneity characteristic of natural systems.IMPORTANCEA central goal of experimental evolution is to infer how microbes evolve in nature from laboratory studies. Here, we evaluate this assumption by comparing genomic evolution of a leaf litter-associated Curtobacterium strain in laboratory and field warming experiments to identify broad patterns rather than isolate the contribution of any single environmental factor. We find that the strong parallelism at coding loci observed under laboratory conditions is reduced in the field, while mutations recurring in the same intergenic regions across field deployments suggest that parallel evolution in nature may more often involve regulatory noncoding regions rather than coding targets. These results show that environmental context reshapes adaptive landscapes and may limit the parallelism of coding-level genomic responses inferred from homogeneous laboratory conditions.

experimental evolution

Valproate vs levetiracetam in juvenile myoclonic epilepsy: systematic review and meta-analysis.

INTRODUCTION: Juvenile myoclonic epilepsy (JME) is a genetic generalized epilepsy syndrome with onset typically in adolescence and a chronic course requiring long-term antiseizure medications (ASMs). Valproate (VPA) is the most effective treatment for seizure control in JME but use is limited by metabolic, cognitive, and teratogenic adverse effects (AEs). Levetiracetam (LEV) is an alternative ASM when VPA is contraindicated or not tolerated. Comparisons of the efficacy and long-term tolerability of VPA and LEV remain limited. METHODS: We conducted a systematic review and meta-analysis using PRISMA guidelines and the Cochrane Handbook. We searched PubMed, Embase, and the Cochrane Library from inception through January 2026 for studies in JME patients comparing LEV and VPA, and included randomized controlled trials and comparative observational studies with ≥ 6 months of follow-up. Primary outcomes were seizure remission and ASM failure or treatment discontinuation. Secondary outcomes included, memory impairment, weight gain or obesity, dizziness, and overall AEs. Risk ratios (RRs) with 95% confidence intervals (CIs) were pooled using random-effects models. Heterogeneity was assessed using the I2 statistic. RESULTS: Seven studies encompassing 1,009 patients were included. VPA was associated with higher pooled seizure remission rates compared with LEV (344 of 574 vs. 169 of 390; RR 1.44, 95% CI 1.27-1.63); however, substantial heterogeneity (I2 = 88.4%) limits confidence in this finding. VPA was associated with a lower risk of drug failure or treatment discontinuation (RR 0.68, 95% CI 0.54-0.86), with no heterogeneity (I2 = 0.0%). VPA was also associated with a higher risk of memory impairment (RR 5.37, 95% CI 2.05-14.04; I2 = 74.5%) and weight gain or obesity (RR 6.40, 95% CI 3.64-11.26; I2 = 35.9%). No significant differences were observed between treatments regarding dizziness (RR 0.91, 95% CI 0.61-1.37; I2 = 21.2%). Sensitivity analyses confirmed the robustness of the pooled estimates. CONCLUSION: VPA was associated with higher seizure remission rates and lower treatment discontinuation compared with LEV; however, these findings must be interpreted with caution given the substantial heterogeneity, the predominance of observational studies, and the serious risk of bias identified in most included studies VPA also demonstrated lower rates of treatment discontinuation, despite a higher burden of cognitive impairment and weight gain. No relevant differences were observed regarding dizziness. Large-scale randomized trials with standardized outcome definitions and longer follow-up are needed to define the comparative risk-benefit profiles of LEV and VPA in JME.

Humans

Non-destructive prediction of lead content in oilseed rape leaves by fluorescence hyperspectral technology based on neural network.

Based on fluorescence hyperspectral imaging (FHSI), this study targeted rapid, non-destructive quantification of lead (Pb) content in oilseed rape leaves treated with varying silicon (Si) concentrations, acquiring fluorescence spectra over the 484.43-1001.61 nm wavelength range. To optimize spectral data quality, preprocessing methods (Savitzky-Golay smoothing, first derivative, detrending) were comprehensively compared. Characteristic wavelengths were then selected via interval variable iterative shrinkage, which effectively compressed data dimensionality and reduced computational load. A hybrid SE-CL1DA model, fusing a 1D convolutional neural network, a long short-term memory network and SE attention mechanism was constructed, with Bayesian optimization tuning hyperparameters to boost stability. The BO-SE-CL1DA outperformed both traditional machine learning and insufficiently optimized deep learning model (Rp2=0.9609, RMSE = 0.0377 mg/kg, RPD = 5.1736), thus enabling accurate Pb estimation, supporting Si-regulated heavy metal stress management and facilitating agricultural contamination monitoring.

Plant Leaves

Assessing the effects of non-invasive transcranial electrical stimulation (tACS and tDCS) on electrophysiological sleep parameters - a systematic review.

Transcranial electrical stimulation (tES), including transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS), is considered a safe method to modulate cortical activity and endogenous brain oscillations. Given the therapeutic potential of tES across various clinical conditions and the central role of sleep in restoration and memory consolidation, numerous studies have investigated its effects on sleep and sleep-related parameters, yielding inconsistent results. This systematic review provides an up-to-date synthesis of 51 studies assessing the impact of tES on objectively measured electrophysiological sleep outcomes in both healthy individuals and clinical populations. The reviewed studies demonstrate heterogeneous effects, reflecting substantial variability in study designs. Nonetheless, consistent trends emerge, including reduced NREM1 and increases in total sleep time, NREM2, and NREM3 following tES. Moreover, slow-oscillatory tES increased slow-wave power during sleep. Here we show that tES, particularly slow-oscillatory tES, may positively influence sleep architecture and continuity by modulating endogenous brain oscillations. However, due to heterogeneous stimulation protocols, inconsistent findings, the limited number of significant effects and substantial risk of bias the current evidence remains inconclusive. Well-designed, large-scale trials targeting specific sleep outcomes are needed to clarify the therapeutic potential of tES.

Humans

Psychological interventions for children with chronic physical conditions: a systematic review assessing the role of coping, emotional and cognitive processes.

OBJECTIVES: Coping, emotional and cognitive processes are crucial in child development, particularly in children with pediatric chronic physical conditions (CPC). No systematic review in pediatric psychology has investigated the effectiveness of interventions on these processes concurrently. This review addresses this gap by focusing on the effectiveness of psychological interventions on coping, emotional and cognitive processes in children with CPCs. METHODS: Five electronic databases were searched for studies assessing at least one of these processes. Only randomized-controlled trials with children (8-12 years) with a CPC (e.g. diabetes, asthma), which implemented a psychological intervention were included. This study is registered in (CRD42021233505). RESULTS: Ten intervention studies were identified. While cognitive interventions (Cogmed) showed some improvements in working memory, the effects varied across studies despite similar methodologies. Coping interventions (e.g. Coping Skills Training) showed little effect on coping strategies or psychological health variables and were no more beneficial than control groups. No study trained coping, emotional processes and cognitive processes together. CONCLUSION: This review shows current limitations in evaluating psychological interventions targeting coping, cognitive or emotional processes in children with CPCs, limiting a comprehensive understanding of the interventions' action mechanisms. Systematically including underlying processes in intervention studies could help to better adjust those interventions.

Humans

Does high fructose consumption trigger microglia activation and neuroinflammation? A systematic review.

This systematic review evaluated the effects of fructose intake on neuroinflammatory markers in rodent models. The search terms Fructose AND neuroinflammation OR Neurodegeneration OR chemokines OR interleukins OR microglia OR behaviour OR memory OR cognition were used in Google Scholar, Scopus and Web of Science. Thirteen animal studies investigating fructose-induced neuroinflammation that matched the eligibility criteria were included in the study. Across the studies, 16 inflammatory markers were identified and significantly altered following exposure to fructose. The findings consistently demonstrated elevated expression of pro-inflammatory cytokines, TNF-α, IL-6, and IL-1β, following fructose administration. Fructose consumption also dysregulated MCP-1, fractalkine, and CX3CR1 levels, thereby promoting inflammatory signalling and microglial activation. Furthermore, fructose exposure significantly increased IBA-1 and CD11b, indicating sustained neuroimmune activation. Alterations in important inflammatory pathways involving TLR4, NLRP3, NF-κB, MyD88, iNOS, and cyclooxygenases (COX-1 and COX-2) were also observed. In contrast, expression of the anti-inflammatory regulator peroxisome proliferator-activated receptor gamma (PPARγ) was reduced after fructose treatment. Overall, the findings suggest that chronic fructose consumption induces neuroinflammation through multiple inflammatory and immune-related mechanisms in the brain. These effects appear to be dose- and duration-dependent and may contribute significantly to neurodegeneration and cognitive impairment.

Microglia

Are there any common effects in preclinical models of micro- and nanoplastic (MNP) exposure? A systematic review.

Micro- and nanoplastics (MNPs) are emerging contaminants detected in food sources and the marine food chain, raising concerns about human health. Although no causal relationship has been established between MNP exposure and specific diseases, growing evidence suggests adverse developmental, behavioral, cognitive and biochemical effects. This systematic review synthesized evidence from common preclinical neurotoxicology models, including C. elegans, D. rerio, D. melanogaster, in vitro systems and rodents, to identify convergent developmental, behavioral and biochemical outcomes. The protocol was preregistered in OSF, followed PRISMA-P guidelines, applied PICOS criteria, and assessed methodological quality using the European Commission's ToxRTool. Overall, 185 studies were included. Consistent findings showed impaired survival and disrupted development across all models. Behavioral alterations affecting anxiety, memory, learning, sociability and locomotor activity were also consistently reported. In addition, numerous studies identified disruptions in the serotonergic (5-HT) system, including changes in neurotransmitter levels, transporters and metabolic enzymes. Despite methodological heterogeneity, these findings indicate that MNP exposure produces reproducible neurodevelopmental and neurochemical alterations across experimental models. Future studies should improve methodological harmonization, strengthen cross-model comparability and identify robust biomarkers and key mechanisms underlying MNP-induced neurotoxicity, facilitating translation to human health risk assessment frameworks.

Animals

Multi‑omics approaches to decipher the molecular mechanisms of exercise‑mediated bone protection: From mechanistic insights to personalized exercise prescription (Review).

The global burden of bone metabolic disorders necessitates a shift from generic exercise recommendations toward personalized prescription strategies. Exercise confers skeletal protection through mechanotransduction, yet the underlying molecular networks remain incompletely understood. Multi‑omics technologies, including transcriptomics, proteomics, metabolomics and single‑cell spatial approaches, have revolutionized the capacity to decode exercise‑mediated bone adaptation at the systems level. The present review synthesizes current single‑omics landscapes and integrative multi‑omics analyses that elucidate the core regulatory networks, mechanobiological coupling mechanisms and multiorgan crosstalk that are implicated in the bone response to mechanical loading. Translational applications across clinical scenarios such as osteoporosis, osteoarthritis and disuse bone loss are evaluated, and the technical, analytical and translational challenges limiting clinical implementation are addressed. Finally, the present review provides a framework for translating multi‑omics molecular signatures into personalized exercise prescriptions for optimized skeletal health.

Humans

Defining Gaslighting in Gender-Based Violence: A Mixed-Methods Systematic Review.

In both public and academic discourse, gaslighting has gained increased attention, especially regarding psychological abuse, power imbalance, and gender-based violence (GBV). However, the term gaslighting is often inconsistently defined and conflated with broader forms of manipulation. It is also largely examined in the context of intimate partner violence (IPV), which ignores its occurrence in other forms of GBV. The present study presents a systematic review that synthesizes interdisciplinary academic literature to create a comprehensive framework of gaslighting. This framework includes the specific tactics that are used by perpetrators of gaslighting, the social-psychological outcomes experienced by survivors, and the role of systemic inequalities and social power dynamics. A search across multiple databases identified 96 records that discussed gaslighting in relation to GBV. Thematic analysis revealed a two-part framework for understanding gaslighting: (a) gaslighting tactics, which were categorized into cognitive and perceptual manipulation, emotional and psychological abuse, power dynamics and control, and additional forms of manipulation and (b) survivor outcomes, including disruptions to perception and memory, emotional distress, social isolation, and resistance strategies. The findings show that gaslighting is more than just an interpersonal act; it is sustained within social structures, where perpetrators use identity factors and forms of marginalization to exploit survivors. Overall, this review presents a comprehensive definition of gaslighting that illustrates its epistemic nature and its intersection with systemic oppression. It is suggested that future research studies gaslighting in GBV contexts beyond IPV, while practice and policy efforts should seek to enhance recognition and support for survivors.

Humans

SGLF-Net:Staged Global-to-Local Cross-Scale Fusion Network for Colonoscopic Polyp Segmentation.

Polyp segmentation in colonoscopy images plays a pivotal role in computer-aided medical diagnosis and the early prevention of colorectal cancer. However, existing methods often suffer from performance degradation when confronted with extreme polyp scale variation and polyp boundary ambiguity. To address these challenges, we propose the Staged Global-to-Local Cross-Scale Fusion Network (SGLF-Net), which adopts a novel staged global-to-local learning paradigm to progressively refine segmentation from coarse global semantics to fine-grained local details. Specifically, the Global Semantic Perception Stage integrates a Swin Transformer Encoder and a Dynamic Attentive Decoder (DAD) to construct comprehensive multi-scale contextual representations. The Local Detail Refinement Stage employs an Edge-aware Dynamic Attentive Decoder (E-DAD) to enhance structural fidelity and boundary precision through explicit edge-guided supervision. Furthermore, we introduce the Cross Spatial-Scale Feature Aggregation and Reconstitution (CSSAR) module, equipped with hybrid attention mechanisms, to facilitate efficient semantic structural interaction between the two cascaded stages. Extensive experiments on five public benchmark datasets demonstrate that SGLF-Net consistently outperforms state-of-the-art methods in both segmentation accuracy and boundary preservation.

Journal Article

Systematic evaluation of one-dimensional-to-two-dimensional near-infrared spectroscopy transformations with deep learning for quantifying coconut sap adulteration.

Near-infrared (NIR) spectroscopy have limitations when combined with deep learning (DL) algorithms because they rely on low-dimensional datasets. Therefore, we investigated the potential of transforming one-dimensional (1D) NIR spectra into two-dimensional (2D) spectrograms using synchronous and asynchronous techniques and the continuous wavelet transform (CWT) and their effectiveness by integrating with DL for detecting adulteration in coconut sap. NIR spectra (12,500-4000 cm-1) were collected from binary mixtures (0%-100%;w/w). The performance of all DL (convolutional neural networks-CNN, AlexNet and ResNet) models was compared with that of partial least squares (PLS). The models were ranked in the mentioned order based on their performances: 2D-CWT > 2D-asynchronous > 2D-synchronous > 1D/2D-PLS. The important features of the best model can be explained and visualized using gradient-weighted-class-activation-mapping. The findings highlight that the 1D-to-2D NIR data transformation combined with DL is a highly robust approach because it addresses the feature representation gap in NIR data and effectively captures the spatial-spectral correlations.

Spectroscopy, Near-Infrared

Overcoming Immunological Barriers in MSC-Derived Insulin-Producing Cells through CRISPR-Based Hypoimmunogenic Engineering and Translational Perspectives for Type 1 Diabetes.

Mesenchymal stromal cell (MSC)-derived insulin-producing cells (IPCs) represent an emerging strategy for β-cell replacement in type 1 diabetes mellitus (T1DM) owing to their differentiation potential, intrinsic immunomodulatory properties, and lower tumorigenic risk compared with pluripotent stem cell-derived platforms. However, accumulating evidence indicates that differentiation-associated immunogenicity, context-dependent immune recognition, and recurrent autoimmune responses may substantially limit long-term graft survival and therapeutic durability following transplantation. This review critically examines the immunological barriers associated with MSC-derived IPCs, including altered MHC expression, susceptibility to alloimmune and autoimmune-mediated rejection, and potential reactivation of autoreactive immune memory. We discuss the application of CRISPR-based hypoimmunogenic engineering strategies targeting antigen presentation pathways, NK-cell activation, and immune checkpoint modulation to generate more immune-evasive MSC-derived IPCs while preserving β-cell functionality. By integrating insights from T1DM immunopathogenesis, MSC biology, genome editing, and translational immunology, we propose a framework linking immune engineering with controlled differentiation, functional maturation, and long-term safety evaluation. In parallel, we comparatively position MSC-derived IPCs alongside clinically advancing iPSC-derived β-cell platforms to highlight their distinct translational niche, including potential advantages related to safety, immunomodulatory capacity, manufacturing accessibility, and scalability, while acknowledging the superior functional maturity and clinical progression currently demonstrated by iPSC-derived systems. Finally, we discuss key translational challenges, including genomic stability, immune-evasion durability, GMP-compliant manufacturing, and the need for rigorous functional and immunological benchmarking prior to clinical application of hypoimmunogenic MSC-derived IPC therapies in T1DM.

Humans

Morphology-Encoded Colorimetric Hydrogen Sensing Using Embedded Reactive Pd Absorbers in Fabry-Perot Cavities.

Chemical reactions offer a powerful strategy for generating visible optical responses through localized changes in absorption, dielectric environment, and interfacial wetting. A palladium (Pd)-embedded Fabry-Perot cavity is introduced as a reaction-active optical platform in which structural color is governed by intracavity absorption coupled with reaction-induced dielectric perturbation. Positioning Pd within the dielectric spacer creates a spatially controllable reactive absorber whose vertical location relative to the standing-wave field dictates wavelength-selective absorption within the cavity. The morphology of the embedded Pd layer provides an additional design parameter by modulating both optical loss and interfacial wetting. Under hydrogen exposure in the presence of oxygen, catalytic water formation at the Pd/polymer interface generates localized dielectric heterogeneity and interfacial water droplets, thereby perturbing the optical path length and amplifying the visible response. As a result, the cavity exhibits pronounced, morphology-dependent color transitions that are inaccessible through dielectric-layer engineering or Pd/PdH refractive-index changes alone, enabling direct visual hydrogen sensing under ambient light, as well as flexible optical devices capable of large-area patterning. These findings establish a design framework for reaction-active optical cavities that translate localized chemistry into a colorimetric hydrogen sensing mechanism.

Fabry–Perot resonator

Proteomics in environmental pollution research: Advances, challenges, and future directions.

Environmental proteomics has emerged as a powerful approach for elucidating the molecular mechanisms underlying pollutant-induced biological effects. Although this field has developed rapidly, the systematic review of recent proteomics applications in environmental pollution research remains limited. This review explored the emerging roles of toxicoproteomics in biomarker discovery and mechanistic elucidation, as well as ecotoxicoproteomics in ecological risk assessment and bioremediation strategies. Here, we review the field, highlighting recent trends such as the integration of proteomics with genomics, transcriptomics, and metabolomics to provide a comprehensive view of biological responses to environmental stressors. We further discuss the growing application of artificial intelligence in improving proteomics data interpretation and accelerating biomarker discovery. In addition, recent technological advances in environmental proteomics are highlighted, including next-generation tissue microarray proteomics, nanoscale proteomics, single-cell proteomics, and spatial proteomics. Despite its potential, proteomics faces challenges, such as high operational costs, computational complexity in analysis, and technical limitations in low-abundance protein detection. We propose that the convergence of proteomics with artificial intelligence and multi-omics approaches offers promising solutions to these challenges, enhancing the practical application of proteomics in environmental monitoring and risk assessment.

Proteomics

Mitochondrial translocation of DNMT3L suppresses oxidative phosphorylation and restrains megakaryopoiesis.

DNMT3L, a catalytically inactive member of the DNA methyltransferase family, is identified here as a negative regulator of megakaryopoiesis. In K562 cells undergoing PMA-induced megakaryocytic differentiation, DNMT3L protein levels declined progressively, and shRNA-mediated depletion enhanced differentiation, whereas overexpression attenuated it. Consistent with these findings, Dnmt3l-knockout mice exhibited elevated peripheral blood platelet counts and expanded bone marrow megakaryocytes. Mechanistically, megakaryocytic differentiation triggered rapid mitochondrial translocation of DNMT3L within 6 h; mitochondrial DNMT3L suppressed oxidative phosphorylation (OXPHOS) capacity and ATP production and downregulated mitochondrial-encoded genes spanning Complex I, III, IV, and ATP synthase, without altering mitochondrial DNA copy number. This metabolic suppression was mediated through compartment-specific remodeling of DNMT3L-containing protein complexes: upon differentiation, DNMT3L selectively dissociated from DNMT1 and DNMT3B in mitochondria, relieving the repressive constraint on OXPHOS, whereas in the nucleus DNMT3L remained associated with DNMT3A, which concomitantly accumulated during differentiation. These findings reveal a previously unrecognized mechanism by which a catalytically inactive epigenetic co-regulator spatially redistributes to coordinate mitochondrial metabolic output with nuclear epigenetic control, thereby facilitating terminal megakaryocytic maturation.

Animals

Beyond species trees: pervasive gene flow limits phylogenomic resolution in the diversification of Juniperus from the Qinghai-Tibet Plateau.

Understanding how lineages diversify despite persistent ancestral polymorphism and recurrent gene flow remains a central challenge in evolutionary biology. Juniperus distributed across the Qinghai-Tibet Plateau provide an ideal system for addressing this question because repeated geological uplift and climatic oscillations have likely promoted cycles of lineage divergence, range shifts, and secondary contact. Here, we combined approximately 1.08 million genome-wide SNPs from 164 individuals representing thirteen Juniperus lineages with phylogenomic datasets comprising 3,381 nuclear single-copy genes and nearly complete plastomes. We detected extensive phylogenomic discordance and cytonuclear incongruence across genomic datasets. Topology weighting, coalescent simulations, quartet-based tests, and analyses of gene flow and reticulation collectively support the interpretation that these patterns were shaped by the combined effects of prolonged incomplete lineage sorting and gene flow during lineage diversification. Ecological niche analyses further provide a spatial and climatic context in which environmentally similar lineages may have had greater opportunities for secondary contact during historical range shifts. Collectively, our results reveal that the evolutionary history of Qinghai-Tibet Plateau Juniperus is characterized by reticulate diversification rather than strictly bifurcating evolution, and demonstrate how genome-wide discordance can provide biological insights into the evolutionary processes underlying lineage diversification.

Gene Flow

A Multi-omics Regulated Cell Death Framework Defines Immune Phenotypes and Guides Precision Therapy in Colorectal Cancer.

Colorectal cancer (CRC) is molecularly and immunologically heterogeneous, contributing to variable treatment response. Because regulated cell death (RCD) intersects with tumor metabolism, immune regulation, and therapeutic susceptibility, we built an RCD-centered framework for CRC stratification. Multi-cohort transcriptomic data were used to infer RCD subtypes with non-negative matrix factorization (NMF) and non-negative least squares (NNLS). Genomic, bulk RNA-seq, single-cell RNA-seq, and spatial transcriptomic datasets were integrated to characterize subtype-associated biology. Machine-learning models were developed for immunotherapy response and survival-risk estimation. Candidate compounds were screened by GDSC2-based drug-sensitivity modeling and molecular docking, and FSTL3 was functionally assessed in vitro. The framework separated CRC samples into two RCD-related phenotypes resembling immune-hot and immune-cold states. RCD1 showed immune activation and higher mutational burden, whereas RCD2 showed immune-suppressed features, intratumoral heterogeneity, and aggressive biology. RCD-associated signatures showed potential for predicting immunotherapy response and survival risk. Dasatinib was prioritized for immune-cold, high-risk tumors, with preliminary evidence supporting its activity in CRC cells, while functional assays suggested a role for FSTL3 in growth, invasion, epithelial-mesenchymal transition, and apoptosis regulation. These findings suggest that RCD-based multi-omics analysis may refine CRC stratification and help generate therapeutic hypotheses.

Colorectal cancer

Wounds that echo: community perceptions of the socio-structural determinants of community violence in post-apartheid South Africa in the context of COVID-19.

The COVID-19 pandemic and its associated public health measures significantly altered the social, economic, and psychological landscape of communities worldwide. In South Africa, the post-COVID-19 period has been marked by a notable surge in homicide rates and interpersonal and community violence. Using a combined structural and social disorganisation framework, this qualitative study critically explores community members' perceptions of the socio-structural factors contributing to community violence, in the context of COVID-19. Utilising data from in-depth interviews and focus group discussions, this study examines the lived experiences of residents in a marginalised high-risk South African community, unpacking the interplay between structural inequities, social disintegration, and community violence. Community violence emerged not as periodic or individual, but as structurally generated, geographically concentrated, and socially normalised. The findings demonstrate that community violence is perceived as being embedded in cycles of survival, where long-standing systemic inequality, economic precarity, spatial disadvantage, and institutional neglect and inequity generate contexts in which community violence becomes normalised and self-reinforcing. The study findings advocate for interventions that not only address immediate catalysts of violence but also the deeper historical and structural determinants of violence in the post-pandemic era, while ensuring preparedness for effective violence prevention during future pandemics.

Humans