Limnology and Oceanography

Microbial degradation of suspended and sinking organic carbon regulates long-term oceanic carbon storage by controlling the efficiency of the biological pump. Yet microbial controls on carbon export and remineralization remain poorly constrained, limiting predictions of how ocean carbon cycling will respond to climate change. Here, we combined in situ sampling with ship-based incubations to quantify prokaryote-driven removal rates of suspended and sinking total organic carbon (TOC). Samples were collected below the mixed layer during three stages of a spring Phaeocystis pouchetii bloom in the Labrador Sea. Phaeocystis blooms can dominate regional phytoplankton biomass and are expected to increase under future climate. Removal rates were used as a proxy for carbon lability and combined with 16S rRNA metabarcoding and carbon composition analyses to link microbial community structure with substrate characteristics. Removal rates of sinking particles (0.02-0.06 d-1) were an order of magnitude higher than those of suspended TOC (0.002 d-1) during bloom-decline and non-bloom. In contrast, during late-bloom, suspended carbon exhibited rates of 0.01 d-1, comparable to sinking particles, and was enriched in exopolymer-rich colonies. Prokaryotic community composition varied primarily among bloom stages rather than carbon fractions, indicating that bloom stage-- and thus particle origin and composition--was the dominant control on bacterial degradation and assembly. Bacterial diversity peaked where carbon was refractory and originated from mixed phytoplankton. Together, these results demonstrate that suspended Phaeocystis-derived carbon can be rapidly remineralized when blooms produce exopolymer-rich colonies and highlight bloom stage as key regulator of microbial carbon processing and biological pump efficiency.

Pacific salmon are keystone species to North Pacific freshwater, coastal, and oceanic ecosystems, but many populations have declined or become more variable in recent decades due to anthropogenic impacts and climate change. Long-term records are needed to understand past changes, identify ecosystem stressors, and guide restoration. We used sedimentary DNA (sedDNA), an emerging paleoecological approach offering broader taxonomic information than traditional methods, to reconstruct ecosystem changes across five Pacific salmon nursery lakes in British Columbia (Canada). DNA metabarcoding targeting the 18S ribosomal RNA gene V7 region was used to track shifts in eukaryotic communities including algae and invertebrates over centuries to millennia. Most lakes showed notable algal community shifts over the past two centuries, with declining green algae and rising diatom relative abundances. Chrysophytes and dinoflagellates also increased over the past century in most lakes, likely driven by stronger thermal stratification, which favored these motile and mixotrophic algae that are capable of vertical migration and flexible nutrient acquisition. We contextualized the trajectories of each core through an ordination analysis based on 98 lakes distributed across British Columbia, which identified land-use changes and longer growing seasons as potential drivers. Network analyses of the sedDNA time series revealed decreasing modularity and increasing connection across lakes, suggesting a shift in resilience mechanisms from between-module buffering by compartmentalized specialists to within-guild insurance via functional overlap among generalists. Our findings demonstrate that sedDNA provides taxonomically rich, long-term insights into aquatic ecological dynamics, which are foundational for understanding and protecting Pacific salmon nursery habitats.

This study investigates the seasonal dynamics of the microbiome within the marine sponge Halichondria panicea from Baltic coastal waters, focusing on its symbiotic relationship with Candidatus Halichondribacter symbioticus. Over 16 months, we observed distinct summer and winter microbial communities, transitioning rapidly between these states during spring and fall. Marine sponges host complex microbiomes composed of diverse microbial taxa that play critical roles in host metabolism and nutrient cycling within marine ecosystems. While our understanding of sponge microbiomes has traditionally been based on static characterizations, the temporal dynamics of these associations across seasonal cycles remain poorly understood. In this study, we investigated temporal variation in bacterial symbionts of Halichondria panicea over 16 months in Baltic coastal waters using high-throughput amplicon sequencing of bacterial 16S rRNA gene sequences. The microbiota of H. panicea exhibited host-specific structure and a high degree of stability across seasons, despite fluctuations in environmental factors such as temperature, salinity, photoperiod intensity, and inorganic nutrient availability. In contrast, bacterial communities in surrounding seawater displayed large seasonal shifts which potentially mix with the sponge bacterial community, suggesting that different degrees of ecological pressures act on free-living and symbiotic marine bacteria. These findings establish an empirical baseline for identifying abnormal shifts in symbiont communities, which could be indicative of environmental stress or biological disturbance events.

Zooplankton communities are influenced by multiple environmental factors, including temperature, nutrient and resource availability, which fluctuate seasonally and across years. While long-term average effects can identify overall drivers, they may overlook dynamic, context-dependent effects that govern short-term changes in diversity and abundance. Understanding and disentangling both perspectives is crucial for identifying and estimating the drivers that shape community structure under varying environmental states. Here, we applied Empirical Dynamic Modeling (CCM, SMap) to a 12-year weekly zooplankton time series to identify causal environmental drivers of taxonomic and morphological diversity and quantify how the influence of each driver shifts over time. We contrast these results with static long-term average effects inferred from Generalized Linear Models which included predictor sets identified using covariate adjustment and accounting for temporal autocorrelation. Drivers linked to long-term average associations differed from those regulating short-term zooplankton dynamics, revealing a decoupling between mean environmental effects and the drivers of temporal variability. Temperature emerged as a persistent regulator of zooplankton dynamics across multiple diversity dimensions, while variables commonly associated with background trophic conditions (e.g. particulate organic matter) were primarily associated with long-term patterns and showed limited dynamical relevance. Importantly, we find evidence for morphological homogenisation in response to short-term fluctuations in chlorophyll a, which was not detectable in long-term average relationships. This contrast highlights that mean environmental associations do not necessarily reflect the mechanisms governing community dynamics. Impacts might be underestimated if average effects appear weak, or misinterpreted if arising mainly from shared trends or seasonality rather than direct mechanisms Integrating both perspectives clarifies the identity and role of environmental drivers, improving inference and prediction of zooplankton community change through time.

Microbial communities are critical to the functioning of ecosystems and shape the ecology and evolution of host organisms. However, we have a limited understanding of how host-associated and free-living microbes differ in their structure and biogeography. Here, we test whether host-associated (fish gut) and free-living (lake bacterioplankton) microbes exhibit different metacommunity structure, spatial turnover, and consistency with neutral expectations using two independent lake systems. We characterized microbial communities in lake water (Vancouver Island and Sierra Nevada) and guts in two fish species (stickleback and brook trout) using 16S amplicon sequencing. We compared alpha and beta diversity within lakes, quantified spatial turnover (distance-decay), and tested for departure from neutral abundance-occurrence expectations between bacterioplankton and fish gut microbiomes. Fish microbiomes had lower alpha diversity compared to bacterioplankton, but higher beta diversity within lakes. Bacterioplankton were more similar across lakes yet showed stronger patterns of spatial turnover with distance than fish gut microbiomes. A neutral model explained a substantial proportion of abundance-occurrence relationships in bacterioplankton communities but performed poorly for fish-associated microbes. Our study indicates that host-associated and free-living microbes have disparate patterns of metacommunity structure and spatial turnover consistent with differences in the strength of neutral ecological processes. Fish microbiomes were less diverse at the local scale but more variable across space and time than bacterioplankton communities, suggestive of potentially strong local selection and/or reduced microbial exchange among hosts compared to environmental communities. Importantly, we observed highly consistent patterns across both lake systems despite differences in host species, sampling design, and region, demonstrating that differences in the distribution of host and environmental microbes are potentially widespread. This study demonstrates how host association fundamentally alters the diversity and spatial distribution of microbes, emphasizing the need to incorporate hosts into broader frameworks of microbial biogeography.

Indo-Pacific humpback dolphin (Sousa chinensis) and finless porpoise (Neophocaena phocaenoides) are increasingly threatened across their native range, yet the relative influence of multiple stressors in shaping their population dynamics remains unclear. Current conservation strategies for both species are limited by incomplete data and limited assessment of affecting factors. Here, we integrated eDNA metabarcoding with Joint Species Distribution Modeling (JSDM) to assess how environmental gradients, pollution, and trophic associations interactively influence cetacean distributions in Hong Kong waters. We show that degraded water quality and intensified human activity negatively associated with cetacean occurrence, with clear species-specific responses to different stressors. S. chinensis covaried most strongly with Secchi disc depth, and presence of vessels, while N. phocaenoides was negatively associated with nitrate nitrogen and microbial pollution (sewage). The JSDM variance partitioning analysis highlighted that the occurrence of S. chinensis was primarily associated with anthropogenic disturbances (30.04%), followed by water physical properties (26.63%), whereas N. phocaenoides was more strongly associated with physical (40.9%) and anthropogenic disturbances (35.2%). By integrating eDNA and JSDM, our approach provides fine-scale diagnostics of species-specific vulnerabilities, supporting adaptive conservation strategies and guiding the realignment of protected areas to mitigate biodiversity loss in urbanized marine ecosystems. Environmental ImplicationOur study demonstrates that hazardous water pollutants, including microbial contamination, nutrient enrichment, and chemical stressors, vessel pressure, show strong, species-specific impacts on resident cetaceans in Hong Kong. By integrating eDNA metabarcoding with joint species distribution models, we provide a diagnostic framework that directly links pollutant profiles to ecological risk. These findings highlight that conventional conservation strategies overlooking pollution drivers are insufficient for marine megafauna persistence. Our approach offers an early-warning system for monitoring hazardous pollutants in coastal ecosystems and supports adaptive management strategies to mitigate biodiversity loss in urbanized seascapes.

Mass mortality of reef-building stony corals has driven widespread community shifts towards reefs dominated by soft corals and macroalgae. Although physical competition for space between these organisms plays an important role, non-contact water-mediated interactions have been proposed to modulate organismal performance and community functioning, yet their independent effects remain poorly resolved. Here, we experimentally tested the hypothesis that water-mediated interactions generate non-additive effects on community productivity, altering ecosystem functioning during phase shifts. Using two controlled incubation experiments with representative stony corals, soft corals, and macroalgae, we compared monoculture baseline productivity with mixed assemblages across a gradient of biomass ratios mimicking phase shift scenarios. We found that reductions in stony coral biomass led to community-level declines in photosynthesis and calcification that exceeded expectations based on monocultures, indicating emergent negative effects of community restructuring. However, these effects were strongly species-dependent, with some assemblages showing only minor deviations from expectations, whereas others exhibited pronounced productivity losses. At the species level, both stony corals reduced photosynthetic efficiency in mixed assemblages, while soft corals maintained efficiency across treatments. Macroalgal responses diverged, with one species exhibiting reduced and another increased photosynthetic efficiency in mixed communities. These species-specific physiological responses scaled up to explain community-level deviations from expected productivity, suggesting that gains in productivity by certain taxa can partially offset, but not fully compensate for, losses in coral-driven functions such as calcification. Together, our findings indicate that sublethal, water-mediated interactions can reorganize holobiont functioning and lead to changes in ecosystem productivity, independent of direct physical competition. By altering community-wide energy acquisition and carbonate production, such interactions may reinforce feedback loops that accelerate ecosystem phase shifts. We argue that incorporating water-mediated interaction effects into ecological theory and ecosystem models is essential for predicting the stability and recovery potential of coral reefs and other transitioning ecosystems under climate change.

The discovery and collection of the enigmatic Golden Orb by the NOAA Ship Okeanos Explorer and ROV Deep Discover in deep Alaskan waters during 2023 has yielded substantial interest by the scientific and public communities alike. Initial field identifications of the specimen collected at 3,250 meters depth ranged from an egg mass to sponge to microbial biofilm. Here we characterize the biology and ecology of the Golden Orb, as well as other specimens of similar appearance identified since the collection of the original material. Through an integrative taxonomic approach including morphological analysis and genomic characterization of the Golden Orb, we identified the presence of cnidocytes of the spirocyst type (restricted to Hexacorallia), as well as metazoan DNA, from which we were able to derive complete mitochondrial genomes and Ultra Conserved Elements. These results indicate that the Golden Orb and a similar specimen from deep equatorial waters represent remnant cuticles belonging to the geographically widespread deep-sea anemone ally Relicanthus daphneae. We also document the presence of cuticle from a collected specimen of R. daphneae from the Southern Ocean and in situ photographic evidence of similar cuticles beneath living individuals. These findings underscore the extent to which the biodiversity and organismal biology of obscure deep sea fauna broadly remain unresolved and highlight the value of whole-specimen collections and rigorous taxonomic follow-up in telepresence-enabled ocean exploration.

Enhanced resilience conferred through sublethal stress pre-exposure may be crucial for reef building corals to cope with variable environments. The effect of stress priming on Acropora cervicornis thermotolerance was evaluated in the context of elevated temperature and ammonium enrichment, 3 and/or 6 M above ambient, respectively. Primed corals were pre-exposed to each stressor individually or in combination for eight days, while non-primed corals remained at ambient conditions. After an eight-day recovery, primed corals and a subset of non-primed corals (naive) were subjected to an acute 15-hour thermal challenge. Coral metabolism, symbiosis, and gene expression were characterized throughout the experiment. Thermal tolerance was quantified as algal symbiont, chlorophyll, and live tissue retention, along with survival probability following acute heating. Primed corals were more likely to retain symbionts and chlorophyll after heat stress and also exhibited slower tissue loss. Moreover, thermal pre-exposure reduced the risk of tissue loss or predicted mortality. Apoptotic regulation differed between primed and naive corals during the initial and secondary heat exposures. Additionally, primed corals exhibited patterns of transcriptional resilience under acute thermal stress. Altogether, results provide support for the capacity of A. cervicornis to gain resilience through pre-exposure to ecologically relevant conditions as well as insights into the molecular mechanisms underpinning this process.

Abstract Objective To address the unresolved bottleneck of selecting cohort-relevant clinical concepts for treatment trajectory analysis in observational health data, we introduce CohortContrast, an OMOP-compatible R package for enrichment-based concept identification, temporal and semantic noise reduction, and concept aggregation, enabling cohort-level characterization and downstream trajectory analysis. Materials and Methods We developed CohortContrast and applied it to OMOP-mapped observational data from the Estonian nationwide OPTIMA database, which includes all cases of lung, breast, and prostate cancer, focusing here on lung and prostate cancer cohorts. The workflow combines target-control statistical enrichment, temporal/global noise filtering, hierarchical concept aggregation and correlation-based merging, with optional patient clustering for downstream trajectory exploration. We validated the approach with a clinician-based plausibility assessment of extracted diagnosis-concept pairs and evaluated a large language model (LLM) as an auxiliary filtering step. Results We analyzed 7,579 lung cancer and 11,547 prostate cancer patients. The workflow reduced concept dimensionality from 5,793 to 296 concepts (94.9%) in lung cancer and from 5,759 to 170 concepts (97.0%) in prostate cancer, and identified three exploratory patient subgroups in both cohorts. In a plausibility assessment of 466 diagnosis-concept pairs, validators rated 31.3% as directly linked and 57.5% as indirectly linked. Discussion CohortContrast reduces manual concept curation by prioritizing and aggregating cohort-relevant concepts while preserving clinically interpretable treatment patterns in OMOP-based real-world data. Conclusion CohortContrast enables scalable reduction of broad OMOP concept spaces into clinically interpretable, cohort-specific representations for exploratory trajectory analysis and real-world evidence research.

Multimodal learning has the potential to improve clinical prediction by integrating complementary data sources, but the incremental value of imaging beyond structured electronic health record (EHR) data remains unclear in real-world settings. We developed a multimodal survival modeling framework integrating optical coherence tomography (OCT) and EHR data to predict time to visual improvement in patients with diabetic macular edema (DME), and evaluated how different ophthalmic foundation model representations contribute to prognostic performance. In a retrospective cohort of 973 patients (1,450 eyes) receiving anti-vascular endothelial growth factor therapy, we compared multimodal models combining 22,227 EHR variables with 196,402 OCT images, with OCT embeddings derived from three ophthalmic foundation models (RETFound, EyeCLIP, and VisionFM). The EHR-only model showed minimal prognostic discrimination (C-index 0.50 [95% CI, 0.45-0.55]). Incorporating OCT improved performance, with the magnitude of improvement depending on the representation. EHR+RETFound achieved the strongest performance (C-index 0.59 [0.54-0.65]), followed by EHR+EyeCLIP (0.57 [0.52-0.62]) and EHR+VisionFM (0.56 [0.51-0.61]). Multimodal models, particularly EHR+RETFound, demonstrated improved risk stratification with clearer separation of Kaplan-Meier curves. Partial information decomposition revealed that prognostic information was dominated by modality-specific contributions, with OCT and EHR providing largely distinct signals and minimal shared information. The magnitude of OCT-specific contribution varied across foundation models and aligned with observed performance differences. These findings indicate that OCT provides complementary prognostic value beyond structured clinical data, but gains are modest and depend strongly on representation choice. Our results highlight both the promise of multimodal modeling for personalized prognosis and the need for rigorous, context-specific evaluation of foundation models in real-world clinical settings.

Drug targets supported by human genetic evidence have significantly higher approval rates, making genome-wide association studies a valuable resource for drug candidate prioritisation. Transcriptome-wide association study signature-matching is an emerging in silico approach that integrates GWAS data with expression quantitative trait loci to generate a disease gene expression signature, which is then compared against drug perturbation databases such as the Connectivity Map. Despite recent adoption, there is no consensus on optimal methodology. Here, we systematically benchmark key parameters, including TWAS method, eQTL tissue model, similarity metric, gene set size, and CMap cell line, using LDL cholesterol, familial combined hyperlipidemia, and asthma as proof-of-concept traits. We demonstrate that while TWAS signature-matching can successfully prioritise known first-line treatments, performance is highly sensitive to parameter choice; for instance, the selection of the cell line used for drug signatures alone can dramatically alter drug prioritisation. Based on these findings, we propose a best-practice framework for robust, genetically-informed drug prioritisation using TWAS signature-matching.

Abstract Background Spontaneous coronary artery dissection (SCAD) is a well-recognised cause of acute coronary syndrome particularly among women without conventional cardiovascular risk factors. Increasing evidence indicates a genetic contribution; however, the underlying genetic architecture of SCAD remains insufficiently understood. Objective The aim of this study was to assess the prevalence of rare variants in previously reported SCAD associated genes and to explore the potential presence of novel genetic alterations in well-characterised Swedish patients with SCAD. Methods The study comprised 201 patients enrolled in SweSCAD, a national project examining the clinical characteristics, aetiology, and outcomes of SCAD. All individuals had a confirmed diagnosis based on invasive coronary angiography. Comprehensive exome sequencing was performed to identify rare variants contributing to disease susceptibility. Results Genetic variants that have been associated with SCAD according to current clinical genetics practice for variant reporting were identified in approximately 4 % of patients. In addition, rare potentially relevant variants were detected in almost 60 % of patients in genes associated with vascular integrity and vascular remodelling. Conclusion This study supports SCAD as a genetically complex arteriopathy, driven by rare high?impact variants together with broader polygenic susceptibility. Variants in collagen, vascular extracellular matrix, and oestrogen?responsive pathways provide biologically plausible links to female?predominant disease. Although the diagnostic yield of clearly actionable variants is modest, these findings support broader genomic evaluation beyond overt syndromic presentations and highlight the need for larger integrative genomic and functional studies to refine risk stratification and management.

Purpose: To test whether histology-derived gene-expression signatures from routine hematoxylin and eosin slides are prognostic for recurrence and predictive of chemotherapy benefit in early breast cancer. Methods: We conducted a multi-cohort study including CALGB 9344 (anthracycline +/- paclitaxel), CALGB 9741 (standard vs dose-dense chemotherapy), a pooled Chicago real-world cohort, and the American Cancer Society (ACS) Cancer Prevention Studies-II and -3. Whole-slide images were processed with a previously described pipeline to generate 61 histology-derived signatures per patient. The primary endpoint was distant recurrence-free interval (DRFI), except in ACS, where breast cancer-specific survival was used. Secondary endpoints include distant recurrence-free survival (DRFS) and overall survival. The most prognostic signature in CALGB 9344, selected by Harrell's C-index, was evaluated in additional cohorts. Signature-treatment interaction was assessed by likelihood-ratio tests. Multivariable Cox models incorporating age, tumor size, nodal status, estrogen/progesterone receptor status, and signature were fit in CALGB 9344 to improve risk stratification. Results: A total of 7,170 patients were included across four cohorts. The top histology-derived signature in CALGB 9344 showed strong prognostic performance for 5-year DRFI (C-index 0.63) and performed well across validation cohorts (C-index 0.60, 0.70, and 0.62 in CALGB 9741, Chicago, and ACS, respectively). The strongest predictive signal for treatment benefit was observed for DRFS. High-risk cases identified by the signature demonstrated greater benefit from taxane in CALGB 9344 (adjusted hazard ratio [aHR] 0.76 for DRFS, 95% CI 0.66-0.88; interaction p=0.028), from dose-dense chemotherapy in CALGB 9741 (aHR 0.69, 95% CI 0.56-0.85; interaction p=0.039), and differential chemotherapy benefit in the Chicago cohort (aHR 0.84, 95% CI 0.59-1.21; interaction p=0.009). Combined clinical-histology models improved risk stratification and identified low-risk groups with a 2%-10% risk of distant recurrence or breast cancer death. Conclusion: Histology-derived signatures from H&E images are broadly prognostic and, unlike clinical factors, may predict chemotherapy benefit.

Earlier menopause is a risk factor for several age-related diseases, including dementia. The biological pathways linking menopause timing to later-life brain aging are not understood. Leveraging large-scale plasma proteomics in postmenopausal women from the UK Biobank (N=15,012), earlier menopause was associated with upregulation of pro-inflammatory and extracellular matrix degradation pathways, plus accelerated aging across proteomic clocks of organ and cellular aging, including brain and oligodendrocyte aging. Elevated GDF15, a canonical aging marker, was the top protein correlate of earlier menopause. We observed robust replication of menopause timing proteomic shifts in the Women's Health Initiative Long Life Study (N=1,210). In UKB, proteins associated with earlier menopause, including GDF15, exhibited concordant associations with incident dementia risk and brain atrophy, cerebral small vessel disease burden, and white matter microstructural integrity. Collectively, our findings identify proteomic signatures linking ovarian aging to brain aging, providing a framework to inform interventions to reduce dementia risk.

Abstract: Background: Vascular access is essential in treating patients undergoing prolonged endovenous therapy such as chemotherapy, antibiotics, and parenteral nutrition. Since the 1990s, when PICCs (peripherally inserted central catheters) appeared, vascular access options have expanded significantly, revolutionizing the treatment landscape for all types of patients. Objective: To analyze and describe the profile of the use of PICCs in a Brazilian quaternary hospital over 10 years with data collected by the infusion therapy team. Evaluating the number of PICCs implanted over the years, patients epidemiology and clinical characteristics, insertion details, associated complications, and the reason for removal. Methods: A retrospective cohort study that employs a quantitative, non-experimental approach to classify and statistically analyze past events associated with 21,652 PICCs implanted from January 2012 to December 2021 in a quaternary hospital at Sao Paulo - Brazil. All the catheters were implanted, and the data was collected by a team of nurses specializing in infusion therapy. We analyzed the number of catheters implanted over the years, insertion characteristics, patients epidemiology and clinical data, possible associated complications, and the reason for removal. Statistical analyses were conducted using R software (version 4.4.1) and SPSS (version 29) for Windows (IBM Corp, Armonk, NY). Results: During the specified period, 21,652 catheters were analyzed. The patients gender distribution was nearly balanced (48.2% versus 51.8%), and the average age was 66 years. Cardiovascular and metabolic issues were the most common comorbidities, and between 2020 and 2021, 29.3% of the sample tested positive for COVID-19. The most common location of hospitalization and implantation was the medical-surgical clinic (31.6% - 41.4%), and the most used type of catheter was the Power Picc (83.9%). The estimated complication incidence density is 2.94 complications per 1,000 catheter-days. Almost all the PICCs (98,2%) were adequately located at the cavo-atrial junction after the first attempt, 82.2% of catheters were removed after therapy, and the median duration of catheter use was 12 days. Conclusion: PICCs are widely employed for drug infusion, with their use growing progressively due to specialized teams greater availability and training. The high efficiency of these devices with a relatively low risk of complications already observed in previous studies was reinforced by the findings of this study of more than 20,000 catheters.

Objectives: To evaluate whether on-site molecular point-of-care testing (POCT) for Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG) is associated with reduced antibiotic overtreatment for presumed sexually transmitted infections (STIs) among adults living with HIV in rural Uganda. Methods: We conducted a single-site quasi-experimental pre-post intervention study at Kumi Hospital, comparing syndromic management (April-August 2024) with CT/NG POCT-guided management (September 2024-January 2025). Adults living with HIV presenting with symptoms suggestive of an STI were included. Overtreatment in the pre-intervention phase was estimated by comparing antibiotic prescribing with the expected number of CT/NG infections based on positivity observed during the intervention phase. Results: A total of 404 participants were included (203 pre-intervention, 201 intervention). During the intervention phase, CT and/or NG were detected in 14 individuals (7.0%). Median test turnaround time was 95 minutes, enabling same-day treatment in 93% of positive cases. Antibiotic prescribing decreased from 99.0% to 11.4% following POCT implementation (P < 0.001), corresponding to an absolute reduction of 87.6 percentage points. Estimated overtreatment declined from 30.0% to 5.0% for NG and from 74.9% to 6.0% for CT (both P < 0.001). Conclusions: Implementation of CT/NG POCT in routine HIV care was associated with a marked reduction in antibiotic prescribing and estimated overtreatment for presumed STIs. These findings support the potential of POCT-guided, aetiology-based STI management to reduce unnecessary antimicrobial exposure in settings where syndromic management remains standard practice.

Objectives: In Latin America, up-to-date information to monitor UNAIDS 95-95-95 HIV targets in key populations, such as men who have sex with men, is limited. Elsewhere, structural homophobia restricts access to ART. Conceptual frameworks suggest that intersecting forms of violence and discrimination may negatively influence HIV care outcomes through psychosocial and structural pathways, although empirical evidence remains limited. The study aimed to assess whether sexual orientation outness and recent homophobic violence are associated with not being on ART among Latin American MSM living with HIV. Methods: This cross-sectional study is a secondary analysis of data from LAMIS-2018, including 7,609 MSM aged 18+ with an HIV diagnosis [&ge;]1 year prior from 18 Latin American countries. Participants self-reported ART status, sociodemographic characteristics, homophobic violence, and sexual orientation outness. Bivariate and multivariate logistic regressions identified those factors associated with not being on ART. Results: Nine percent of MSM with HIV were not on ART, 18% reported low sexual orientation outness, and 27% experienced homophobic violence, especially in Andean and Central American countries. Not being on ART was associated with recent homophobic violence (aPR=1.25), low outness (aPR=1.22), unemployment (aPR=1.27), and residence in the Andean subregion (aPR=1.87), Mexico (aPR=1.28), or the Southern Cone (aPR=1.45) versus Brazil. Protective factors included being older (25-39: aPR=0.72; >39: aPR=0.49), living in large cities (aPR=0.72), having a stable partner (aPR=0.78), and university education (aPR=0.74). Conclusions: Recent homophobic violence and low sexual orientation outness were associated with not being on ART among MSM in Latin America. While access varies across countries, structural factors such as stigma and violence may limit engagement in care. Addressing these barriers alongside strengthening health systems may be key to improving ART uptake and advancing progress toward the 95-95-95 targets.

An increasing number of studies highlight the role of saccadic remodulation in compensatory mechanisms following vestibular injury, and the reappearance of SHIMP saccades correlates with symptom improvement measured by the Dizziness Handicap Inventory (DHI). To investigate the influence of attentional processes and working memory on visuo-vestibular interaction, three independent but interrelated experiments were conducted. In the first two experiments, healthy subjects and patients with unilateral or bilateral vestibular deficits underwent vHIT in SHIMP mode and the Functional Head Impulse Test (fHIT), performed first separately and subsequently simultaneously. Mean latency and clustering of SHIMP saccades, together with Landolt C recognition rates, were analyzed. Differences between separate and combined protocols were assessed, and, in patients, correlated with symptom severity measured by the DHI, to determine whether the near-simultaneous execution of tasks mediated by shared parietal cortical substrates influenced performance. In the third experiment, vHIT in HIMP mode and fHIT were performed using separate and combined protocols to evaluate whether recognition-related cognitive load affected recovery saccade latency and clustering. Results suggest that visual recognition modulates visuo-vestibular interaction, supporting integrated dual-task protocols for ecological balance assessment and helping explain clinical discrepancies.

Early detection of breast cancer remains essential for improving clinical outcomes, and complementary non-invasive approaches are needed to support existing screening methods, particularly for women with dense breast tissue. We have previously reported plasma lipid biomarker discovery using untargeted high-resolution liquid chromatography tandem mass spectrometry (LC-MS/MS). In this study, we performed biomarker confirmation and developed machine-learning models applied to targeted plasma lipid measurements for the non-invasive detection of early-stage breast cancer across international cohorts with independent external validation. Targeted LC-MS/MS was used to quantify candidate lipid panels in plasma samples from European discovery cohorts (n = 554) and an independent Australian cohort (n = 266) used for external validation. Data-driven feature selection identified a 15-lipid panel with strong performance in European cohorts (AUC >= 0.94). External validation prior to confidence stratification yielded 76% sensitivity, 64% specificity, and an AUC of 0.81 in the Australian validation cohort. Clinical assay development requires iterative panel and model testing to support translational feasibility and performance in the intended-use population. An analytically viable panel, excluding lipids requiring complex and costly synthesis, achieved comparable accuracy with improved assay robustness. Confidence-based analysis showed enhanced performance for predictions made with moderate to high confidence, with sensitivity up to 89% and AUC up to 0.85, suggesting that ongoing research should focus on strategies to enhance diagnostic model confidence. Importantly, model predictions were independent of breast density, tumour size, grade, subtype, and morphology, indicating biological specificity of the lipid signature. These results demonstrate that calibrated machine-learning models applied to plasma lipid biomarkers can support non-invasive breast cancer detection. Expanding training datasets to include greater diversity will further improve performance in the ongoing development of this lipid-based detection approach.