Citations

Abstract Stroke thrombi are heterogeneous; their composition influences their mechanical properties, including stiffness, elasticity, and adhesiveness, which in turn affect their response to therapy. Systemic characterization of the thrombi components could help select therapeutic techniques and anticipate outcomes. We conducted a systematic review and meta-analysis to evaluate associations between thrombi composition and angiographic and functional outcomes in acute ischemic stroke (AIS). Up to May 2025, relevant studies were sourced from four databases: PubMed, Web of Science, Scopus, and Embase. We included cohort studies whose populations are patients with AIS in relation to thrombi composition. Thirty-six studies involving 5,462 patients matched our eligibility criteria. Angiographic success (Thrombolysis in Cerebral Infarction ≥ 2b/3) did not differ significantly between red blood cell (RBC)-rich and fibrin-rich thrombi (risk ratio [RR] 1.02, 95% confidence interval [CI] 0.85–1.22; p = 0.84; I 2 = 63.8%; very low certainty). However, thrombi from patients with good angiographic outcomes had a significantly higher RBC content compared to patients with poor outcomes (mean difference = 7.22%, 95% CI 3.31–11.13; p < 0.001; low certainty). No significant difference was observed between RBC-rich and fibrin-rich thrombi regarding functional independence at 90 days (modified Rankin Scale 0–2) (RR = 1.22, 95% CI 0.73–2.05; p = 0.31; low certainty). While RBC-rich thrombi are associated with more favorable angiographic performance, thrombus composition alone does not predict functional outcomes. Stroke outcomes remain multifactorial, dominated by time to reperfusion, collateral circulation, and baseline severity. Preinterventional assessment of clot composition via imaging or biomarkers may support individualized therapy but should complement, not replace, other prognostic determinants.

Background: Several breast cancer (BC) risk prediction models have been developed to provide personal risk assessments. Though individually validated, their performance has not been systematically evaluated across a wide range of populations or ages. Methods: We harmonized individual-level baseline questionnaire data and incident BC diagnoses from 21 cohorts from North America, Europe, and Australia participating in the Breast Cancer Risk Prediction Project. Five-year absolute risk of invasive BC was estimated for five established risk prediction models using classical risk factors only. Discrimination was evaluated by area under the curve (AUC). Calibration was assessed using average and risk-decile specific expected to observed (E/O) ratios. Performance metrics were meta-analyzed across cohorts and models. Metaregression tested associations between cohort characteristics and performance metrics. Results: This analysis included 1,595,977 women aged 20-75 years, enrolled in studies between 1976-2015, with 19,062 (1.2%) invasive BC cases ascertained within 5 years from exposure assessment. Age-adjusted AUCs were similar across models and cohorts (pooled AUCs by model: 0.57-0.58), while E/O ratios varied substantially (pooled E/O ratios by model: 0.83-1.25). Overestimation was common among predicted high-risk individuals (>3%). No appreciable differences in model performance by cohort age, birth year, race, and variable missingness emerged. Calibration improved after assigning race-specific incidence rates. Conclusion: Existing BC risk prediction models provided similar risk discrimination across multiple cohorts, although there was overestimation of risk for high-risk individuals. Performance variation across cohorts was not driven by specific characteristics, which supports development of a unified risk model for diverse populations that leverages appropriate incidence rates.

Abstract Background Breast cancer is etiologically heterogeneous, but which risk factors differ in their associations across tumor subtypes remains unclear. We conducted a large, pooled analysis to evaluate independent, dose-response associations between breast cancer risk factors and quantitative tumor features. Methods Analyses of 15,731 invasive breast cancers from 24 studies evaluated associations (p-trend) between reproductive and hormonal factors, body mass index (BMI), alcohol, smoking, and family history in relation to quantitative immunohistochemistry measures on tissue microarrays (ER, PR, HER2, KI67, TP53) and tumor grade. Analyses in a subset of 10 population-based studies estimated subtype-specific odds ratios (ORs) comparing cases to controls. A Bayesian False Discovery Probability (BFDP) &lt;0.2 was used to identify associations with strong statistical evidence. Results Nulliparity and later age at menopause were associated with higher ER-positivity (p-trend=0.021 and 0.001, respectively), with corresponding OR[ER+] (95% CI) = 1.49 (1.16-1.90) for nulliparous vs. parous and 1.07 (1.03-1.11) per 5 years. Current combined menopausal hormone therapy (MHT) use was associated with lower grade (p-trend&lt;0.001), with OR [grade1] = 3.37 (2.69-4.21) for current vs. never users. Higher BMI was associated with lower ER-positivity and higher grade in premenopausal women (p-trend&lt;0.001 and &lt;0.001), with OR[ER+] = 0.80 (0.74-0.87) and OR[grade1] = 0.75 (0.63-0.88) per 5 units, and with higher PR-positivity and higher grade in postmenopausal women (p-trend&lt;0.001 and &lt;0.001), with OR[PR+] = 1.08 (1.03-1.14) and OR[grade 3] = 1.10 (1.04-1.17) per 5 units. Conclusion This pooled analysis of 15,731 cases showed that nulliparity, age at menopause, MHT, and BMI have independent, dose-response associations with ER, PR, and grade, clarifying patterns of etiologic heterogeneity. Associations with HER2, KI67 and TP53, or other risk factors did not meet our threshold for strong evidence.

Abstract Polygenic risk scores (PRSs), which quantify inherited susceptibility to complex traits and diseases, have emerged as valuable tools for risk stratification and precision medicine. Despite their promise, PRS developed on European cohorts often demonstrate substantially reduced predictive accuracy in non-European populations, due to differences in genetic architecture. The disproportionate representation of European ancestry cohorts in genome-wide association studies (GWAS) leads to inequitable deployment of PRS technologies across diverse populations. Here, we introduce PRANA (Polygenic Risk Adaptation via Neural-network Architecture), a deep learning framework that adapts an existing PRS developed on one population to other ancestries. Unlike methods that require large-scale GWAS in the target population, PRANA leverages pre-trained PRS models derived from European cohorts and adapts them using modestly sized cohorts from the target population. We evaluated PRANA on seven complex traits in South Asian, East Asian and Ashkenazi Jewish populations, as well as in selected smaller East Asian subpopulations where the scarcity of training data poses a particular challenge. PRANA mostly improved predictive performance of the baseline PRS models by 5%-20% in terms of effect size (β) and Nagelkerke’s R², and, in most cases, outperformed existing cross-ancestry multi-PRS approaches. These results highlight PRANA as a scalable and practical strategy to reduce disparities in genomic risk prediction and advance the equitable application of PRS in diverse populations.

<title>RESUMEN</title> <p>A pesar de que la Práctica Basada en la Evidencia (PBE) ha estandarizado los tratamientos psicológicos, la realidad clínica sigue enfrentándose a una barrera persistente: la dificultad de traducir los resultados de ensayos clínicos grupales a la singularidad de cada paciente en contextos reales. Como respuesta, la Salud Mental de Precisión (SMP) propone una evolución del modelo actual, integrando el cuidado basado en la medición y la toma de decisiones basada en datos para ajustar la intervención a las necesidades específicas de cada persona, gracias en gran medida a desarrollos tecnológicos recientes como la Inteligencia Artificial (IA). En este contexto, el presente artículo persigue dos objetivos fundamentales. En primer lugar, sintetiza los pilares operativos de la SMP, analizando cómo la medición rutinaria y las predicciones algorítmicas pueden potenciar el juicio clínico del profesional. En segundo lugar, se introduce el proyecto NOVA (Navigating Outcomes via Analytics), un proyecto traslacional diseñado para superar las barreras actitudinales, tecnológicas, estadísticas y contextuales de la implementación de herramientas de SMP en España. Se discute sobre los pasos necesarios para cerrar la brecha entre la innovación tecnológica y la práctica asistencial en psicología, acelerando la adopción sostenible de una psicología de precisión en el mundo real.</p>

Background and Objectives: Obstructive sleep apnea (OSA) is a heterogeneous disease with diverse clinical presentations and high global prevalence. Obesity is a common comorbidity in OSA, but its relationship with symptom subtypes remains unclear. This study aimed to evaluate the association between OSA symptom subtypes and obesity in a clinical cohort. Methods: This observational study analyzed data from the Santiago Obstructive Sleep Apnea (SantOSA) prospective clinical cohort, including adults with OSA confirmed by home sleep apnea testing. Symptom subtypes were identified using latent class analysis. Associations between obesity and symptom subtypes were evaluated using multivariable regression models adjusted for age, sex, tobacco use, RDI, T90%, TST, hypertension, and diabetes. Statistical significance was set at p < 0.05. Results: A total of 1167 patients were included (943 men). Latent class analysis identified three symptom subtypes: non-sleepy, disturbed sleep, and excessive daytime sleepiness. Among obese patients, 30.7%, 50.0%, and 19.3% were classified into these subtypes, respectively. Obesity prevalence was 50.2%, and compared with non-obese OSA patients, obese patients showed a higher prevalence of severe OSA (46.1% vs. 26.9%), hypertension (54.4% vs. 34.9%), and diabetes (37.2% vs. 19.4%), as well as higher ESS scores and higher RDI and T90% values (all p < 0.01). In adjusted analyses, obesity remained independently associated with the excessive daytime sleepiness subtype after controlling for age, sex, tobacco use, RDI, T90%, TST, and comorbidities. Conclusions: Obesity is highly prevalent in OSA and is associated with specific symptom-defined phenotypes, particularly excessive daytime sleepiness and disturbed sleep. These findings support the relevance of considering symptom profiles alongside traditional severity metrics.

Evidence-Based Practice (EBP) has increased the availability of psychological treatments, yet many people do not benefit from therapy, some report deteriorations in symptoms, and dropout rates remain high. Precision Mental Health (PMH) is proposed as an extension of EBP by combining systematic measurement with predictive analytics to support the right intervention, at the right time, for the right person. Recent advances in Artificial Intelligence (AI) make PMH increasingly feasible in routine psychotherapy; however, the implementation of these approaches in routine care is still incipient. In this context, the present article has two main aims. First, we summarize key advances in PMH, particularly measurement-based care and data-informed decision making. Second, we introduce the NOVA project (Navigating Outcomes via Analytics), a multi-phase translational program designed to implement PMH in real-world psychological services. Guided by the Implementing Precision Methods framework, NOVA integrates (i) stakeholder-informed work on clinician acceptability and intention to use, (ii) pragmatic evaluation of decision support tools in routine care, (iii) development of robust and interpretable predictive models, and (iv) training and dissemination activities aligned with responsible innovation and professional competencies for AI-supported precision care. By detailing NOVA’s implementation pathway, we aim to provide a concrete roadmap for bridging AI innovation and psychological practice, accelerating the sustainable adoption of PMH in real-world settings.

Background Delayed aneurysm rupture (DAR) is a potentially devastating complication after flow-diverters (FD) deployment and their exact mechanisms are still unclear. The purpose of this meta-analysis is to synthesize the latest evidence on DAR after FD treatment and identify predictive factors. Methods A systematic review and meta-analysis were conducted according to established protocols. Searches were conducted in PubMed, Scopus, Web of Science, and Embase databases up to June 2024 using variations of “aneurysm “and “delayed aneurysm rupture” terms. Original studies reporting DAR and including more than five patients were included. All analyses were conducted in R software (version 4.4.2), employing the “meta” and “tableone” packages. Results 64 studies with 9820 patients were included, large aneurysm size (OR=1.08, 95% CI (1.02, 1.14), P value=0.008), intra-aneurysmal thrombus (OR=1.05, 95% CI (1.01, 1.09), P value=0.010) and posterior circulation location (OR=1.02, 95% CI (1.00, 1.04), P value=0.014) were significant risk factors for DAR. The pooled prevalence of DAR was 1.40%, with a rate of 1.18% after removing outliers. DAR occurred within 30 days in 66.7% of cases, with a mortality rate of 73.3% at discharge. Conclusion DAR following flow diversion treatment is an uncommon but devastating complication and remains a limitation of the technique. It is influenced by factors such as large aneurysm size, intra-aneurysmal thrombosis, and posterior circulation involvement. The first 30 days post-procedure represent a particularly high-risk period. Despite advancements in FD technology, significant gaps remain, highlighting the need for further research to improve risk assessment and treatment strategies.

Abstract Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adults, with a dismal 5-year survival rate of ~6.5%. Vasogenic cerebral edema is a severe complication that occurs in the majority of GBM patients, leading to a significant increase in intracranial pressure, neurological deficits, and increased mortality rates. GBM-associated edema is almost exclusively managed by the corticosteroid dexamethasone, the use of which is associated with immunosuppression and interference with radiation therapy. While there is considerable research on edema formation, little is known about how edema resolution occurs in GBM, particularly in response to dexamethasone or other anti-edema therapies. This gap in understanding highlights the need for new approaches to treat edema effectively. The water channel aquaporin-4 (AQP4), a key player in the brain’s water homeostasis, is upregulated in GBM compared to the normal brain. To investigate the role of AQP4 in GBM-associated edema, we used the RCAS/tv-a system to generate de novo GBM with different driver mutations in Aqp4 knockout (KO) and wild-type (WT) mice. Our results indicated that Aqp4 KO tumor-bearing mice had shortened survival and increased edema content. Additionally, Aqp4 loss decreased astrocyte coverage specifically in the tumor core but not the tumor periphery. Our single cell RNA-seq data suggested that Aqp4 loss led to an astrocytic shift towards a less reactive, progenitor-like state, while also decreasing the myeloid and CD8+ T cell population in the tumor microenvironment. Interestingly, tumor-bearing mice treated with anti-VEGFA antibody, which has anti-edema properties, showed increased expression of Aqp4 and reduced vessel leakage. This suggests that AQP4 plays a critical role in mediating the effects of anti-VEGFA therapy. Overall, this study highlights the important role of AQP4 in the resolution of edema, while also revealing its additional effects on astrocyte activation and the immune microenvironment in GBM.