ORDNA : Deep‐learning‐based ordination for raw environmental DNA samples

Abstract Environmental DNA (eDNA) metabarcoding has revolutionized biodiversity monitoring, offering non‐invasive tools to assess ecosystem health. The complexity of eDNA metabarcoding data poses major challenges for conventional ordination methods in understanding assemblage similarities and assessing biodiversity patterns. Here, we introduce ORDNA (ORDination via Deep Neural Algorithm), a new deep learning method tailored for eDNA sample ordination. Leveraging artificial neural networks, ORDNA processes raw sequences from eDNA samples directly, bypassing potentially biased and cumbersome expert‐based bioinformatic steps. The method is trained with a contrastive self‐supervised learning approach, the triplet loss, to derive a two‐dimensional representation of eDNA samples based on their read composition. We apply ORDNA to four distinct eDNA datasets, demonstrating its robustness and superiority over traditional ordination techniques in capturing and visualizing ecological patterns. Our results he potential of deep learning in advancing eDNA analysis, with ORDNA serving as a promising tool for more accurate and efficient biodiversity assessments.

Self‐Organizing Ovarian Somatic Organoids Preserve Cellular Heterogeneity and Reveal Cellular Contributions to Ovarian Aging

ABSTRACT Ovarian somatic cells are essential for reproductive function, but no existing ex vivo models recapitulate the cellular heterogeneity or interactions within this compartment. We engineered an ovarian somatic organoid model by culturing a stroma‐enriched fraction of mouse ovaries in scaffold‐free agarose micromolds. Self‐organized ovarian somatic organoids maintained diverse cell populations, produced extracellular matrix, and secreted hormones. Organoids generated from reproductively old mice exhibited reduced aggregation and growth compared to young counterparts, as well as differences in cellular composition. Interestingly, matrix fibroblasts from old mice demonstrated upregulation of pathways associated with the actin cytoskeleton and downregulation of cell adhesion pathways, indicative of increased cellular stiffness that may impair organoid aggregation. Cellular morphology, which is regulated by the cytoskeleton, significantly changed with age and in response to actin modulation. Moreover, actin modulation altered organoid aggregation efficiency. Overall, ovarian somatic organoids have advanced knowledge of cellular contributions to ovarian aging.

Biologically Younger Individuals, as Identified by MARK ‐ AGE Biological Age Scores, Display a Distinct Favourable Blood Chemistry Profile Regardless of Age

ABSTRACT Biomarkers of ageing are defined as age‐related changes in body function or composition that could serve as a measure of ‘biological’ age and predict the onset of age‐related diseases and/or residual life expectancy. We conducted the MARK‐AGE Study, a European population study (3300 subjects aged 35–74) to identify a powerful set of biomarkers of ageing. A total of 362 clinical‐chemistry, genetic, cellular or molecular biomarkers were analysed for each subject. Using statistical models as well as machine learning we derived mathematical formulas for females and for males that yield a ‘bioage score’ of an individual, based on sets of 10 biomarkers for females and 10 for males. Collectively, these biomarkers model chronological age of our study population and, thus yield the ‘biological’ age of a certain person. ‘Age difference’ (defined as biological minus chronological age) should then identify biologically older or younger individuals. Using our set of biomarkers, subjects with Down Syndrome and smoking females are biologically older, whereas postmenopausal females taking hormone replacement therapy are biologically younger. Strikingly, our data reveal that age difference of MARK‐AGE subjects, but not chronological age, is linearly correlated with levels of HDL, 25‐hydroxy‐Vitamin D, and CD3+ CD4+/CD45+ ratio in such a way that biologically younger subjects display values that are favourable to good health, whereas other markers such as glucose and HbA1c are correlated with chronological age, but not age difference. This dichotomy of correlations may point to different roles of such markers, that is, drivers of the ageing process versus bystanders of ageing.

Resilience in Bipolar Disorder Compared to Clinical and Non‐Clinical Populations: A Systematic Review and Meta‐Analysis

ABSTRACT Introduction Resilience is present in both clinical and non‐clinical populations; yet, there is a paucity of literature examining its role in bipolar disorder (BD). The goal of the present systematic review and meta‐analysis was to substantiate the extant literature investigating resilience in BD in comparison to clinical and non‐clinical populations. Method PubMed/MEDLINE, PsycINFO, and Scopus were systematically searched from inception to August 8th, 2024. Results Twenty‐eight studies using a validated resilience scale with a total of 3094 people with BD, 4100 healthy controls, and 1768 with other mental diagnoses were included in the systematic review, and 21 were analyzed in a random effects meta‐analysis. A statistically significant result with a medium effect (SMD = −0.787, p  < 0.001) indicated that people with BD reported lower levels of resilience than healthy controls. Similarly, patients with BD showed higher levels of resilience than patients with schizophrenia (SCZ) (SMD = 0.336, p  = 0.013). No significant differences were found between BD and major depressive disorder (MDD). Conclusion Findings should be interpreted with caution due to the high heterogeneity observed and methodological challenges in the definition and measurement of resilience. Future research should aim to better characterize resilience in BD by improving its assessment as a standardized element of clinical evaluation. This will provide a basis for strategies to reduce the burden of this chronic condition.

Care coordination in screening, brief intervention, and referral to treatment (SBIRT): A scoping review

Abstract Background and Objectives Implementation of screening, brief intervention, and referral to treatment (SBIRT) for substance use in primary care remains challenging. Care coordination (CC) may strengthen SBIRT by supporting brief interventions, improving referral follow‐through, and enhancing continuity. CC models and outcomes vary, however, and no prior scoping review has synthesized these. Methods A comprehensive search of 10 resources, including Embase, MEDLINE, and PsycINFO, was conducted on 4/15/2025. Eligible studies described outpatient SBIRT models adding CC for any age group. Articles were excluded if they lacked CC, presented no original data, or were not in English. Relevant implementation and patient‐level outcomes were extracted to describe CC models and their impact. Results Of 737 abstracts and 50 full texts reviewed, 15 met the inclusion criteria. Studies spanned primary care, specialty care, and age ranges. CC models included embedded behavioral health providers, centralized linkage managers, and remote coordinators using phone or digital platforms. CC was associated with higher screening completion, brief intervention delivery, referral initiation, and treatment engagement. Successful integration incorporated structured communication, follow‐up protocols, and accessible technology. Barriers included limited billing infrastructure, weak electronic record integration, and unclear roles. Discussion and Conclusions CC appears to enhance SBIRT implementation and treatment engagement across outpatient settings. Embedded approaches offer relational benefits, while remote and digital strategies show promise for scalability. Comparative studies are needed to assess effectiveness, cost, and contextual fit. Scientific Significance Provides the first review of CC models within SBIRT, highlighting their implementation impact and guiding future optimization.

Regional and Socioeconomic Disparities in Frailty Across Tasmania: Evidence From Island Study Linking Ageing and Neurodegenerative Disease

ABSTRACT Objective Although frailty appears higher in rural and socioeconomically disadvantaged areas, existing evidence often lacks adjustment for possible population confounders. This study examined the independent associations between geographic remoteness and area‐level socioeconomic status with frailty. Methods We constructed a 33‐item frailty index using data from 5740 participants of the Island Study Linking Ageing and Neurodegenerative Disease (ISLAND), a web‐based longitudinal cohort of adults aged 50 years and over in Tasmania, Australia. After linking participant postcodes to Modified Monash Model remoteness and Index of Relative Socioeconomic Advantage and Disadvantage, we examined frailty distribution and its associations with geographic and sociodemographic factors using descriptive statistics, spatial mapping and multivariable linear regression models. Results The analytical sample mean age was 69.3 years (SD = 8.0) and most were women (72%). Frailty index scores followed a gamma distribution (mean score = 0.16, SD = 0.09), increased with age and were highest in central and western areas of Tasmania. After adjustment for age, gender, education, retirement and migrant status, frailty index scores were significantly higher in rural towns (β = 0.011 [95% confidence interval, CI = 0.005, 0.016]) and remote communities (β = 0.023 [95% CI = 0.009, 0.038]) than regional centres. Similarly, after full adjustment, compared with areas of the highest socioeconomic advantage, frailty was significantly higher in areas of middle (β = 0.013 [95% CI = 0.007, 0.018]) or low (β = 0.024 [95% CI = 0.018, 0.030]) socioeconomic advantage. Conclusions The distribution of frailty across Tasmania varied by geographic remoteness and socioeconomic disadvantage. Integrating frailty assessment into regional health planning may support targeted interventions for vulnerable subpopulations, particularly in rural and disadvantaged communities.

Correspondence: Dupilumab‐Induced Blood Eosinophilia—Why We Need to Look Beyond the Blood

Allergological Work‐Up in Cephalosporin Allergy Diagnosis and Delabelling: The Experience From Two European Allergy Centres

ABSTRACT Background Diagnostic strategies for cephalosporin allergy are not well defined due to differences in inclusion criteria among studies and a lack of standardised diagnostic tests. Our aim was to describe the characteristics of patients with a suspicion of cephalosporin allergy and to analyse the role of in vivo tests. Methods Patients with suspected cephalosporin allergy were prospectively evaluated (2019–2023). Diagnosis was achieved using clinical history, skin tests ( STs ) and, if negative, drug provocation tests ( DPTs ). A randomised group of patients who tolerated DPT were retested by STs at 2–8 weeks. Results 211 patients were evaluated, with 55.4% reporting IRs and 44.5% NIRs . Skin was involved in half of the patients reporting IRs and in all reporting NIRs ( p  < 0.0001). Anaphylaxis was experienced by 18% and shock by 7.1%. The cephalosporin most commonly involved was cefuroxime (63.6%) ( p  < 0.0001) in the Spanish cohort and cefazolin (51%) ( p  < 0.0001) in the Italian one. Allergy was confirmed in 57.2% of patients reporting IRs (47% by STs and 9.4% by DPT ) and 14.9% of NIRs (5.3% by STs and 8.5% by DPT ). In the positive‐ ST group, the percentage of grade II and III reactions was higher ( p  = 0.02) and the interval reaction‐study shorter ( p  = 0.0007) than in negative‐ ST . Of the 48 patients retested, 1 (2.9%) who reported an IR and 1 (7.1%) who reported NIR resulted positive. Conclusions The patterns of cephalosporin allergy may differ across different regions, being STs and DPT useful for diagnosis. Further studies are needed to confirm the role of retesting, as well as to identify which patients would benefit most from this procedure.

Comorbid Chronic Rhinosinusitis and Asthma: Shared Risk Factors and Treatment Implications—An EAACI Task Force Report

ABSTRACT Chronic rhinosinusitis (CRS) and asthma are prevalent conditions that often coexist. These diseases share common inflammatory mechanisms, such as T‐helper cell 2 (T2)‐high inflammation, driven by interleukin (IL)‐4, IL‐5, and IL‐13 cytokines. The frequent comorbidity between CRS, especially CRS with nasal polyps (CRSwNP), and asthma exacerbates disease severity, impairs quality of life, and complicates treatment. Patients with NSAID‐exacerbated respiratory disease (N‐ERD) represent a severe phenotype of this disease, characterized by the coexistence of CRSwNP, asthma, and NSAID hypersensitivity, which poses unique therapeutic challenges. This EAACI Task Force explores the shared risk factors, including genetic predispositions, epithelial barrier dysfunction, microbiome dysbiosis, underlying CRS, and asthma. It also evaluates current therapeutic strategies such as biologics, aspirin therapy after desensitization (ATAD), and endoscopic sinus surgery (ESS). Biologics have shown their effectiveness and safety in the treatment of asthma and CRS. Dupilumab, mepolizumab, depemokimab, and omalizumab have emerged as transformative therapies, particularly for patients with severe type 2 inflammation. Tezepelulumab is effective for both T2‐high and T2‐low asthma and CRSwNP. Itepekimab has shown its effect in asthma and is under investigation for CRSwNP. Omalizumab is effective in allergic asthma and CRSwNP. ATAD provides an additional disease‐modifying approach for N‐ERD, though patient adherence and tolerability remain critical challenges. ESS significantly improves asthma control, reduces medication use, and enhances sinonasal outcomes, particularly in severe asthma cases; however, these patients often need recurring surgeries. Despite these advances, treatment outcomes vary based on individual phenotypes and endotypes, underscoring the need for personalized approaches. The report highlights gaps in the literature, such as the lack of head‐to‐head trials comparing biologics, ATAD, and surgery. Future research should focus on refining treatment algorithms, identifying biomarkers for treatment selection, and assessing long‐term outcomes to optimize care for patients with CRS, asthma, and N‐ERD.

Integrating generative artificial intelligence, predictive analytics and human intelligence in peri‐operative care