Climate change is altering not only the mean conditions of marine environments, but also their temporal variability and predictability. As these alterations are not uniform across seascapes, their biological effects are expected to accentuate intra-specific differences in the adaptive capacity (e.g., plasticity and evolutionary potential) of natural populations. To test this theoretical framework, we assessed the phenotypic and genetic profiles of mussel from three study sites across a multi-driver heterogeneous environmental mosaic in Chilean Patagonia. Our study reveals that temporal variability, predictability, and exposure to extreme events (low pH/low salinity), collectively, can modulate the plasticity and optimal conditions of mussels. Despite these phenotypic differences, we observed low genetic differentiation, likely resulting from significant gene flow induced by aquaculture, ultimately diminishing variation among individuals from different geographic areas. Our findings underscore how variability and predictability are essential factors shaping phenotypic diversity, even at small spatial scales. Balancing these factors could enhance species resilience and ecological success, crucial for biodiversity conservation amidst climate change.
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http://dx.doi.org/10.1016/j.scitotenv.2024.176772 | DOI Listing |
J Osteopath Med
January 2025
McAllen Department of Trauma, South Texas Health System, McAllen, TX, USA.
Context: The injuries caused by falls-from-height (FFH) are a significant public health concern. FFH is one of the most common causes of polytrauma. The injuries persist to be significant adverse events and a challenge regarding injury severity assessment to identify patients at high risk upon admission.
View Article and Find Full Text PDFJ Eval Clin Pract
February 2025
California State University Monterey Bay, Seaside, California, USA.
Rationale: Obesity is an increasing medical issue not responding well to behavioural treatments beyond their initial weeks/months.
Aims And Objectives: Before suggesting surgical or pharmacological interventions, medical professionals might consider referrals to cost-effective, community-based behavioural treatments if stronger theoretical/empirical bases were demonstrated. Thus, evaluation of such is warranted.
J Diabetes Sci Technol
January 2025
Unit of Endocrine Diseases and Diabetology, Department of Medicine, ASST Papa Giovanni XXIII, Bergamo, Italy.
Aims: According to the 2023 International Consensus, glucose metrics derived from two-week-long continuous glucose monitoring (CGM) can be extrapolated up to 90 days before. However, no studies have focused on adults with type 1 diabetes (T1D) on multiple daily injections (MDIs) and with second-generation intermittently scanned CGM (isCGM) sensors in a real-world setting.
Methods: This real-world, retrospective study included 539 90-day isCGM data from 367 adults with T1D on MDI therapy.
Hum Reprod Open
November 2024
Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Study Question: How accurately can artificial intelligence (AI) models predict sperm retrieval in non-obstructive azoospermia (NOA) patients undergoing micro-testicular sperm extraction (m-TESE) surgery?
Summary Answer: AI predictive models hold significant promise in predicting successful sperm retrieval in NOA patients undergoing m-TESE, although limitations regarding variability of study designs, small sample sizes, and a lack of validation studies restrict the overall generalizability of studies in this area.
What Is Known Already: Previous studies have explored various predictors of successful sperm retrieval in m-TESE, including clinical and hormonal factors. However, no consistent predictive model has yet been established.
J Diabetes Metab Disord
June 2025
Department of Traditional Medicine, School of Persian Medicine, Tabriz University of Medical Sciences, Tabriz, Iran.
Objectives: This study was designed to characterize the prevalence, pattern of herbal use, and related factors among diabetic patients in Tabriz, Iran.
Methods: A descriptive cross-sectional study was carried out on 322 diabetic patients with random cluster sampling of specialized and subspecialized clinics in Tabriz, Iran. Binary logistic regression analysis was performed to evaluate the association between predictor variables (sociodemographic and disease-related characteristics and patient preference for treatment type) with herb use Interviews were conducted using a structured questionnaire from October 1, 2022, to April 23, 2023.
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