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Analysis of risk factors related to perioperative neurocognitive disorders in elderly patients with hip fractures
Comparative analysis of consistency of adaptations to interval interventions individualized using sport-specific techniques in well-trained soccer players
Author Correction: Cannabis pollen dispersal across the United States
Large-scale prospective serum metabolomic profiling reveals candidate predictive biomarkers for suspected preeclampsia patients
Discrete vulnerability to pharmacological CDK2 inhibition is governed by heterogeneity of the cancer cell cycle
Abstract Cyclin dependent kinase 2 (CDK2) regulates cell cycle and is an emerging target for cancer therapy. There are relatively small numbers of tumor models that exhibit strong dependence on CDK2 and undergo G1 cell cycle arrest following CDK2 inhibition. The expression of P16INK4A and cyclin E1 determines this sensitivity to CDK2 inhibition. The co-expression of these genes occurs in breast cancer patients highlighting their clinical significance as predictive biomarkers for CDK2-targeted therapies. In cancer models that are genetically independent of CDK2, pharmacological inhibitors suppress cell proliferation by inducing 4N cell cycle arrest and increasing the expressions of phospho-CDK1 (Y15) and cyclin B1. CRISPR screens identify CDK2 loss as a mediator of resistance to a CDK2 inhibitor, INX-315. Furthermore, CDK2 deletion reverses the G2/M block induced by CDK2 inhibitors and restores cell proliferation. Complementary drug screens define multiple means to cooperate with CDK2 inhibition beyond G1/S. These include the depletion of mitotic regulators as well as CDK4/6 inhibitors cooperate with CDK2 inhibition in multiple phases of the cell cycle. Overall, this study underscores two fundamentally distinct features of response to CDK2 inhibitors that are conditioned by tumor context and could serve as the basis for differential therapeutic strategies in a wide range of cancers.
Diabetic retinopathy further increases risk of cardiovascular disease mortality in a high-risk cohort
Abstract Diabetic retinopathy (DR) is the most common microvascular complication of diabetes and is strongly linked with systemic vascular comorbidities. This study investigated if DR predicts risk of cardiovascular disease (CVD) mortality in a high CVD risk cohort. This was a prospective cohort study of 1582 adult participants who presented to a tertiary Australian hospital for evaluation of acute coronary syndrome by coronary angiography. Participants were concurrently examined for DR from mydriatic fundus photographs which were mask-graded according to International Clinical Classification categories of no DR, mild non-proliferative DR, moderate-to-severe NPDR, and proliferative DR. Coronary artery disease was graded from coronary angiograms using the Gensini score. CVD mortality follow-up was assessed 9 years after baseline examination using linkage with the Australian National Death Index. At baseline, 355 (22.4%) participants had any DR. There were 181 (11.4%) fatal CVD events after 9-years follow-up. After controlling for age, sex, BMI, diabetes, total cholesterol, smoking status, hypertension, previous myocardial infarction and stroke, any DR was associated with 1.8-fold higher risk of CVD mortality (Hazard Ratio [HR] 1.84, 95% Confidence Interval [95% CI: 1.30–2.61]). Mild non-proliferative DR (1.85 [1.26–2.72]) and proliferative DR (5.27 [2.32–12.00]) were associated with greater CVD mortality risk. Further adjustment for coronary artery disease using Gensini scores and excluding patients without diabetes had minimal impact on the association. The increased risk of CVD mortality was significant in both men (2.25 [1.60–3.19]) and women (2.38 [1.24–4.58]) with any DR. In individuals with high CVD risk, presence of DR independently predicts increased CVD mortality. This likely reflects additional contribution of microvascular disease to CVD mortality. Individuals with DR may benefit from a comprehensive cardiovascular risk assessment, lifestyle changes, more intensive cardiovascular management and follow-up to minimise risk of death from CVD events.
Observation of anti-PT-symmetry and higher-order exceptional point PT-symmetry in ternary systems for single-mode operation
Elevated high-precision mapping and localization technology for periodic inspections
Prevalence of fatigue and perceived fatigability in older adults: a systematic review and meta-analysis
Abstract Fatigue is a common health complaint in older adults, but its prevalence varies widely among studies due to differences in populations and assessment tools. The objective of this review is to systematically evaluate the prevalence of fatigue and perceived fatigability in older adults with PRISMA 2020. Four databases—PubMed, Embase, Web of Science (WoS), and Cochrane Library—were systematically searched as of December 27, 2023. Cochrane Q tests and the I 2 statistic were used using Stata16.0 to assess between-study heterogeneity. A total of 21 studies involving 17843 participants were included in this study. The prevalence of fatigue in older adults was 42.6%, and the prevalence of perceived physical fatigability and mental fatigability was 58.2% and 24.0%. Meta-analysis showed that the prevalence of perceived physical fatigability among older adults was very high. This prevalence varied with regional economic development level, age of the subjects, sample size, and representativeness of the subjects. Fatigue is a health dilemma faced by most older adults. To improve quality of life, early and regular fatigue assessment should be part of routine health screening for older adults.
Incorporating health literacy principles into the adaptation of a methods motivational interviewing approach for enrolling black adults in a pilot randomized trial
Boosting biogas production through innovative data-driven modeling and optimization methods at NJWTP
Trends in the disease burden of cystic echinococcosis in China, 1990–2044 analysis and forecasting study
Integrating regression and multiobjective optimization techniques to analyze scientific perception
Diploid aposporous sunflower forms triploid BIII progeny displaying increased apospory levels and non-random genetic mutations
Numerical study on the cyclic behavior of the stiffened steel plate shear walls containing sandwich panels with corrugated core
Natural selection and adaptive traits in the Maniq, a nomadic hunter-gatherer society from Mainland Southeast Asia
Abstract Asia is home to diverse hunter-gatherer populations characterized by significant morphological, anthropological, cultural, and linguistic diversity. Despite their importance in understanding ancestral human subsistence, little is known about the essential genetic adaptations of these groups. This study investigates the evolutionary pressures shaping the genome of the Maniq population, a nomadic hunter-gatherer group inhabiting the rainforests of southern Thailand. Using genome-wide approaches, including iHS, xp-EHH, PBE, and beta statistics, we identified signatures of positive and balancing selection. Genes under positive selection were enriched in pathways related to immunity, metabolic regulation, structural adaptation, cardiovascular performance, and neuromodulatory traits. Several genes associated with the Southeast Asian ‘negrito-like’ phenotype were also under positive selection. Balancing selection was primarily detected in immune-related genes, particularly within the HLA region, underscoring the critical role of genetic diversity in surviving pathogen-rich environments. Additionally, balancing selection in olfactory receptor genes highlights their importance in environmental sensing and adaptation. These results reveal the intricate interplay of positive and balancing selection in shaping the genetic landscape of the Maniq population and highlight their adaptations to the ecological and lifestyle challenges of life in the rainforest. This study contributes to our understanding of human evolutionary processes in tropical environments and hunter-gatherer societies.
Factor structure and measurement invariance of exercise self-efficacy scale among secondary school students in China
Precision and efficiency in skin cancer segmentation through a dual encoder deep learning model
Flexible and cost-effective deep learning for accelerated multi-parametric relaxometry using phase-cycled bSSFP
Abstract To accelerate the clinical adoption of quantitative magnetic resonance imaging (qMRI), frameworks are needed that not only allow for rapid acquisition, but also flexibility, cost efficiency, and high accuracy in parameter mapping. In this study, feed-forward deep neural network (DNN)- and iterative fitting-based frameworks are compared for multi-parametric (MP) relaxometry based on phase-cycled balanced steady-state free precession (pc-bSSFP) imaging. The performance of supervised DNNs (SVNN), self-supervised physics-informed DNNs (PINN), and an iterative fitting framework termed motion-insensitive rapid configuration relaxometry (MIRACLE) was evaluated in silico and in vivo in brain tissue of healthy subjects, including Monte Carlo sampling to simulate noise. DNNs were trained on three distinct in silico parameter distributions and at different signal-to-noise-ratios. The PINN framework, which incorporates physical knowledge into the training process, ensured more consistent inference and increased robustness to training data distribution compared to the SVNN. Furthermore, DNNs utilizing the full information of the underlying complex-valued MR data demonstrated ability to accelerate the data acquisition by a factor of 3. Whole-brain relaxometry using DNNs proved to be effective and adaptive, suggesting the potential for low-cost DNN retraining. This work emphasizes the advantages of in silico DNN MP-qMRI pipelines for rapid data generation and DNN training without extensive dictionary generation, long parameter inference times, or prolonged data acquisition, highlighting the flexible and rapid nature of lightweight machine learning applications for MP-qMRI.