The majority of Americans, accounting for 51% of the population, take 2 or more drugs daily. Unfortunately, nearly 100,000 people die annually as a result of adverse drug reactions (ADRs), making it the 4th most common cause of mortality in the USA. Drug-drug interactions (DDls) and their impact on patients represent critical challenges for the healthcare system. To reduce the incidence of ADRs, this study focuses on identifying DDls using a machine-learning approach. Drug-related information was obtained from various free databases, including DrugBank, BioGRID and Comparative Toxicogenomics Database. Eight similarity matrices between drugs were created as covariates in the model in order to assess their infiuence on DDls. Three distinct machine learning algorithms were considered, namely, logistic regression (LR), extreme Gradient Boosting (XGBoost) and neural network (NN). Our study examined 22 notable drugs and their interactions with 841 other drugs from DrugBank. The accuracy of the machine learning approaches ranged from 68% to 78%, while the F1 scores ranged from 78% to 83%. Our study indicates that enzyme and target similarity are the most significant parameters in identifying DDls. Finally, our data-driven approach reveals that machine learning methods can accurately predict DDls and provide additional insights in a timely and cost-effective manner.

The accumulation of proteins such as amyloid-beta and tau, which disrupt normal cellular processes, characterizes Alzheimer's disease (AD). Cognitive decline is strongly linked to tau pathology, which initially manifests in the medial temporal lobe (MTL). To investigated the association between cognitive performance and regional tau accumulation, as measured by positron emission tomography (PET) imaging, in cognitively normal older adults. Understanding this relationship is critical for early intervention before noticeable cognitive decline emerges. Retrospective study. Tau PET scans were conducted on 440 participants enrolled in the anti-amyloid treatment in asymptomatic Alzheimer's (A4) study. The participants, aged 65-85, were cognitively unimpaired and had complete demographic and genetic profiles. Tau levels in the MTL and temporal neocortex (NEO) was quantified using composite metrics. Cognitive function was evaluated using the preclinical Alzheimer's cognitive composite (PACC) and its individual components. Multiple linear regression models were applied to determine the associations between tau burden and cognitive outcomes, including interaction terms to evaluate the moderating roles of sex and apolipoprotein E (APOE)-ε4 genotype. The average participant age was 71.8 years (standard deviation = 4.84), with females comprising 58% of the sample. Greater tau accumulation in both tauMTL and tauNEO regions was significantly associated with lower cognitive scores. Specifically, reduced PACC scores (p < 0.001) corresponded with higher tau levels in both regions, primarily influenced by declines in Delayed Logical Memory and Free and Cued Selective Reminding test scores. Additionally, tauNEO levels were modestly linked to Mini-Mental State Examination scores. The effects of sex and APOE-ε4 status were minimal. Elevated tau deposition in the MTL and NEO is associated with diminished cognitive function, particularly in memory and processing speed domains. Notably, tau accumulation in the MTL showed a strong association with poorer outcomes on memory-related cognitive measures. The limited influence of sex and APOE-ε4 genotype highlights tau pathology as a key contributor to early cognitive decline in preclinical AD.

Background: Subjective cognitive impairment (SCI), a potential precursor to measurable cognitive decline, is a known predictor of Alzheimer's disease (AD). Mood symptoms, including depressive and anxiety symptoms, negatively impact both subjective and objective cognitive measures, even when accounting for tau and amyloid pathology. This highlights a critical yet underexplored area in understanding how mood symptoms independently influence cognitive outcomes beyond established AD biomarkers. This study examines the cross‐sectional relationship between mood symptoms and subjective (Cognitive Function Instrument, CFI) and objective (Preclinical Alzheimer Cognitive Composite, PACC) cognitive measures, while accounting for tau and amyloid burden. Method: We analyzed 447 participants from the A4 study, all of whom underwent tau PET and amyloid PET. Depressive symptoms were assessed using the Geriatric Depression Scale (GDS) and anxiety symptoms were assessed using the State‐Trait Anxiety Inventory (STAI), respectively. Multiple linear regression models were used to examine the cross‐sectional associations between mood symptoms and cognitive outcomes, adjusting for age, education, amyloid and tau burden (as measured in the medial temporal lobe; tau _MTL ) at baseline. Result: Higher levels of depressive symptoms were significantly associated with poorer subjective cognition (CFI = 0.047, p <0.001) and objective cognition (PACC = ‐0.099, p = 0.025), even after accounting for tau and amyloid pathology. Anxiety symptoms showed no significant associations with either CFI or PACC scores. Tau burden (tau _MTL ) was independently associated with lower PACC scores (=‐0.145, p = 0.001), while amyloid burden did not show significant associations with either cognitive measure. Conclusion: Depressive symptoms are strongly associated with both subjective and objective cognitive measures, independent of tau and amyloid pathology, highlighting the importance of addressing mood symptoms in individuals at risk for AD. Anxiety symptoms, in contrast, appear to have minimal influence on cognition in this cohort. These findings underscore the value of incorporating assessments of mood symptoms into AD research to refine early detection strategies and intervention approaches.

This study investigates the determinants of electric vehicle (EV) pricing through an interpretable machine learning framework. Using XGBoost on a comprehensive dataset of technical specifications, we achieve high predictive accuracy (R²=0.958) while employing SHAP and LIME to deconstruct the model's decision logic. Our analysis reveals a fundamental shift in automotive valuation principles: integrated performance characteristics synthesized via Principal Component Analysis. It emerges as the primary price driver, followed by vehicle dimensions, which exhibit non-linear threshold effects. Notably, traditional differentiators had minimal impact, suggesting that electric powertrains are redefining conventional automotive hierarchies. The complementary interpretability methods consistently demonstrate that EV pricing rewards engineering substance over traditional status markers, with performance bundles and dimensional thresholds creating clear market stratification. These findings provide manufacturers with quantifiable engineering targets for premium positioning and offer consumers unprecedented transparency into feature valuation. The study establishes that interpretable machine learning not only predicts prices but also uncovers the emerging economic logic governing the electric vehicle revolution, where technological integration and physical proportions supersede historical automotive status symbols.

This study examined the relationship between horizontal and vertical jumps and speed performance in U-12 soccer players. Twenty-six amateur soccer players from a soccer club participated in the study. Speed tests (0-5m/0-10m/0-20m and 0-30m), horizontal jump tests, and vertical jump tests were applied. In the literature review, studies examining the relationship between speed performance with vertical and horizontal jump performance in young soccer players were observed, but, to our knowledge, no study has been found that evaluates horizontal jump as a standing long jump with hands on hips in young soccer players. The relationship between variables was measured by Spearman correlation, with a significance level of α set at 0.05.The mean values of the measured parameters of the U-12 soccer players who participated in the study were; age 10.88 ± 0.81 years, height 142.92 ± 4.65 cm, body weight 35 ± 3.44 kg, 5m speed time 2.36 ± 0.13 sec, 10m speed time 3.45 ± 0.16 sec, 20m speed time 5.40 ± 0.35 sec, 30m speed time 7.19 ± 0.45 sec, horizontal jump (SLJHH) value 140.42 ± 12.85 cm, horizontal jump (SLJHF) value 163.84 ± 13.79 cm, vertical jump (VJ) value 26.57 ± 4.33 cm. The study findings indicated significant relationships between 5m and SLJHF/VJ, 10m and SLJHF/SLJHW, and 20m and 30m with SLJHF/SLJHH/VJ (p 0.05). In conclusion, a negative relationship was found between horizontal and vertical jump and speed performance in U-12 soccer players. Therefore, when evaluating speed performance in this age group, horizontal and vertical jump performance should also be taken into consideration. Developing lower extremity strength should be prioritized in training programs tailored to this age group, aiming to improve speed performance.