Browse Articles

Long-term outcomes of biodegradable versus 2nd generation durable polymer drug-eluting stents in PCI: Protocol for a systematic review and meta-analysis

Background More than 3 million individuals globally experience STEMI each year, with percutaneous coronary intervention (PCI) as the preferred revascularization method. While second-generation Drug Eluting Stents (DES) reduce restenosis compared to bare-metal stents, complications such as neoatherosclerosis and stent thrombosis remain. Second-generation stents, including durable polymer (DP-DES) and biodegradable polymer (BP-DES), aim to improve outcomes, though guidelines do not specify a preference. Given mixed results from prior studies and new long-term data, we aim to perform a systematic review and meta-analysis comparing long-term outcomes of DP-DES vs. BP-DES following PCI. Methods This protocol has been developed following the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols. MEDLINE, Embase, and Scopus databases will be searched for eligible observational and interventional studies from inception up to 5th of October 2024. Screening (title/abstract and full text), data extraction, risk of bias assessment, and quality of evidence assessment will be conducted by two independent reviewers. A random-effects model will be used to meta-analyse outcomes. Discussion DES have greatly advanced PCI for STEMI. However, long-term stent thrombosis remains an issue due to chronic inflammation and impaired healing from the stent’s polymer coating. To overcome this, BP-DES were introduced to dissolve their coating within 2–9 months. However, whether BP-DES offers superior long-term outcomes compared to second-generation DP-DES remains uncertain. While previous meta-analyses have shown similar outcomes, recent studies suggest BP-DES may offer better long-term results. This review will compare long-term outcomes (≥5 years) of BP-DES vs. DP-DES, providing important insights to inform clinical practice. Systematic review registration: PROSPERO (CRD42024592579)

Neighborhood walkability and cardiometabolic disease in Texas

Abstract Cardiometabolic diseases (CMDs) affect significant numbers of adults in the United States, with 11% diagnosed with diabetes and 10% with cardiovascular diseases. Walking plays a crucial role in reducing health risks, particularly obesity and diabetes. We aim to explore the association between neighborhood walkability and CMD measures in Texas, while controlling for age, sex, racial/ethnic background, and family history of diabetes. We collected 1994 observations for the year 2019, merging data from the Texas Behavioral Risk Factor Surveillance System and the Environmental Protection Agency. We employed multilevel linear regression and multilevel logistic regression analyses to assess the association between CMD measures and neighborhood walkability. Our findings revealed that higher neighborhood walkability is significantly associated with a lower body mass index (BMI) (β = − 0.28, CI − 0.45 to − 0.10) and a reduced risk of diabetes (OR 0.93, CI 0.86–0.99), indicating that when walkability increases by one unit, an individual’s BMI decreases by 0.28 kg/m2 and the odds of having diabetes decrease by 7%. We also found that African Americans living in communities with lower walkability scores compared to other racial/ethnic groups. Our findings highlight the need for urban planning policies promoting walkable neighborhoods, suggesting community-based approaches to health promotion.

Imaging of coexisting classical and non-classical oriented attachment growth pathways in covalent organic framework microcrystals

The association between adult-life smoking and age-related cognitive decline in Danish men

Background Most previous studies of effects of smoking on age-related cognitive decline have compared cognitive decline in current smokers, former smokers, and never smokers rather than investigating the effects of pack-years. The aim of the present study was to analyze the association between smoking and age-related cognitive decline in a sample of men administered the same intelligence test in young adulthood and late midlife, using pack-years between the two assessments as the primary measure of exposure to smoking. Methods In 5052 men, scores on a military intelligence test (BPP, Børge Priens Prøve) were available from young adulthood and a late midlife follow-up assessment including the same intelligence test and a comprehensive questionnaire on socio-demographic factors, lifestyle, and health. Information on smoking was self-reported at follow up for eight age periods, and pack-years were calculated from age 19 based on information on daily smoking and the duration of each age period. The differences in cognitive decline between adult-life smokers and non-smokers and the differences between light, moderate, and heavy smokers defined by pack-years were analyzed in linear regression models. Results All smoking variables were only weakly associated with cognitive decline. Comparison of adult-life smokers and non-smokers showed less cognitive decline among smokers (1.12 IQ points, p <  0.001). Among smokers, analyses of pack-years suggested a weak dose-response relationship with more decline in heavy smokers than in light smokers (1.33 IQ points, p =  0.001). Independent of pack-years, current smoking was associated with larger cognitive decline than former smoking (1.73 IQ points, p <  0.001). Conclusion Smoking explained negligible fractions of the variance in cognitive decline, and thus our results did not indicate that smoking is a strong predictor of cognitive decline. The effects of pack-years suggest a relatively weak, possibly cumulative effect of smoking across the adult lifespan. The difference in decline between smokers and non-smokers may reflect participation bias and selective attrition at follow-up while the effects of current smoking may reflect either temporary effects of smoking or individual and life-style characteristics associated with continuation of smoking into late midlife.

Maternal and paternal lineage analysis of Island Southeast Asian goats reveals continental propagation routes and introgression through the Indian ocean

Deformation resistant monolithic hierarchical textures inducing stretchable superamphiphobicity with environmental adaptability and flame retardancy

Key parameters and sensitivity analysis of lower limb muscle strength in young men with different gait patterns

Objective To explore the functional characteristics and principal component differences of electromyography in different phases of the gait cycle, to provide key parameters for identifying a complete gait, and to provide a reference for joint moment solving in the lower limb. Methods Twenty young men were selected to measure the natural gait EMG of 14 muscles of the lower limb using VICON and NORAXON devices. Gait was classified into two categories according to the Niyogi S A classification, integral EMG differences were compared, and principal component analysis was performed on the differing muscles to calculate Cohen’s d values for significant differences and ΔIEMG values for non-significant differences. Results (1) Significant differences existed in the integral EMG of the left semitendinosus, right semitendinosus, right biceps femoris, and left gastrocnemius muscles, both lateral and medial. (2) Principal component analysis showed significant differences in the left semitendinosus for principal component five (P < 0.1, ES = 1.40); right biceps femoris for principal component three (ES = 0.63, 10%-30%); and left gastrocnemius medial for principal component four (P < 0.05, ES = 1.81, 40%-60%). The ΔIEMG% of the right semitendinosus principal components I-IV were 97.96%, 92.24%, 87.26%, and 75.08%, respectively; and the ΔIEMG% of the left gastrocnemius medial principal components I-IV were 90.95%, 75.08%, 96.37%, and 85.39%, respectively. Conclusion (1) Left semitendinosus, right semitendinosus, right biceps femoris, and left gastrocnemius can be used as the main muscles for gait recognition. (2) The left semitendinosus principal component V, the right biceps femoris principal component III, and the left gastrocnemius medial principal component IV are sensitive indicators for gait stage classification.

Forecasting solar energetic particles using multi-source data from solar flares, CMEs, and radio bursts with machine learning approaches

Abstract This study presents a consistent method to the inherently imbalanced problem of predicting solar energetic particle (SEP) events, using a variety of datasets that include solar flares, coronal mass ejections (CMEs), and radio bursts. We applied several machine learning (ML) methods, including Random Forests (RF), Decision Trees (dtree), and Support Vector Machines (SVM) with both linear (linSVM) and nonlinear (svm) kernels. To assess model performance, we used standard metrics such as Probability of Detection (POD), False Alarm Rate (FAR), True Skill Statistic (TSS), and Heidke Skill Score (HSS). Our results show that the RF model consistently outperforms the other algorithms across datasets containing flares, CMEs, and radio bursts. For the sweep frequency dataset, RF achieved a POD of $$0.85 (\pm 0.08)$$ , a FAR of $$0.30 (\pm 0.05)$$ , a TSS of $$0.78 (\pm 0.07)$$ ,and a HSS of $$0.71 (\pm 0.03$$ ). For the fixed-frequency dataset, RF produced a POD of $$0.76 (\pm 0.12)$$ , a FAR of $$0.31 (\pm 0.08)$$ , a TSS of $$0.71 (\pm 0.11)$$ ,and a HSS of $$0.67 (\pm 0.06$$ ). Key features for SEP prediction include CME linear speed and angular width across both datasets. For sweep frequency, flare intensity and integral soft X-ray (SXR) flux are crucial, while for fixed frequency, the rise time and duration of radio bursts at 1415 MHz are significant.

Predicting driving comfort in autonomous vehicles using road information and multi-head attention models

Aetiology, antimicrobial susceptibility patterns and factors associated with bacteriuria among HIV-infected women attending Prevention of Mother-to-Child Transmission (PMTCT) clinic at Bukoba Municipality, Tanzania

Background Bacteriuria is the detection of significant bacteria in urine in the presence or absence of signs and symptoms of urinary tract infection (UTI). Bacteriuria in pregnant and lactating HIV-infected women can cause serious complications to women and fetuses for pregnant women. Due to the importance of bacteriuria, we determined the etiology, antimicrobial susceptibility patterns, and factors associated with bacteriuria in HIV-infected women. Methods We conducted a cross-sectional study from January to April 2022 among HIV-infected women attending the Prevention of Mother-to-Child Transmission (PMTCT) clinic at Bukoba Municipality, Tanzania. Clean-catch midstream urine specimens were collected for culture on MacConkey and blood agars. We used colonial characteristics, Gram staining reactions, and biochemical tests to identify bacteria isolates. Data were collected using a structured questionnaire. We used STATA version 15.0 for analysis. An association with bacteriuria was performed using modified poisson regressions. A p-value ≤ 0.05 was regarded as statistically significant. Results Of the 290 participants, 66 (22.8%) had significant bacteriuria. The predominant bacteria isolates were Escherichia coli 21 (31.8%). Among gram-negative bacteria, 17 (34.0%) were extended-spectrum beta-lactamase producers, and 1 (25.0%) of Staphylococcus aureus were Methicillin-resistant. Escherichia coli showed a high rate of resistance against trimethoprim-sulfamethoxazole 21 (100%), and amoxicillin clavulanic acid 20 (95.0%). Staphylococcus aureus was highly resistant to penicillin 4 (100%) and trimethoprim-sulfamethoxazole 4 (100%). The proportion of multi-drug resistant (MDR) strains was 45 (68.2%). Conclusions The prevalence of bacteriuria in HIV-infected women was relatively high. The pathogens were most resistant to trimethoprim-sulfamethoxazole, penicillin and amoxicillin clavulanic acid and more than two-third were MDR. The findings emphasize that the use of antimicrobial agents should be supported by culture and Antimicrobial Susceptibility Testing (AST) results.

The association positive and negative empathy have with depressive symptoms, resilience, and posttraumatic growth

Abstract Empathy, the ability to understand and respond to others’ emotional experiences, is often regarded as a universally positive trait. However, its role in psychological adjustment following adversity is more complex. The current study examined the relationships between empathy – measured globally and through its positive (compassionate concern) and negative (callousness) dimensions – and three outcomes of stress or trauma: depressive symptoms, resilience, and posttraumatic growth (PTG). College students ( N  = 403) completed online surveys assessing these variables, with controls for age, sex, and personality traits. Hierarchical regression analyses showed that global and positive empathy were positively associated with PTG, indicating empathy’s role in fostering personal and relational growth. However, positive empathy also predicted depressive symptoms, reflecting its potential to heighten vulnerability to emotional distress. Negative empathy was inversely related to PTG but unrelated to depressive symptoms or resilience. Resilience demonstrated weaker links with empathy, instead aligning more closely with personality traits like extraversion and conscientiousness. These findings highlight empathy’s dual impact, where it can contribute to personal growth while also increasing susceptibility to distress. Future research should explore empathy’s cognitive and affective components and develop strategies to minimize its negative effects while enhancing adaptive outcomes like PTG.

Complexions at the iron-magnetite interface

Abstract Synthesizing distinct phases and controlling crystalline defects are key concepts in materials design. These approaches are often decoupled, with the former grounded in equilibrium thermodynamics and the latter in nonequilibrium kinetics. By unifying them through defect phase diagrams, we can apply phase equilibrium models to thermodynamically evaluate defects—including dislocations, grain boundaries, and phase boundaries—establishing a theoretical framework linking material imperfections to properties. Using scanning transmission electron microscopy (STEM) with differential phase contrast (DPC) imaging, we achieve the simultaneous imaging of heavy Fe and light O atoms, precisely mapping the atomic structure and chemical composition at the iron-magnetite (Fe/Fe3O4) interface. We identify a well-ordered two-layer interface-stabilized phase state (referred to as complexion) at the Fe[001]/Fe3O4[001] interface. Using density-functional theory (DFT), we explain the observed complexion and map out various interface-stabilized phases as a function of the O chemical potential. The formation of complexions increases interface adhesion by 20% and alters charge transfer between adjacent materials, impacting transport properties. Our findings highlight the potential of tunable defect-stabilized phase states as a degree of freedom in materials design, enabling optimized corrosion protection, catalysis, and redox-driven phase transitions, with applications in materials sustainability, efficient energy conversion, and green steel production.

Correction: Diagnostic performance of two rapid tests for syphilis screening in people living with HIV in Cali, Colombia

Smart green spectrophotometric estimation and content uniformity testing of chlorphenoxamine HCl and caffeine in bulk forms and combined pharmaceutical formulation

Abstract Chlorphenoxamine hydrochloride is a chemical that has attracted interest because of its notable anti-histaminic and anti-cholinergic characteristics. Moreover, it has been recognized as a highly efficient tool in the fight against several lethal viral diseases, such as Severe Acute Respiratory Syndrome Coronavirus. In this study, five efficient and straightforward univariate spectrophotometric approaches are proposed for accurately measuring the quantities of Caffeine and Chlorphenoxamine HCl in bulk forms and combined pharmaceutical formulations. Notably, these methods based on an advanced approach using the factorized response spectrum and not require any initial processing.They have been classified into three spectrophotometric platform windows. The study of Window I focuses on absorption spectra of substances in their original states(zero-order). It includes, absorbance resolution method (AR), extended absorbance difference method (EAD), and factorized zero order method (FZM). Window II focuses on the factorized derivative method (FDM), while Window III focuses on factorized ratio difference method (FRM). These approaches successfully measured concentration of Caffeine and Chlorphenoxamine HCl within a range of 3–35.0 and 3–45.0 μg/mL, respectively. The factorized response spectrum’s exclusivity stems from its capacity to fully separate the mentioned components in mixture and recover the pure spectra. Validation of the suggested approaches has been conducted according to guidelines established by International Council for Harmonization, which demonstrated acceptable levels of accuracy and precision. The scope of this work has been expanded to include verification of content uniformity of dosage units according to recommendations outlined in United States Pharmacopoeia. Greenness profile of the proposed approaches has been properly assessed using state-of-the-art software metrics, in comparison to the reported one. Finally, the proposed methods demonstrated strong compliance with the recently established principles of white field of analytical chemistry.

Selective Photocatalytic Aerobic Oxidation of Methane to Methyl Hydroperoxide by ZnO-Loaded Single-Atomic Ruthenium Oxide Catalyst

Hypergraph reconstruction from dynamics

Abstract A plethora of methods have been developed in the past two decades to infer the underlying network structure of an interconnected system from its collective dynamics. However, methods capable of inferring nonpairwise interactions are only starting to appear. Here, we develop an inference algorithm based on sparse identification of nonlinear dynamics (SINDy) to reconstruct hypergraphs and simplicial complexes from time-series data. Our model-free method does not require information about node dynamics or coupling functions, making it applicable to complex systems that do not have a reliable mathematical description. We first benchmark the new method on synthetic data generated from Kuramoto and Lorenz dynamics. We then use it to infer the effective connectivity in the brain from resting-state EEG data, which reveals significant contributions from non-pairwise interactions in shaping the macroscopic brain dynamics.

Ageing and digital shopping: Measurement and validation of an innovative framework

Senior citizens are the fastest growing demographic in the world. Amid an intensification of digitalisation across every sector, evidence suggests older people are slow to adopt and use many online tools and services. Moreover, despite studies showing differences in the online behaviour of older people compared to the rest of the population, established models specifically dedicated to explaining their behaviour have remained limited. Therefore, based on components of UTAUT, we propose a new conceptual model that specifically focuses on senior citizens. We introduce four new constructs: health needs, place of settlement (rural/urban), perceived trust, and perceived risk. Data were collected from 320 seniors in Russia and a structural equation modelling was used for data analysis. With a cumulative variance of 86%, the test and validation results demonstrate that our proposed model provides a better explanation of older people’s online shopping behaviour than the original UTAUT model. This model provides an important framework for future studies on the digital shopping behaviours of seniors.

Learning features for offline handwritten signature verification using spatial transformer network

Staphylococcus aureus ST764-SCCmecII high-risk clone in bloodstream infections revealed through national genomic surveillance integrating clinical data

Social media as a workplace panopticon: The development and validation of social media monitoring by workplace contacts scale

The monitoring of employees’ private social network accounts by employers and colleagues has become increasingly prevalent, yet research in this area remains limited. To address this gap, the present study developed and validated a scale to measure social media monitoring by workplace contacts (SMMWC). The scale, comprising fifteen items, was developed using Hinkin’s (1998) approach to scale development and has four dimensions based on the concept of panoptic effect by Foucault (1977) and Botan (1996). While Study 1, based on 334 employees, focused on scale development, Study 2, based on 302 employees, replicated the factor structure of the SMMWC scale and examined its impact on outcomes, using a time-lagged design. The SMMWC scale demonstrated strong psychometric properties, including factorial validity; discriminant validity with electronic performance monitoring and user perceptions of social media monitoring; and criterion-related validity with online disclosure, social capital, emotional exhaustion, and self-concept clarity. Notably, SMMWC was positively associated with online disclosure in both the studies and was significantly related to emotional exhaustion and self-concept clarity in Study 2, suggesting that SMMWC can influence employees’ online behavior and psychological well-being.