Browse Articles

Mixed provenance of organic carbon in Northeast Atlantic temperate intertidal seagrass sediments

Optimization of separation and purification processes in diethyl ether production for improved efficiency and sustainability

Could bridging science and the arts be the key to better policies?

Effectiveness of a nurse-led pain management model following TACE: a retrospective analysis

An effective approach to cartilage regeneration using antler stem cell-conditioned medium

Africa needs to invest in science communication — here’s how

A new Drosophila model of prolonged inactivity shortens lifespan and impairs muscle function

Abstract Prolonged inactivity due to medical conditions can cause chronic muscle disuse and lead to physical incapacity and poor quality of life. Here, we developed a Drosophila model of confinement inactivity (CI) to observe its effects on lifespan and muscle function. We found that, similar to mammalian models and humans, CI negatively impacted longevity and function in Drosophila. Confined flies had impaired mobility, shorter lifespan, and reduced muscle integrity compared to their freely mobile siblings. These findings establish a new, highly efficient platform for studying long term effects of chronic sedentary behavior and muscle disuse in the genetically tractable Drosophila model. In addition, we found that temporarily removing flies from CI for scheduled bouts of forced physical exercise ameliorated negative effects, in part by improving muscle homeostasis. Finally, we tested whether muscle overexpression of 3 exercise-responsive genes, dPGC-1α , dFNDC5 , or dSesn , could prevent the negative impact of CI on fly aging, even without physical exercise. We previously established that overexpression of these factors phenocopies exercise effects in aging wild-type and disease model flies. We found that when overexpressed in muscle, dSesn prevented premature declines in endurance, and dFNDC5 protected speed and endurance. This new model can be used in the future for mechanistic studies to identify preventative and therapeutic targets for diseases associated with chronic inactivity.

A numerical study of the relevance of the electrode-tissue contact area in the application of soft coagulation

Abstract Monopolar electrocoagulation is a well-established surgical technique to achieve hemostasis by selectively destroying biological tissue through the application of high-frequency alternating current. However, this technique is associated with unwanted tissue damage. In this context, computational simulation is a valuable tool that can improve our understanding of such complex processes and highlight important application parameters in the direction of an improved control function to achieve safer and more reliable results. Despite its critical role in surgical applications, the influence of the electrode-tissue contact area has received little to no attention in previous simulation studies. To address this gap, the present study investigates the sensitivity of temperature distribution and necrotic volume formation to variations in electrode-tissue contact area. For this purpose, a multiphysics finite element model was developed to simulate HF current induced soft coagulation using a ball electrode under varying contact areas. Our findings demonstrate that variations in the contact area significantly impact temperature development and, consequently, necrosis formation. These results highlight the crucial role of the contact area in the electrocoagulation process and its associated necrosis formation. Furthermore, it was observed that when the boiling point of water is reached inside the tissue, complete necrosis has not yet formed at the contact site, which could lead to further undesired effects. Consequently, it is essential to consider the contact area in computational simulations and the development of novel control features for safer and more reliable electrocoagulation.

Risk assessment of corn borer based on feature optimization and weighted spatial clustering: a case study in Shandong Province, China

Parkinson disease detection based on in-air dynamics feature extraction and selection using machine learning

Explainable machine learning for predicting ICU mortality in myocardial infarction patients using pseudo-dynamic data

Abstract Myocardial infarction (MI) remains one of the greatest contributors to mortality, and patients admitted to the intensive care unit (ICU) with myocardial infarction are at higher risk of death. In this study, we use two retrospective cohorts extracted from two US-based ICU databases, eICU and MIMIC-IV, to develop an explainable pseudo-dynamic machine learning framework for mortality prediction in the ICU. The method provides accurate prediction for ICU patients up to 24 hours before the event and provides time-resolved interpretability. We compare standard supervised machine learning algorithms with novel tabular deep learning approaches and find that an integrated XGBoost model in our EHR time-series extraction framework (XMI-ICU) performs best. The framework was evaluated on a held-out test set from eICU and externally validated on the MIMIC-IV cohort using the most important features identified by time-resolved Shapley values. XMI-ICU achieved AUROCs of 92.0 (balanced accuracy of 82.3) for a 6-hour prediction of mortality. We demonstrate that XMI-ICU maintains reliable predictive performance across different prediction horizons (6, 12, 18, and 24 hours) during ICU stay while also achieving successful external validation in a separate patient cohort from MIMIC-IV without any previous training on that dataset. We also evaluated the framework for clinical risk analysis by comparing it to the standard APACHE IV system in active use. We show that our framework successfully leverages time-series physiological measurements from ICU health records by translating them into stacked static prediction problems for mortality in heart attack patients and can offer clinical insight from time-resolved interpretability through the use of Shapley values.

How to detect consciousness in people, animals and maybe even AI

Incidence of intraventricular haemorrhage, associated risk factors and short-term outcomes among preterm neonates in a tertiary referral hospital in Kenya

Background Intraventricular hemorrhage (IVH) stands as the predominant cause of brain injury with the incidence increasing with the decrease in birth weight and gestational age. In developed countries, a reduction in IVH incidence by implementing small baby neuroprotective protocols has been reported. The incidence of IVH within our setting is largely unexplored. This study aimed to fill this gap by determining the incidence and associated risk factors of IVH to guide in implementing a small baby neuroprotective protocol that could potentially reduce the incidence of IVH. Method A retrospective cohort study from January 2020 to December 2023 included all Preterm babies with gestation age < 32 weeks or birthweights of ≤1500g admitted to the neonatal intensive care unit (NICU) at The Aga Khan University Hospital, Nairobi. The primary outcome was the occurrence of IVH, while secondary outcomes were associated risk factors and short-term outcomes of IVH. Results A total of 526 babies were admitted to the NICU during the study period. Of these, 135 preterm infants were recruited, and 45 (33.3%) developed IVH, predominantly grade 1 IVH, which occurred predominantly between days 0–28 of life. Logistic regression analysis identified that exposure to antenatal steroids exhibited significantly lower odds of IVH occurrence (AOR 0.075, 95% CI 0.007–0.757). Resuscitation in the NICU had a 3 times higher risk of developing IVH (AOR 2.773, 95% CI 0.867–8.874). Treatment with normal saline bolus and inotropes had 4 times higher odds of IVH occurrence (OR 3.5, 95% CI: 1.043–11.885). A higher mortality rate was observed in preterms with IVH (26.6% vs 13.3%). Post-hemorrhagic ventricular dilation (22% vs 6.7%) and periventricular leukomalacia (20% vs 6.7%) were significantly higher among preterms with IVH. Conclusion The findings elicited from this study lay a foundation for the implementation of neuroprotective protocols which may potentially reduce the magnitude of IVH in this highly vulnerable age group.

Smartphone dependence, addiction, and insomnia among medical students during the COVID-19 pandemic

Introduction During the COVID-19 pandemic, smartphone use among students increased, even before going to sleep, which resulted in an increased risk of dependence, addiction, and sleep problems such as insomnia and daytime somnolence. The objective of this study was to determine the association between different levels of problematic smartphone use (dependence and addiction) and insomnia among medical students. In this study, “dependence” refers to subclinical, yet compulsive, smartphone use, while “addiction” denotes a more severe behavioral disruption involving loss of control and functional impairment. Materials and methods This was a secondary cross-sectional study of a cohort of medical students from Piura (Peru) during the COVID-19 pandemic in 2020. The study included 370 participants. Insomnia was assessed using the Insomnia Severity Index, and the extent of smartphone use was determined using the Smartphone Dependence and Addiction Scale. A chi-squared test was used for the bivariate analysis, and prevalence ratios (PR) were estimated using Poisson regression with crude and adjusted models and 95% confidence intervals (CI). Results Insomnia was reported by 67.8% of participants. Smartphone dependence was identified in 67.8% of students, and 10.8% met the criteria for addiction. The prevalence of insomnia was notably higher among students aged ≥26 years (80.0%) and among those with symptoms of depression (79.3%) and anxiety (81.9%). After adjusting for potential confounders, students classified as dependent were 1.43 times more likely to report insomnia (aPR: 1.43; 95% CI: 1.10–1.86), while those classified as addicted showed an even higher association (aPR: 1.66; 95% CI: 1.26–2.17), compared to nondependent users. Conclusion Both smartphone dependence and addiction were significantly associated with a higher prevalence of insomnia among medical students. These findings highlight the need for preventive strategies targeting problematic smartphone use to promote better sleep health in academic settings.

A bearing fault diagnosis method based on hybrid artificial intelligence models

The working state of rolling bearing severely affects the performance of industrial equipment. Addressing the issue of that the difficulty of incipient weak signals feature extraction influences the rolling bearing diagnosis accuracy, an efficient bearing fault diagnostic technique, a proposition is forwarded for hybrid artificial intelligence models, which integrates Improved Harris Hawks Optimization (IHHO) into the optimization of Deep Belief Networks and Extreme Learning Machines (DBN-ELM). The process employs Maximum Second-order Cyclostationary Blind Deconvolution (CYCBD) to filter out noise from the vibration signals emitted by bearings; secondly, considering the issue with the conventional Harris Hawks Optimization (HHO) algorithm which tends to prematurely converge to local optima, the differential evolution mutation operator is introduced and the escape energy factor is improved from linear to nonlinear in IHHO; then, a double-layer network model based on DBN-ELM is proposed, to avoid the number of hidden layer nodes of DBN from human experience interference, and IHHO is used to optimize DBN structure, which is denoted as IHHO-DBN-ELM method; with the optimal structure is obtained by using a combined IHHO optimized DBN and ELM; in conclusion, the proposed IHHO-DBN-ELM approach is applied to the bearing fault detection using the Western Reserve University’s bearing fault dataset. The outcome of the experiments demonstrates that IHHO-DBN-ELM technique successfully extracts fault characteristics from the raw time-domain signals, thereby offering enhanced diagnostic accuracy and superior generalization capabilities.

Post-caesarean section infection burden, antimicrobial resistance pattern, and associated factors at all Africa Leprosy Rehabilitation and Training Center Comprehensive Specialized Hospital, Addis Ababa, Ethiopia

Background Surgical site infections are a major concern in maternal healthcare, especially after cesarean sections (CSs), which are among the most common surgical procedures globally. The World Health Organization reports a pooled prevalence of SSIs in low- and middle-income countries at 5.6 per 100 surgical patients. The global incidence of SSIs after CS is approximately 5.63%, with significant variations: 0.15% in China, 8.02% in India, and 12.6% in Nepal. In sub-Saharan Africa, the incidence is 7.3%, while Ethiopia reports rates as high as 10.4%. Risk factors include older age, prolonged hospitalization, and emergency surgeries, with common pathogens being S. aureus and E. coli. Surgical site infections complicate recovery and contribute to maternal morbidity and mortality, particularly in resource-limited settings like Ethiopia, leading to increased hospitalization and healthcare costs. Objective This study aimed to assess the burden of post-cesarean section bacterial infections, antimicrobial resistance pattern, and associated factors among mothers attending postnatal care services. Methods A cross-sectional study was conducted among 226 participants at All ALERT Comprehensive Specialized Hospital from December 1, 2020, to May 30, 2021. Wound swabs were collected, processed for the isolation of bacterial pathogens, and bacterial susceptibility tests were conducted on the isolates following standard procedures. A structured questionnaire was used to identify potential risk factors. Data were entered and analyzed using SPSS software version 20. Statistical significance of associated factors was determined using odds ratios (95% CI). Bivariate and multivariable logistic regression was performed. Results Among the 226 post-cesarean section patients included in the study, 134/226 (59.29%) showed bacterial growth. The predominant bacterial isolates were coagulase -negative Staphylococcus (CONS) 62/134 (46.27%), S. aureus 46/134 (34.33%), and, from Gram-negative bacteria, E. coli 8/134 (5.97%) and Klebsiella species 8/134 (5.97%). The most resistant antibiotics were penicillin, tetracycline, ampicillin, and cefazolin, while the most effective antibiotics included ciprofloxacin, gentamicin, linezolid, and meropenem. Multidrug-resistant bacteria were also identified. Obstructed labor (cephalopelvic disproportion) and maternal education level beyond college were significantly associated with post-cesarean surgical site infections. Conclusion The prevalence of wound infections following CSs at ALERT Comprehensive Specialized Hospital was found to be high. Infections caused by antibiotic-resistant bacteria contribute to increased maternal morbidity, mortality, and healthcare costs. These findings emphasize the urgent need for strict adherence to infection prevention strategies during the preoperative, intraoperative, and postoperative phases. Additionally, ensuring appropriate and effective treatment is crucial to reducing the risk of surgical site infections.

Exploring the potential of synthetic and biological fungicides for managing the fungus-farming ambrosia beetle Xylosandrus compactus

Little is known about effective control strategies targeting the invasive ambrosia beetle Xylosandrus compactus. This fungus-farming beetle is highly dependent on its primary nutritional fungal mutualist Ambrosiella xylebori. Traditionally, insect pest control programs target the pest directly. Here, we tested the potential of synthetic and microbial based fungicides to suppress the fungal mutualist, consequently hampering the beetle development. Thiophanate-methyl application to bay laurel (Laurus nobilis L.) stem sections proved to be effective in reducing the mutualist fungus occurrence in infested galleries, as well as to reduce the mean X. compactus brood size. Thiophanate-methyl and azoxystrobin significantly reduced the mean beetle brood size in extended laboratory conditions. Similarly, these two fungicides were the most effective in reducing the fungal lesion length, both when tested by soil or spray applications. Overall, thiophanate-methyl showed the highest reduction of the X. compactus brood size by spray application. No or low impact on X. compactus infestations was observed when testing the triazole mefentrifuconazole. Among tested microbial based fungicides, Trichoderma asperellum T34 was the only one causing a reduction of the fungal lesion length. To the best of our knowledge, this study provides, for the first time, baseline data on the potential of fungicides for disrupting the mutualistic interaction between X. compactus and its primary mutualist A. xylebori. These findings will help in developing novel and effective integrated pest management approaches based on the mycobiome alteration and targeting X. compactus in its invaded range.

Determine the accuracy of CBCT reconstructed panoramic images in periodontal assessment

Objectives Periodontal probing and intraoral radiography are conventional methods for periodontal assessment. Evaluating periodontium using CBCT-reconstructed multiplanar or cross-sectional views is labor-intensive and time-consuming. The objective of this study is to evaluate the accuracy of CBCT-simulated panoramic projections for periodontal assessment and determine whether a more efficient evaluation method can be developed. Methods CBCTs taken at East Carolina University School of Dental Medicine from 2014–2023 were screened. The inclusion criteria were: (1) diagnostic-quality CBCT scans covering maxilla and/or mandible; (2) full-mouth surveys (FMXs) taken within 6 months of the CBCT scans; and (3) clinical attachment loss (CAL) noted in periodontal charting. The cementoenamel junction (CEJ) to alveolar crest distance on mesial and distal surfaces of maxillary and mandibular central incisors, canines, first premolars, and first molars were measured on bitewings/periapical radiographs (BW/PA) and CBCT-simulated panoramic images. CALs were extracted from periodontal chartings. One-way ANOVA followed by Tukey’s HSD post hoc test, Pearson correlation, and Bland Altman analysis were used to analyze the data. Results CBCT, BW/PA, and CAL demonstrated strong correlations, with BW/PA and CBCT producing comparable results in periodontal assessments for most sites. For mandibular central incisors and canines, CBCT and PA showed significantly more bone loss compared to CAL values. For the mandibular molars, CBCT revealed significantly more bone loss compared to BW. Conclusions CBCT-reconstructed panoramic imaging has proven to be a reliable tool for periodontal assessment. Some discrepancies among the evaluation methods are likely due to periodontal configurations or conditions, operator errors, and the projection geometry of intraoral radiographs.

Effects of repetitive head impacts from a single season on the cognitive functioning of youth male soccer players

Repetitive head impacts have long-lasting negative effects on the cognition of athletes. For example, repetitive head impacts accumulated by adult professional soccer players throughout their careers result in long-term negative consequences on cognition. However, these effects on youth soccer players have not been extensively studied and need to be further evaluated. The purposes of this study were to quantify head impact exposure in youth elite soccer and to examine the effects of repetitive head impacts on the cognitive function of youth male soccer players. A prospective cohort study of a single boys U13 soccer team of 18 players (12.9 ± 0.2 years of age) was completed throughout a single soccer season (five months in duration). Head impact frequency data were recorded using impact monitoring mouthguards worn during practices and matches and subsequently video-verified. Cognitive function was assessed using cognitive-motor integration (CMI) tasks conducted before the first season match, every subsequent four weeks, and after the last match. Peak path velocity, absolute error, reaction time, full path movement time, and the number of direction reversals were CMI task outcome measures. Over a single soccer season, 1089 head impacts occurred with more head impacts occurring in practices (62.7%) than matches (37.3%). Midfielders experienced the most head impacts during practices (53.6% of practice impacts), and defenders experienced the most head impacts during matches (47.8% of match impacts). Repetitive head impact exposure by position was associated with significant decreases in absolute error (p < 0.001), increases in peak velocity (p < 0.001), and increases in reaction time (p < 0.001). Repetitive head impacts within a single soccer season were associated with measurable declines in some aspects of youth soccer players’ cognitive function. Therefore, coaches should aim to reduce the number of head impacts experienced by male youth soccer players with a specific focus on reducing head impact exposure during practices.

Transcriptome combined single-cell sequencing explores molecular mechanisms of ANGPTL4 in sepsis-induced acute lung injury

Objective Sepsis-induced acute lung injury (ALI) constitutes a critical clinical syndrome associated with high mortality rates, yet its molecular mechanisms remain inadequately elucidated. Recent evidence indicates that ANGPTL4 may influence inflammatory responses and endothelial barrier integrity; however, its cell-specific regulatory mechanisms in sepsis-associated ALI are not well understood. This study utilizes transcriptome profiling combined with single-cell sequencing to systematically analyze the spatiotemporal expression patterns and functional networks of ANGPTL4 during the progression of ALI. Methods Gene expression profiles from acute lung injury patients were obtained from the Gene Expression Omnibus (GEO) database. Single-cell and intercellular communication analyses identified candidate gene sets. GSEA examined gene-immune cell relationships, while gene enrichment analysis explored key gene mechanisms. miRNA networks identified target miRNAs for these key genes. Molecular docking with AutoDock and the CTD database predicted drugs interacting with ANGPTL4. Additionally, in vitro experiments confirmed the Angptl4 gene expression level in sepsis-induced acute lung injury. Results Angptl4 is a crucial marker for acute lung injury progression, potentially affecting pathways like the pentose phosphate pathway, fatty acid degradation, and PPAR signaling. It may interact with Q9BY76-Quercetin, but this requires further investigation. In vitro studies show a notable increase in Angptl4 expression compared to controls. Conclusion The increased expression of ANGPTL4 may influence disease progression through mechanisms involving fatty acid metabolism, PPAR signaling, and the pentose phosphate pathway in murine models. Furthermore, its dual role in regulating inflammation through interactions with both pro-inflammatory and anti-inflammatory cells underscores its pivotal contribution to the pathogenesis of acute lung injury (ALI), thereby supporting the development of targeted therapies for sepsis-induced lung injury.