Browse Articles

Investigating the key principles in two-step heterogeneous transfer learning for early laryngeal cancer identification

Experimental and analytical study on axial behaviour of square corrugated concrete filled single and double skin tube stub columns

Abstract Concrete-filled double-skin steel tubular (CFDST) columns have become widely utilized in building construction and bridges, thanks to their exceptional structural capabilities. Therefore, this study investigates the axial compressive behavior of square CFDST columns. The study aims to explore the influence of external and internal plate shapes (flat or corrugated plates) and different widths of internal steel tubes on the axial compressive behavior. The effects of varying internal widths of the internal steel tube (60 mm, 116 mm, and 160 mm) on the performance of CFDST columns were examined. Additionally, the study compared the performance of concrete-filled steel tubular (CFST) and CFDST columns with external flat or corrugated plates. The findings indicated that incorporating internal corrugated plates notably improved both the load-carrying capacity and ductility of the specimens. Notably, CFDST columns featuring corrugated internal plates (116 mm width) exhibited strength enhancements of 25.3% and 7.4% compared to internal widths of 160 mm and 60 mm, respectively. Furthermore, the study proposed two machine-learning models, namely Artificial Neural Network (ANN) and Gaussian Process Regression (GPR), to estimate the ultimate compressive strength of square CFDST columns. The findings indicated that the GPR model outperformed the ANN model in predicting the bearing capacity of square CFDST columns. Additionally, the Shapley Additive Explanation technique was employed for feature analysis. The outcomes of this analysis revealed that parameters such as section width and concrete strength positively influence the compressive strength index.

Versatile application of fast green FCF as a visible cholangiogram in adult mice to medium-sized mammals

Diversity and biogeography of the bacterial microbiome in glacier-fed streams

Urinary prostaglandin metabolites evaluation for activity monitoring in inflammatory bowel disease

Opposite outcomes of triglyceride-glucose index and associated cardiovascular mortality risk in type 2 diabetes mellitus participants by different obesity criteria

Predictive modelling and identification of critical variables of mortality risk in COVID-19 patients

Abstract South Africa was the most affected country in Africa by the coronavirus disease 2019 (COVID-19) pandemic, where over 4 million confirmed cases of COVID-19 and over 102,000 deaths have been recorded since 2019. Aside from clinical methods, artificial intelligence (AI)-based solutions such as machine learning (ML) models have been employed in treating COVID-19 cases. However, limited application of AI for COVID-19 in Africa has been reported in the literature. This study aimed to investigate the performance and interpretability of several ML algorithms, including deep multilayer perceptron (Deep MLP), support vector machine (SVM) and Extreme gradient boosting trees (XGBoost) for predicting COVID-19 mortality risk with an emphasis on the effect of cross-validation (CV) and principal component analysis (PCA) on the results. For this purpose, a dataset with 154 features from 490 COVID-19 patients admitted into the intensive care unit (ICU) of Tygerberg Hospital in Cape Town, South Africa, during the first wave of COVID-19 in 2020 was retrospectively analysed. Our results show that Deep MLP had the best overall performance (F1 = 0.92; area under the curve (AUC) = 0.94) when CV and the synthetic minority oversampling technique (SMOTE) were applied without PCA. By using the Shapley Additive exPlanations (SHAP) model to interpret the mortality risk predictions, we identified the Length of stay (LOS) in the hospital, LOS in the ICU, Time to ICU from admission, days discharged alive or death, D-dimer (blood clotting factor), and blood pH as the six most critical variables for mortality risk prediction. Also, Age at admission, Pf ratio (PaO2/FiO2 ratio), troponin T (TropT), ferritin, ventilation, C-reactive protein (CRP), and symptoms of acute respiratory distress syndrome (ARDS) were associated with the severity and fatality of COVID-19 cases. The study reveals how ML could assist medical practitioners in making informed decisions on handling critically ill COVID-19 patients with comorbidities. It also offers insight into the combined effect of CV, PCA, and SMOTE on the performance of ML models for COVID-19 mortality risk prediction, which has been little explored.

Publisher Correction: A generic self-learning emotional framework for machines

Striking a Balance — Advancing Physician Collective-Bargaining Rights and Patient Protections

EEG-based functional and effective connectivity patterns during emotional episodes using graph theoretical analysis

Learning the fitness dynamics of pathogens from phylogenies

Abstract The dynamics of the genetic diversity of pathogens, including the emergence of lineages with increased fitness, is a foundational concept of disease ecology with key public-health implications. However, the identification of such lineages and estimation of associated fitness remain challenging, and is rarely done outside densely sampled systems 1,2 . Here we present phylowave, a scalable approach that summarizes changes in population composition in phylogenetic trees, enabling the automatic detection of lineages based on shared fitness and evolutionary relationships. We use our approach on a broad set of viruses and bacteria (SARS-CoV-2, influenza A subtype H3N2, Bordetella pertussis and Mycobacterium tuberculosis ), which include both well-studied and understudied threats to human health. We show that phylowave recovers the main known circulating lineages for each pathogen and that it can detect specific amino acid changes linked to fitness changes. Furthermore, phylowave identifies previously undetected lineages with increased fitness, including three co-circulating B.   pertussis lineages. Inference using phylowave is robust to uneven and limited observations. This widely applicable approach provides an avenue to monitor evolution in real time to support public-health action and explore fundamental drivers of pathogen fitness.

Sex, Gender, and Sexuality in the <i>Journal</i>

Identifying risk factors and constructing a predictive model for heart failure combined with intracardiac thrombus in non-compaction cardiomyopathy patients

KRAS Oncoprotein Signaling in Cancer

The segmentation of nanoparticles with a novel approach of HRU2-Net†

Abstract Nanoparticles have great potential for the application in new energy and aerospace fields. The distribution of nanoparticle sizes is a critical determinant of material properties and serves as a significant parameter in defining the characteristics of zero-dimensional nanomaterials. In this study, we proposed HRU2-Net†, an enhancement of the U2-Net† model, featuring multi-level semantic information fusion. This approach exhibits strong competitiveness and refined segmentation capabilities for nanoparticle segmentation. It achieves a Mean intersection over union (MIoU) of 87.31%, with an accuracy rate exceeding 97.31%, leading to a significant improvement in segmentation effectiveness and precision. The results show that the deep learning-based method significantly enhances the efficacy of nanomaterial research, which holds substantial significance for the advancement of nanomaterial science.

Influenza Vaccination Strategies in Patients with Hematologic Cancer

Daily walking habits can mitigate age-related decline in static balance: a longitudinal study among aircraft assemblers

Abstract Improving physical balance among older workers is essential for preventing falls in workplace. We aimed to elucidate the age-related decline in one-leg standing time with eyes closed, an indicator of static balance, and mitigating influence of daily walking habits on this decline in Japan. This longitudinal study involved 249 manufacturing workers, including seven females, aged 20–66 years engaged in tasks performed at height in the aircraft and spacecraft machinery industry. The participants underwent a one-leg standing test and annual health checkups through the Kanagawa Health Service Association between 2017 and 2019. The outcome measure was one-leg standing time up to 30 s. The coefficient (β) of one-leg standing time against aging was estimated using two-level multilevel linear regression with random intercepts. We also estimated the β of daily walking habits at least one hour per day. The quadratic spline curve showed an almost linear trend of one-leg standing time with age. The one-leg standing time significantly decreased with age (adjusted β = − 0.22; 95% confidence interval [CI] − 0.31 to − 0.14). Meanwhile, walking habits showed a preventive effect (β = 1.76; 95% CI 0.49 to 3.04). Age-related decline in one-leg standing time may be mitigated by simple daily walking habits.

Inavolisib Therapy in Advanced Breast Cancer

Treatment of patients with tumor/treatment-related hypothalamic obesity in the first two years following surgical treatment or radiotherapy

Abstract Survivors of sellar/suprasellar tumors involving hypothalamic structures face a risk of impaired quality of life, including tumor- and/or treatment-related hypothalamic obesity (TTR-HO) defined as abnormal weight gain resulting in severe persistent obesity due to physical, tumor- and/or treatment related damage of the hypothalamus. We analyze German claims data to better understand treatment pathways for patients living TTR-HO during the two years following the index surgical treatment. A database algorithm identified patients with TTR-HO in a representative German payer claims database between 2010 and 2021 (n = 5.42 million patients). Claims from 37 patients with TTR-HO were analyzed on a quarterly basis over 2 years. The analysis considered inpatient, outpatient, and prescription data. In the follow-up period, patients with TTR-HO are hospitalized 3.68 times on average; 37% of hospitalizations in year 1 and 31% in year 2 are due to TTR-HO. On average, patients see a general practitioner 12.27 times and various specialists 20.45 times. The need for complex neuroendocrine therapy develops quickly, with most patients having 2–3 neuroendocrine prescriptions in any given quarter. The management of patients with TTR-HO requires frequent inpatient and outpatient visits for tumor follow-up and management of incident comorbidities, and most patients with TTR-HO require intense polytherapy.

Survival with Trastuzumab Emtansine in Residual HER2-Positive Breast Cancer