Browse Articles

The association between care modality and hospitalizations and emergency department visits for ambulatory care-sensitive conditions during and after the pandemic in Ontario, Canada

The COVID-19 pandemic required a rapid transition to virtual care as a key strategy to maintain healthcare access while minimizing virus transmission risks. However, the impact of this shift on hospitalizations and emergency department (ED) visits for ambulatory care-sensitive conditions (ACSCs) remains unclear. This study aims to assess the relationship between the modality of outpatient care for ACSCs and their outcomes in Ontario, Canada. In this population-based retrospective cohort study, we analyzed hospitalization and ED visit data for ACSCs, including diabetes, epilepsy, congestive heart failure, hypertension, and angina, during the pandemic (April 2020 to April 2023) and post-pandemic (May 2023 to August 2023) periods. Monthly trends in hospitalizations and ED visits were evaluated using Generalized Additive Models and Generalized Additive Mixed Models, accounting for the effects of virtual and in-person care within 30 days and 60 days preceding each event. Despite a notable decrease in virtual visits and a corresponding rise in in-person visits, overall hospitalizations and ED visits for ACSCs remained relatively stable. Our analysis found no significant association between care modality and changes in hospitalizations and ED visits, suggesting that virtual care, particularly during the early pandemic, effectively supported chronic disease management and contributed to the stability of acute care needs. In conclusion, virtual care proved to be a sustainable component of ACSC management during and after the COVID-19 pandemic, complementing in-person care.

Association between estimated pulse wave velocity and mortality risk in patients with acute ischemic stroke

Development and optimization of an injectable in-situ gel system for sustained release of anti-tuberculosis drugs

Short-term effects of pet acquisition and loss on well-being in an unbiased sample during the COVID-19 pandemic

Abstract Research on the impact of pet ownership on well-being has produced mixed results, often influenced by a focus on pet enthusiasts. To address this bias, we conducted a longitudinal study in Hungary using a stratified random sample based on gender, age, education, and settlement type. Unlike previous studies, our participants were not particularly devoted to pets and were not necessarily the primary caretakers, offering a more balanced perspective on how pet acquisition affects well-being. Among the 2783 respondents who participated three times in data collection, 65 acquired a pet, and 75 lost a pet during the COVID-19 pandemic. Pet acquisition initially increased cheerfulness, but this effect was short-lived, lasting only 1–4 months. Over a longer period (up to 6 months), pet acquisition—particularly dog acquisition—was linked to declines in calmness, activity, cheerfulness, and life satisfaction. Importantly, neither mental nor physical well-being was linked to future pet acquisition, and losing a pet had no significant effect on well-being. These findings challenge the widely held belief that pet acquisition leads to lasting improvements in well-being, suggesting instead that the demands of pet care—especially for dogs—can outweigh initial benefits. Moreover, the results underscore the context-dependent nature of the human-animal bond. The stress and uncertainty of a global crisis may alter the typical emotional and psychological benefits of pet ownership.

The benefits of chronic sport participation and acute exercise on mental health and executive functioning in adolescents

Development and validation of a predictive model for continuous renal replacement therapy in sepsis patients using the MIMIC-IV database

Abstract To develop and validate a dynamic nomogram for predicting the need for continuous renal replacement therapy (CRRT) in septic patients in the intensive care unit (ICU). Data were extracted from the MIMIC-IV 3.0 database and divided into a training set and a validation set in a 7:3 ratio. Relevant risk factors were identified through LASSO regression, and a binary logistic regression model was subsequently developed. The CRRT risk nomogram was visualized using R language, with the DynNom package employed to create a dynamic nomogram. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), Harrell’s C-index, and calibration curves. The clinical utility of the model was evaluated via decision curve analysis (DCA). A total of 7361 septic patients were included in this study, of which 525 required CRRT. The study identified several predictive factors for CRRT, including respiratory rate, oxygen saturation, international normalized ratio (INR), activated partial thromboplastin time (APTT), creatinine, lactate, pH, body weight, renal disease, and severe liver disease. The C-index was 0.871. The AUCs for the training and validation sets were 0.87 (95% CI: 0.8535–0.8883) and 0.86 (95% CI: 0.8282–0.8887), respectively. The calibration curves demonstrated good predictive consistency. DCA confirmed the model’s significant clinical value. The dynamic nomogram is available for visualization at: https://zhong-hua-min-zu-wan-sui.shinyapps.io/CRRT_prediction_nomogram/. We have developed a dynamic nomogram based on the MIMIC-IV database, incorporating 10 clinical features, to predict the probability of CRRT requirement in septic patients. Internal validation showed that this model exhibits robust predictive performance.

Shared gene signatures and molecular mechanisms link ankylosing spondylitis and rheumatoid arthritis

SARS-CoV-2 bioaerosol transmission in experimentally infected American mink

Abstract The SARS-CoV-2 BA.1 (Omicron) variant, which emerged in late 2021, is more transmissible than earlier variants but causes milder symptoms in humans. Mink farms, where animals are housed in close quarters, present a high risk for virus transmission and mutation, necessitating strict control measures due to documented cases of mink-to-human and human-to-mink transmission. Hence, we aimed to detect infectious airborne SARS-CoV-2 using BioSampler-air collectors and to investigate aerosol transmission between groups of American mink ( Neovison vison ). Two groups (male and female) were infected with the BA.1 variant, and samples were collected from aerosols, saliva, feces, and surfaces. The results indicated that infectious viruses were predominantly detected in aerosol samples over a three-day period in both groups. Surface, saliva, and fecal samples also showed potential for virus transmission. Notably, infectious viruses were cultivated from aerosol samples, confirming aerosol transmission among American mink. This study highlights the importance of immediate sample culturing to improve infectious virus detection and emphasizes the need for enhanced preventive measures on mink farms to mitigate the spread of viruses.

Brain dynamics of the interplay between auditory selective attention and working memory during melody encoding

Triggering ferroptosis in drug-tolerant cancer cells

CpgD is a phosphoglycerate cytidylyltransferase required for ceramide diphosphoglycerate synthesis

Microsporidia infection alters C. elegans lipid levels

Microsporidia are fungal-related obligate intracellular parasites that infect many types of animals. Microsporidia have exceptionally reduced genomes resulting in limited metabolic capabilities and are thought to be reliant on host metabolism to fuel their own growth. Here, we investigate the impact of microsporidia infection on host lipid metabolism using the nematode Caenorhabditis elegans along with its natural microsporidian pathogen Nematocida parisii . We show that infection causes an increase in the level of C. elegans lipid droplet associated lipase, ATGL-1, and a decrease in host fat levels. A mutation that decreases ATGL-1 activity and overexpression of ATGL-1 did not significantly change N. parisii infection levels. Using lipidomics we show that N. parisii infection decreases C. elegans triglyceride levels and results in increased ceramides that we speculate are synthesized by N. parisii . Mutations in host genes involved in ceramide synthesis did not significantly change the levels of N. parisii infection. Together these results show that microsporidia can cause changes to lipid metabolism of their hosts, but some individual mutations of C. elegans lipid enzymes do not alter microsporidian growth.

Machine learning based on a generative adversarial tri-model

Heterogeneity analysis of the effects of new quality productive forces on ecological resilience in the Yangtze River Delta Economic Belt

Tracking 35 years of progress in metallic materials for extreme environments via text mining

Effect of bonding characteristics of major constituents of mineral filler-based glass fiber reinforced with epoxy composites

Abstract In the present study, attempt has been made in understanding the bonding behaviour of mineral filler when it is introduced with epoxy matrix structured with e-glass fibre at molecular level. Firstly, filler content in GFRP composite was analysed through Fourier Transform infrared spectroscopy (FTIR). Here, Silicon dioxide has been chosen as a representative for E-glass fibre as Silicon dioxide holds major part in the composition of an E-glass fibre. DFT simulation techniques has been employed to study the reaction in between them. In order to increase the binding capability, wollastonite has been introduced into the system and many possible configurations were modelled for study. Out of all the models, the model with the highest dipole moment and stability has been considered. Spectral studies such as NMR, VCD and IR studies has been done to witness the oxygen atoms in the glass fibre acted as the connecting bridge in between the silicon atoms of the glass fibre and the carbon atoms of the epoxy resin. But these alone were not enough to obtain a stable structure that was described above. The calcium atoms in the wollastonite acted as better electron bridges and support for the complex. This work majorly focusses on the interactions between epoxy resin(ly556) and SiO2 molecule and the filler material wollastonite (CaSiO3).

Harnessing molluscan shell waste for sustainable tribology by integrating biogenic fillers in eco-friendly brake pad development

Practice patterns of vascular neurologists in timing anticoagulation for high risk stroke mechanisms versus atrial fibrillation

RIME optimization with dynamic multi-dimensional random mechanism and Nelder–Mead simplex for photovoltaic parameter estimation

Abstract Solar photovoltaic technology is efficient and clean, but extracting photovoltaic cell parameters is challenging due to various influencing factors. The rime optimization algorithm (RIME) is a recently proposed metaheuristic algorithm (MAs). This paper introduces the dynamic multi-dimensional random mechanism (DMRM) combined with the Nelder–Mead simplex (NMs) to propose an enhanced version of RIME, called DNMRIME. DMRM improves the convergence accuracy of RIME by random non-periodic convergence, and NMs accelerate convergence, enabling DNMRIME to escape local optima and perform better on hybrid and composite functions. To evaluate the performance of DNMRIME, a qualitative analysis and an ablation study were conducted on CEC 2017. To verify its effectiveness, DNMRIME was compared with 14 well-known MAs, including some champion algorithms, and the results of the Wilcoxon signed rank test showed that DNMRIME ranked first. To extract parameters on SDM, DDM, TDM, and PV, DNMRIME was applied, resulting in mean RMSE values of 9.8602188324E − 04, 9.8296993325E − 04, 9.8393451046E − 04, and 2.4250748704E − 03 respectively. Moreover, under varying temperature and irradiation conditions on three manufacturers (KC200GT, ST40, SM55), DNMRIME extracted parameters with simulation data matching the actual data. Therefore, unlike previous studies, this study proposes DMRM and DNMRIME, demonstrating the efficiency and practicality of DNMRIME and further highlighting potential value of DNMRIME in photovoltaic parameter extraction. The source code of DNMRIME is available at https://github.com/zyetpink/DNMRIME-Solar-Model-dataset.

A highly generalized federated learning algorithm for brain tumor segmentation