Browse Articles

Associations of the gut microbiome and inflammatory markers with mental health symptoms: a cross-sectional study on Danish adolescents

Abstract Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder that often persists into adulthood and is accompanied by comorbid mental health problems. This cross-sectional cohort study analyzed 411 18-year-olds from the Danish COPSAC2000 birth cohort to investigate the relationship between the gut microbiome, fasting and postprandial systemic inflammation, ADHD symptoms, and symptoms of anxiety, stress, and depression. ADHD was assessed using the Adult ADHD Self-Report Scale (ASRS), while depression, stress, and anxiety were evaluated with the Depression, Anxiety, and Stress Scale 21 (DASS-21). Fecal metagenomic data and inflammation levels, measured as glycosylated protein A (GlycA), were analyzed following a standardized meal challenge. In males, higher ADHD symptom scores correlated significantly with increased abundance of a tryptophan biosynthesis pathway (MetaCyc Metabolic Pathways Database) and elevated fasting and postprandial GlycA levels (p < 0.05). While the severity of depression, anxiety, and stress symptoms showed weak associations with GlycA and the gut microbiome, our findings indicate a significant link between ADHD symptoms and postprandial inflammation, warranting further investigation into underlying mechanisms.

Brain functional and structural alteration following acute carbon monoxide poisoning contribute to delayed neurological sequelae

A high-resolution field of view expansion method for single-pixel imaging systems based on DMD and scanning mirror

The nitrogen-vacancy defect in Si1-xGex

Abstract Defect processes and energetics in semiconducting alloys is scientifically and technologically important as silicon germanium (Si1 − xGex) is a mainstream nanoelectronic material. It is established that point defects and defect clusters have an increasing role in the physical properties of Si1 − xGex particularly with the ever-decreasing critical dimensions of nanoelectronic devices. Nitrogen-vacancy defects in Si1 − xGex are bound and have the potential to change the optical and electronic properties and thus need to be investigated as absolute control is required in nanoelectronic devices. The nitrogen-vacancy defects are not extensively studied in Si1 − xGex random semiconductor alloys. Here we employ density functional theory (DFT) in conjunction with the special quasirandom structures (SQS) method to calculate the binding energies of substitutional nitrogen-vacancy pairs (NV) in Si1 − x Ge x alloys. This is a non-trivial problem as the energetics of these defect pairs are dependent upon the nearest neighbour Ge concentration and the composition of Si1 − x Ge x . The criterion for NV stability is binding energy and here it is shown that the most bound NV defects will form in high Si-content Si1 − x Ge x alloys.

Author Correction: Predicting the risk category of thymoma with machine learning-based computed tomography radiomics signatures and their between-imaging phase differences

Multidimensional sleep impairment predicts steatotic liver disease spectrum risk

Abstract To evaluate the correlation between various sleep and the risk of NAFLD\MAFLD\MASLD. This study included 4772 subjects from the National Health and Nutrition Examination Survey data from 2017 to 2020. Poor sleep factors were defined as: ①abnormal sleep duration (< 7 h or > 8 h); ②snoring; ③sleep apnea; ④self-reported sleep disorder; ⑤ daytime sleepiness. The frequency of each sleep factor was scored, and the scores of all components were summed to obtain a sleep score ranging from 0 to 12. The higher the score, the less healthy the sleep pattern. Then we divided the overall sleep pattern into mild (sleep score 0–3 points), moderate (sleep score 4–7 points) or severe (sleep score 8–12 points) sleep pattern according to the distribution of sleep scores. Multiple logistic regression and restricted cubic spline graph analysis were used to determine the association between sleep and NAFLD\MAFLD\MASLD. In Model 1 and Model 2, sleep score as a continuous or categorical variable had an effect on NAFLD\MAFLD\MASLD(p <0.05). The risk of NAFLD\MAFLD\MASLD was higher in subjects with severe sleep patterns (p < 0.05). Snoring and sleepy during day was associated with NAFLD\MAFLD\MASLD (p < 0.05). We then drew a restricted cubic spline plot and found that sleep duration was nonlinearly associated with MAFLD\MASLD (p < 0.01), and the risk of MAFLD\MASLD was lower when the sleep duration was 7.5 ~ 9.5 h/d. In this nationally representative survey, severe sleep patterns were associated with an increased risk of NAFLD/MAFLD/MASLD. It is worth noting that sleep duration was nonlinearly associated with MAFLD and MASLD.

Inhibiting Overoxidation of Dynamically Evolved RuO₂ to Achieve a Win–Win in Activity–Stability for Acidic Water Electrolysis

Bremsstrahlung radiation from toroidal plasmas generated through hydrodynamic shear

Wearable-derived short sleep duration is associated with higher C-reactive protein in a placebo-controlled vaccine trial among young adults

Steel surface defect detection based on multi-layer fusion networks

PKD1 mutation perturbs morphogenesis in tubular epithelial organoids derived from human pluripotent stem cells

RORα fine-tunes the circadian control of hepatic triglyceride synthesis and gluconeogenesis

Reaction wetting and interfacial properties calculation of NiFe binary alloy droplets on Fe substrate

Author Correction: Predicting mortality after transcatheter aortic valve replacement using preprocedural CT

New lasso-shaped antibiotic kills drug-resistant bacteria

Combining laser biomimetic surface microtextured and PTFE solid lubricant for improved friction property of Ti6Al4V

Ecological evaluation of dominant roadside plants through APTI and API for sustainable green belt development in cosmopolitan city (Lahore) of Pakistan

A map of mitochondrial biology reveals the energy landscape of the human brain

Multi-omics integration identifies molecular markers and biological pathways for carcass and meat quality traits in Nellore cattle

Deep neural networks excel in COVID-19 disease severity prediction—a meta-regression analysis

Abstract COVID-19 is a disease in which early prognosis of severity is critical for desired patient outcomes and for the management of limited resources like intensive care unit beds and ventilation equipment. Many prognostic statistical tools have been developed for the prediction of disease severity, but it is still unclear which ones should be used in practice. We aim to guide clinicians in choosing the best available tools to make optimal decisions and assess their role in resource management and assess what can be learned from the COVID-19 scenario for development of prediction models in similar medical applications. Using the five major medical databases: MEDLINE (via PubMed), Embase, Cochrane Library (CENTRAL), Cochrane COVID-19 Study Register, and Scopus, we conducted a comprehensive systematic review of prediction tools between 2020 January and 2023 April for hospitalized COVID-19 patients. We identified both the relevant confounding factors of tool performance using the MetaForest algorithm and the best tools—comparing linear, machine learning, and deep learning methods—with mixed-effects meta-regression models. The risk of bias was evaluated using the PROBAST tool. Our systematic search identified eligible 27,312 studies, out of which 290 were eligible for data extraction, reporting on 430 independent evaluations of severity prediction tools with ~ 2.8 million patients. Neural Network-based tools have the highest performance with a pooled AUC of 0.893 (0.748–1.000), 0.752 (0.614–0.853) sensitivity, 0.914 (0.849–0.952) specificity, using clinical, laboratory, and imaging data. The relevant confounders of performance are the geographic region of patients, the rate of severe cases, and the use of C-Reactive Protein as input data. 88% of studies have a high risk of bias, mostly because of deficiencies in the data analysis. All investigated tools in use aid decision-making for COVID-19 severity prediction, but Machine Learning tools, specifically Neural Networks clearly outperform other methods, especially in cases when the basic characteristics of severe and non-severe patient groups are similar, and without the need for more data. When highly specific biomarkers are not available—such as in the case of COVID-19—practitioners should abandon general clinical severity scores and turn to disease specific Machine Learning tools.