Browse Articles

Assessment of the efficacy of on-demand tegoprazan therapy in gastroesophageal reflux disease through a randomized controlled trial

Deciphering the effect of UM171 on human hematopoietic progenitor cell fate through clonal analysis

Dynamics of specialization in neural modules under resource constraints

AbstractThe brain is structurally and functionally modular, although recent evidence has raised questions about the extent of both types of modularity. Using a simple, toy artificial neural network setup that allows for precise control, we find that structural modularity does not in general guarantee functional specialization (across multiple measures of specialization). Further, in this setup (1) specialization only emerges when features of the environment are meaningfully separable, (2) specialization preferentially emerges when the network is strongly resource-constrained, and (3) these findings are qualitatively similar across several different variations of network architectures. Finally, we show that functional specialization varies dynamically across time, and these dynamics depend on both the timing and bandwidth of information flow in the network. We conclude that a static notion of specialization is likely too simple a framework for understanding intelligence in situations of real-world complexity, from biology to brain-inspired neuromorphic systems.

Comparison of core temperature using tracheal thermometer and pulmonary artery catheter in adult patients undergoing coronary artery bypass graft surgery

Background Monitoring core temperature is important for patients under anesthesia. Esophageal and pulmonary artery blood temperatures can be used for measuring core temperature during general anesthesia. However, these methods pose challenges, especially when the placement of an esophageal thermometer and pulmonary artery catheter (PAC) is either impractical or not the preferred approach. An endotracheal tube (ETT) with a thermometer on the cuff allows for the measurement of tracheal temperature, providing a suitable alternative to core temperature measurement. This study aimed to assess the clinical reliability and accuracy of the thermometer in the ETT in comparison to the core temperature measured using a PAC. Methods Eleven patients who underwent coronary artery bypass graft (CABG) surgery were enrolled in this study. The patients were intubated using an ETT equipped with a thermometer on the cuff, and a PAC was inserted. Temperature measurements of both the trachea and pulmonary artery blood were recorded at 5-minute intervals for 1 hour before starting cardiopulmonary bypass. The agreement between the two temperature measurement methods was investigated using the Bland-Altman plot with multiple measurements per subject, and the correlation was evaluated using the concordance correlation coefficient (CCC). Results Eleven patients with a total of 143 pairs of data were included for analysis. The mean difference between the tracheal and pulmonary artery temperatures was −0.10°C. The 95% limit of agreement (LoA), calculated as ± 1.96 standard deviation, ranged from −0.35°C to 0.15°C. The 95% confidence interval for the lower and upper LoA was −0.51°C to −0.27°C and 0.07°C to 0.31°C, respectively. The maximum allowed difference (Δ) was set at 0.5°C. The majority of temperature differences fell within the LoA and were well below the maximum allowed difference. The CCC was 0.95, which indicates a substantial strength of agreement. Conclusions The agreement between the tracheal and pulmonary artery temperature measurements using the ETT thermometer and pulmonary artery catheter, respectively, was found to be clinically reliable and accurate. Therefore, the tracheal temperature measurement can effectively represent the core temperature of the patients. Employing an ETT equipped with a thermometer on the cuff can serve as a reliable and independent method for measuring core temperature. Trial registration Clinical trial registration number: NCT05595616.

Prevalence and determinants of full vaccination coverage according to the national schedule among children aged 12–35 months in Ghana

Prognostic signature and therapeutic drug identification for dilated cardiomyopathy based on necroptosis via bioinformatics and experimental validation

Photoinduced hidden monoclinic metallic phase of VO2 driven by local nucleation

Exploring transition states of protein conformational changes via out-of-distribution detection in the hyperspherical latent space

Seroconversion and dynamics of IgG anti-SARS-CoV-2 antibodies during the pandemic: A two-month observation cohort study on the population of Sleman in Indonesia

Background This study describes the seroconversion and serodynamics of IgG antibodies against the RBD of SARS-CoV-2 in the general population of Sleman District, Yogyakarta Special Province. We aim to identify possible factors that correlate with the seroconversion and serodynamics of IgG antibodies against the RBD of SARS-CoV-2. Methods We performed a longitudinal study of the population at Health and Demographic Surveillance System (HDSS) Sleman, Yogyakarta, Indonesia. Study subjects were recruited between April and December 2021 using convenience sampling and were followed up 2 times, i.e. 4–5 and 8–9 weeks. The inclusion criteria for subjects were age ≥ 18 years, absence of flu-like symptoms, and negative COVID-19 by using GeNose C19® screening. A community-based survey on demographics, comorbidities and smoking habits were documented at baseline, while a history of vaccination, COVID-19-related symptoms, mobility, and preventive measures, weight and height as well as a venous blood draw, were collected at each visit. The anti-RBD-SARS-CoV-2 IgG antibody concentration from blood plasma was measured using chemiluminescent microplate immunoassay (CMIA). Descriptive analysis was performed based on IgG seropositivity by using chi-squared test or Fisher’s exact test, as appropriate. Logistic regression was subsequently performed to identify factors that were correlated with IgG seropositivity. Further, a grouping of subjects based on IgG seropositivity was done to analyze factors that might correlate with seroconversion and serodynamics of anti-RBD-SARS-CoV-2 IgG antibody. A P value ≤ 0.05 was considered to indicate a significant difference. Results Three hundred eighty-five (385) participants were analyzed. At baseline, 307 out of 385 (79.7%) subjects were seropositive for the IgG antibody against the RBD of SARS-CoV-2. Descriptive analysis showed that sex, marital status, smoking habits, obesity, vaccination status, and preventive measures were different between the IgG anti-RBD-SARS-CoV-2 seropositive and negative individuals (p≤ 0.05). Further analysis showed that, vaccination was the factor most strongly correlated with seropositivity [OR = 20.58; 95% CI 10.82, 39.15]. Based on the correlation, we separated subjects into 4 groups. Group 1 (seronegative-unvaccinated individuals; 50 subjects); Group 2 (seronegative-vaccinated individuals; 27 subjects); Group 3 (seropositive-unvaccinated individuals; 25 subjects); and Group 4 (seropositive-vaccinated individuals; 282 subjects). During monitoring, 27/49 (55.10%), 5/25 (20%), 9/22 (40.91%), and 27/257 (10.51%) of subjects in Group 1, 2, 3, and 4 respectively, received 1 or 2 doses of COVID19 vaccine. When comparing seroconversion at baseline and monitoring 2, positive IgG seroconversion was observed in Group 1 (from 0/51 (0%) to 23/49 (46.94%)) and Group 2 (from 0/27 (0%) to 10/25 (40%)), but negative seroconversion was observed in Group 4 (from 282/0 (100%) to 248/257 (96.50%)); while, all subjects in Group 3 remained seropositive at the end of monitoring. This evidence suggested for hybrid immunity, on which infection and vaccine simultaneously contributes to anti-RBD-SARS-CoV-2 IgG seroconversion. Conclusions A high seroprevalence of the IgG antibody against RBD-SARS-CoV-2 in the Sleman population was found to correlate with COVID-19 vaccination and as infection occurred, thus enhancing hybrid immunity. We also identified nonresponder and rapid antibody decaying individuals, that call for targeted vaccinations in addition to annual universal boosting.

Evaluating the impact of smart city construction on sewage treatment in China from a synergistic perspective

The dynamics of thrombolysis over time in acute immunologic reactions

Multimaterial cryogenic printing of three-dimensional soft hydrogel machines

High-purity hydrogen production from dehydrogenation of methylcyclohexane catalyzed by zeolite-encapsulated subnanometer platinum-iron clusters

Correction: Emphasizing speed or accuracy in an eye-tracking version of the Trail-Making-Test: Towards experimental diagnostics for decomposing executive functions

In silico analysis of mitochondrial DNA genes: implication for conservation of Tor putitora (Hamilton, 1822)

Modeling synergy and individual effects of X-ray induced photodynamic therapy components

Optimizing saffron cormlet production through substrate composition nutrient concentration and irrigation management in soilless cultivation

Structural basis for catalysis and selectivity of phospholipid synthesis by eukaryotic choline-phosphotransferase

Oriented wide-bandgap perovskites for monolithic silicon-based tandems with over 1000 hours operational stability

Finite mixtures of functional graphical models: Uncovering heterogeneous dependencies in high-dimensional data

Graphical models have been widely used to explicitly capture the statistical relationships among the variables of interest in the form of a graph. The central question in these models is to infer significant conditional dependencies or independencies from high-dimensional data. In the current literature, it is common to assume that the high-dimensional data come from a homogeneous source and follow a parametric graphical model. However, in real-world context the observed data often come from different sources and may have heterogeneous dependencies across the whole population. In addition, for time-dependent data, many work has been done to estimate discrete correlation structures at each time point but less work has been done to estimate global correlation structures over all time points. In this work, we propose finite mixtures of functional graphical models (MFGM), which detect the heterogeneous subgroups of the population and estimate single graph for each subgroup by considering the correlation structures. We further design an estimation method for MFGM using an iterative Expectation-Maximization (EM) algorithm and functional graphical lasso (fglasso). Numerically, we demonstrate the performance of our method in simulation studies and apply our method to high-dimensional electroencephalogram (EEG) dataset taken from an alcoholism study.