Browse Articles

Treatment outcome and determinant factors of bacterial meningitis at pediatric ward: a multicenter study from Northwest Ethiopia

There is no six-year periodicity in tidal forcing

A scoring system with high predictive performance for poor outcomes in acute carbon monoxide poisoning

The effect of students’ attitudes towards elderly patients on satisfaction with removable complete dentures

Changes in thyroid surgery over last 25 years

Association between pre-season lower limb interlimb asymmetry and non-contact lower limb injuries in elite male volleyball players

Design of a multi-epitope vaccine against Staphylococcus Aureus lukotoxin ED using in silico approaches

Amplified quantum battery via dynamical modulation

An LLM-based hybrid approach for enhanced automated essay scoring

Evaluation of serum calprotectin levels in patients with polycystic ovary syndrome

Automating multi-task learning on optical neural networks with weight sharing and physical rotation

TAS2R38 gene methylation is associated with syndrome Coronavirus 2 (SARS-CoV-2) infection and clinical symptoms

Abstract TAS2R38 is the T2R receptor primarily associated with the innate immune response of the respiratory system. It activates a response mediated by nitric oxide (NO), which has been shown to inhibit the replication of SARS-CoV-2. TAS2R38 polymorphisms (SNPs) that decrease receptor functionality contributing to individual differences in susceptibility to airway infections. DNA methylation (DNAm) may affect gene expression influencing disease development, including COVID-19. We analyzed the effect of SARS-CoV-2 on the methylation pattern of TAS2R38 (at cg25481253, a CpG site located in the coding region) during infection and after the cessation of the exposure to the virus, also considering the disease severity and TAS2R38 SNPs. Our results showed a positive relationship between TAS2R38 DNAm levels and disease severity in the COVID-19 patients and a return to a normal state after the infection. In addition, our results showed an association between DNAm level and the TAS2R38 genotype in participants who recovered from the disease. PAV/PAV genotypes showed lower TAS2R38 DNAm levels than heterozygous and AVI homozygous. In conclusion, our results clearly indicate the involvement of TAS2R38 DNAm alteration in COVID-19 severity and suggest a role of the methylation changes at cg25481253 in the regulation of the TAS2R38 expression.

A novel integration of Hodrick–Prescott filter (Hp-filter) and wavelet transform (WT) with optimize support vector machine (PSO-SVM) in predicting solar radiation

Abstract Previous research has shown that predicting solar radiation is a challenging issue due to highly nonlinear and noisy climate data. Various hybrid approaches have been applied earlier for solar radiation prediction, which integrates the Wavelet Transform with various Machine Learning models. This research, therefore, intends to further improve the performance of these existing hybrid models. To address the limitations in handling nonlinear and noisy climate patterns, this study proposes a multi-hybrid model for accurately predicting solar radiation that incorporates the Hodrick–Prescott Filter (HP-Filter), Discrete Wavelet Transform (DWT), and Support Vector Machine (SVM). The collected data from the Bangladesh Meteorological Department for two different geological locations in Bangladesh, namely Dhaka and Chittagong, is divided into three categories for modeling: 70% for training, 15% for validation, and 15% for testing, whereas the model hyper-parameters of the SVM were optimized using the Particle Swarm Optimization algorithm. The proposed approach applies the Hodrick–Prescott Filter before analyzing DWT to strengthen the SVM model’s ability to capture complicated climate patterns in great detail and also make the model more precise and reliable. Several performance metrics, such as Mean Squared Error (MSE), Root Mean Squared Error, Mean Absolute Error, Mean Absolute Percentage Error, and Coefficient of Determination (R2), were considered for model evaluation. The results showed that it improves upon traditional SVM by 99.76% and 99.77% and hybrid DWT-SVM by 39% and 57% in terms of MSE reduction at Dhaka and Chittagong, respectively. R2 also improved by 49% and 54% over traditional SVM and by 4.40% and 3.16% over hybrid DWT-SVM model. The model well captures the complex nonlinear trend existing in solar radiation; thus, it shows its potential to be applied to other regions for efficient prediction of solar radiation.

Clinical performance validation and four diagnostic strategy assessments of high-sensitivity troponin I assays

Optimal application research of superconducting fault current limiters on medium voltage direct current shipboard power system

Spatial ecology of two emblematic deep-sea crustaceans in the Salas y Gómez, Nazca and Juan Fernández ridges Southeast Pacific

Abstract The Salas & Gómez, Nazca, and Juan Fernández ridges are among the least studied regions on the planet. Ecological knowledge of deep-sea organisms inhabiting islands and seamounts along these ridges is limited. Using various sources, including published data and in situ information from five scientific expeditions in the region, we analyzed the spatial ecology of Paromola rathbuni and Projasus bahamondei, two mobile species of benthic megafauna. Random Forest models detected different combinations of abiotic variables affecting species distribution. The distribution of P. bahamondei was primarily influenced by dissolved oxygen, longitude (W), and temperature. The distribution of P. rathbuni was mainly explained by longitude (W), followed by depth, temperature, and oxygen. Generalized Linear Models quantified significant effects of low dissolved oxygen and temperature on species presence, influencing their preference for bathymetric ranges (P. bahamondei at 400–500 m, P. rathbuni at 300–400 m). A distribution break to the west of ~ 85°W was confirmed, potentially due to the weakening of the Oxygen Minimum Zone altering oxygen and thermal conditions. This feature could act as a barrier for both species despite their high dispersal potential. Under projected climate change, shifts in latitudinal, longitudinal, and bathymetric distribution patterns are expected for both species.

Investigation of deformation of existing tunnel due to above excavation unloading considering tunnel lateral response

Partial organic substitution for chemical fertilizer reduces N2O emissions but increases the risk of N loss through nitrification in Tibetan farmland

Graphical model analysis of subjective well-being and various factors in Japanese adults from the Iwaki cross-sectional study

Direct cell interactions potentially regulate transcriptional programmes that control the responses of high grade serous ovarian cancer patients to therapy

Abstract The tumour microenvironment is composed of a complex cellular network involving cancer, stromal and immune cells in dynamic interactions. A large proportion of this network relies on direct physical interactions between cells, which may impact patient responses to clinical therapy. Doublets in scRNA-seq are usually excluded from analysis. However, they may represent directly interacting cells. To decipher the physical interaction landscape in relation to clinical prognosis, we inferred a physical cell–cell interaction (PCI) network from ‘biological’ doublets in a scRNA-seq dataset of approximately 18,000 cells, obtained from 7 treatment-naive ovarian cancer patients. Focusing on cancer-stromal PCIs, we uncovered molecular interaction networks and transcriptional landscapes that stratified patients in respect to their clinical responses to standard therapy. Good responders featured PCIs involving immune cells interacting with other cell types including cancer cells. Poor responders lacked immune cell interactions, but showed a high enrichment of cancer-stromal PCIs. To explore the molecular differences between cancer-stromal PCIs between responders and non-responders, we identified correlating gene signatures. We constructed ligand-receptor interaction networks and identified associated downstream pathways. The reconstruction of gene regulatory networks and trajectory analysis revealed distinct transcription factor (TF) clusters and gene modules that separated doublet cells by clinical outcomes. Our results indicate (i) that transcriptional changes resulting from PCIs predict the response of ovarian cancer patients to standard therapy, (ii) that immune reactivity of the host against the tumour enhances the efficacy of therapy, and (iii) that cancer-stromal cell interaction can have a dual effect either supporting or inhibiting therapy responses.