Category: Nevin Manimala

Sci Rep. 2024 Feb 17;14(1):3954. doi: 10.1038/s41598-024-54571-2.

ABSTRACT

This study investigates the impact of water levels and soil texture on the migration and transformation of nitrate (NO₃^–-N) and ammonium (NH₄⁺-N) within a soil column. The concentrations of NO₃^–-N gradually decreased from an initial concentration of 34.19 ± 0.86 mg/L to 14.33 ± 0.77 mg/L on day 70, exhibiting fluctuations and migration influenced by water levels and soil texture. Higher water levels were associated with decreased NO₃^–-N concentrations, while lower water levels resulted in increased concentrations. The retention and absorption capacity for NO₃^–-N were highest in fine sand soil, followed by medium sand and coarse sand, highlighting the significance of soil texture in nitrate movement and retention. The analysis of variance (ANOVA) confirmed statistically significant variations in pH, dissolve oxygen and oxidation-reduction potential across the soil columns (p < 0.05). Fluctuating water levels influenced the migration and transformation of NO₃^–-N, with distinct patterns observed in different soil textures. Water level fluctuations also impacted the migration and transformation of NH₄⁺-N, with higher water levels associated with increased concentrations and lower water levels resulting in decreased concentrations. Among the soil types considered, medium sand exhibited the highest absorption capacity for NH₄⁺-N. These findings underscore the significant roles of water levels, soil texture, and soil type in the migration, transformation, and absorption of nitrogen compounds within soil columns. The results contribute to a better understanding of nitrogen dynamics under varying water levels and environmental conditions, providing valuable insights into the patterns of nitrogen migration and transformation in small-scale soil column experiments.

PMID:38368500 | DOI:10.1038/s41598-024-54571-2

Sci Rep. 2024 Feb 17;14(1):3980. doi: 10.1038/s41598-024-54679-5.

ABSTRACT

This study examined the impact of the COVID-19 pandemic on marriage, divorce, birth, and death rates using the Poisson regression model and an interrupted time-series Poisson regression model. Before the pandemic, marriage and birth rates were decreasing, while divorce and death rates were increasing, with only the trend in birth rates being statistically significant. The immediate effect of the pandemic was a significant decrease in the divorce rate, but there were non-significant effects on birth and marriage rates. However, in the months following the onset of the pandemic, there was a statistically significant sustained effect on increasing death and divorce rates. Forecasts based on pre-pandemic data showed that by the end of 2020, marriage, divorce, death, and birth rates were higher compared to pre-pandemic levels. In conclusion, the pandemic has greatly impacted society, particularly in terms of death and divorce rates. Birth rates were not immediately affected to the time lag between decisions and actual births. Fear of COVID-19 may have increased death rates as people avoided seeking medical help. Vaccination and effective treatment strategies are vital in reducing the pandemic’s impact on mortality. Supporting families financially is important due to the role of economic issues in couples’ decisions.

PMID:38368489 | DOI:10.1038/s41598-024-54679-5

Sci Rep. 2024 Feb 17;14(1):3984. doi: 10.1038/s41598-024-54541-8.

ABSTRACT

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia in the elderly worldwide. The exact etiology of AD, particularly its genetic mechanisms, remains incompletely understood. Traditional genome-wide association studies (GWAS), which primarily focus on single-nucleotide polymorphisms (SNPs) with main effects, provide limited explanations for the “missing heritability” of AD, while there is growing evidence supporting the important role of epistasis. In this study, we performed a genome-wide SNP-SNP interaction detection using a linear regression model and employed multiple GPUs for parallel computing, significantly enhancing the speed of whole-genome analysis. The cerebrospinal fluid (CSF) phosphorylated tau (P-tau)/amyloid-[Formula: see text] (A[Formula: see text]) ratio was used as a quantitative trait (QT) to enhance statistical power. Age, gender, and clinical diagnosis were included as covariates to control for potential non-genetic factors influencing AD. We identified 961 pairs of statistically significant SNP-SNP interactions, explaining a high-level variance of P-tau/A[Formula: see text] level, all of which exhibited marginal main effects. Additionally, we replicated 432 previously reported AD-related genes and found 11 gene-gene interaction pairs overlapping with the protein-protein interaction (PPI) network. Our findings may contribute to partially explain the “missing heritability” of AD. The identified subnetwork may be associated with synaptic dysfunction, Wnt signaling pathway, oligodendrocytes, inflammation, hippocampus, and neuronal cells.

PMID:38368488 | DOI:10.1038/s41598-024-54541-8

Sci Data. 2024 Feb 17;11(1):217. doi: 10.1038/s41597-024-03039-z.

ABSTRACT

In this paper, we present MatKG, a knowledge graph in materials science that offers a repository of entities and relationships extracted from scientific literature. Using advanced natural language processing techniques, MatKG includes an array of entities, including materials, properties, applications, characterization and synthesis methods, descriptors, and symmetry phase labels. The graph is formulated based on statistical metrics, encompassing over 70,000 entities and 5.4 million unique triples. To enhance accessibility and utility, we have serialized MatKG in both CSV and RDF formats and made these, along with the code base, available to the research community. As the largest knowledge graph in materials science to date, MatKG provides structured organization of domain-specific data. Its deployment holds promise for various applications, including material discovery, recommendation systems, and advanced analytics.

PMID:38368452 | DOI:10.1038/s41597-024-03039-z

Sci Rep. 2024 Feb 17;14(1):3957. doi: 10.1038/s41598-024-53576-1.

ABSTRACT

This article presents a study, intending to design a model with 90% reliability, which helps in the prediction of school dropouts in higher and secondary education institutions, implementing machine learning techniques. The collection of information was carried out with open data from the 2015 Intercensal Survey and the 2010 and 2020 Population and Housing censuses carried out by the National Institute of Statistics and Geography, which contain information about the inhabitants and homes. in the 32 federal entities of Mexico. The data were homologated and twenty variables were selected, based on the correlation. After cleaning the data, there was a sample of 1,080,782 records in total. Supervised learning was used to create the model, automating data processing with training and testing, applying the following techniques, Artificial Neural Networks, Support Vector Machines, Linear Ridge and Lasso Regression, Bayesian Optimization, Random Forest, the first two with a reliability greater than 99% and the last with 91%.

PMID:38368429 | DOI:10.1038/s41598-024-53576-1

Eur J Med Res. 2024 Feb 17;29(1):133. doi: 10.1186/s40001-024-01722-w.

ABSTRACT

BACKGROUND: Studies in many populations have reported associations between circulating cytokine levels and various physiological or pathological conditions. However, the reliability of cytokine measurements in population studies, which measure cytokines in multiple assays over a prolonged period, has not been adequately examined; nor has stability during sample storage or intra-individual variation been assessed.

METHODS: We assessed (1) analytical reliability in short- and long-term repeated measurements; (2) stability and analytical reliability during long-term sample storage, and (3) variability within individuals over seasons, of four cytokines-osteopontin (OPN), osteoprotegerin (OPG), vascular endothelial growth factor-A (VEGF-A), and interleukin-17A (IL-17A). Measurements in plasma or serum samples were made with commercial kits according to standard procedures. Estimation was performed by fitting a random or mixed effects linear model on the log scale.

RESULTS: In repeated assays over a short period, OPN, OPG, and VEGF-A had acceptable reliability, with intra- and inter-assay coefficients of variation (CV) less than 0.11. Reliability of IL-17A was poor, with inter- and intra-assay CV 0.85 and 0.43, respectively. During long-term storage, OPG significantly decayed (- 33% per year; 95% confidence interval [- 54, – 3.7]), but not OPN or VEGF-A (- 0.3% or – 6.3% per year, respectively). Intra- and inter-assay CV over a long period were comparable to that in a short period except for a slight increase in inter-assay CV of VEGF-A. Within-individual variation was small for OPN and VEGF-A, with intra-class correlations (ICC) 0.68 and 0.83, respectively, but large for OPG (ICC 0.11).

CONCLUSIONS: We conclude that OPN and VEGF-A can be reliably measured in a large population, that IL-17A is suitable only for small experiments, and that OPG should be assessed with caution due to degradation during storage and intra-individual variation. The overall results of our study illustrate the need for validation under relevant conditions when measuring circulating cytokines in population studies.

PMID:38368424 | DOI:10.1186/s40001-024-01722-w

Cell Death Discov. 2024 Feb 17;10(1):85. doi: 10.1038/s41420-024-01857-z.

ABSTRACT

Through GWAS studies we identified PATJ associated with functional outcome after ischemic stroke (IS). The aim of this study was to determine PATJ role in brain endothelial cells (ECs) in the context of stroke outcome. PATJ expression analyses in patient’s blood revealed that: (i) the risk allele of rs76221407 induces higher expression of PATJ, (ii) PATJ is downregulated 24 h after IS, and (iii) its expression is significantly lower in those patients with functional independence, measured at 3 months with the modified Rankin scale ((mRS) ≤2), compared to those patients with marked disability (mRS = 4-5). In mice brains, PATJ was also downregulated in the injured hemisphere at 48 h after ischemia. Oxygen-glucose deprivation and hypoxia-dependent of Hypoxia Inducible Factor-1α also caused PATJ depletion in ECs. To study the effects of PATJ downregulation, we generated PATJ-knockdown human microvascular ECs. Their transcriptomic profile evidenced a complex cell reprogramming involving Notch, TGF-ß, PI3K/Akt, and Hippo signaling that translates in morphological and functional changes compatible with endothelial to mesenchymal transition (EndMT). PATJ depletion caused loss of cell-cell adhesion, upregulation of metalloproteases, actin cytoskeleton remodeling, cytoplasmic accumulation of the signal transducer C-terminal transmembrane Mucin 1 (MUC1-C) and downregulation of Notch and Hippo signaling. The EndMT phenotype of PATJ-depleted cells was associated with the nuclear recruitment of MUC1-C, YAP/TAZ, β-catenin, and ZEB1. Our results suggest that PATJ downregulation 24 h after IS promotes EndMT, an initial step prior to secondary activation of a pro-angiogenic program. This effect is associated with functional independence suggesting that activation of EndMT shortly after stroke onset is beneficial for stroke recovery.

PMID:38368420 | DOI:10.1038/s41420-024-01857-z

Int J Oral Sci. 2024 Feb 18;16(1):14. doi: 10.1038/s41368-023-00273-w.

ABSTRACT

Oral cancer (OC) is the most common form of head and neck cancer. Despite the high incidence and unfavourable patient outcomes, currently, there are no biomarkers for the early detection of OC. This study aims to discover, develop, and validate a novel saliva-based microRNA signature for early diagnosis and prediction of OC risk in oral potentially malignant disorders (OPMD). The Cancer Genome Atlas (TCGA) miRNA sequencing data and small RNA sequencing data of saliva samples were used to discover differentially expressed miRNAs. Identified miRNAs were validated in saliva samples of OC (n = 50), OPMD (n = 52), and controls (n = 60) using quantitative real-time PCR. Eight differentially expressed miRNAs (miR-7-5p, miR-10b-5p, miR-182-5p, miR-215-5p, miR-431-5p, miR-486-3p, miR-3614-5p, and miR-4707-3p) were identified in the discovery phase and were validated. The efficiency of our eight-miRNA signature to discriminate OC and controls was: area under curve (AUC): 0.954, sensitivity: 86%, specificity: 90%, positive predictive value (PPV): 87.8% and negative predictive value (NPV): 88.5% whereas between OC and OPMD was: AUC: 0.911, sensitivity: 90%, specificity: 82.7%, PPV: 74.2% and NPV: 89.6%. We have developed a risk probability score to predict the presence or risk of OC in OPMD patients. We established a salivary miRNA signature that can aid in diagnosing and predicting OC, revolutionising the management of patients with OPMD. Together, our results shed new light on the management of OC by salivary miRNAs to the clinical utility of using miRNAs derived from saliva samples.

PMID:38368395 | DOI:10.1038/s41368-023-00273-w

Trials. 2024 Feb 17;25(1):131. doi: 10.1186/s13063-024-07957-6.

ABSTRACT

BACKGROUND: We evaluated the clinical and cost-effectiveness of manualised sensory integration therapy (SIT) for autistic children with sensory processing difficulties in a two-arm randomised controlled trial. Trial processes and contextual factors which may have affected intervention outcomes were explored within a nested process evaluation. This paper details the process evaluation methods and results. We also discuss implications for evaluation of individual level, tailored interventions in similar populations.

METHODS: The process evaluation was conducted in line with Medical Research Council guidance. Recruitment, demographics, retention, adherence, and adverse effects are reported using descriptive statistics. Fidelity of intervention delivery is reported according to the intervention scoring manual. Qualitative interviews with therapists and carers were undertaken to explore the acceptability of the intervention and trial processes. Qualitative interviews with carers explored potential contamination.

RESULTS: Recruitment, reach and retention within the trial met expected thresholds. One hundred thirty-eight children and carers were recruited (92% of those screened and 53.5% of those who expressed an interest) with 77.5% retained at 6 months and 69.9% at 12 months post-randomisation. The intervention was delivered with structural and process fidelity with the majority (78.3%) receiving a ‘sufficient dose’ of intervention. However, there was considerable individual variability in the receipt of sessions. Carers and therapists reported that trial processes were generally acceptable though logistical challenges such as appointment times, travel and COVID restrictions were frequent barriers to receiving the intervention. No adverse effects were reported.

CONCLUSIONS: The process evaluation was highly valuable in identifying contextual factors that could impact the effectiveness of this individualised intervention. Rigorous evaluations of interventions for autistic children are important, especially given the limitations such as limited sample sizes and short-term follow-up as faced by previous research. One of the challenges lies in the variability of outcomes considered important by caregivers, as each autistic child faces unique challenges. It is crucial to consider the role of parents or other caregivers in facilitating access to these interventions and how this may impact effectiveness.

TRIAL REGISTRATION: This trial is registered as ISRCTN14716440. August 11, 2016.

PMID:38368387 | DOI:10.1186/s13063-024-07957-6

Syst Rev. 2024 Feb 17;13(1):69. doi: 10.1186/s13643-024-02472-w.

ABSTRACT

Systematic reviews and meta-analyses typically require significant time and effort. Machine learning models have the potential to enhance screening efficiency in these processes. To effectively evaluate such models, fully labeled datasets-detailing all records screened by humans and their labeling decisions-are imperative. This paper presents the creation of a comprehensive dataset for a systematic review of treatments for Borderline Personality Disorder, as reported by Oud et al. (2018) for running a simulation study. The authors adhered to the PRISMA guidelines and published both the search query and the list of included records, but the complete dataset with all labels was not disclosed. We replicated their search and, facing the absence of initial screening data, introduced a Noisy Label Filter (NLF) procedure using active learning to validate noisy labels. Following the NLF application, no further relevant records were found. A simulation study employing the reconstructed dataset demonstrated that active learning could reduce screening time by 82.30% compared to random reading. The paper discusses potential causes for discrepancies, provides recommendations, and introduces a decision tree to assist in reconstructing datasets for the purpose of running simulation studies.

PMID:38368379 | DOI:10.1186/s13643-024-02472-w