Nevin Manimala

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

Nat Commun. 2024 Feb 17;15(1):1471. doi: 10.1038/s41467-024-45719-9.

ABSTRACT

How paracrine signals are interpreted to yield multiple cell fate decisions in a dynamic context during human development in vivo and in vitro remains poorly understood. Here we report an automated tracking method to follow signaling histories linked to cell fate in large numbers of human pluripotent stem cells (hPSCs). Using an unbiased statistical approach, we discover that measured BMP signaling history correlates strongly with fate in individual cells. We find that BMP response in hPSCs varies more strongly in the duration of signaling than the level. However, both the level and duration of signaling activity control cell fate choices only by changing the time integral. Therefore, signaling duration and level are interchangeable in this context. In a stem cell model for patterning of the human embryo, we show that signaling histories predict the fate pattern and that the integral model correctly predicts changes in cell fate domains when signaling is perturbed. Our data suggest that mechanistically, BMP signaling is integrated by SOX2.

PMID:38368368 | DOI:10.1038/s41467-024-45719-9

BMC Surg. 2024 Feb 17;24(1):62. doi: 10.1186/s12893-024-02359-6.

ABSTRACT

BACKGROUND: Hepatic artery thrombosis (HAT) is one of the critical conditions after an orthotopic liver transplant (OLT) and leads to severe problems if not corrected promptly. However, multiple treatments have been proposed for HAT, in which surgical revascularization with either auto-hepatic conduit interposition (AHCI) or revision of the anastomosis is more familiar indeed indicated for some patients and in specific situations. In this study, we want to evaluate the success and outcomes of treating early HAT (E-HAT), which defines HAT within 30 days after OLT with either of the surgical revascularization techniques.

METHOD: In this retrospective study, we collected information from the medical records of patients who underwent either of the surgical revascularization procedures for E-HAT after OLT. Patients who needed early retransplantation (RT) or died without surgical intervention for E-HAT were excluded. Demographic data, OLT surgery information, and data regarding E-HAT were gathered. The study outcomes were secondary management for E-HAT in case of improper inflow, biliary complications (BC), RT, and death.

RESULTS: A total of 37 adult patients with E-HAT after OLT included in this study. These E-HATs were diagnosed within a mean of 4.6 ± 3.6 days after OLT. Two patients had their HA revised for the initial management of E-HAT; however, it changed to AHCI intraoperatively and finally needed RT. Two and nine patients from the AHCI and revision groups had re-thrombosis (12.5% vs. 47.3%, respectively, p = 0.03). RT was used to manage rethrombosis in all patients of AHCI and two patients of the revision group (22.2%). In comparison to the AHCI, revision group had statistically insignificant higher rates of BC (47.4% vs. 31.2%); however, RT for nonvascular etiologies (12.5% vs. 5.3%) and death (12.5% vs. 10.5%) were nonsignificantly higher in AHCI group. All patients with more than one HA exploration who were in the revision group had BC; however, 28.5% of patients with just one HA exploration experienced BC (p < 0.001).

CONCLUSION: Arterial conduit interposition seems a better approach for the initial management of E-HAT in comparison to revision of the HA anastomosis due to the lower risk of re-thrombosis and the number of HA explorations; indeed, BC, RT, and death remain because they are somewhat related to the ischemic event of E-HAT than to a surgical treatment itself.

PMID:38368356 | DOI:10.1186/s12893-024-02359-6

J Neuroinflammation. 2024 Feb 17;21(1):52. doi: 10.1186/s12974-024-03036-4.

ABSTRACT

Establishing biomarkers to predict multiple sclerosis diagnosis and prognosis has been challenging using a single biomarker approach. We hypothesised that a combination of biomarkers would increase the accuracy of prediction models to differentiate multiple sclerosis from other neurological disorders and enhance prognostication for people with multiple sclerosis. We measured 24 fluid biomarkers in the blood and cerebrospinal fluid of 77 people with multiple sclerosis and 80 people with other neurological disorders, using ELISA or Single Molecule Array assays. Primary outcomes were multiple sclerosis versus any other diagnosis, time to first relapse, and time to disability milestone (Expanded Disability Status Scale 6), adjusted for age and sex. Multivariate prediction models were calculated using the area under the curve value for diagnostic prediction, and concordance statistics (the percentage of each pair of events that are correctly ordered in time for each of the Cox regression models) for prognostic predictions. Predictions using combinations of biomarkers were considerably better than single biomarker predictions. The combination of cerebrospinal fluid [chitinase-3-like-1 + TNF-receptor-1 + CD27] and serum [osteopontin + MCP-1] had an area under the curve of 0.97 for diagnosis of multiple sclerosis, compared to the best discriminative single marker in blood (osteopontin: area under the curve 0.84) and in cerebrospinal fluid (chitinase-3-like-1 area under the curve 0.84). Prediction for time to next relapse was optimal with a combination of cerebrospinal fluid[vitamin D binding protein + Factor I + C1inhibitor] + serum[Factor B + Interleukin-4 + C1inhibitor] (concordance 0.80), and time to Expanded Disability Status Scale 6 with cerebrospinal fluid [C9 + Neurofilament-light] + serum[chitinase-3-like-1 + CCL27 + vitamin D binding protein + C1inhibitor] (concordance 0.98). A combination of fluid biomarkers has a higher accuracy to differentiate multiple sclerosis from other neurological disorders and significantly improved the prediction of the development of sustained disability in multiple sclerosis. Serum models rivalled those of cerebrospinal fluid, holding promise for a non-invasive approach. The utility of our biomarker models can only be established by robust validation in different and varied cohorts.

PMID:38368354 | DOI:10.1186/s12974-024-03036-4