Analysing an Imbalanced Stroke Prediction Dataset Using Machine Learning Techniques

A stroke is a medical condition characterized by the rupture of blood vessels within the brain which can lead to brain damage. Various symptoms may be exhibited when the brain's supply of blood and essential nutrients is disrupted. To forecast the possibility of brain stroke occurring at an early stage using Machine Learning (ML) and Deep Learning (DL) is the main objective of this study. Timely detection of the various warning signs of a stroke can significantly reduce its severity. This paper performed a comprehensive analysis of features to enhance stroke prediction effectiveness. A reliable dataset for stroke prediction is taken from the Kaggle website to gauge the effectiveness of the proposed algorithm. The dataset has a class imbalance problem which means the total number of negative samples is higher than the total number of positive samples. The results are reported based on a balanced dataset created using oversampling techniques. The proposed work used Smote and Adasyn to handle imbalanced problem for better evaluation metrics. Additionally, the hybrid Neural Network and Random Forest (NN-RF) utilizing the balanced dataset by Adasyn oversampling achieves the highest F1-score of 75% compared to the original unbalanced dataset and other benchmarking algorithms. The proposed algorithm with balanced data utilizing hybrid NN-RF achieves an accuracy of 84%. Advanced ML techniques coupled with thorough data analysis enhance stroke prediction. This study underscores the significance of data-driven methodologies, resulting in improved accuracy and comprehension of stroke risk factors. Applying these methodologies to medical fields can enhance patient care and public health outcomes. By integrating our discoveries, we can enhance the efficiency and effectiveness of the public health system

Association of urinary uromodulin with kidney function decline and mortality: the health ABC study .

BackgroundUrine uromodulin (uUMOD) is a protein secreted by the kidney tubule. Recent studies have suggested that higher uUMOD may be associated with improved kidney and mortality outcomes.MethodsUsing a case-cohort design, we evaluated the association between baseline uUMOD levels and ≥ 30% estimated glomerular filtration rate (eGFR) decline, incident chronic kidney disease (CKD), rapid kidney function decline, and mortality using standard and modified Cox proportional hazards regression.ResultsThe median value of uUMOD was 25.8 µg/mL, mean age of participants was 74 years, 48% were women, and 39% were black. Persons with higher uUMOD had lower prevalence of diabetes and coronary artery disease (CAD), and had lower systolic blood pressure. Persons with higher uUMOD also had higher eGFR, lower urinary albumin to creatinine ratio (ACR), and lower C-reactive protein (CRP). There was no association of uUMOD with > 30% eGFR decline. In comparison to those in the lowest quartile of uUMOD, those in the highest quartile had a significantly (53%) lower risk of incident CKD (CI 73%, 18%) and a 51% lower risk of rapid kidney function decline (CI 76%, 1%) after multivariable adjustment. Higher uUMOD was associated with lower risk of mortality in demographic adjusted models, but not after multivariable adjustment.ConclusionHigher levels of uUMOD are associated with lower risk of incident CKD and rapid kidney function decline. Additional studies are needed in the general population and in persons with advanced CKD to confirm these findings.


Integrative genomic analysis identifies ancestry-related expression quantitative trait loci on DNA polymerase β and supports the association of genetic ancestry with survival disparities in head and neck squamous cell carcinoma

BACKGROUND: African Americans with head and neck squamous cell carcinoma (HNSCC) have a lower survival rate than whites. This study investigated the functional importance of ancestry-informative single-nucleotide polymorphisms (SNPs) in HNSCC and also examined the effect of functionally important genetic elements on racial disparities in HNSCC survival. METHODS: Ancestry-informative SNPs, RNA sequencing, methylation, and copy number variation data for 316 oral cavity and laryngeal cancer patients were analyzed across 178 DNA repair genes. The results of expression quantitative trait locus (eQTL) analyses were also replicated with a Gene Expression Omnibus (GEO) data set. The effects of eQTLs on overall survival (OS) and disease-free survival (DFS) were evaluated. RESULTS: Five ancestry-related SNPs were identified as cis-eQTLs in the DNA polymerase β (POLB) gene (false discovery rate [FDR] < 0.01). The homozygous/heterozygous genotypes containing the African allele showed higher POLB expression than the homozygous white allele genotype (P < .001). A replication study using a GEO data set validated all 5 eQTLs and also showed a statistically significant difference in POLB expression based on genetic ancestry (P = .002). An association was observed between these eQTLs and OS (P < .037; FDR < 0.0363) as well as DFS (P = .018 to .0629; FDR < 0.079) for oral cavity and laryngeal cancer patients treated with platinum-based chemotherapy and/or radiotherapy. Genotypes containing the African allele were associated with poor OS/DFS in comparison with homozygous genotypes harboring the white allele. CONCLUSIONS: Analyses show that ancestry-related alleles could act as eQTLs in HNSCC and support the association of ancestry-related genetic factors with survival disparities in patients diagnosed with oral cavity and laryngeal cancer. Cancer 2017;123:849-60. © 2016 American Cancer Society

An unusual congregation of organisms in the catches off Kovalam, Madras

The fishermen belonging to Kovalam had a hectic activity in harvesting huge quantities of fish from the Kovalam bay from 26-8-'87 to 4-9-'87. Fishermen employed all available gears for catching the fish and prawns. According to them, this was due to the appearance of 'Vandal thanneer' or turbid water close to the shore. The present account embodies the results of the observations made on this unusual phenomenon

Simultaneous non-negative matrix factorization for multiple large scale gene expression datasets in toxicology

Non-negative matrix factorization is a useful tool for reducing the dimension of large datasets. This work considers simultaneous non-negative matrix factorization of multiple sources of data. In particular, we perform the first study that involves more than two datasets. We discuss the algorithmic issues required to convert the approach into a practical computational tool and apply the technique to new gene expression data quantifying the molecular changes in four tissue types due to different dosages of an experimental panPPAR agonist in mouse. This study is of interest in toxicology because, whilst PPARs form potential therapeutic targets for diabetes, it is known that they can induce serious side-effects. Our results show that the practical simultaneous non-negative matrix factorization developed here can add value to the data analysis. In particular, we find that factorizing the data as a single object allows us to distinguish between the four tissue types, but does not correctly reproduce the known dosage level groups. Applying our new approach, which treats the four tissue types as providing distinct, but related, datasets, we find that the dosage level groups are respected. The new algorithm then provides separate gene list orderings that can be studied for each tissue type, and compared with the ordering arising from the single factorization. We find that many of our conclusions can be corroborated with known biological behaviour, and others offer new insights into the toxicological effects. Overall, the algorithm shows promise for early detection of toxicity in the drug discovery process

Investigation on thermophysical properties of multi-walled carbon nanotubes enhanced salt hydrate phase change material

Thermal Energy Storage (TES) is a valuable tool for improving the energy efficiency of renewable energy conversion systems. One of the most effective methods for harnessing thermal energy from solar sources is through energy storage using phase change materials (PCMs). However, the thermal performance of PCMs is hindered by their low thermal conductivity. This research focuses on enhancing the thermal performance of salt hydrate PCM using multi-walled carbon nanotubes (MWCNTs) and surfactants. Through experimental investigations, a salt hydrate PCM with varying concentrations of MWCNTs (ranging from 0.1% to 0.9%) was prepared using a two-step technique and their thermophysical properties were thoroughly characterized. Various techniques such as field emission scanning electron microscope, thermal conductivity analyzer, ultraviolet-visible spectrum, thermogravimetric analyzer, and Fourier transform infrared spectroscopy were utilized to study the effect of surfactant on the nanocomposites and examine their morphology, thermal conductivity, optical properties, thermal stability, and chemical stability. The results indicated that the inclusion of MWCNTs with salt hydrate significantly improved the thermal conductivity by 68.09% at a concentration of 0.7 wt %, compared to pure salt hydrate. However, this enhancement in thermal performance was accompanied by a reduction in optical transmittance in the developed nanocomposite PCM. Additionally, the formulated nanocomposite demonstrated excellent thermal and chemical stability up to temperatures as high as 468 °C. As a result, this nanocomposite shows great promise as a potential candidate for solar TES applications, offering favourable characteristics for efficient energy storage from solar sources

Urinary nitrate might be an early biomarker for pediatric acute kidney injury in the emergency department

NO is involved in normal kidney function and perturbed in acute kidney injury (AKI). We hypothesized that urinary concentration of NO metabolites, nitrite, and nitrate would be lower in children with early AKI presenting to the emergency department (ED), when serum creatinine (SCr) was uninformative. Patients up to 19 y were recruited if they had a urinalysis and SCr obtained for routine care. Primary outcome, AKI, was defined by pediatric Risk, Injury, Failure, Loss of function, End-stage renal disease (pRIFLE) criteria. Urinary nitrite and nitrate were determined by HPLC. A total of 252 patients were enrolled, the majority (93%) of whom were without AKI. Although 18 (7%) had AKI by pRIFLE, 50% may not have had it identified by the SCr value alone at the time of visit. Median urinary nitrate was lower for injury versus risk (p = 0.03); this difference remained significant when the injury group was compared against the combined risk and no AKI groups (p = 0.01). Urinary nitrite was not significantly different between groups. Thus, low urinary nitrate is associated with AKI in the pediatric ED even when SCr is normal. Predictive potential of this putative urinary biomarker for AKI needs further evaluation in sicker patients

Advancements in additive manufacturing: Innovations in direct ink writing materials and their transformative practical applications

The domain of three-dimensional (3D) printing holds limitless potential, encompassing a diverse range of materials and applications. This review offers a comprehensive exploration of the Direct Ink Writing (DIW) technique within additive manufacturing, alongside recent breakthroughs in material advancement. The purview extends to encompass DIW methodologies, graphene oxide, hydrogels, shape-memory polymers, ceramics, polymers, and composite-based materials. The discussion delves into the multifaceted potential of 3D printing materials and their prospective applications, notably emphasizing the transformative role of DIW. The versatility of DIW is showcased in various fields, including energy storage, electronics, soft robotics, and biomedical applications. Through an in-depth analysis of capabilities of DIW and the diverse materials it encompasses, this review sheds light on the promising avenues that lie ahead in the evolving landscape of additive manufacturing