UNASSIGNED: To screen for long non-coding RNA (lncRNA) molecular markers characteristic of osteoarthritis (OA) by utilizing the Gene Expression Omnibus (GEO) database combined with machine learning.
UNASSIGNED: The samples of 185 OA patients and 76 healthy individuals as normal controls were included in the study. GEO datasets were screened for differentially expressed lncRNAs. Three algorithms, the least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF), were used to screen for candidate lncRNA models and receiver operating characteristic (ROC) curves were plotted to evaluate the models. We collected the peripheral blood samples of 30 clinical OA patients and 15 health controls and measured the immunoinflammatory indicators. RT-PCR was performed for quantitative analysis of the expression of lncRNA molecular markers in peripheral blood mononuclear cells (PBMC). Pearson analysis was performed to examine the correlation between lncRNA and indicators for inflammation of the immune system.
UNASSIGNED: A total of 14 key markers were identified with LASSO, 6 genes were identified with SVM-RFE, and 24 genes were identified with RF. Venn diagram was used to screen for overlapping genes identified with the three algorithms, showing HOTAIR, H19, MIR155 HG, and NKILA to be the overlapping genes. The ROC curves showed that these four lncRNAs all had an area under the curve ( AUC) greater than 0.7. The RT-PCR findings revealed relatively elevated expression of HOTAIR, H19, and MIR155HG and decreased expression of NKILA in the PBMC of OA patients compared with those of the normal group ( P<0.01). The results were consistent with the bioinformatics predictions. Pearson analysis showed that the candidate lncRNAs were correlated with clinical indicators for inflammation.
UNASSIGNED: HOTAIR, H19, MIR155 HG, and NKILA can be used as molecular markers for the clinical diagnosis of OA and are correlate with clinical indicators of inflammation of the immune system.

The large-scale usage of petro-chemical-based plastics has proved to be a significant source of environmental pollution due to their non-biodegradable nature. Microbes-based enzymes such as esterases, cutinases, and lipases have shown the ability to degrade synthetic plastic. However, the degradation of plastics by enzymes is primarily limited by the unavailability of a robust enzymatic system, i.e., low activity and stability towards plastic degradation. Recently, the machine learning strategy involved structure-based and deep neural networks show desirable potential to generate functional, active stable, and tolerant polyethylene terephthalate (PET) degrading enzyme (FAST-PETase). FAST-PETase showed the highest PET hydrolytic activity among known enzymes or their variants and degraded broad ranges of plastics. The development of a closed-loop circular economy-based system of plastic degradation to monomers by FAST-PETase followed by the re-polymerization of monomers into clean plastics can be a more sustainable approach. As an alternative to synthetic plastics, diverse microbes can produce polyhydroxyalkanoates, and their degradation by microbes has been well-established. This article discusses recent updates in the enzymatic degradation of plastics for sustainable development.

UNASSIGNED: Diabetic kidney disease (DKD) is the leading cause of chronic kidney disease and end-stage renal disease worldwide. Early diagnosis is critical to prevent its progression. The aim of this study was to identify potential diagnostic biomarkers for DKD, illustrate the biological processes related to the biomarkers and investigate the relationship between them and immune cell infiltration.
UNASSIGNED: Gene expression profiles (GSE30528, GSE96804, and GSE99339) for samples obtained from DKD and controls were downloaded from the Gene Expression Omnibus database as a training set, and the gene expression profiles (GSE47185 and GSE30122) were downloaded as a validation set. Differentially expressed genes (DEGs) were identified using the training set, and functional correlation analyses were performed. The least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), and random forests (RF) were performed to identify potential diagnostic biomarkers. To evaluate the diagnostic efficacy of these potential biomarkers, receiver operating characteristic (ROC) curves were plotted separately for the training and validation sets, and immunohistochemical (IHC) staining for biomarkers was performed in the DKD and control kidney tissues. In addition, the CIBERSORT, XCELL and TIMER algorithms were employed to assess the infiltration of immune cells in DKD, and the relationships between the biomarkers and infiltrating immune cells were also investigated.
UNASSIGNED: A total of 95 DEGs were identified. Using three machine learning algorithms, DUSP1 and PRKAR2B were identified as potential biomarker genes for the diagnosis of DKD. The diagnostic efficacy of DUSP1 and PRKAR2B was assessed using the areas under the curves in the ROC analysis of the training set (0.945 and 0.932, respectively) and validation set (0.789 and 0.709, respectively). IHC staining suggested that the expression levels of DUSP1 and PRKAR2B were significantly lower in DKD patients compared to normal. Immune cell infiltration analysis showed that B memory cells, gamma delta T cells, macrophages, and neutrophils may be involved in the development of DKD. Furthermore, both of the candidate genes are associated with these immune cell subtypes to varying extents.
UNASSIGNED: DUSP1 and PRKAR2B are potential diagnostic markers of DKD, and they are closely associated with immune cell infiltration.

Half of the patients with heart failure (HF) have preserved ejection fraction (HFpEF). To date, there are no specific markers to distinguish this subgroup. The main objective of this work was to stratify HF patients using current biochemical markers coupled with clinical data. The cohort study included HFpEF (n = 24) and heart failure with reduced ejection fraction (HFrEF) (n = 34) patients as usually considered in clinical practice based on cardiac imaging (EF ≥ 50% for HFpEF; EF < 50% for HFrEF). Routine blood tests consisted of measuring biomarkers of renal and heart functions, inflammation, and iron metabolism. A multi-test approach and analysis of peripheral blood samples aimed to establish a computerized Machine Learning strategy to provide a blood signature to distinguish HFpEF and HFrEF. Based on logistic regression, demographic characteristics and clinical biomarkers showed no statistical significance to differentiate the HFpEF and HFrEF patient subgroups. Hence a multivariate factorial discriminant analysis, performed blindly using the data set, allowed us to stratify the two HF groups. Consequently, a Machine Learning (ML) strategy was developed using the same variables in a genetic algorithm approach. ML provided very encouraging explorative results when considering the small size of the samples applied. The accuracy and the sensitivity were high for both validation and test groups (69% and 100%, 64% and 75%, respectively). Sensitivity was 100% for the validation and 75% for the test group, whereas specificity was 44% and 55% for the validation and test groups because of the small number of samples. Lastly, the precision was acceptable, with 58% in the validation and 60% in the test group. Combining biochemical and clinical markers is an excellent entry to develop a computer classification tool to diagnose HFpEF. This translational approach is a springboard for improving new personalized treatment methods and identifying \"high-yield\" populations for clinical trials.

The causal role of somatic mutation and its interrelationship with gene expression profile during tumor development has already been observed, which plays a major role to decide the cancer grades and overall survival. Accurate and robust prediction of tumor grades and patients\' overall survival are important for prognosis, risk factors identification and betterment of the treatment strategy, especially for highly lethal tumors, like gliomas. Here, with the help of more accurate and widely used machine learning-based approaches, we propose an integrative computational pipeline that incorporates somatic mutations and gene expression profile for survival and grade prediction of glioma patients and simultaneously relates it to the drugs to be administered. This study gives us a clear understanding that the same drug is not effective for the treatment of same grade of cancer if the gene mutations are different. The alteration in a specific gene plays a very important role in tumor progression and should also be considered for the selection of appropriate drugs. This proposed framework includes all the necessary factors required for enhancement of therapeutic designs and could be useful for clinicians in determining an accurate and personalized treatment strategy for individual patients suffering from different life threatening diseases.