Improving the Post-Operative Prediction of BCR-Free Survival Time with mRNA Variables and Machine Learning.

Predicting the risk of, and time to biochemical recurrence (BCR) in prostate cancer patients post-operatively is critical in patient treatment decision pathways following surgical intervention. This study aimed to investigate the predictive potential of mRNA information to improve upon reference nomograms and clinical-only models, using a dataset of 187 patients that includes over 20,000 features. Several machine learning methodologies were implemented for the analysis of censored patient follow-up information with such high-dimensional genomic data. Our findings demonstrated the potential of inclusion of mRNA information for BCR-free survival prediction. A random survival forest pipeline was found to achieve high predictive performance with respect to discrimination, calibration, and net benefit. Two mRNA variables, namely ESM1 and DHAH8, were identified as consistently strong predictors with this dataset.

Endoplasmic Reticulum Stress-Regulated Chaperones as a Serum Biomarker Panel for Parkinson's Disease.

Examination of post-mortem brain tissues has previously revealed a strong association between Parkinson's disease (PD) pathophysiology and endoplasmic reticulum (ER) stress. Evidence in the literature regarding the circulation of ER stress-regulated factors released from neurons provides a rationale for investigating ER stress biomarkers in the blood to aid diagnosis of PD. The levels of ER stress-regulated proteins in serum collected from 29 PD patients and 24 non-PD controls were measured using enzyme-linked immunosorbent assays. A panel of four biomarkers, protein disulfide-isomerase A1, protein disulfide-isomerase A3, mesencephalic astrocyte-derived neurotrophic factor, and clusterin, together with age and gender had higher ability (area under the curve 0.64, sensitivity 66%, specificity 57%) and net benefit to discriminate PD patients from the non-PD group compared with other analyzed models. Addition of oligomeric and total α-synuclein to the model did not improve the diagnostic power of the biomarker panel. We provide evidence that ER stress-regulated proteins merit further investigation for their potential as diagnostic biomarkers of PD.

Immunoarray Measurements of Parathyroid Hormone-Related Peptides Combined with Other Biomarkers to Diagnose Aggressive Prostate Cancer.

Parathyroid hormone-related peptide (PTHrP) is related to bone metastasis and hypercalcemia in prostate and breast cancers and should be an excellent biomarker for aggressive forms of these cancers. Current clinical detection protocols for PTHrP are immunoradiometric assay and radioimmunoassay but are not sensitive enough to detect PTHrPs at early stages. We recently evaluated a prostate cancer biomarker panel, including serum monocyte differentiation antigen (CD-14), ETS-related gene protein, pigment epithelial-derived factor, and insulin-like growth factor-1, with promise for identifying aggressive prostate cancers. This panel predicted the need for patient biopsy better than PSA alone. In the present paper, we report an ultrasensitive microfluidic assay for PTHrPs and evaluate their diagnostic value and the value of including them with our prior biomarker panel to diagnose aggressive forms of prostate cancer. The immunoarray features screen-printed carbon sensor electrodes coated with 5 nm glutathione gold nanoparticles with capture antibodies attached. PTHrPs are bound to a secondary antibody attached to a polyhorseradish peroxidase label and delivered to the sensors to provide high sensitivity when activated by H2O2 and a mediator. We obtained an unprecedented 0.3 fg mL-1 limit of detection for PTHrP bioactive moieties PTHrP 1-173 and PTHrP 1-86. We also report the first study of PTHrPs in a large serum pool to identify aggressive malignancies. In assays of 130 human patient serum samples, PTHrP levels distinguished between aggressive and indolent prostate cancers with 83-91% clinical sensitivity and 78-96% specificity. Logistic regression identified the best predictive model as a combination of PTHrP 1-86 and vascular endothelial growth factor-D. PTHrP 1-173 alone also showed a high ability to differentiate aggressive and indolent cancers.

Investigation of light aromatics removal from industrial wastewater using nano metal organic framework.

In this study, the adsorption of benzoic acid and phenols in the aqueous phase by MOF-Cu adsorbent was investigated. A high-performance liquid chromatography (HPLC) device was used to analyze the concentration of contaminants in the solution. Three isotherms, Freundlich, Langmuir, and Temkin were performed for adsorption of Benzoic Acid (BA) and Phenol contaminants. Correlation factor for adsorption isotherms were fitted into Langmuir aqueous BA and Phenol would be 99.89 and 99.98%, respectively. The equilibrium adsorption capacity MOF-Cu of BA and Phenol is 636.73 and 524.42 mg/g, respectively. In this study, high contaminant adsorption with π-π interaction and hydrogen bonding leads to the high capacity of MOFCu. In addition, the increase in adsorption capacity of benzoic acid is due to the electronegative property of oxygen in the carbonyl group and the similarity of the carboxylic acid functional group with the adsorbent. The result shows, that at initial time adsorption, has been a non-linear trend. In addition, the first-order kinetic model is not a suitable option for fitting the experimental data of adsorption kinetics and the adsorption kinetics of BA and Phenol is very well compatible with the semi-second order with the correlation Factor being 99.7 and 99.78, respectively. Also, the equilibrium adsorption capacity in pseudo-second order kinetic for BA and Phenol is 613.5 and 523.56 mg/g respectively.

Integrating Serum Biomarkers into Prediction Models for Biochemical Recurrence Following Radical Prostatectomy.

This study undertook to predict biochemical recurrence (BCR) in prostate cancer patients after radical prostatectomy using serum biomarkers and clinical features. Three radical prostatectomy cohorts were used to build and validate a model of clinical variables and serum biomarkers to predict BCR. The Cox proportional hazard model with stepwise selection technique was used to develop the model. Model evaluation was quantified by the AUC, calibration, and decision curve analysis. Cross-validation techniques were used to prevent overfitting in the Irish training cohort, and the Austrian and Norwegian independent cohorts were used as validation cohorts. The integration of serum biomarkers with the clinical variables (AUC = 0.695) improved significantly the predictive ability of BCR compared to the clinical variables (AUC = 0.604) or biomarkers alone (AUC = 0.573). This model was well calibrated and demonstrated a significant improvement in the predictive ability in the Austrian and Norwegian validation cohorts (AUC of 0.724 and 0.606), compared to the clinical model (AUC of 0.665 and 0.511). This study shows that the pre-operative biomarker PEDF can improve the accuracy of the clinical factors to predict BCR. This model can be employed prior to treatment and could improve clinical decision making, impacting on patients' outcomes and quality of life.

Prostate Cancer Diagnosis in the Clinic Using an 8-Protein Biomarker Panel.

The inability to distinguish aggressive from indolent prostate cancer is a longstanding clinical problem. Prostate specific antigen (PSA) tests and digital rectal exams cannot differentiate these forms. Because only ∼10% of diagnosed prostate cancer cases are aggressive, existing practice often results in overtreatment including unnecessary surgeries that degrade patients' quality of life. Here, we describe a fast microfluidic immunoarray optimized to determine 8-proteins simultaneously in 5 µL of blood serum for prostate cancer diagnostics. Using polymeric horseradish peroxidase (poly-HRP, 400 HRPs) labels to provide large signal amplification and limits of detection in the sub-fg mL-1 range, a protocol was devised for the optimization of the fast, accurate assays of 100-fold diluted serum samples. Analysis of 130 prostate cancer patient serum samples revealed that some members of the protein panel can distinguish aggressive from indolent cancers. Logistic regression was used to identify a subset of the panel, combining biomarker proteins ETS-related gene protein (ERG), insulin-like growth factor-1 (IGF-1), pigment epithelial-derived factor (PEDF), and serum monocyte differentiation antigen (CD-14) to predict whether a given patient should be referred for biopsy, which gave a much better predictive accuracy than PSA alone. This represents the first prostate cancer blood test that can predict which patients will have a high biopsy Gleason score, a standard pathology score used to grade tumors.