Supervised machine learning based multi-task artificial intelligence classification of retinopathies

Artificial intelligence (AI) classification holds promise as a novel and affordable screening tool for clinical management of ocular diseases. Rural and underserved areas, which suffer from lack of access to experienced ophthalmologists may particularly benefit from this technology. Quantitative optical coherence tomography angiography (OCTA) imaging provides excellent capability to identify subtle vascular distortions, which are useful for classifying retinovascular diseases. However, application of AI for differentiation and classification of multiple eye diseases is not yet established. In this study, we demonstrate supervised machine learning based multi-task OCTA classification. We sought 1) to differentiate normal from diseased ocular conditions, 2) to differentiate different ocular disease conditions from each other, and 3) to stage the severity of each ocular condition. Quantitative OCTA features, including blood vessel tortuosity (BVT), blood vascular caliber (BVC), vessel perimeter index (VPI), blood vessel density (BVD), foveal avascular zone (FAZ) area (FAZ-A), and FAZ contour irregularity (FAZ-CI) were fully automatically extracted from the OCTA images. A stepwise backward elimination approach was employed to identify sensitive OCTA features and optimal-feature-combinations for the multi-task classification. For proof-of-concept demonstration, diabetic retinopathy (DR) and sickle cell retinopathy (SCR) were used to validate the supervised machine leaning classifier. The presented AI classification methodology is applicable and can be readily extended to other ocular diseases, holding promise to enable a mass-screening platform for clinical deployment and telemedicine.Comment: Supplemental material attached at the en

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.11Ysciescopu

Assessment of porcine endogenous retrovirus transmission across an alginate barrier used for the encapsulation of porcine islets

BACKGROUND: Subcutaneous implantation of a macroencapsulated patch containing human allogenic islets has been successfully used to alleviate type 1 diabetes mellitus (T1DM) in a human recipient without the need for immunosuppression. The use of encapsulated porcine islets to treat T1DM has also been reported. Although no evidence of pathogen transfer using this technology has been reported to date, we deemed it appropriate to determine if the encapsulation technology would prevent the release of virus, in particular, the porcine endogenous retrovirus (PERV). METHODS: HEK293 (human epithelial kidney) and swine testis (ST) cells were co-cultured with macroencapsulated pig islets embedded in an alginate patch, macroencapsulated PK15 (swine kidney epithelial) cells embedded in an alginate patch and free PK15 cells. Cells and supernatant were harvested at weekly time points from the cultures for up to 60 days and screened for evidence of PERV release using qRT-PCR to detect PERV RNA and SG-PERT to detect reverse transcriptase (RT). RESULTS: No PERV virus, or evidence of PERV replication, was detected in the culture medium of HEK293 or pig cells cultured with encapsulated porcine islets. Increased PERV activity relative to the background was not detected in ST cells cultured with encapsulated PK15 cells. However, PERV was detected in 1 of the 3 experimental replicates of HEK293 cells cultured with encapsulated PK15 cells. Both HEK293 and ST cells cultured with free PK15 cells showed an increase in RT detection. CONCLUSIONS: With the exception of 1 replicate, there does not appear to be evidence of transmission of replication competent PERV from the encapsulated islet cells or the positive control PK15 cells across the alginate barrier. The detection of PERV would suggest the alginate barrier of this replicate may have become compromised, emphasizing the importance of quality control when producing encapsulated islet patches

Dual expression recombinase based (DERB) single vector system for high throughput screening and verification of protein interactions in living cells

Identification of novel protein interactions and their mediators is fundamental in understanding cellular processes and is necessary for protein-targeted therapy. Evidently high throughput formatting of these applications in living cells would be beneficial, however no adequate system exists. We present a novel platform technology for the high throughput screening and verification of protein interactions in living cells. The platform's series of Dual Expression Recombinase Based (DERB) destiny vectors individually encode two sets of recombinase recognizable sequences for inserting the protein open reading frame (ORF) of interest, two sets of promoters and reporter tags in frame with the ORFs for detecting interactions. Introduction into living cells (prokaryotic and eukaryotic) enables the detection of protein interactions by fluorescence resonance energy transfer (FRET) or bimolecular fluorescence complementation (BiFC). The DERB platform shows advantages over current commercialized systems by DERB vectors validated through proof-of-principle experiments and the identification of an unknown interaction