Tissue-or cell-specific targeting of vectors is critical to the success of gene therapy. We describe a novel approach to virus-mediated gene therapy, where viral replication and associated cytotoxicity are limited to a specific cell type by the regulated expression of an essential immediate-early viral gene product. This is illustrated with a herpes simplex virus type 1 (HSV-1) vector (G92A) whose growth is restricted to albuminexpressing cells. G92A was constructed by inserting an albumin enhancer/promoter-ICP4 transgene into the thymidine kinase gene of mutant HSV-1 d120, deleted for both copies of the ICP4 gene. This vector also contains the Escherichia coli lacZ gene under control of the thymidine kinase promoter, a viral early promoter, to permit easy detection of infected cells containing replicating vector. In the adult, albumin is expressed uniquely in the liver and in hepatocellular carcinoma and is transcriptionally regulated. The plaquing efficiency of G92A is >10 3 times higher on human hepatoma cells than on non-albumin-expressing human cells. The growth kinetics of G92A in albumin-expressing cells is delayed compared with that of wild-type HSV-1, likely due to aberrant expression of ICP4 protein. Cells undergoing a productive infection expressed detectable levels of ICP4 protein, as well as the reporter gene product ␤-galactosidase. Confining a productive, cytotoxic viral infection to a specific cell type should be useful for tumor therapy and the ablation of specific cell types for the generation of animal models of disease.

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Cancer is the common name used to categorize a collection of diseases. In the United States, there were an estimated 1.8 million new cancer cases and 600,000 cancer deaths in 2020. Though it has been proven that an early diagnosis can significantly reduce cancer mortality, cancer screening is inaccessible to much of the world’s population. Machine learning approaches are increasingly successful in image-based diagnosis, disease prognosis, and risk assessment. A literature search with the Google Scholar and PubMed databases from January 2020 to June 2021 determined that currently, no machine learning model (n=0/417) has an accuracy of 90% or higher in diagnosing multiple cancers. We propose our model HOPE, the Heuristic Oncological Prognosis Evaluator, a transfer learning diagnostic tool for the screening of patients with common cancers. By applying this approach to magnetic resonance (MRI) and digital whole slide pathology images, HOPE 2.0 demonstrates an overall accuracy of 95.52% in classifying brain, breast, colorectal, and lung cancer. HOPE 2.0 is a unique state-of-the-art model, as it possesses the ability to analyze multiple types of image data (radiology and pathology) and has an accuracy higher than existing models. HOPE 2.0 may ultimately aid in accelerating the diagnosis of multiple cancer types, resulting in improved clinical outcomes compared to previous research that focused on singular cancer diagnosis.

Machine learning approaches have been used to develop methods for the automatic detection of Parkinson’s Disease with voice recordings being the most used data type due to the simple and non-invasive nature of acquiring data. While most studies used voice samples recorded under controlled conditions, a few studies have used voice samples acquired via telephone. Although voice recordings captured via telephone or mobile devices allow much easier and wider access for data collection, current conflicting performance results limit their clinical applicability. The contribution of this study is two-fold: First, we show the reliability of telephone-collected voice recordings of the sustained vowel /a/ by collecting samples from 50 people with Parkinson’s Disease and 50 healthy controls and applying machine learning classification with voice features related to phonation. Second, we utilize a novel application of a convolutional neural network with transfer learning to analyze the spectrograms of the sustained vowel from these samples. This approach considers speech intensity estimates across time and frequency scales rather than collapsing measurements across time., We show the superiority of this pre-trained Inception V3 convolutional neural network model with transfer-learning for the task of classifying people with Parkinson’s Disease as distinct from healthy controls.