Articles

This paper leverages gene and cell therapy research in diverse disorders ranging from monogenic to infectious diseases to cancer and emerging breakthroughs, where one can harness individual genes or a synthetic gene sequence designed based on a shared molecular pattern in infected cells to better fight various disorders [1]. A pivotal task is to predict the performances of candidate gene therapies to guide clinical translational research using methods such as retrospective bioinformatic analyses. Implementing them to a large-scale gene therapy database reveals that it is feasible to construct and apply well-performing interpretable, supervised learning models [2]. Preliminary evidence of machine learning approaches' statistical significance helps clinicians and biomedical researchers, market participants, and regulatory and economic experts derive relevant, practical applications, thereby enhancing the deployment of gene therapy and genomics to achieve positive, long-term growth for humanity while alleviating the ongoing worldwide economic burden precipitated by prolonged and recurring diseases. Deploying machine learning techniques to accelerate gene and cell therapy drug development and trials shall also mitigate the existing obstacle of limited patient access to emerging, transformative medical innovations such as gene therapy due to skyrocketing prices, which often herald gene therapy products as the world's most expensive medicines [3]. Moreover, in preventing patients from accessing effective, life-saving genetic medicines, there commonly exists a multidimensional access gap encompassing the availability, affordability, and quality or acceptability of these clinical treatments. The ensuing substantial gap has repeatedly been documented and mainly emanates from differential institutional and socio-political choices around resource allocation at international and domestic levels [4]. Particularly, it is also due to the stringent licensure and regulatory approval processes underpinned by insufficient evidence for novel safety and clinical efficacy profiles for genetic therapies in multiple micro-local diagnoses and subpopulations. We believe that a higher likelihood of gene therapy adoption shall result when the clinical evidence path contains adequate representation from the most diverse and relevant patient populations [5].

<i><b>Background. </b></i>Guanylate cyclase 1 soluble subunit alpha 2 (<i>sGC</i><i>α2</i>), also known as <i>GUCY1A2</i>, was reported to be upregulated and promoted tumorigenesis in cervical cancer. But whether <i>GUCY1A2</i> was abnormally expressed and its prognostic value in gastric cancer was unknown. The current study aimed to find out the prognostic value of <i>GUCY1A2</i> in gastric cancer by analyzing data from The Cancer Genome Atlas (TCGA) database. <i><b>Methods.</b></i> Wilcoxon signed-rank test, cox regression analysis and multivariant analysis were used to analyze the relationship between clinical characteristic and <i>GUCY1A2</i> expression level. Kaplan-Meier method was used to analyze the association of <i>GUCY1A2</i> and overall survival. Gene set enrichment analysis (GSEA) was used to identify <i>GUCY1A2</i>-related signaling pathway. <i><b>Results.</b></i> Compared to normal tissue, expression of <i>GUCY1A2</i> was significantly increased in gastric cancer (p<0.001). Increased <i>GUCY1A2</i> was associated with advanced T stage (p=0.012) and poor survival (p=0.022). Univariate analysis showed that high <i>GUCY1A2 </i>expression was associated with a poor overall survival (HR:1.44, 95% confidence interval [CI]: 1.03-2.02, p=0.03). Multivariate analysis indicated that <i>GUCY1A3</i> remained an independent prognostic predictor of overall survival (HR:1.75, 95% confidence interval [CI]: 1.20-2.56, p=0.00). GSEA revealed that calcium signaling pathway, <i>MAPK </i>signaling pathway, <i>TGF-β</i> signaling pathway and <i>Wnt</i> signaling pathway were enriched in <i>GUCY1A2 </i>high expression phenotype. <i><b>Conclusions.</b></i> <i>GUCY1A2</i> maybe a potential prognostic predictor of poor survival in gastric cancer. But it need to be further validated clinically.

Advances of wearables, sensors, smart devices, and electronic health records have generated patient-oriented longitudinal data sources that are analyzed with advanced analytical tools to generate enormous opportunities to understand patient health conditions and needs, transforming healthcare significantly from conventional paradigms to more patient-specific and preventive approaches. Artificial intelligence (AI) with a machine learning methodology is prominently considered as it is uniquely suitable to derive predictions and recommendations from complex patient datasets. Recent studies have shown that precise data aggregation methods exhibit an important role in the precision and reliability of clinical outcome distribution models. There is an essential need to develop an effective and powerful multifunctional machine learning platform to enable healthcare professionals to comprehend challenging biomedical multifactorial datasets to understand patient-specific scenarios and to make better clinical decisions, potentially leading to the optimist patient outcomes. There is a substantial drive to develop the networking and interoperability of clinical systems, the laboratory, and public health. These steps are delivered in concert with efforts at enabling usefully analytic tools and technologies for making sense of the eruption of overall patient’s information from various sources. However, the full efficiency of this technology can only be eliminated when ethical, legal, and social challenges related to reducing the privacy of healthcare information are successfully absorbed. Public and media are to be informed about the capabilities and limitations of the technologies and the paramount to be balanced is juvenile public healthcare data privacy debate. While this is ongoing, the measures have been progressed from patient data protection abuses for progress to realize the full potential of AI technology for hosting the health system, with benefits for all stakeholders. Any protection program should be based on fairness, transparency, and a full commitment to data privacy. On-going innovative systems that use AI to manage clinical data and analyzes are proposed. These tools can be used by healthcare providers, especially in defining specific scenarios related to biomedical data management and analysis. These platforms ensure that the significant and potentially predictive parameters associated with the diagnosis, treatment, and progression of the disease have been recognized. With the systematic use of these solutions, this work can contribute to the realization of noticeable improvements in the provision of real-time, personalized, and efficient medicine at a reduced cost [1].