The development of machine learning in lung surgery: A narrative review.

Background Machine learning reflects an artificial intelligence that allows applications to improve their accuracy to predict outcomes, eliminating the need to conduct explicit programming on them. The medical field has increased its focus on establishing tools for integrating machine learning algorithms in laboratory and clinical settings. Despite their importance, their incorporation is minimal in the medical sector yet. The primary goal of this study is to review the development of machine learning in the field of thoracic surgery, especially lung surgery. Methods This article used the Preferred Reporting Items for Systematic and Meta-analyses (PRISMA). The sources used to gather data are the PubMed, Cochrane, and CINAHL databases and the Google Scholar search engine. Results The study included 19 articles, where ten concentrated on the application of machine learning in especially lung surgery, six focused on the benefits and limitations of machine learning algorithms in lung surgery, and three provided an overview of the future of machine learning in lung surgery. Conclusion The outcome of this study indicates that the field of lung surgery has attempted to integrate machine learning algorithms. However, the implementation rate is low, owing to the newness of the concept and the various challenges it encompasses. Also, this study reveals the absence of sufficient literature discussing the application of machine learning in lung surgery. The necessity for future research on the topic area remains evident

The Development of Healthcare Jobs in the COVID-Pandemic-A New Economic Market

BACKGROUND The COVID-19 pandemic commenced in China and has caused the death of numerous people globally. Despite the adverse effects, the outbreak has created room for job opportunities in healthcare, particularly the pharmaceutical domain. The main goal of this study is to examine how the current pandemic has triggered job creation in the healthcare domain and created a new economic market. METHODS The study used the Preferred Reporting Items for Systematic and Meta-analyses for Scoping Review (PRISMA-ScR) to structure the manuscript and the subheadings to use. The source used to gather data is the PubMed database. RESULTS The study exclusively included fourteen articles, five of which focused on the pharmaceutical sector, three focused on vaccine sales, three on vaccination centers, and three on testing centers. CONCLUSION The COVID-19 pandemic has created job opportunities in the healthcare sector. Most jobs are in the pharmaceutical sector, vaccination, and testing centers. However, more comprehensive research on the topic is necessary to gather conclusive outcomes on whether these jobs will be relevant after the pandemic

The development of machine learning in bariatric surgery

BackgroundMachine learning (ML), is an approach to data analysis that makes the process of analytical model building automatic. The significance of ML stems from its potential to evaluate big data and achieve quicker and more accurate outcomes. ML has recently witnessed increased adoption in the medical domain. Bariatric surgery, otherwise referred to as weight loss surgery, reflects the series of procedures performed on people demonstrating obesity. This systematic scoping review aims to explore the development of ML in bariatric surgery.MethodsThe study used the Preferred Reporting Items for Systematic and Meta-analyses for Scoping Review (PRISMA-ScR). A comprehensive literature search was performed of several databases including PubMed, Cochrane, and IEEE, and search engines namely Google Scholar. Eligible studies included journals published from 2016 to the current date. The PRESS checklist was used to evaluate the consistency demonstrated during the process.ResultsA total of seventeen articles qualified for inclusion in the study. Out of the included studies, sixteen concentrated on the role of ML algorithms in prediction, while one addressed ML's diagnostic capacity. Most articles (n = 15) were journal publications, whereas the rest (n = 2) were papers from conference proceedings. Most included reports were from the United States (n = 6). Most studies addressed neural networks, with convolutional neural networks as the most prevalent. Also, the data type used in most articles (n = 13) was derived from hospital databases, with very few articles (n = 4) collecting original data via observation.ConclusionsThis study indicates that ML has numerous benefits in bariatric surgery, however its current application is limited. The evidence suggests that bariatric surgeons can benefit from ML algorithms since they will facilitate the prediction and evaluation of patient outcomes. Also, ML approaches to enhance work processes by making data categorization and analysis easier. However, further large multicenter studies are required to validate results internally and externally as well as explore and address limitations of ML application in bariatric surgery

Prognostic value of immunohistochemical markers for locally advanced rectal cancer

The aim of this study is to reveal the potential roles of apoptosis markers (Bcl2 and p53), proliferation markers (Ki-67 and CyclD1), and the neuroendocrine marker Chromogranin A as markers for the radioresistance of rectal cancer. Statistically significant differences were found in the expression of p53, Ki-67, and Chromogranin A in groups of patients with and without a favorable prognosis after radiotherapy. The survival analysis revealed that the marker of neuroendocrine differentiation, Chromogranin A, also demonstrated a high prognostic significance, indicating a poor prognosis. Markers of proliferation and apoptosis had no prognostic value for patients who received preoperative radiotherapy. Higher Chromogranin A values were predictors of poor prognosis. The results obtained from studying the Chromogranin A expression suggest that the secretion of biologically active substances by neuroendocrine cells causes an increase in tumor aggressiveness

Machine learning in pancreas surgery, what is new? literature review

BackgroundMachine learning (ML) is an inquiry domain that aims to establish methodologies that leverage information to enhance performance of various applications. In the healthcare domain, the ML concept has gained prominence over the years. As a result, the adoption of ML algorithms has become expansive. The aim of this scoping review is to evaluate the application of ML in pancreatic surgery.MethodsWe integrated the preferred reporting items for systematic reviews and meta-analyses for scoping reviews. Articles that contained relevant data specializing in ML in pancreas surgery were included.ResultsA search of the following four databases PubMed, Cochrane, EMBASE, and IEEE and files adopted from Google and Google Scholar was 21. The main features of included studies revolved around the year of publication, the country, and the type of article. Additionally, all the included articles were published within January 2019 to May 2022.ConclusionThe integration of ML in pancreas surgery has gained much attention in previous years. The outcomes derived from this study indicate an extensive literature gap on the topic despite efforts by various researchers. Hence, future studies exploring how pancreas surgeons can apply different learning algorithms to perform essential practices may ultimately improve patient outcomes

Systematic identification of novel cancer genes through analysis of deep shRNA perturbation screens.

Systematic perturbation screens provide comprehensive resources for the elucidation of cancer driver genes. The perturbation of many genes in relatively few cell lines in such functional screens necessitates the development of specialized computational tools with sufficient statistical power. Here we developed APSiC (Analysis of Perturbation Screens for identifying novel Cancer genes) to identify genetic drivers and effectors in perturbation screens even with few samples. Applying APSiC to the shRNA screen Project DRIVE, APSiC identified well-known and novel putative mutational and amplified cancer genes across all cancer types and in specific cancer types. Additionally, APSiC discovered tumor-promoting and tumor-suppressive effectors, respectively, for individual cancer types, including genes involved in cell cycle control, Wnt/β-catenin and hippo signalling pathways. We functionally demonstrated that LRRC4B, a putative novel tumor-suppressive effector, suppresses proliferation by delaying cell cycle and modulates apoptosis in breast cancer. We demonstrate APSiC is a robust statistical framework for discovery of novel cancer genes through analysis of large-scale perturbation screens. The analysis of DRIVE using APSiC is provided as a web portal and represents a valuable resource for the discovery of novel cancer genes

Standardizing Patient-Derived Organoid Generation Workflow to Avoid Microbial Contamination From Colorectal Cancer Tissues.

The use of patient-derived organoids (PDO) as a valuable alternative to in vivo models significantly increased over the last years in cancer research. The ability of PDOs to genetically resemble tumor heterogeneity makes them a powerful tool for personalized drug screening. Despite the extensive optimization of protocols for the generation of PDOs from colorectal tissue, there is still a lack of standardization of tissue handling prior to processing, leading to microbial contamination of the organoid culture. Here, using a cohort of 16 patients diagnosed with colorectal carcinoma (CRC), we aimed to test the efficacy of phosphate-buffered saline (PBS), penicillin/streptomycin (P/S), and Primocin, alone or in combination, in preventing organoid cultures contamination when used in washing steps prior to tissue processing. Each CRC tissue was divided into 5 tissue pieces, and treated with each different washing solution, or none. After the washing steps, all samples were processed for organoid generation following the same standard protocol. We detected contamination in 62.5% of the non-washed samples, while the use of PBS or P/S-containing PBS reduced the contamination rate to 50% and 25%, respectively. Notably, none of the organoid cultures washed with PBS/Primocin-containing solution were contaminated. Interestingly, addition of P/S to the washing solution reduced the percentage of living cells compared to Primocin. Taken together, our results demonstrate that, prior to tissue processing, adding Primocin to the tissue washing solution is able to eliminate the risk of microbial contamination in PDO cultures, and that the use of P/S negatively impacts organoids growth. We believe that our easy-to-apply protocol might help increase the success rate of organoid generation from CRC patients

Transcriptional Enhancer Factor Domain Family member 4 Exerts an Oncogenic Role in Hepatocellular Carcinoma by Hippo-Independent Regulation of Heat Shock Protein 70 Family Members.

Transcriptional enhancer factor domain family member 4 (TEAD4) is a downstream effector of the conserved Hippo signaling pathway, regulating the expression of genes involved in cell proliferation and differentiation. It is up-regulated in several cancer types and is associated with metastasis and poor prognosis. However, its role in hepatocellular carcinoma (HCC) remains largely unexplored. Using data from The Cancer Genome Atlas, we found that TEAD4 was overexpressed in HCC and was associated with aggressive HCC features and worse outcome. Overexpression of TEAD4 significantly increased proliferation and migration rates in HCC cells in vitro as well as tumor growth in vivo. Additionally, RNA sequencing analysis of TEAD4-overexpressing HCC cells demonstrated that TEAD4 overexpression was associated with the up-regulation of genes involved in epithelial-to-mesenchymal transition, proliferation, and protein-folding pathways. Among the most up-regulated genes following TEAD4 overexpression were the 70-kDa heat shock protein (HSP70) family members HSPA6 and HSPA1A. Chromatin immunoprecipitation-quantitative real-time polymerase chain reaction experiments demonstrated that TEAD4 regulates HSPA6 and HSPA1A expression by directly binding to their promoter and enhancer regions. The pharmacologic inhibition of HSP70 expression in TEAD4-overexpressing cells reduced the effect of TEAD4 on cell proliferation. Finally, by overexpressing TEAD4 in yes-associated protein (YAP)/transcriptional coactivator with PDZ binding motif (TAZ)-knockdown HCC cells, we showed that the effect of TEAD4 on cell proliferation and its regulation of HSP70 expression does not require YAP and TAZ, the main effectors of the Hippo signaling pathway. Conclusion: A novel Hippo-independent mechanism for TEAD4 promotes cell proliferation and tumor growth in HCC by directly regulating HSP70 family members

Genomic analysis of focal nodular hyperplasia with associated hepatocellular carcinoma unveils its malignant potential: a case report.

Background Focal nodular hyperplasia (FNH) is typically considered a benign tumor of the liver without malignant potential. The co-occurrence of FNH and hepatocellular carcinoma (HCC) has been reported in rare cases. In this study we sought to investigate the clonal relationship between these lesions in a patient with FNH-HCC co-occurrence. Methods A 74-year-old female patient underwent liver tumor resection. The resected nodule was subjected to histologic analyses using hematoxylin and eosin stain and immunohistochemistry. DNA extracted from microdissected FNH and HCC regions was subjected to whole exome sequencing. Clonality analysis were performed using PyClone. Results Histologic analysis reveals that the nodule consists of an FNH and two adjoining HCC components with distinct histopathological features. Immunophenotypic characterization and genomic analyses suggest that the FNH is clonally related to the HCC components, and is composed of multiple clones at diagnosis, that are likely to have progressed to HCC through clonal selection and/or the acquisition of additional genetic events. Conclusion To the best of our knowledge, our work is the first study showing a clonal relationship between FNH and HCC. We show that FNH may possess the capability to undergo malignant transformation and to progress to HCC in very rare cases

GATA3 and MDM2 are synthetic lethal in estrogen receptor-positive breast cancers.

Synthetic lethal interactions, where the simultaneous but not individual inactivation of two genes is lethal to the cell, have been successfully exploited to treat cancer. GATA3 is frequently mutated in estrogen receptor (ER)-positive breast cancers and its deficiency defines a subset of patients with poor response to hormonal therapy and poor prognosis. However, GATA3 is not yet targetable. Here we show that GATA3 and MDM2 are synthetically lethal in ER-positive breast cancer. Depletion and pharmacological inhibition of MDM2 significantly impaired tumor growth in GATA3-deficient models in vitro, in vivo and in patient-derived organoids/xenograft (PDOs/PDX) harboring GATA3 somatic mutations. The synthetic lethality requires p53 and acts via the PI3K/Akt/mTOR pathway. Our results present MDM2 as a therapeutic target in the substantial cohort of ER-positive, GATA3-mutant breast cancer patients. With MDM2 inhibitors widely available, our findings can be rapidly translated into clinical trials to evaluate in-patient efficacy