EGFR signaling pathway as therapeutic target in human cancers.

Spheroids and organoids are important novel players in medical and life science research. They are gradually replacing two-dimensional (2D) cell cultures. Indeed, three-dimensional (3D) cultures are closer to the in vivo reality and open promising perspectives for academic research, drug screening, and personalized medicine. A large variety of cells and tissues, including tumor cells, can be the starting material for the generation of 3D cultures, including primary tissues, stem cells, or cell lines. A panoply of methods has been developed to generate 3D structures, including spontaneous or forced cell aggregation, air–liquid interface conditions, low cell attachment supports, magnetic levitation, and scaffold-based technologies. The choice of the most appropriate method depends on (i) the origin of the tissue, (ii) the presence or absence of a disease, and (iii) the intended application. This review summarizes methods and approaches for the generation of cancer spheroids and organoids, including their advantages and limitations. We also highlight some of the challenges and unresolved issues in the field of cancer spheroids and organoids, and discuss possible therapeutic applications.

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Cancer Spheroids and Organoids as Novel Tools for Research and Therapy: State of the Art and Challenges to Guide Precision Medicine

Abstract Lung cancer is the most common cancer and the leading cause of cancer-related death worldwide. However, mechanisms of its progression remained unclear and new treatments against this disease are rapidly emerging. As a novel preclinical model, patient-derived organoid (PDO) can also be established from the patient’s tumor tissue and cultured in the laboratory, which preserves the key biological characteristics of the original tumor. Compared to the patient-derived xenograft (PDX) model of lung cancer, the culture success rate is improved, and the time and cost of model establishment are largely reduced. PDO is also expected to provide a more individual model to predict the efficacy of anti-cancer treatment in vitro. This paper summarizes the current application of PDO in the translational research of lung cancer. 

/pdf/the-application-of-patient-derived-organoid-in-the-research-27ywzwvu.pdf

The application of patient-derived organoid in the research of lung cancer

The impact of artificial intelligence (AI) will be felt not only in the arena of patient care and deliverable therapies but will also be uniquely disruptive in medical education and healthcare simulation (HCS), in particular. As HCS is intertwined with computer technology, it offers opportunities for rapid scalability with AI and, therefore, will be the most practical place to test new AI applications. This will ensure the acquisition of AI literacy for graduates from the country’s various healthcare professional schools. Artificial intelligence has proven to be a useful adjunct in developing interprofessional education and team and leadership skills assessments. Outcome-driven medical simulation has been extensively used to train students in image-centric disciplines such as radiology, ultrasound, echocardiography, and pathology. Allowing students and trainees in healthcare to first apply diagnostic decision support systems (DDSS) under simulated conditions leads to improved diagnostic accuracy, enhanced communication with patients, safer triage decisions, and improved outcomes from rapid response teams. However, the issue of bias, hallucinations, and the uncertainty of emergent properties may undermine the faith of healthcare professionals as they see AI systems deployed in the clinical setting and participating in diagnostic judgments. Also, the demands of ensuring AI literacy in our healthcare professional curricula will place burdens on simulation assets and faculty to adapt to a rapidly changing technological landscape. Nevertheless, the introduction of AI will place increased emphasis on virtual reality platforms, thereby improving the availability of self-directed learning and making it available 24/7, along with uniquely personalized evaluations and customized coaching. Yet, caution must be exercised concerning AI, especially as society’s earlier, delayed, and muted responses to the inherent dangers of social media raise serious questions about whether the American government and its citizenry can anticipate the security and privacy guardrails that need to be in place to protect our healthcare practitioners, medical students, and patients.

Artificial Intelligence and Healthcare Simulation: The Shifting Landscape of Medical Education

Lung cancer is a public health problem worldwide. Currently, identifying genetic mutations in the epidermal growth factor receptor (EGFR) has brought significant changes in diagnosing and managing patients with lung cancer. The presence of multiple mutations, defined as the presence of more than one EGFR mutation, has been reported in a few studies. Therefore, we carried out this systematic review to describe the most common multiple mutations in the EGFR gene.We conduct a systematic review of descriptive studies, cohorts, and clinical trials published in Scopus, PubMed, Scielo, and Virtual Health Library literature. The inclusion criteria for the systematic review were descriptive studies, cohorts, and clinical trials with the presence of multiple mutations in the EGFR gene. It was followed the Preferred Reporting Items for Systematic Meta-Analyses (PRISMA) guidelines.In the systematic review, 41 articles were included. Four hundred and forty-six cases with multiple mutations in the EGFR gene were found (0.95% of the patients included in the studies). The most prevalent dual mutations observed were T790M + L858R and deletions in exon 19 + T790M. Triple mutations were found in 9 cases (2.017%). According to reports, the presence of T790M mutation in the multiple mutations has been associated with poor clinical outcomes.The presence of multiple mutations in the EGFR gene is rare. It is of great importance to consider the T790M mutation since it generates resistance to pharmacological management and has worse outcomes. The most important limitation was that clinical information data and follow-up could not be collected in a large percentage of patients. Therefore, future work should be focused on clinical characteristics, follow-up and repercussions in the treatment of patients with multiple mutations. 

/pdf/multiple-mutations-in-the-egfr-gene-in-lung-cancer-a-1yijguw5.pdf

Multiple mutations in the EGFR gene in lung cancer: a systematic review

Lung adenocarcinoma organoids harboring EGFR 19Del and L643V double mutations respond to osimertinib and gefitinib: A case report.

Background The unannounced standardized patient (USP) is the gold standard for primary health care (PHC) quality assessment but has many restrictions associated with high human and resource costs. Virtual patient (VP) is a valid, low-cost software option for simulating clinical scenarios and is widely used in medical education. It is unclear whether VP can be used to assess the quality of PHC. Objective This study aimed to examine the agreement between VP and USP assessments of PHC quality and to identify factors influencing the VP-USP agreement. Methods Eleven matched VP and USP case designs were developed based on clinical guidelines and were implemented in a convenience sample of urban PHC facilities in the capital cities of the 7 study provinces. A total of 720 USP visits were conducted, during which on-duty PHC providers who met the inclusion criteria were randomly selected by the USPs. The same providers underwent a VP assessment using the same case condition at least a week later. The VP-USP agreement was measured by the concordance correlation coefficient (CCC) for continuity scores and the weighted κ for diagnoses. Multiple linear regression was used to identify factors influencing the VP-USP agreement. Results Only 146 VP scores were matched with the corresponding USP scores. The CCC for medical history was 0.37 (95% CI 0.24-0.49); for physical examination, 0.27 (95% CI 0.12-0.42); for laboratory and imaging tests, –0.03 (95% CI –0.20 to 0.14); and for treatment, 0.22 (95% CI 0.07-0.37). The weighted κ for diagnosis was 0.32 (95% CI 0.13-0.52). The multiple linear regression model indicated that the VP tests were significantly influenced by the different case conditions and the city where the test took place. Conclusions There was low agreement between VPs and USPs in PHC quality assessment. This may reflect the “know-do” gap. VP test results were also influenced by different case conditions, interactive design, and usability. Modifications to VPs and the reasons for the low VP-USP agreement require further study.

The Agreement Between Virtual Patient and Unannounced Standardized Patient Assessments in Evaluating Primary Health Care Quality: Multicenter, Cross-sectional Pilot Study in 7 Provinces of China

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Lung adenocarcinoma organoids harboring EGFR 19Del and L643V double mutations respond to osimertinib and gefitinib: A case report.

The Agreement Between Virtual Patient and Unannounced Standardized Patient Assessments in Evaluating Primary Health Care Quality: Multicenter, Cross-sectional Pilot Study in 7 Provinces of China