Quantitative Evaluation of Cancer Stigma among Non-Patient Population in Oman.

BACKGROUND: Cancer is widely considered as one of the most stigmatized diseases globally, despite scientific advances in the medicine. While most existing literatures focuses on cancer stigma as perceived by patients, there has been limited research on  stigma towards cancer among the non-cancer population. In 2014, Marlow et al developed and validated the "Cancer Stigma Scale" (CASS) specifically for the non-Cancer population. This study aims to quantitatively evaluate cancer stigma within the non-patient population in Oman. METHODS: This is a cross-sectional study conducted in Oman. The Cancer Stigma Scale (CASS) has been used to evaluate the cancer-related stigma among the non-cancer patient population in Oman. RESULTS: A total of 510 participants completed the survey of whom 57.6% were male. The personal responsibility section had the highest mean score, followed by the avoidance and financial discrimination. The lowest mean scores were observed in the danger and policy opposition sections. Female participants showed ore disagreement  with cancer stigma statements compared to males. Participants who knew someone with cancer expressed more disagreement with stigma statements than those  who did not know anyone with cancer. CONCLUSION: This study provides a baseline measurement of  cancer-related stigma among non-cancer patients in Oman, tilizing the CASS in a representative sample of the population. The results indicate generally low levels of stigma, though certain aspects are more pronounced, varying according to the participants' gender, age, and personal connections to someone with cancer.

Early Onset Colorectal Cancer in Arabs, Are We Dealing with a Distinct Disease?

Early-onset colorectal cancer (EOCRC) incidence is increasing worldwide. Efforts are directed to understand the biological and clinical signatures of EOCRC compared to late-onset colorectal cancer (LOCRC). EOCRC is thought to present differently across different ethnic groups and geographical regions. This study was an attempt to contribute with data from the Arab world toward the understanding of the clinicopathological parameters of EOCRC compared to LOCRC. Data from 254 CRC patients diagnosed at Sultan Qaboos University Hospital from the period 2015-2020 were studied. About 32.6% of all diagnosed CRC patients are below 50 years old, with no differences in gender distribution between EOCRC and LOCRC (p-value 0.417). Rectal involvement and tumor laterality were comparable among the two groups. Adenocarcinoma accounts for 83.3% and 94.2% of EOCRC and LOCRC, respectively. More mucinous and signet ring adenocarcinoma (8.3% each) were reported in EOCRC than LOCRC (2.9% and 2.2%, respectively). MLH1 and PMS2 loss are more common among LOCRC, but MSH6 loss is more frequent in EOCRC. The overall survival of EOCRC and LOCRC was comparable (median survival 64.88 and 67.24 months, respectively). This study showed comparable clinicopathological parameters between EOCRC and LOCRC from Arabs, which adds to the bigger picture of understand the disease.

Teaching, Learning and Assessing Anatomy with Artificial Intelligence: The Road to a Better Future.

Anatomy is taught in the early years of an undergraduate medical curriculum. The subject is volatile and of voluminous content, given the complex nature of the human body. Students frequently face learning constraints in these fledgling years of medical education, often resulting in a spiraling dwindling academic performance. Hence, there have been continued efforts directed at developing new curricula and incorporating new methods of teaching, learning and assessment that are aimed at logical learning and long-term retention of anatomical knowledge, which is a mainstay of all medical practice. In recent years, artificial intelligence (AI) has gained in popularity. AI uses machine learning models to store, compute, analyze and even augment huge amounts of data to be retrieved when needed, while simultaneously the machine itself can be programmed for deep learning, improving its own efficiency through complex neural networks. There are numerous specific benefits to incorporating AI in education, which include in-depth learning, storage of large electronic data, teaching from remote locations, engagement of fewer personnel in teaching, quick feedback from responders, innovative assessment methods and user-friendly alternatives. AI has long been a part of medical diagnostics and treatment planning. Extensive literature is available on uses of AI in clinical settings, e.g., in Radiology, but to the best of our knowledge there is a paucity of published data on AI used for teaching, learning and assessment in anatomy. In the present review, we highlight recent novel and advanced AI techniques such as Artificial Neural Networks (ANN), or more complex Convoluted Neural Networks (CNN) and Bayesian U-Net, which are used for teaching anatomy. We also address the main advantages and limitations of the use of AI in medical education and lessons learnt from AI application during the COVID-19 pandemic. In the future, studies with AI in anatomy education could be advantageous for both students to develop professional expertise and for instructors to develop improved teaching methods for this vast and complex subject, especially with the increasing paucity of cadavers in many medical schools. We also suggest some novel examples of how AI could be incorporated to deliver augmented reality experiences, especially with reference to complex regions in the human body, such as neural pathways in the brain, complex developmental processes in the embryo or in complicated miniature regions such as the middle and inner ear. AI can change the face of assessment techniques and broaden their dimensions to suit individual learners.

A Review of Radiomics in Predicting Therapeutic Response in Colorectal Liver Metastases: From Traditional to Artificial Intelligence Techniques.

An early evaluation of colorectal cancer liver metastasis (CRCLM) is crucial in determining treatment options that ultimately affect patient survival rates and outcomes. Radiomics (quantitative imaging features) have recently gained popularity in diagnostic and therapeutic strategies. Despite this, radiomics faces many challenges and limitations. This study sheds light on these limitations by reviewing the studies that used radiomics to predict therapeutic response in CRCLM. Despite radiomics' potential to enhance clinical decision-making, it lacks standardization. According to the results of this study, the instability of radiomics quantification is caused by changes in CT scan parameters used to obtain CT scans, lesion segmentation methods used for contouring liver metastases, feature extraction methods, and dataset size used for experimentation and validation. Accordingly, the study recommends combining radiomics with deep learning to improve prediction accuracy.

Proteomics: Concepts and applications in human medicine.

Proteomics is the complete evaluation of the function and structure of proteins to understand an organism's nature. Mass spectrometry is an essential tool that is used for profiling proteins in the cell. However, biomarker discovery remains the major challenge of proteomics because of their complexity and dynamicity. Therefore, combining the proteomics approach with genomics and bioinformatics will provide an understanding of the information of biological systems and their disease alteration. However, most studies have investigated a small part of the proteins in the blood. This review highlights the types of proteomics, the available proteomic techniques, and their applications in different research fields.