EliasHossain/CancerAbstracts huggingface dataset:
Category: Colon_Cancer
A case of spontaneous regression of transverse colon cancer is reported. A 64-year-old man was diagnosed as having cancer of the transverse colon at a local hospital. Initial and second colonoscopy examinations revealed a typical cancer of the transverse colon, which was diagnosed as moderately differentiated adenocarcinoma. The patient underwent right hemicolectomy 6 weeks after the initial colonoscopy. The resected specimen showed only a scar at the tumor site, and no cancerous tissue was proven histologically. The patient is alive with no evidence of recurrence 1 year after surgery. Although an antitumor immune response is the most likely explanation, the exact nature of the phenomenon was unclear. We describe this rare case and review the literature pertaining to spontaneous regression of colorectal cancer.
Category: Thyroid_Cancer
Thyroid cancer has shown a parallel increase with diabetes in the last few years. This narrative review aims to explain the association between these two entities, focusing on insulin resistance as the mediator and exploring the effects of antidiabetic agents on thyroid cancer incidence and progression.We searched Pubmed for English-written articles on insulin resistance, diabetes, antidiabetic treatments, and thyroid cancer reported from January 2019 to April 2023. Exclusion criteria were preclinical and clinical studies involving a population with thyroid dysfunction, benign nodular goiter, or those that only analyzed thyroid cancer's association with obesity.The results of the narrative literature review revealed 96 articles. Additionally, four studies from a manual search were retrieved. After the exclusion criteria were applied, we included 20 studies. Out of 8 studies on insulin-resistant or Metabolic Syndrome patients, all suggest a positive association with thyroid cancer. At the same time, for diabetes, four out of five publications support a link with thyroid cancer. The seven remaining studies on antidiabetics suggest that metformin might benefit thyroid cancer. In contrast, the evidence for an association between Glucagon-like peptide-1 receptor agonists (GLP-1 RA) and increased thyroid cancer findings is limited.In conclusion, the association between thyroid cancer and diabetes may be explained by insulin resistance, as shown in observational studies. However, the causal role is yet to be defined. Although the wide use of different antidiabetic agents has been related to thyroid cancer prevalence and progression, future research with drugs such as metformin or GLP-1 RA is still needed.
Category: Lung_Cancer
Pleural collections on the side of an obstructing bronchial cancer pose a particular challenge. All efforts should be placed into determining whether the collection is malignant or para-malignant with its significant implications on cancer staging. This article discusses various diagnostic modalities and therapeutic interventions needed for the optimal management of patients presenting with this situation. The order of interventions is dictated by the individual circumstances that patients present with, often requiring the pleural interventions to take place ahead of the bronchial obstruction management.
Category: Thyroid_Cancer
Cancer is a multistep phenomenon, and multiple cellular genetic lesions are involved in the emergence of the malignant neoplasm. Several early events, including ras mutations in follicular thyroid carcinoma and RET gene rearrangement in papillary tumors, have been implicated in the neoplastic transformation of thyrocytes. The stepwise array of mutations or deletions described to date in candidate oncogenes and tumor suppressor genes that are necessary for the advance to malignancy and for tumor progression await further investigation. The identification of additional, thyroid-cancer-specific lesions in these pathways is being further studied also.
Category: Generic
Artificial intelligence (AI) has given the electrocardiogram (ECG) and clinicians reading them super-human diagnostic abilities. Trained without hard-coded rules by finding often subclinical patterns in huge datasets, AI transforms the ECG, a ubiquitous, non-invasive cardiac test that is integrated into practice workflows, into a screening tool and predictor of cardiac and non-cardiac diseases, often in asymptomatic individuals. This review describes the mathematical background behind supervised AI algorithms, and discusses selected AI ECG cardiac screening algorithms including those for the detection of left ventricular dysfunction, episodic atrial fibrillation from a tracing recorded during normal sinus rhythm, and other structural and valvular diseases. The ability to learn from big data sets, without the need to understand the biological mechanism, has created opportunities for detecting non-cardiac diseases as COVID-19 and introduced challenges with regards to data privacy. Like all medical tests, the AI ECG must be carefully vetted and validated in real-world clinical environments. Finally, with mobile form factors that allow acquisition of medical-grade ECGs from smartphones and wearables, the use of AI may enable massive scalability to democratize healthcare.
Category: Colon_Cancer
Classical Non-homologous End Joining (NHEJ) pathway is the mainstay of cellular response to DNA double strand breaks. While aberrant expression of genes involved in this pathway has been linked with genomic instability and drug resistance in several cancers, limited information is available about its clinical significance in colon cancer. We performed a comprehensive analysis of seven essential genes, including XRCC5, XRCC6, PRKDC, LIG4, XRCC4, NHEJ1, and PAXX of this pathway, in colon cancer using multi-omics datasets, and studied their associations with molecular and clinicopathological features, including age, gender, stage, KRAS mutation, BRAF mutation, microsatellite instability status and promoter DNA methylation in TCGA colon cancer dataset. This analysis revealed upregulation of XRCC5, PRKDC, and PAXX in colon cancer compared to normal colon tissues, while LIG4 and NHEJ1 (XLF) displayed downregulation. The expression of these genes was independent of age and KRAS status, while XRCC5, PRKDC, and LIG4 exhibited reduced expression in BRAF mutant tumors. Interestingly, we observed a strong association between XRCC6, XRCC5, PRKDC and LIG4 overexpression and microsatellite instability status of the tumors. In multivariate analysis, high PAXX expression emerged as an independent prognostic marker for poor overall and disease specific survival. We also observed hypomethylation of PAXX promoter in tumors, which exhibited a strong correlation with its overexpression. Furthermore, PAXX overexpression was also associated with several oncogenic pathways as well as a reduction in numbers of tumor-infiltrating lymphocytes.
Category: Generic
Pancreatic cancer is the deadliest disease, with a five-year overall survival rate of just 11%. The pancreatic cancer patients diagnosed with early screening have a median overall survival of nearly ten years, compared with 1.5 years for those not diagnosed with early screening. Therefore, early diagnosis and early treatment of pancreatic cancer are particularly critical. However, as a rare disease, the general screening cost of pancreatic cancer is high, the accuracy of existing tumor markers is not enough, and the efficacy of treatment methods is not exact. In terms of early diagnosis, artificial intelligence technology can quickly locate high-risk groups through medical images, pathological examination, biomarkers, and other aspects, then screening pancreatic cancer lesions early. At the same time, the artificial intelligence algorithm can also be used to predict the survival time, recurrence risk, metastasis, and therapy response which could affect the prognosis. In addition, artificial intelligence is widely used in pancreatic cancer health records, estimating medical imaging parameters, developing computer-aided diagnosis systems, etc. Advances in AI applications for pancreatic cancer will require a concerted effort among clinicians, basic scientists, statisticians, and engineers. Although it has some limitations, it will play an essential role in overcoming pancreatic cancer in the foreseeable future due to its mighty computing power.
Category: Generic
In Nature, the adaptability of many organisms and their capability to survive in challenging and dynamically changing environments are closely linked to their characteristics and the morphology of their body parts [...].
Category: Generic
The increasing availability of Electric Vehicles (EVs) is driving a shift away from traditional gasoline-powered vehicles. Subsequently, the demand for Electric Vehicle Charging Systems (EVCS) is rising, leading to the significant growth of EVCS as public and private charging infrastructure. The cybersecurity-related risks in EVCS have significantly increased due to the growing network of EVCS. In this context, this paper presents a cybersecurity risk analysis of the network of EVCS. Firstly, the recent advancements in the EVCS network, recent EV adaptation trends, and charging use cases are described as a background of the research area. Secondly, cybersecurity aspects in EVCS have been presented considering infrastructure and protocol-centric vulnerabilities with possible cyber-attack scenarios. Thirdly, threats in EVCS have been validated with real-time data-centric analysis of EV charging sessions. The paper also highlights potential open research issues in EV cyber research as new knowledge for domain researchers and practitioners.
Category: Colon_Cancer
Glycosylation changes occur widely in colon tumours, suggesting glycosylated molecules as potential biomarkers for colon cancer diagnostics. In this study, proteoglycans (PGs) expression levels and their transcriptional patterns are investigated in human colon tumours in vivo and carcinoma cells in vitro. According to RT-PCR analysis, normal and cancer colon tissues expressed a specific set of PGs (syndecan-1, perlecan, decorin, biglycan, versican, NG2/CSPG4, serglycin, lumican, CD44), while the expression of glypican-1, brevican and aggrecan was almost undetectable. Overall transcriptional activity of the PGs in normal and cancer tissues was similar, although expression patterns were different. Expression of decorin and perlecan was down-regulated 2-fold in colon tumours, while biglycan and versican expression was significantly up-regulated (6-fold and 3-fold, respectively). Expression of collagen1A1 was also increased 6-fold in colon tumours. However, conventional HCT-116 colon carcinoma and AG2 colon cancer-initiating cells did not express biglycan and decorin and were versican-positive and -negative, respectively, demonstrating an extracellular origin of the PGs in cancer tissue. Selective expression of heparan sulfate (HS) proteoglycans syndecan-1 and perlecan in the AG2 colon cancer-initiating cell line suggests these PGs as potential biomarkers for cancer stem cells. Overall transcriptional activity of the HS biosynthetic system was similar in normal and cancer tissues, although significant up-regulation of extracellular sulfatases SULF1/2 argues for a possible distortion of HS sulfation patterns in colon tumours. Taken together, the obtained results suggest versican, biglycan, collagen1A1 and SULF1/2 expression as potential microenvironmental biomarkers and/or targets for colon cancer diagnostics and treatment.
Category: Generic
Many disciplines, including the broad Field of Information (iField), have been offering Data Science (DS) programs. There have been significant efforts exploring an individual discipline's identity and unique contributions to the broader DS education landscape. To advance DS education in the iField, the iSchool Data Science Curriculum Committee (iDSCC) was formed and charged with building and recommending a DS education framework for iSchools. This paper reports on the research process and findings of a series of studies to address important questions: What is the iField identity in the multidisciplinary DS education landscape? What is the status of DS education in iField schools? What knowledge and skills should be included in the core curriculum for iField DS education? What are the jobs available for DS graduates from the iField? What are the differences between graduate-level and undergraduate-level DS education? Answers to these questions will not only distinguish an iField approach to DS education but also define critical components of DS curriculum. The results will inform individual DS programs in the iField to develop curriculum to support undergraduate and graduate DS education in their local context.
Category: Colon_Cancer
Inherited colorectal cancer syndromes are responsible for a small percentage of all colorectal cancers, but affected individuals are at increased risk of gastrointestinal and extraintestinal malignancies. Gene testing plays an important diagnostic role and guides continued care to the patient and family members. Predisposition to colorectal cancer outside these inherited syndromes is less well defined, but recently established screening guidelines should prove to reduce the incidence of colorectal cancer in those with a familial risk. Colorectal cancer is preventable through recognition, treatment, and proper screening of those at risk.
Category: Generic
Medication treatment for opioid use disorder (MOUD) is an effective evidence-based therapy for decreasing opioid-related adverse outcomes. Effective strategies for retaining persons on MOUD, an essential step to improving outcomes, are needed as roughly half of all persons initiating MOUD discontinue within a year. Data science may be valuable and promising for improving MOUD retention by using "big data" (e.g., electronic health record data, claims data mobile/sensor data, social media data) and specific machine learning techniques (e.g., predictive modeling, natural language processing, reinforcement learning) to individualize patient care. Maximizing the utility of data science to improve MOUD retention requires a three-pronged approach: (1) increasing funding for data science research for OUD, (2) integrating data from multiple sources including treatment for OUD and general medical care as well as data not specific to medical care (e.g., mobile, sensor, and social media data), and (3) applying multiple data science approaches with integrated big data to provide insights and optimize advances in the OUD and overall addiction fields.
Category: Lung_Cancer
The morphology of the tumor and the anatomic extent of the disease are important factors influencing treatment selection and ultimately survival for patients with lung cancer. The American Joint Committee TNM system provides a method for consistent reproducible description of the primary tumor (T), the status of the regional lymph nodes (N), and the presence or absence of distant metastasis (M). The TNM subsets thus classified can be grouped into three "stages" of disease such that the survival expectations for patients in each stage and cell type are similar. This classification of patients with respect to estimates of their prognosis is essential for valid comparisons of treatment modalities and meaningful communication of end results information.Clinical characteristics which influence survival are reflected in the staging recommendations. The size of the lesion, the proximal margination, and the presence or absence of other pulmonary complications are features which distinguish the T classification as T1, T2, or T3. The presence or absence of lymph node involvement has an important bearing on survival expectations. Advancing from no nodal involvement, N0, to involvement of the peribronchial and hilar nodes, N1, and then to the mediastinal nodes, N2, causes progressive erosion in survival expectations. The tumor morphology and specific nodes that are involved are important components of this relationship. The presence of distant metastasis, M1, is synonymous with an extremely poor prognosis. Using these prognostic elements, the TNM subsets are combined into three stages of disease so that patients in each group will have a generally similar life expectancy, the survival for patients with stage I disease being significantly greater than that for patients with stage II disease which is significantly greater than survival for patients with stage III disease.Improvements in the outcome for lung cancer patients depend upon the depth and scope of our scientific understandings and our ability to communicate our observations to one another. Measures of response to treatment can be translated into therapeutic practice only if uniform evaluators are used. Accordingly, a reproducible valid system for staging of lung cancer is recommended.
Category: Colon_Cancer
The association between physical job activity and colon cancer was examined in a 19-year follow-up study of 1.1 million Swedish men. The relative risk (RR) of colon cancer in men employed in sedentary occupations was estimated at 1.3 (1.2-1.5, 90% confidence interval), with the highest risk for the transverse colon including flexures (RR = 1.6) and the lowest risk for the sigmoid (RR = 1.2). The relative risk for rectal cancer was not elevated. Age, population density, and social class did not confound the relative risk and were controlled for, whereas marital status and geographic region did not. Furthermore, food habits and physical activity during leisure time were examined in the same occupations in another sample and it was judged unlikely that confounding from these factors could explain the association.
Category: Generic
Over recent years, the use of statistical learning techniques applied to chemical problems has gained substantial momentum. This is particularly apparent in the realm of physical chemistry, where the balance between empiricism and physics-based theory has traditionally been rather in favor of the latter. In this guest Editorial for the special topic issue on "Machine Learning Meets Chemical Physics," a brief rationale is provided, followed by an overview of the topics covered. We conclude by making some general remarks.
Category: Generic
We extend techniques and learnings about the stochastic properties of nonlinear responses from finance to medicine, particularly oncology, where it can inform dosing and intervention. We define antifragility. We propose uses of risk analysis for medical problems, through the properties of nonlinear responses (convex or concave). We (1) link the convexity/concavity of the dose-response function to the statistical properties of the results; (2) define "antifragility" as a mathematical property for local beneficial convex responses and the generalization of "fragility" as its opposite, locally concave in the tails of the statistical distribution; (3) propose mathematically tractable relations between dosage, severity of conditions, and iatrogenics. In short, we propose a framework to integrate the necessary consequences of nonlinearities in evidence-based oncology and more general clinical risk management.
Category: Lung_Cancer
Several factors are known to promote the development of a lung cancer. Smoking, occupation, environment, chronic bronchitis, and scars in the lung are all risk factors. Many studies have stressed the importance of nutrition, in particular vitamins. Vitamin A is necessary for cell differentiation. Retrospective and prospective studies have proven the inverse relation between provitamin A, beta-carotene, and lung cancer of the squamous and oat cell types. Studies in which beta-carotene or natural vitamin A are supplied to smokers, controls, and patients after resection for lung cancer are in progress. The study of other vitamins such as vitamin C and vitamin E has not led to definitive conclusions. The trace element selenium may also exert a beneficial effect.
Category: Lung_Cancer
Lung cancer screening with low-dose chest computed tomography is now recommended for high-risk individuals by the US Preventive Services Task Force. This recommendation was informed by several randomized controlled trials, the largest of which, the National Lung Screening Trial, demonstrated a 20% relative reduction in lung cancer mortality with annual low-dose chest computed tomography compared with chest radiography.
Category: Generic
With a massive influx of multimodality data, the role of data analytics in health informatics has grown rapidly in the last decade. This has also prompted increasing interests in the generation of analytical, data driven models based on machine learning in health informatics. Deep learning, a technique with its foundation in artificial neural networks, is emerging in recent years as a powerful tool for machine learning, promising to reshape the future of artificial intelligence. Rapid improvements in computational power, fast data storage, and parallelization have also contributed to the rapid uptake of the technology in addition to its predictive power and ability to generate automatically optimized high-level features and semantic interpretation from the input data. This article presents a comprehensive up-to-date review of research employing deep learning in health informatics, providing a critical analysis of the relative merit, and potential pitfalls of the technique as well as its future outlook. The paper mainly focuses on key applications of deep learning in the fields of translational bioinformatics, medical imaging, pervasive sensing, medical informatics, and public health.
Category: Thyroid_Cancer
Ultrasound is the first-line diagnostic tool for diagnosis of thyroid diseases. The low aggressiveness of many thyroid cancers coupled with high sensitivity of sonography can lead to cancer diagnosis and treatment with no effect on outcomes. Ultrasound is recognized as the most important driver of thyroid cancer overdiagnosis. Ultrasound should not be used as a general screening tool and should be reserved for patients at high risk of thyroid cancer and in the diagnostic management of incidentally discovered thyroid nodules. With prescreening risk stratification and application of consensus criteria for nodule biopsy, the value of the diagnostic ultrasound can be maximized.
Category: Lung_Cancer
The National Lung Cancer Screening Trial (NLST) demonstrated a mortality reduction benefit associated with low-dose computed tomography (LDCT) screening for lung cancer. There has been considerable debate regarding the benefits and harms of LDCT lung cancer screening, including the challenges related to its practical implementation. One of the controversies regards overdiagnosis, which conceptually denotes diagnosing a cancer that, either because of its indolent, low-aggressiveness biologic behavior or because of limited life expectancy, is unlikely to result in significant morbidity during the patient's remainder lifetime. In theory, diagnosing and treating these cancers offer no measurable benefit while incurring costs and risks. Therefore, if a screening test detects a substantial number of overdiagnosed cancers, it is less likely to be effective. It has been argued that LDCT screening for lung cancer results in an unacceptably high rate of overdiagnosis. This article aims to defend the opposite stance. Overdiagnosis does exist and to a certain extent is inherent to any cancer-screening test. Nonetheless, the concept is less dualistic and more nuanced than it has been suggested. Furthermore, the average estimates of overdiagnosis in LDCT lung cancer screening based on the totality of published data are likely much lower than the highest published estimates, if a careful definition of a positive screening test reflecting our current understanding of lung cancer biology is utilized. This article presents evidence on why reports of overdiagnosis in lung cancer screening have been exaggerated.