Notes

Monitoring associated with strangles in UK horses among 2015 along with 2019 according to lab discovery of Streptococcus equi.
Additionally, 131-iodine-meta-iodobenzylguanidine (131-I-MIBG) initially had been used for gastroenteropancreatic neuroendocrine (carcinoid) tumors. However, recently clinical trials have start enrolling patients to evaluate efficacy of 131-I-MIBG in patients with small cell carcinoma of the lung. In the era of precision medicine and personalized targeted therapeutics, Theranostics can play a key pivotal in improving diagnostic and therapeutic specificity by increasing potency of these targeted small molecules and antibodies with radioisotopes. In this review, we will review various clinically relevant Theranostics agent and their utility in thoracic disorders, notably within oncology.Thyroid cancer affects 1.3% of the population with increasing rates of incidence over the last decade (approximately 2% per year). Although the overall prognosis is good in the differentiated subtypes, there has been a slow but steady increase in rate of deaths associated with thyroid cancer (approximately 0.7% per year over the last decade). Thyroid cancer is usually detected when (I) patients feel a lump in the neck; (II) a routine clinical exam is performed; (III) an incidental thyroid nodule is identified on diagnostic imaging (e.g., CT neck or chest, carotid ultrasound, PET scan acquired for non-thyroid pathology). Identification of suspicious thyroid nodules results in further diagnostic work-up including laboratory assessment, further imaging, and biopsy. Accurate diagnosis is required for clinical staging and optimal patient treatment design. In this review, we aim to discuss utility of various imaging modalities and their role in thyroid cancer diagnosis and management. Additionally, we aim to highlight emerging diagnostic techniques that aim to improve diagnostic specificity and accuracy in thyroid cancer, thus paving way for precision medicine.Within the past decade, immunotherapy has revolutionized the treatment of advanced non-small lung cancer (NSCLC). Immune checkpoint inhibitors (ICIs) such as pembrolizumab, nivolumab, atezolizumab, and durvalumab have shown superiority over chemotherapy regimens in patients with programmed death-ligand 1 (PD-L1) expression. Several predictive molecular biomarkers, including PD-L1 expression and high tumor mutation burden, have shown utility in discovering lung cancer patient groups that would benefit from ICIs. However, there remains to be a reliable imaging biomarker that would clearly select patients, through baseline or restaging imaging, who would respond or have a prolonged response to ICIs. The purpose of this review is to highlight the role of ICIs in patients with advanced NSCLC and past or current studies in potential biomarkers as well as future directions on the role of imaging in immunotherapy.In the era of Precision Medicine, diagnostic imaging plays a key role in initial diagnosis and treatment response assessment in thoracic manifestation of various rheumatic disorders; resulting in increased dependency on imaging for treatment planning. Chest radiographs serve as a good initial screening tool for assessment of emergent and urgent thoracic conditions, e.g., pneumothorax, pulmonary edema, consolidation and pleural effusions. Cross-sectional imaging techniques, e.g., computed tomography (CT) and positron emission tomography-computed tomography (PET-CT) are most commonly utilized to evaluate more detailed pulmonary and mediastinal manifestations of rheumatic conditions. Magnetic resonance imaging (MRI) and ultrasound are most commonly used in cardiovascular, neural and musculoskeletal structures. This review article aims to highly key common thoracic imaging findings of rheumatic disorders, highlighting imaging test of choice for the particular disorder.Machine learning (ML) and artificial intelligence (AI) are aiding in improving sensitivity and specificity of diagnostic imaging. The rapid adoption of these advanced ML algorithms is transforming imaging analysis; taking us from noninvasive detection of pathology to noninvasive precise diagnosis of the pathology by identifying whether detected abnormality is a secondary to infection, inflammation and/or neoplasm. This is led to the emergence of "Radiobiogenomics"; referring to the concept of identifying biologic (genomic, proteomic) alterations in the detected lesion. Radiobiogenomic involves image segmentation, feature extraction, and ML model to predict underlying tumor genotype and clinical outcomes. Lung cancer is the most common cause of cancer related death worldwide. There are several histologic subtypes of lung cancer, e.g., small cell lung cancer (SCLC), non-small cell lung cancer (NSCLC) (adenocarcinoma, squamous cell carcinoma). These variable histologic subtypes not only appear different at microscopic level, but these also differ at genetic and transcription level. This intrinsic heterogeneity reveals itself as different morphologic appearances on diagnostic imaging, such as CT, PET/CT and MRI. Traditional evaluation of imaging findings of lung cancer is limited to morphologic characteristics, such as lesion size, margins, density. L-685,458 purchase Radiomics takes image analysis a step further by looking at imaging phenotype with higher order statistics in efforts to quantify intralesional heterogeneity. This heterogeneity, in turn, can be potentially used to extract intralesional genomic and proteomic data. This review aims to highlight novel concepts in ML and AI and their potential applications in identifying radiobiogenomics of lung cancer.With emerging promising therapeutic regimens in non-small cell lung cancer (NSCLC), the standard-of-care treatments for a variety of histologic and mutated subgroups in NSCLC has been regularly shifting in response to landmark clinical trials. However, with the availability of a range of therapeutic agents, clear grouping of patient populations to appropriate treatment strategies is essential. In this review, we illustrate past and current treatment strategies in NSCLC, specifically focusing on targeted therapy and immunotherapy. We describe a complex clinical scenario that oncologists will encounter of patients with multiple actionable mutations such as epidermal growth factor receptor (EGFR) sensitizing mutations and high expression of programmed death-ligand 1 (PD-L1). Recent data regarding sequential therapy of EGFR tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) demonstrate severe adverse interactions between the therapies that impact patient quality-of-life and outcomes. As we enter further into an era of personalized and precision medicine, guidelines and standard-of-care therapies are essential to define separate patient groups based on molecular testing, histology, comorbidities, and more.
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