Mark A. Micale, J Lynne Williams, Sumi Dinda, and Sara Rivard
Publication Date: 2022
- Coverage of physical therapy patient management includes acute care, outpatient, and multidisciplinary clinical settings, along with in-depth therapeutic management interventions.
- Content on the continuum of cancer care addresses the primordial, primary, secondary, tertiary, and quaternary stages in prevention and treatment.
- Focus on clinicians includes the professional roles, responsibilities, self-care, and values of the oncology rehabilitation clinician as an integral member of the cancer care team.
Use of Immunohistochemistry to Determine Expression of Rab5 Subfamily of GTPases in Mature and Developmental Brains
Kwok Ling Kam, Paige Parrack, Marcellus Banworth, Sheeja Aravindan, Guangpu Li, and Kar Ming Fung
Publication Date: 8-29-2021
Rab GTPases are essentially molecular switches. They serve as master regulators in intracellular membrane trafficking from the formation and transport of vesicles at the originating organelle to its fusion to the membrane at the target organelle. Their functions are diversified and each has their specific subcellular location. Their expression may vary significantly in the same cell when the level of protein production is significantly different in different physiologic status. One of the best examples is the transition from fetal to mature status of cells. Expression and localization of Rab GTPases in mature and developing brains have not been well studied. Immunohistochemistry is an efficient way in the detection, semiquantitation, and localization of Rab GTPases in tissue sections. It is inexpensive and fast which allow efficient mass screening of many sections. In this chapter, we describe the immunohistochemical assay protocol for analyzing several Rab protein expressions of the Rab5 subfamily, including Rab5, Rab17, Rab22, and Rab31, in developmental (fetal) and mature human brains.
Barbara S. Ducatman
Publication Date: 2-21-2020
Concise yet comprehensive, Cytology: Diagnostic Principles and Clinical Correlates is a practical guide to the diagnostic interpretation of virtually any cytological specimen you may encounter. This highly useful bench manual covers all organ systems and situations in which cytology is used, including gynecologic, non-gynecologic, and FNA samples, with an in-depth differential diagnosis discussion for all major entities. As with previous editions, the revised 5th Edition focuses on practical issues in diagnosis and the use of cytology in clinical care, making it ideal for both trainee and practicing pathologists.
Barbara S. Ducatman and Kurt D. Bernacki
Publication Date: 12-5-2019
Fine-needle aspiration (FNA) and small core needle biopsy are mainstays in the evaluation of liver masses. They are usually performed percutaneously with guidance by computed tomography (CT), ultrasonography, or magnetic resonance imaging (MRI), and its principal value is in the diagnosis of malignancies. FNA can also be performed with the aid of endoscopic ultrasonography (EUS).
Junyan Wu, Jia-Xing Zhong, Eric Z. Chen, Jingwei Zhang, Jay J. Ye, and Limin Yu
Publication Date: 11-14-2019
Deep learning has been used to identify Basal Cell Carcinoma (BCC) from pathology images. The traditional patch-based strategy has the problem of integrating patch level information into the whole image level prediction. Also, it is often difficult to obtain sufficient high-quality patch labels such as pixel-wise segmentation masks. Benefiting from the recent development of Graph-CNN (GCN), we propose a new weakly- and semi-supervised GCN architecture to model patch-patch relation and provide patch-aware interpretability. Integrating prior knowledge and structure information, without relying on pixel-wise segmentation labels, our whole image level prediction achieves state-of-art performance with mAP 0.9556 and AUC 0.9502. Further visualization demonstrates that our model is implicitly consistent with the pixel-wise segmentation labels, which indicates our model can identify the region of interests without relying on the pixel-wise labels.
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