In Vivo Microstructure Imaging in Oropharyngeal Squamous Cell Carcinoma Using the Random Walk With Barriers Model.

BACKGROUND: Apparent diffusion coefficient is not specifically sensitive to tumor microstructure and therapy-induced cellular changes. PURPOSE: To investigate time-dependent diffusion imaging with the short-time-limit random walk with barriers model (STL-RWBM) for quantifying microstructure parameters and early cancer cellular response to therapy. STUDY TYPE: Prospective. POPULATION: Twenty-seven patients (median age of 58 years and 7.4% of females) with p16+/p16- oropharyngeal/oral cavity squamous cell carcinomas (OPSCC/OCSCC) underwent MRI scans before therapy, of which 16 patients had second scans at 2 weeks of the 7-weeks chemoradiation therapy (CRT). FIELD STRENGTH/SEQUENCE: 3-T, diffusion sequence with oscillating gradient spine echo (OGSE) and pulse gradient spin echo (PGSE). ASSESSMENT: Diffusion weighted images were acquired using OGSE and PGSE. Effective diffusion times were derived for the STL-RWBM to estimate free diffusion coefficient D0 , volume-to-surface area ratio of cellular membranes V/S, and cell membrane permeability κ. Mean values of these parameters were calculated in tumor volumes. STATISTICAL TESTS: Tumor microstructure parameters were compared with clinical stages of p16+ I-II OPSCC, p16+ III OPSCC, and p16- IV OCSCC by Spearman's rank correlation and with digital pathological analysis of a resected tissue sample. Tumor microstructure parameter responses during CRT in the 16 patients were assessed by paired t-tests. A P-value of <0.05 was considered statistically significant. RESULTS: The derived effective diffusion times affected estimated values of V/S and κ by 40%. The tumor V/S values were significantly correlated with clinical stages (r = 0.47) as an increase from low to high clinical stages. The in vivo estimated cell size agreed with one from pathological analysis of a tissue sample. Early tumor cellular responses showed a significant increase in D0 (14%, P = 0.03) and non-significant increases in κ (56%, P = 0.6) and V/S (10%, P = 0.1). DATA CONCLUSION: Effective diffusion time estimation might impact microstructure parameter estimation. The tumor V/S was correlated with OPSCC/OCSCC clinical stages. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 1.

Banff Digital Pathology Working Group: Image Bank, Artificial Intelligence Algorithm, and Challenge Trial Developments.

The Banff Digital Pathology Working Group (DPWG) was established with the goal to establish a digital pathology repository; develop, validate, and share models for image analysis; and foster collaborations using regular videoconferencing. During the calls, a variety of artificial intelligence (AI)-based support systems for transplantation pathology were presented. Potential collaborations in a competition/trial on AI applied to kidney transplant specimens, including the DIAGGRAFT challenge (staining of biopsies at multiple institutions, pathologists' visual assessment, and development and validation of new and pre-existing Banff scoring algorithms), were also discussed. To determine the next steps, a survey was conducted, primarily focusing on the feasibility of establishing a digital pathology repository and identifying potential hosts. Sixteen of the 35 respondents (46%) had access to a server hosting a digital pathology repository, with 2 respondents that could serve as a potential host at no cost to the DPWG. The 16 digital pathology repositories collected specimens from various organs, with the largest constituent being kidney (n = 12,870 specimens). A DPWG pilot digital pathology repository was established, and there are plans for a competition/trial with the DIAGGRAFT project. Utilizing existing resources and previously established models, the Banff DPWG is establishing new resources for the Banff community.

Challenges in the Development, Deployment, and Regulation of Artificial Intelligence in Anatomic Pathology.

Significant advances in artificial intelligence (AI), deep learning, and other machine-learning approaches have been made in recent years, with applications found in almost every industry, including health care. AI is capable of completing a spectrum of mundane to complex medically oriented tasks previously performed only by boarded physicians, most recently assisting with the detection of cancers difficult to find on histopathology slides. Although computers will likely not replace pathologists any time soon, properly designed AI-based tools hold great potential for increasing workflow efficiency and diagnostic accuracy in pathology. Recent trends, such as data augmentation, crowdsourcing for generating annotated data sets, and unsupervised learning with molecular and/or clinical outcomes versus human diagnoses as a source of ground truth, are eliminating the direct role of pathologists in algorithm development. Proper integration of AI-based systems into anatomic-pathology practice will necessarily require fully digital imaging platforms, an overhaul of legacy information-technology infrastructures, modification of laboratory/pathologist workflows, appropriate reimbursement/cost-offsetting models, and ultimately, the active participation of pathologists to encourage buy-in and oversight. Regulations tailored to the nature and limitations of AI are currently in development and, when instituted, are expected to promote safe and effective use. This review addresses the challenges in AI development, deployment, and regulation to be overcome prior to its widespread adoption in anatomic pathology.

Rationale and design of the Kidney Precision Medicine Project.

Chronic kidney disease (CKD) and acute kidney injury (AKI) are common, heterogeneous, and morbid diseases. Mechanistic characterization of CKD and AKI in patients may facilitate a precision-medicine approach to prevention, diagnosis, and treatment. The Kidney Precision Medicine Project aims to ethically and safely obtain kidney biopsies from participants with CKD or AKI, create a reference kidney atlas, and characterize disease subgroups to stratify patients based on molecular features of disease, clinical characteristics, and associated outcomes. An additional aim is to identify critical cells, pathways, and targets for novel therapies and preventive strategies. This project is a multicenter prospective cohort study of adults with CKD or AKI who undergo a protocol kidney biopsy for research purposes. This investigation focuses on kidney diseases that are most prevalent and therefore substantially burden the public health, including CKD attributed to diabetes or hypertension and AKI attributed to ischemic and toxic injuries. Reference kidney tissues (for example, living-donor kidney biopsies) will also be evaluated. Traditional and digital pathology will be combined with transcriptomic, proteomic, and metabolomic analysis of the kidney tissue as well as deep clinical phenotyping for supervised and unsupervised subgroup analysis and systems biology analysis. Participants will be followed prospectively for 10 years to ascertain clinical outcomes. Cell types, locations, and functions will be characterized in health and disease in an open, searchable, online kidney tissue atlas. All data from the Kidney Precision Medicine Project will be made readily available for broad use by scientists, clinicians, and patients.

An Overview of the Effect of Aging on the Female Urethra.

Urethral function declines by roughly 15% per decade and profoundly contributes to the pathogenesis of urinary incontinence. Individuals with poor urethral function are more likely to fail surgical management for stress incontinence that focus on improving urethral support. The reduced number of intramuscular nerves and the morphologic changes in muscle and connective tissue collectively impact urethral function as women age. Imaging technologies like MRI and ultrasound have advanced our understanding of these changes. However, substantial knowledge gaps remain. Addressing these gaps can be crucial for developing better prevention and treatment strategies, ultimately enhancing the quality of life for aging women.

Evaluating Use of Generative Artificial Intelligence in Clinical Pathology Practice: Opportunities and the Way Forward.

CONTEXT.­: Generative artificial intelligence (GAI) technologies are likely to dramatically impact health care workflows in clinical pathology (CP). Applications in CP include education, data mining, decision support, result summaries, and patient trend assessments. OBJECTIVE.­: To review use cases of GAI in CP, with a particular focus on large language models. Specific examples are provided for the applications of GAI in the subspecialties of clinical chemistry, microbiology, hematopathology, and molecular diagnostics. Additionally, the review addresses potential pitfalls of GAI paradigms. DATA SOURCES.­: Current literature on GAI in health care was reviewed broadly. The use case scenarios for each CP subspecialty review common data sources generated in each subspecialty. The potential for utilization of CP data in the GAI context was subsequently assessed, focusing on issues such as future reporting paradigms, impact on quality metrics, and potential for translational research activities. CONCLUSIONS.­: GAI is a powerful tool with the potential to revolutionize health care for patients and practitioners alike. However, GAI must be implemented with much caution considering various shortcomings of the technology such as biases, hallucinations, practical challenges of implementing GAI in existing CP workflows, and end-user acceptance. Human-in-the-loop models of GAI implementation have the potential to revolutionize CP by delivering deeper, meaningful insights into patient outcomes both at an individual and population level.

Isolation of rare circulating tumour cells in cancer patients by microchip technology.

Viable tumour-derived epithelial cells (circulating tumour cells or CTCs) have been identified in peripheral blood from cancer patients and are probably the origin of intractable metastatic disease. Although extremely rare, CTCs represent a potential alternative to invasive biopsies as a source of tumour tissue for the detection, characterization and monitoring of non-haematologic cancers. The ability to identify, isolate, propagate and molecularly characterize CTC subpopulations could further the discovery of cancer stem cell biomarkers and expand the understanding of the biology of metastasis. Current strategies for isolating CTCs are limited to complex analytic approaches that generate very low yield and purity. Here we describe the development of a unique microfluidic platform (the 'CTC-chip') capable of efficient and selective separation of viable CTCs from peripheral whole blood samples, mediated by the interaction of target CTCs with antibody (EpCAM)-coated microposts under precisely controlled laminar flow conditions, and without requisite pre-labelling or processing of samples. The CTC-chip successfully identified CTCs in the peripheral blood of patients with metastatic lung, prostate, pancreatic, breast and colon cancer in 115 of 116 (99%) samples, with a range of 5-1,281 CTCs per ml and approximately 50% purity. In addition, CTCs were isolated in 7/7 patients with early-stage prostate cancer. Given the high sensitivity and specificity of the CTC-chip, we tested its potential utility in monitoring response to anti-cancer therapy. In a small cohort of patients with metastatic cancer undergoing systemic treatment, temporal changes in CTC numbers correlated reasonably well with the clinical course of disease as measured by standard radiographic methods. Thus, the CTC-chip provides a new and effective tool for accurate identification and measurement of CTCs in patients with cancer. It has broad implications in advancing both cancer biology research and clinical cancer management, including the detection, diagnosis and monitoring of cancer.

Analysis of factorial time-course microarrays with application to a clinical study of burn injury.

Time-course microarray experiments are capable of capturing dynamic gene expression profiles. It is important to study how these dynamic profiles depend on the multiple factors that characterize the experimental condition under which the time course is observed. Analytic methods are needed to simultaneously handle the time course and factorial structure in the data. We developed a method to evaluate factor effects by pooling information across the time course while accounting for multiple testing and nonnormality of the microarray data. The method effectively extracts gene-specific response features and models their dependency on the experimental factors. Both longitudinal and cross-sectional time-course data can be handled by our approach. The method was used to analyze the impact of age on the temporal gene response to burn injury in a large-scale clinical study. Our analysis reveals that 21% of the genes responsive to burn are age-specific, among which expressions of mitochondria and immunoglobulin genes are differentially perturbed in pediatric and adult patients by burn injury. These new findings in the body's response to burn injury between children and adults support further investigations of therapeutic options targeting specific age groups. The methodology proposed here has been implemented in R package "TANOVA" and submitted to the Comprehensive R Archive Network at http://www.r-project.org/. It is also available for download at http://gluegrant1.stanford.edu/TANOVA/.

Providing specimen transport through an online marketplace in the Northern region of Ghana.

Background: Integrated diagnostic networks, which are themselves dependent on robust specimen transport solutions, are fundamental to effective healthcare systems. Objective: This study aimed to pilot an online marketplace for the transport of specimens throughout a laboratory network in Ghana. Methods: Independent drivers were matched with health facilities that required specimen transport using a suite of mobile applications and web portals developed for this study. This marketplace was piloted with seven drivers, two laboratories, and five health facilities in Ghana's Northern region from March 2019 to October 2019. Results: During the pilot, 182 deliveries were completed for 691 patients, including 4118 laboratory tests for antenatal care, disease surveillance, and clinical testing. Testing included 34 tests for communicable and non-communicable diseases. All but two specimens (laboratory cancellations) were successfully delivered and tested. The median time from request to encrypted emailing of results was 19.7 h, while that for a drop-off request was 0.9 h. In the midwife registry, the median time from patient visit to result recording was 1 day, compared to 4 days in the same months in 2018, and the number of mothers without documented testing decreased from 41 to 3. Similarly, the proportion of tuberculosis specimen deliveries from Buipe Polyclinic to Tamale Zonal Laboratory taking over 1 day fell from 62% at baseline to 3% during the pilot. Conclusion: An online marketplace successfully orchestrated the delivery of laboratory specimens under a variety of clinical circumstances, reducing overall turn-around time without diminution of the overall specimen delivery process. What this study adds: This study established the efficacy of an online marketplace to orchestrate timely and high-quality delivery of specimens within a laboratory network.

Dysfunction of the aging female mouse urethra is associated with striated muscle loss and increased fibrosis: an initial report.

The decline of urethral function with advancing age plays a major role in urinary incontinence in women, impairing quality of life and economically burdening the health care system. However, none of the current urinary incontinence treatments address the declining urethral function with aging, and the mechanisms by which aging impacts urethra physiology remain little known or explored. Here, we have compared functional, morphometric, and global gene expression of urethral tissues between young and old female mice. Bladder leak point pressure (LPP) measurement showed that the aged female mice had 26.55% lower LPP compared to younger mice. Vectorized Scale-Invariant Pattern Recognition (VIPR) analysis of the relative abundance of different tissue components revealed that the mid-urethra of old female mice contains less striated muscle, more extracellular matrix/fibrosis, and diminished elastin fibers ratio compared to young mice. Gene expression profiling analysis (bulk RNA-seq of the whole urethra) showed more down-regulated genes in aged than young mice. Immune response and muscle-related (striated and smooth) pathways were predominantly enriched. In contrast, keratinization, skin development, and cell differentiation pathways were significantly downregulated in aged urethral tissues compared to those from young female mice. These results suggest that molecular pathways (i.e., ACVR1/FST signaling and CTGF/TGF-ß signaling) leading to a decreased striated muscle mass and an increase in fibrous extracellular matrix in the process of aging deserve further investigation for their roles in the declined urethral function.

A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen.

Tamoxifen significantly reduces tumor recurrence in certain patients with early-stage estrogen receptor-positive breast cancer, but markers predictive of treatment failure have not been identified. Here, we generated gene expression profiles of hormone receptor-positive primary breast cancers in a set of 60 patients treated with adjuvant tamoxifen monotherapy. An expression signature predictive of disease-free survival was reduced to a two-gene ratio, HOXB13 versus IL17BR, which outperformed existing biomarkers. Ectopic expression of HOXB13 in MCF10A breast epithelial cells enhances motility and invasion in vitro, and its expression is increased in both preinvasive and invasive primary breast cancer. The HOXB13:IL17BR expression ratio may be useful for identifying patients appropriate for alternative therapeutic regimens in early-stage breast cancer.

Semiautomated laser capture microdissection of lung adenocarcinoma cytology samples.

OBJECTIVE: In the past decade molecular diagnostics has changed the clinical management of lung adenocarcinoma patients. Molecular diagnostics, however, is largely dependent on the quantity and quality of the tumor DNA that is retrieved from the tissue or cytology samples. Frequently, patients are diagnosed on cytology specimens where the tumor cells are scattered within the cell block, making selecting for tumor enrichment difficult. In the past we have used laser capture microdissection (LCM) to select for pure populations of tumor cells to increase the sensitivity of molecular assays. This study explores several methods for semiautomated computer-guided LCM. STUDY DESIGN: Hematoxylin and eosin- or TTF-1-immunostained slides from a pleural effusion cell block with metastatic lung adenocarcinoma were used for LCM with either AutoScan or a recently described pattern-matching algorithm, spatially invariant vector quantization (SIVQ), to define morphologic predicates (vectors) to select cells of interest. RESULTS: We retrieved pure populations of tumor cells using both algorithm-guided LCM approaches with slight variations in cellular retrievals. Both methods were semiautomated, requiring minimum technical supervision. CONCLUSION: In this study we demonstrate the first semiautomated, computer-guided LCM of a cytology specimen using SIVQ and AutoScan, a first step towards the long-term goal of integrating LCM into the clinical cytology-molecular workflow.

A Study of Thyroid Fine Needle Aspiration of Follicular Adenoma in the "Atypia of Undetermined Significance" Bethesda Category Using Digital Image Analysis.

BACKGROUND: Originally designed for computerized image analysis, ThinPrep is underutilized in that role outside gynecological cytology. It can be used to address the inter/intra-observer variability in the evaluation of thyroid fine needle aspiration (TFNA) biopsy and help pathologists to gain additional insight into thyroid cytomorphology. METHODS: We designed and validated a feature engineering and supervised machine learning-based digital image analysis method using ImageJ and Python scikit-learn . The method was trained and validated from 400 low power (100x) and 400 high power (400x) images generated from 40 TFNA cases. RESULT: The area under the curve (AUC) for receiver operating characteristics (ROC) is 0.75 (0.74-0.82) for model based from low-power images and 0.74 (0.69-0.79) for the model based from high-power images. Cytomorphologic features were synthesized using feature engineering and when performed in isolation, they achieved AUC of 0.71 (0.64-0.77) for chromatin, 0.70 (0.64-0.73) for cellularity, 0.65 (0.60-0.69) for cytoarchitecture, 0.57 (0.51-0.61) for nuclear size, and 0.63 (0.57-0.68) for nuclear shape. CONCLUSION: Our study proves that ThinPrep is an excellent preparation method for digital image analysis of thyroid cytomorphology. It can be used to quantitatively harvest morphologic information for diagnostic purpose.

Quantitative Image Analysis as an Adjunct to Manual Scoring of ER, PgR, and HER2 in Invasive Breast Carcinoma.

OBJECTIVES: Biomarker expression evaluation for estrogen receptor (ER), progesterone receptor (PgR), and human epidermal growth factor receptor 2 (HER2) is an essential prognostic and predictive parameter for breast cancer and critical for guiding hormonal and neoadjuvant therapy. This study compared quantitative image analysis (QIA) with pathologists' scoring for ER, PgR, and HER2. METHODS: A retrospective analysis was undertaken of 1,367 invasive breast carcinomas, including all histopathology subtypes, for which ER, PgR, and HER2 were analyzed by manual scoring and QIA. The resulting scores were compared, and in a subset of HER2 cases (n = 373, 26%), scores were correlated with available fluorescence in situ hybridization (FISH) results. RESULTS: Concordance between QIA and manual scores for ER, PgR, and HER2 was 93%, 96%, and 90%, respectively. Discordant cases had low positive scores (1%-10%) for ER (n = 33), were due to nonrepresentative region selection (eg, ductal carcinoma in situ) or tumor heterogeneity for PgR (n = 43), and were of one-step difference (negative to equivocal, equivocal to positive, or vice versa) for HER2 (n = 90). Among HER2 cases where FISH results were available, only four (1.0%) showed discordant QIA and FISH results. CONCLUSIONS: QIA is a computer-aided diagnostic support tool for pathologists. It significantly improves ER, PgR, and HER2 scoring standardization. QIA demonstrated excellent concordance with pathologists' scores. To avoid pitfalls, pathologist oversight of representative region selection is recommended.

Contemporary Whole Slide Imaging Devices and Their Applications within the Modern Pathology Department: A Selected Hardware Review.

Digital pathology (DP) has disrupted the practice of traditional pathology, including applications in education, research, and clinical practice. Contemporary whole slide imaging (WSI) devices include technological advances that help address some of the challenges facing modern pathology, such as increasing workloads with fewer subspecialized pathologists, expanding integrated delivery networks with global reach, and greater customization when working up cases for precision medicine. This review focuses on integral hardware components of 43 market available and soon-to-be released digital WSI devices utilized throughout the world. Components such as objective lens type and magnification, scanning camera, illumination, and slide capacity were evaluated with respect to scan time, throughput, accuracy of scanning, and image quality. This analysis of assorted modern WSI devices offers essential, valuable information for successfully selecting and implementing a digital WSI solution for any given pathology practice.

Digital pathology and computational image analysis in nephropathology.

The emergence of digital pathology - an image-based environment for the acquisition, management and interpretation of pathology information supported by computational techniques for data extraction and analysis - is changing the pathology ecosystem. In particular, by virtue of our new-found ability to generate and curate digital libraries, the field of machine vision can now be effectively applied to histopathological subject matter by individuals who do not have deep expertise in machine vision techniques. Although these novel approaches have already advanced the detection, classification, and prognostication of diseases in the fields of radiology and oncology, renal pathology is just entering the digital era, with the establishment of consortia and digital pathology repositories for the collection, analysis and integration of pathology data with other domains. The development of machine-learning approaches for the extraction of information from image data, allows for tissue interrogation in a way that was not previously possible. The application of these novel tools are placing pathology centre stage in the process of defining new, integrated, biologically and clinically homogeneous disease categories, to identify patients at risk of progression, and shifting current paradigms for the treatment and prevention of kidney diseases.

Imputing HbA1c from capillary blood glucose levels in patients with type 2 diabetes in Sri Lanka: a cross-sectional study.

OBJECTIVE: To develop a population-specific methodology for estimating glycaemic control that optimises resource allocation for patients with diabetes in rural Sri Lanka. DESIGN: Cross-sectional study. SETTING: Trincomalee, Sri Lanka. PARTICIPANTS: Patients with non-insulin-treated type 2 diabetes (n=220) from three hospitals in Trincomalee, Sri Lanka. OUTCOME MEASURE: Cross-validation was used to build and validate linear regression models to identify predictors of haemoglobin A1c (HbA1c). Validation of models that regress HbA1c on known determinants of glycaemic control was thus the major outcome. These models were then used to devise an algorithm for categorising the patients based on estimated levels of glycaemic control. RESULTS: Time since last oral intake other than water and capillary blood glucose were the statistically significant predictors of HbA1c and thus included in the final models. In order to minimise type II error (misclassifying a high-risk individual as low-risk or moderate-risk), an algorithm for interpreting estimated glycaemic control was created. With this algorithm, 97.2% of the diabetic patients with HbA1c ≥9.0% were correctly identified. CONCLUSIONS: Our calibrated algorithm represents a highly sensitive approach for detecting patients with high-risk diabetes while optimising the allocation of HbA1c testing. Implementation of these methods will optimise the usage of resources devoted to the management of diabetes in Trincomalee, Sri Lanka. Further external validation with diverse patient populations is required before applying our algorithm more widely.

Display Characteristics and Their Impact on Digital Pathology: A Current Review of Pathologists' Future "Microscope".

Digital displays (monitors) are an indispensable component of a pathologists' daily workflow, from writing reports, viewing whole-slide images, or browsing the Internet. Due to a paucity of literature and experience surrounding display use and standardization in pathology, the Food and Drug Administration's (FDA) has currently restricted FDA-cleared whole-slide imaging systems to a specific model of display for each system, which at this time consists of only medical-grade (MG) displays. Further, given that a pathologists' display will essentially become their new surrogate "microscope," it becomes exceedingly important that all pathologists have a basic understanding of fundamental display properties and their functional consequences. This review seeks to: (a) define and summarize the current and emerging display technology, terminology, features, and regulation as they pertain to pathologists and review the current literature on the impact of different display types (e.g. MG vs. consumer off the shelf vs. professional grade) on pathologists' diagnostic performance and (b) discuss the impact of the recent digital pathology device componentization and the coronavirus disease 2019 public emergency on the pixel pathway and display use for remote digital pathology. Display technology has changed dramatically over the past 20 years and continues to change at a rapid rate. There is a paucity of published studies to date that investigate how display type affects pathologist performance, with more research necessary in order to develop standards and minimum specifications for displays in digital pathology. Given the complexity of modern displays, pathologists must become better informed regarding display technology if they wish to have more choice over their future "microscopes."

A new model for diabetes-focused capacity building - lessons from Sri Lanka.

Sri Lanka is experiencing a rapid increase in the number of people with diabetes mellitus (DM) due to population growth and aging. Physician shortages, outdated technology, and insufficient health education have contributed to the difficulties associated with managing the burden of disease. New models of chronic disease management are needed to address the increasing prevalence of DM. Medical students, business students, and faculty members from the University of Michigan partnered with the Grace Girls' Home, Trincomalee General Hospital, and Selvanayakapuram Central Hospital to identify and train diabetes-focused medical assistants (MAs) to collect and enter patient data and educate patients about their disease. Return visits to these MAs were encouraged so that patient progress and disease progression could be tracked longitudinally. Data entry was conducted through a cloud-based mechanism, facilitating patient management and descriptive characterization of the population. We implemented this pilot program in June 2016 in coordination with Trincomalee General Hospital and Selvanayakapuram Central Hospital. Over a 12-month period, 93 patients were systematically assessed by the medical assistants. All patients received education and were provided materials after the visit to better inform them about the importance of controlling their disease. Fifteen percent (14/93) of patients returned for follow-up consultation. Trained MAs have the potential to provide support to physicians working in congested health systems in low-resource settings. Public investment in training programs for MAs and greater acceptance by physicians and patients will be essential for handling the growing burden associated with chronic illnesses like DM. Trained MAs may also play a role in improved patient education and awareness regarding diabetes self-management.