Citations

Diabetes is one of the main causes of the rising cases of blindness in adults. This microvascular complication of diabetes is termed diabetic retinopathy (DR) and is associated with an expanding risk of cardiovascular events in diabetes patients. DR, in its various forms, is seen to be a powerful indicator of atherosclerosis. Further, the macrovascular complication of diabetes leads to coronary artery disease (CAD). Thus, the timely identification of cardiovascular disease (CVD) complications in DR patients is of utmost importance. Since CAD risk assessment is expensive for low-income countries, it is important to look for surrogate biomarkers for risk stratification of CVD in DR patients. Due to the common genetic makeup between the coronary and carotid arteries, low-cost, high-resolution imaging such as carotid B-mode ultrasound (US) can be used for arterial tissue characterization and risk stratification in DR patients. The advent of artificial intelligence (AI) techniques has facilitated the handling of large cohorts in a big data framework to identify atherosclerotic plaque features in arterial ultrasound. This enables timely CVD risk assessment and risk stratification of patients with DR. Thus, this review focuses on understanding the pathophysiology of DR, retinal and CAD imaging, the role of surrogate markers for CVD, and finally, the CVD risk stratification of DR patients. The review shows a step-by-step cyclic activity of how diabetes and atherosclerotic disease cause DR, leading to the worsening of CVD. We propose a solution to how AI can help in the identification of CVD risk. Lastly, we analyze the role of DR/CVD in the COVID-19 framework.

Kanack and colleagues analyze anti-platelet factor 4 antibodies from 5 patients with vaccine-induced thrombotic thrombocytopenia (VITT) secondary to COVID-19 adenoviral vaccination and antibodies from patients with spontaneous heparin-induced thrombocytopenia (HIT) and classical HIT. VITT antibodies are monoclonal or oligoclonal, similar to spontaneous HIT, whereas classical HIT antibodies are polyclonal. Heparin inhibits antibody-induced platelet activation in VITT, suggesting that heparin should be considered for the treatment of VITT.

Abstract Improving the chances of curing patients with cancer who have had surgery to remove metastatic sites of disease is a priority area for cancer research. Pexa-Vec (Pexastimogene Devacirepvec; JX-594, TG6006) is a principally immunotherapeutic oncolytic virus that has reached late-phase clinical trials. We report the results of a single-center, nonrandomized biological end point study (trial registration: EudraCT number 2012-000704-15), which builds on the success of the presurgical intravenous delivery of oncolytic viruses to tumors. Nine patients with either colorectal cancer liver metastases or metastatic melanoma were treated with a single intravenous infusion of Pexa-Vec ahead of planned surgical resection of the metastases. Grade 3 and 4 Pexa-Vec–associated side effects were lymphopaenia and neutropaenia. Pexa-Vec was peripherally carried in plasma and was not associated with peripheral blood mononuclear cells. Upon surgical resection, Pexa-Vec was found in the majority of analyzed tumors. Pexa-Vec therapy associated with IFNα secretion, chemokine induction, and resulted in transient innate and long-lived adaptive anticancer immunity. In the 2 patients with significant and complete tumor necrosis, a reduction in the peripheral T-cell receptor diversity was observed at the time of surgery. These results support the development of presurgical oncolytic vaccinia virus-based therapies to stimulate anticancer immunity and increase the chances to cure patients with cancer.

Hodgkin lymphoma (HL) is an uncommon malignancy of B-cell origin. Classical HL (cHL) and nodular lymphocyte–predominant HL are the 2 main types of HL. The cure rates for HL have increased so markedly with the advent of modern treatment options that overriding treatment considerations often relate to long-term toxicity. These NCCN Guidelines Insights discuss the recent updates to the NCCN Guidelines for HL focusing on (1) radiation therapy dose constraints in the management of patients with HL, and (2) the management of advanced-stage and relapsed or refractory cHL.

Abstract Inhibitory receptors have a critical role in the regulation of immunity. Siglecs are a family of primarily inhibitory receptors expressed by immune cells that recognize specific sialic acid modifications on cell surface glycans. Many tumors have increased sialic acid incorporation. Overexpression of the sialyltransferase ST8Sia6 on tumors led to altered immune responses and increased tumor growth. In this study, we examined the role of ST8Sia6 on immune cells in regulating antitumor immunity. ST8Sia6 knockout mice had an enhanced immune response to tumors. The loss of ST8Sia6 promoted an enhanced intratumoral activation of macrophages and dendritic cells, including upregulation of CD40. Intratumoral regulatory T cells exhibited a more inflammatory phenotype in ST8Sia6 knockout mice. Using adoptive transfer studies, the change in regulatory T cell phenotype was not cell intrinsic and depended on the loss of ST8Sia6 expression in APCs. Thus, ST8Sia6 generates ligands for Siglecs that dampen antitumor immunity.

Background: Administrative data such as Medicare data are commonly used for health services research and comparative effectiveness studies. These databases are readily available and a good source of long-term data on healthcare utilization and health outcomes in real-world settings. Outcomes are captured using diagnostic codes, but the evidence on the validity of these codes is scarce in contemporary Medicare data. Methods: We linked the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study data using SSN, date of birth and sex to 2003-2009 Medicare claims. In addition to the events captured by REGARDS phone interviews, medical charts were pursued for Medicare-identified strokes not previously identified in REGRADS. Events were adjudicated by stroke specialists using the retrieved medical charts. Using all the adjudicated strokes as gold standard, we calculated the sensitivity, specificity, PPV and NPV of inpatient stroke algorithms with ICD-9-CM codes [430, 431, 433.x1, 434.x1, 436] in the primary discharge diagnosis. Results: We successfully linked 15,089 REGARDS participants with mean age of 69.3, 52% female and 37% Black. We adjudicated 457 strokes, of which 48 were identified by claims only and not by interviews. The tested algorithms had high specificity (99.8-100%), PPV (88.6-90.5%) and NPV (98.9-99.9%), but low sensitivity (58.6-67.4%) (Table). Conclusions: High specificity and PPV of our algorithms to identify strokes in contemporary Medicare populations support their use in etiological and comparative effectiveness studies. While our inpatient Medicare algorithms identified 12% extra cases of strokes, their usefulness for estimating stroke incidence or related healthcare utilizations is limited by their low sensitivity. Linking cohorts to Medicare data is feasible and should be considered to increase the completeness of follow-up. Further studies are needed to evaluate more sensitive Medicare algorithms.

AimsBarrett's oesophagus with indefinite for dysplasia (BE‐IND) is a subjective diagnosis with a low interobserver agreement (IOA) among pathologists and uncertain clinical implications. This study aimed to assess the utility of p53 immunohistochemistry (p53‐IHC) in assessing BE‐IND specimens.Methods and resultsArchive endoscopic biopsies with a BE‐IND diagnosis from two academic centres were analysed. First, haematoxylin and eosin‐stained slides (H&E) were reviewed by four expert gastrointestinal (GI) pathologists allocated into two groups (A and B). After a washout period of at least 8 weeks, H&E slides were reassessed side‐to‐side with p53‐IHC available. We compared the rate of changed diagnosis and the IOA for all BE grades before and after p53‐IHC. We included 216 BE‐IND specimens from 185 patients, 44.0 and 32.9% of which were confirmed after H&E slide revision by groups A and B, respectively. More than half the cases were reclassified to a non‐dysplastic BE (NDBE), while 5.6% of cases in group A and 7.4% in group B were reclassified to definite dysplasia. The IOA for NDBE, BE‐IND, low‐grade dysplasia (LGD) and high‐grade dysplasia (HGD)/intramucosal cancer (IMC) was 0.31, 0.21, −0.03 and −0.02, respectively. Use of p53‐IHC led to a >40% reduction in BE‐IND diagnoses (P < 0.001) and increased IOA for all BE grades [κ = 0.46 (NDBE), 0.26 (BE‐IND), 0.49 (LGD), 0.35 (HGD/IMC)]. An aberrant p53‐IHC pattern significantly increased the likelihood of reclassifying BE‐IND to definite dysplasia (odds ratio = 44.3, 95% confidence interval = 18.8–113.0).ConclusionP53‐IHC reduces the rate of BE‐IND diagnoses and improves the IOA among pathologists when reporting BE with equivocal epithelial changes.

(1) Background: COVID-19 computed tomography (CT) lung segmentation is critical for COVID lung severity diagnosis. Earlier proposed approaches during 2020–2021 were semiautomated or automated but not accurate, user-friendly, and industry-standard benchmarked. The proposed study compared the COVID Lung Image Analysis System, COVLIAS 1.0 (GBTI, Inc., and AtheroPointTM, Roseville, CA, USA, referred to as COVLIAS), against MedSeg, a web-based Artificial Intelligence (AI) segmentation tool, where COVLIAS uses hybrid deep learning (HDL) models for CT lung segmentation. (2) Materials and Methods: The proposed study used 5000 ITALIAN COVID-19 positive CT lung images collected from 72 patients (experimental data) that confirmed the reverse transcription-polymerase chain reaction (RT-PCR) test. Two hybrid AI models from the COVLIAS system, namely, VGG-SegNet (HDL 1) and ResNet-SegNet (HDL 2), were used to segment the CT lungs. As part of the results, we compared both COVLIAS and MedSeg against two manual delineations (MD 1 and MD 2) using (i) Bland–Altman plots, (ii) Correlation coefficient (CC) plots, (iii) Receiver operating characteristic curve, and (iv) Figure of Merit and (v) visual overlays. A cohort of 500 CROATIA COVID-19 positive CT lung images (validation data) was used. A previously trained COVLIAS model was directly applied to the validation data (as part of Unseen-AI) to segment the CT lungs and compare them against MedSeg. (3) Result: For the experimental data, the four CCs between COVLIAS (HDL 1) vs. MD 1, COVLIAS (HDL 1) vs. MD 2, COVLIAS (HDL 2) vs. MD 1, and COVLIAS (HDL 2) vs. MD 2 were 0.96, 0.96, 0.96, and 0.96, respectively. The mean value of the COVLIAS system for the above four readings was 0.96. CC between MedSeg vs. MD 1 and MedSeg vs. MD 2 was 0.98 and 0.98, respectively. Both had a mean value of 0.98. On the validation data, the CC between COVLIAS (HDL 1) vs. MedSeg and COVLIAS (HDL 2) vs. MedSeg was 0.98 and 0.99, respectively. For the experimental data, the difference between the mean values for COVLIAS and MedSeg showed a difference of <2.5%, meeting the standard of equivalence. The average running times for COVLIAS and MedSeg on a single lung CT slice were ~4 s and ~10 s, respectively. (4) Conclusions: The performances of COVLIAS and MedSeg were similar. However, COVLIAS showed improved computing time over MedSeg.