Deep learning-based histotype diagnosis of ovarian carcinoma whole-slide pathology images.

Ovarian carcinoma has the highest mortality of all female reproductive cancers and current treatment has become histotype-specific. Pathologists diagnose five common histotypes by microscopic examination, however, histotype determination is not straightforward, with only moderate interobserver agreement between general pathologists (Cohen's kappa 0.54-0.67). We hypothesized that machine learning (ML)-based image classification models may be able to recognize ovarian carcinoma histotype sufficiently well that they could aid pathologists in diagnosis. We trained four different artificial intelligence (AI) algorithms based on deep convolutional neural networks to automatically classify hematoxylin and eosin-stained whole slide images. Performance was assessed through cross-validation on the training set (948 slides corresponding to 485 patients), and on an independent test set of 60 patients from another institution. The best-performing model achieved a diagnostic concordance of 81.38% (Cohen's kappa of 0.7378) in our training set, and 80.97% concordance (Cohen's kappa 0.7547) on the external dataset. Eight cases misclassified by ML in the external set were reviewed by two subspecialty pathologists blinded to the diagnoses, molecular and immunophenotype data, and ML-based predictions. Interestingly, in 4 of 8 cases from the external dataset, the expert review pathologists rendered diagnoses, based on blind review of the whole section slides classified by AI, that were in agreement with AI rather than the integrated reference diagnosis. The performance characteristics of our classifiers indicate potential for improved diagnostic performance if used as an adjunct to conventional histopathology.

Gastrointestinal symptoms and the severity of COVID-19: Disorders of gut-brain interaction are an outcome.

BACKGROUND: Many of the studies on COVID-19 severity and its associated symptoms focus on hospitalized patients. The aim of this study was to investigate the relationship between acute GI symptoms and COVID-19 severity in a clustering-based approach and to determine the risks and epidemiological features of post-COVID-19 Disorders of Gut-Brain Interaction (DGBI) by including both hospitalized and ambulatory patients. METHODS: The study utilized a two-phase Internet-based survey on: (1) COVID-19 patients' demographics, comorbidities, symptoms, complications, and hospitalizations and (2) post-COVID-19 DGBI diagnosed according to Rome IV criteria in association with anxiety (GAD-7) and depression (PHQ-9). Statistical analyses included univariate and multivariate tests. RESULTS: Five distinct clusters of symptomatic subjects were identified based on the presence of GI symptoms, loss of smell, and chest pain, among 1114 participants who tested positive for SARS-CoV-2. GI symptoms were found to be independent risk factors for severe COVID-19; however, they did not always coincide with other severity-related factors such as age >65 years, diabetes mellitus, and Vitamin D deficiency. Of the 164 subjects with a positive test who participated in Phase-2, 108 (66%) fulfilled the criteria for at least one DGBI. The majority (n = 81; 75%) were new-onset DGBI post-COVID-19. Overall, 86% of subjects with one or more post-COVID-19 DGBI had at least one GI symptom during the acute phase of COVID-19, while 14% did not. Depression (65%), but not anxiety (48%), was significantly more common in those with post-COVID-19 DGBI. CONCLUSION: GI symptoms are associated with a severe COVID-19 among survivors. Long-haulers may develop post-COVID-19 DGBI. Psychiatric disorders are common in post-COVID-19 DGBI.

The utility of color normalization for AI-based diagnosis of hematoxylin and eosin-stained pathology images.

The color variation of hematoxylin and eosin (H&E)-stained tissues has presented a challenge for applications of artificial intelligence (AI) in digital pathology. Many color normalization algorithms have been developed in recent years in order to reduce the color variation between H&E images. However, previous efforts in benchmarking these algorithms have produced conflicting results and none have sufficiently assessed the efficacy of the various color normalization methods for improving diagnostic performance of AI systems. In this study, we systematically investigated eight color normalization algorithms for AI-based classification of H&E-stained histopathology slides, in the context of using images both from one center and from multiple centers. Our results show that color normalization does not consistently improve classification performance when both training and testing data are from a single center. However, using four multi-center datasets of two cancer types (ovarian and pleural) and objective functions, we show that color normalization can significantly improve the classification accuracy of images from external datasets (ovarian cancer: 0.25 AUC increase, p = 1.6 e-05; pleural cancer: 0.21 AUC increase, p = 1.4 e-10). Furthermore, we introduce a novel augmentation strategy by mixing color-normalized images using three easily accessible algorithms that consistently improves the diagnosis of test images from external centers, even when the individual normalization methods had varied results. We anticipate our study to be a starting point for reliable use of color normalization to improve AI-based, digital pathology-empowered diagnosis of cancers sourced from multiple centers. © 2021 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Adhesion of Blood Clots Can Be Enhanced When Copolymerized with a Macromer That Is Crosslinked by Coagulation Factor XIIIa.

The adhesion of blood clots to blood vessels, such as through the adhesion of fibrin, is essential in hemostasis. While numerous strategies for initiating clot formation and preventing clot lysis are being developed to create improved hemostatic agents, strategies for enhancing clot adhesion have not been widely explored. Here, we show that adhesion of blood clots can be increased by adding a previously characterized synthetic polymer that is crosslinked by coagulation factor XIIIa during clotting. Addition of the polymer to normal plasma increased the adhesive strength of clots by 2-fold. It also recovered the adhesive strength of nonadhesive fibrinogen-deficient whole blood clots from <0.06 kPa to 1.9 ± 0.14 kPa, which is similar to the adhesive strength of a fibrinogen-rich clot (1.8 ± 0.64 kPa). The polymer also enabled plasma clots to remain adhered under fibrinolytic conditions. By demonstrating that the adhesive strength of clots can be increased with a synthetic material, this provides a potential strategy for creating advanced hemostatic materials, such as treatments for fibrinogen deficiency in trauma-induced coagulopathy.