Uterine injury during diestrus leads to placental and embryonic defects in future pregnancies in mice†.

Uterine injury from procedures such as Cesarean sections (C-sections) often have severe consequences on subsequent pregnancy outcomes, leading to disorders such as placenta previa, placenta accreta, and infertility. With rates of C-section at ~30% of deliveries in the USA and projected to continue to climb, a deeper understanding of the mechanisms by which these pregnancy disorders arise and opportunities for intervention are needed. Here we describe a rodent model of uterine injury on subsequent in utero outcomes. We observed three distinct phenotypes: increased rates of resorption and death, embryo spacing defects, and placenta accreta-like features of reduced decidua and expansion of invasive trophoblasts. We show that the appearance of embryo spacing defects depends entirely on the phase of estrous cycle at the time of injury. Using RNA-seq, we identified perturbations in the expression of components of the COX/prostaglandin pathway after recovery from injury, a pathway that has previously been demonstrated to play an important role in embryo spacing. Therefore, we demonstrate that uterine damage in this mouse model causes morphological and molecular changes that ultimately lead to placental and embryonic developmental defects.

Clinical Features of SARS-CoV-2 Infection During Pregnancy and Associated Placental Pathologies.

SUMMARY: We reviewed the clinicopathologic findings of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-exposed placentas at our institution. We identified patients diagnosed with SARS-CoV-2 during pregnancy (March-October 2020). Clinical data included gestational age at diagnosis and delivery and maternal symptoms. Hematoxylin and eosin slides were reviewed for maternal vascular malperfusion, fetal vascular malperfusion, chronic villitis, amniotic fluid infection, intervillous thrombi, fibrin deposition, and infarction. Immunohistochemistry (IHC) for coronavirus spike protein and RNA in situ hybridization (ISH) for SARS-CoV-2 was performed on a subset of blocks. A review of placentas from age-matched patients received March-October 2019 was conducted as a comparison cohort. A total of 151 patients were identified. Placentas in the 2 groups were similar in weight for gestational age and had similar rates of maternal vascular malperfusion, fetal vascular malperfusion, amniotic fluid infection, intervillous thrombi, fibrin deposition, and infarction. Chronic villitis was the only significantly different pathologic finding between cases and controls (29% of cases showed chronic villitis vs. 8% of controls, P <0.001). Overall, 146/151 (96.7%) cases were negative for IHC and 129/133 (97%) cases were negative for RNA ISH. There were 4 cases that stained positively for IHC/ISH, 2 of which showed massive perivillous fibrin deposition, inflammation, and decidual arteriopathy. Coronavirus disease 2019 (COVID-19)-positive patients were more likely to self-identify as Hispanic and more likely to have public health insurance. Our data suggests SARS-CoV-2 exposed placentas that stain positively for SARS-CoV-2 show abnormal fibrin deposition, inflammatory changes, and decidual arteriopathy. The group of patients with clinical COVID-19 are more likely to show chronic villitis. IHC and ISH evidence of viral infection is rare.

A pathologist-AI collaboration framework for enhancing diagnostic accuracies and efficiencies.

In pathology, the deployment of artificial intelligence (AI) in clinical settings is constrained by limitations in data collection and in model transparency and interpretability. Here we describe a digital pathology framework, nuclei.io, that incorporates active learning and human-in-the-loop real-time feedback for the rapid creation of diverse datasets and models. We validate the effectiveness of the framework via two crossover user studies that leveraged collaboration between the AI and the pathologist, including the identification of plasma cells in endometrial biopsies and the detection of colorectal cancer metastasis in lymph nodes. In both studies, nuclei.io yielded considerable diagnostic performance improvements. Collaboration between clinicians and AI will aid digital pathology by enhancing accuracies and efficiencies.