Expert Agreement on the Presence and Spatial Localization of Melanocytic Features in Dermoscopy.

Dermoscopy aids in melanoma detection; however, agreement on dermoscopic features, including those of high clinical relevance, remains poor. In this study, we attempted to evaluate agreement among experts on exemplar images not only for the presence of melanocytic-specific features but also for spatial localization. This was a cross-sectional, multicenter, observational study. Dermoscopy images exhibiting at least 1 of 31 melanocytic-specific features were submitted by 25 world experts as exemplars. Using a web-based platform that allows for image markup of specific contrast-defined regions (superpixels), 20 expert readers annotated 248 dermoscopic images in collections of 62 images. Each collection was reviewed by five independent readers. A total of 4,507 feature observations were performed. Good-to-excellent agreement was found for 14 of 31 features (45.2%), with eight achieving excellent agreement (Gwet's AC >0.75) and seven of them being melanoma-specific features. These features were peppering/granularity (0.91), shiny white streaks (0.89), typical pigment network (0.83), blotch irregular (0.82), negative network (0.81), irregular globules (0.78), dotted vessels (0.77), and blue-whitish veil (0.76). By utilizing an exemplar dataset, a good-to-excellent agreement was found for 14 features that have previously been shown useful in discriminating nevi from melanoma. All images are public (www.isic-archive.com) and can be used for education, scientific communication, and machine learning experiments.

A population-level digital histologic biomarker for enhanced prognosis of invasive breast cancer.

Breast cancer is a heterogeneous disease with variable survival outcomes. Pathologists grade the microscopic appearance of breast tissue using the Nottingham criteria, which are qualitative and do not account for noncancerous elements within the tumor microenvironment. Here we present the Histomic Prognostic Signature (HiPS), a comprehensive, interpretable scoring of the survival risk incurred by breast tumor microenvironment morphology. HiPS uses deep learning to accurately map cellular and tissue structures to measure epithelial, stromal, immune, and spatial interaction features. It was developed using a population-level cohort from the Cancer Prevention Study-II and validated using data from three independent cohorts, including the Prostate, Lung, Colorectal, and Ovarian Cancer trial, Cancer Prevention Study-3, and The Cancer Genome Atlas. HiPS consistently outperformed pathologists in predicting survival outcomes, independent of tumor-node-metastasis stage and pertinent variables. This was largely driven by stromal and immune features. In conclusion, HiPS is a robustly validated biomarker to support pathologists and improve patient prognosis.

Toward a generalizable machine learning workflow for neurodegenerative disease staging with focus on neurofibrillary tangles.

Machine learning (ML) has increasingly been used to assist and expand current practices in neuropathology. However, generating large imaging datasets with quality labels is challenging in fields which demand high levels of expertise. Further complicating matters is the often seen disagreement between experts in neuropathology-related tasks, both at the case level and at a more granular level. Neurofibrillary tangles (NFTs) are a hallmark pathological feature of Alzheimer disease, and are associated with disease progression which warrants further investigation and granular quantification at a scale not currently accessible in routine human assessment. In this work, we first provide a baseline of annotator/rater agreement for the tasks of Braak NFT staging between experts and NFT detection using both experts and novices in neuropathology. We use a whole-slide-image (WSI) cohort of neuropathology cases from Emory University Hospital immunohistochemically stained for Tau. We develop a workflow for gathering annotations of the early stage formation of NFTs (Pre-NFTs) and mature intracellular (iNFTs) and show ML models can be trained to learn annotator nuances for the task of NFT detection in WSIs. We utilize a model-assisted-labeling approach and demonstrate ML models can be used to aid in labeling large datasets efficiently. We also show these models can be used to extract case-level features, which predict Braak NFT stages comparable to expert human raters, and do so at scale. This study provides a generalizable workflow for various pathology and related fields, and also provides a technique for accomplishing a high-level neuropathology task with limited human annotations.

Survey of Neuroanatomic Sampling and Staining Procedures in Alzheimer Disease Research Center Brain Banks.

The collection of post-mortem brain tissue has been a core function of the Alzheimer Disease Research Center's (ADRCs) network located within the United States since its inception. Individual brain banks and centers follow detailed protocols to record, store, and manage complex datasets that include clinical data, demographics, and when post-mortem tissue is available, a detailed neuropathological assessment. Since each institution often has specific research foci, there can be variability in tissue collection and processing workflows. While published guidelines exist for select diseases, such as those put forth by the National Institute on Aging and Alzheimer Association (NIA-AA), it is of importance to denote the current practices across institutions. To this end a survey was developed and sent to United States based brain bank leaders, collecting data on brain region sampling, including anatomic landmarks used, staining (including antibodies used), as well as whole-slide-image scanning hardware. We distributed this survey to 40 brain banks and obtained a response rate of 95% (38 / 40). Most brain banks followed guidelines defined by the NIA-AA, having H&E staining in all recommended regions and targeted region-based amyloid beta, tau, and alpha-synuclein immunohistochemical staining. However, sampling consistency varied related to key anatomic landmarks/locations in select regions, such as the striatum, periventricular white matter, and parietal cortex. This study highlights the diversity and similarities amongst brain banks and discusses considerations when amalgamating data/samples across multiple centers. This survey aids in establishing benchmarks to enhance dialogues on divergent workflows in a feasible way.

A Trial of Adding Lung Protective Strategies to Existing Enhanced Recovery After Surgery Protocols and Its Effect on Improving Postoperative Lung Function.

Background: With this rising popularization of enhanced recovery after surgery (ERAS) protocols, it is important to ask if the current and developing pathways are fully comprehensive for the patient's perioperative experience. Many current pathways discuss aspects of care including fluid management, pain management, and anti-emetic medication regiments, but few delineate recommendations for lung protective strategies. The hypothesis was that intraoperative lung protective strategies would results in improved postoperative lung function. Methods: One hundred patients at the Medical University of South Carolina undergoing hepatobiliary and colorectal surgeries were randomized to receive intraoperative lung protective techniques or a standard intraoperative ventilation management. Three maximum vital capacity breaths were recorded preoperatively, and postoperatively 30 min, 1 h, and 2 h after anesthesia stop time. Average maximum capacity breaths from all four data collection interactions were analyzed between both study and control cohorts. Results: There was no significant difference in the preoperative inspiratory capacity between the control and the ERAS group (2,043.3 ± 628.4 mL vs. 2,012.2 ± 895.2 mL; P = 0.84). Additional data analysis showed no statistically significant difference between ERAS and control groups: total average of the inspiratory capacity volumes (1,253.5 ± 593.7 mL vs. 1,390.4 ± 964.9 mL; P = 0.47), preoperative oxygen saturation (97.76±2.3% vs. 98.04±1.7%; P = 0.50), the postoperative oxygen saturation (98.51±1.4% vs. 96.83±14.2%; P = 0.40), and change in inspiratory capacity (95% confidence interval (CI) (-211.2 - 366.6); P = 0.60). Conclusions: No statistically significant difference in postoperative inspiratory capacities were seen after the implementation of intraoperative lung protective strategies. The addition of other indicators of postoperative lung function like pneumonia incidence or length of inpatient stay while receiving oxygen treatment could provide a fuller picture in future studies, but a higher power will be needed.

An Automated Pipeline for Differential Cell Counts on Whole-Slide Bone Marrow Aspirate Smears.

The pathologic diagnosis of bone marrow disorders relies in part on the microscopic analysis of bone marrow aspirate (BMA) smears and the manual counting of marrow nucleated cells to obtain a differential cell count (DCC). This manual process has significant limitations, including the analysis of only a small subset of optimal slide areas and nucleated cells, as well as interobserver variability due to differences in cell selection and classification. To address these shortcomings, we developed an automated machine learning-based pipeline for obtaining 11-component DCCs on whole-slide BMAs. This pipeline uses a sequential process of identifying optimal BMA regions with high proportions of marrow nucleated cells, detecting individual cells within these optimal areas, and classifying these cells into 1 of 11 DCC components. Convolutional neural network models were trained on 396,048 BMA region, 28,914 cell boundary, and 1,510,976 cell class images from manual annotations. The resulting automated pipeline produced 11-component DCCs that demonstrated a high statistical and diagnostic concordance with manual DCCs among a heterogeneous group of testing BMA slides with varying pathologies and cellularities. Additionally, we demonstrated that an automated analysis can reduce the intraslide variance in DCCs by analyzing the whole slide and marrow nucleated cells within all optimal regions. Finally, the pipeline outputs of region classification, cell detection, and cell classification can be visualized using whole-slide image analysis software. This study demonstrates the feasibility of a fully automated pipeline for generating DCCs on scanned whole-slide BMA images, with the potential for improving the current standard of practice for utilizing BMA smears in the laboratory analysis of hematologic disorders.

The Association of Improved Overall Survival with NSAIDs in Non-Small Cell Lung Cancer Patients Receiving Immune Checkpoint Inhibitors.

BACKGROUND: Immune checkpoint inhibitors (ICI) are commonly used in the management of patients with advanced non-small cell lung cancer (NSCLC), but response is suboptimal. Preclinical data suggest ICI efficacy may be enhanced with concomitant nonsteroidal anti-inflammatory (NSAID) medications. PATIENTS AND METHODS: In this retrospective study, the Veterans Health Administration Corporate Data Warehouse was queried for patients diagnosed with NSCLC and treated with ICI from 2010 to 2018. Concomitant NSAID use was defined as NSAID dispensation by a VA pharmacy within 90 days of the any ICI infusion. To mitigate immortal time bias, patients who started NSAIDs 60 or more days after ICI initiation were excluded from analysis. Survival was measured from start of ICI. RESULTS: We identified 3634 patients with NSCLC receiving ICI; 2336 (64.3%) were exposed to concomitant NSAIDs. On multivariable analysis, NSAIDs were associated with better overall survival (HR = 0.90; 95% CI, 0.83-0.98; P = .010). When stratifying by NSAID type, diclofenac was the only NSAID with significant association with overall survival (HR = 0.75; 95% CI, 0.68-0.83; P < .001). Propensity score matching of the original cohort yielded 1251 patients per cohort balanced in characteristics. NSAIDs remained associated with improved overall survival (HR = 0.85; 95% CI, 0.78-0.92; P < .001). CONCLUSION: This study of Veterans with NSCLC treated with ICI demonstrated that concomitant NSAIDs are associated with longer OS. This may indicate that NSAIDs can enhance ICI-induced antitumor immunity and should prospectively validated.

The status of digital pathology and associated infrastructure within Alzheimer's Disease Centers.

Digital pathology (DP) has transformative potential, especially for Alzheimer disease and related disorders. However, infrastructure barriers may limit adoption. To provide benchmarks and insights into implementation barriers, a survey was conducted in 2019 within National Institutes of Health's Alzheimer's Disease Centers (ADCs). Questions covered infrastructure, funding sources, and data management related to digital pathology. Of the 35 ADCs to which the survey was sent, 33 responded. Most respondents (81%) stated that their ADC had digital slide scanner access, with the most frequent brand being Aperio/Leica (62.9%). Approximately a third of respondents stated there were fees to utilize the scanner. For DP and machine learning (ML) resources, 41% of respondents stated none was supported by their ADC. For scanner purchasing and operations, 50% of respondents stated they received institutional support. Some were unsure of the file size of scanned digital images (37%) and total amount of storage space files occupied (50%). Most (76%) were aware of other departments at their institution working with ML; a similar (76%) percentage were unaware of multiuniversity or industry partnerships. These results demonstrate many ADCs have access to a digital slide scanner; additional investigations are needed to further understand hurdles to implement DP and ML workflows.

Impact of concomitant fibrates on immunotherapy outcomes for advanced non-small cell lung cancer.

BACKGROUND: Peroxisome proliferator-activated receptor agonists such as fibrates restore oxidative metabolism in cytotoxic T-lymphocytes, thereby enhancing response to immune checkpoint inhibitors (ICI) in preclinical models. However, there is no evidence in humans on the clinical impact of fibrates as an adjunct to ICI. METHODS: In this cohort study of Veterans with non-small cell lung cancer (NSCLC) receiving ICI, fibrate exposure was defined as a prescription filled within 90 days of an ICI infusion. Overall survival (OS), measured from the start of ICI, was compared between exposed and unexposed Veterans. Cox multivariable analysis (MVA) was used to identify factors associated with OS. A sensitivity analysis of Veterans with stage IV NSCLC who received docetaxel without ICI was similarly performed. RESULTS: The ICI cohort included 3593 Veterans, of whom 301 (8.5%) coincidentally received a fibrate. Veterans receiving fibrates were more likely to be older, white, male, and married, and to have greater comorbidity burden, but less likely to receive chemotherapy. Coincidental fibrates were associated with improved OS both on MVA (HR 0.86, 95%CI 0.75-0.99) and in a matched subset (HR 0.75, 95%CI 0.63-0.90). In contrast, among the cohort of 968 Veterans treated with chemotherapy, fibrates did not have a significant impact on OS by MVA (HR 0.99, 95%CI 0.79-1.25) or in a matched subset (HR 1.02, 95%CI CI 0.75-1.39). CONCLUSIONS: Concomitant fibrates are associated with improved OS among NSCLC patients receiving ICI but not among those receiving chemotherapy. This hypothesis-generating observation supports a potential role for fibrates as an adjunct to immunotherapy.

Reexpansion Pulmonary Edema following Tube Thoracostomy in a Pediatric Patient with Anterior Mediastinal Mass.

Reexpansion pulmonary edema (RPE) is an exceedingly rare and potentially fatal complication of a rapidly reexpanded lung following evacuation of air or fluid from the pleural space secondary to conditions such as a mediastinal mass, pleural effusion, or pneumothorax. Clinical presentations can range from mild radiographic changes to acute respiratory failure and hemodynamic instability. The rapidly progressive nature of the disease makes it important for clinicians to appropriately diagnose and manage patients who develop RPE. We present a case of a child with a large malignant pleural effusion who developed severe RPE after tube thoracostomy and ultimately required venoarterial extracorporeal membrane oxygenation (VA-ECMO). The patient was 7-year-old Caucasian male with newly diagnosed ambiguous T cell myeloid leukemia. A chest computerized tomography (CT) demonstrated a large pleural effusion causing tracheal shift and left bronchus compression as well as an anterior mediastinal mass causing compression of the right atria and right ventricle. Tube thoracostomy was performed in the operating room (OR) with deep sedation. The procedure was complicated with hypoxemia, bradycardia, and pulseless cardiac arrest. After return of spontaneous circulation, the child continued to have refractory hypoxemia, profound hypotension, and frothy secretions. Endotracheal intubation was performed with a size 5.0 cuffed endotracheal tube. Chest radiograph demonstrated opacification of the left hemithorax with chest infiltrates. Patient required VA-ECMO for circulatory support. Supportive therapy of RPE was continued and decannulation was done on day three. Tracheal extubation was performed on day five.

NuCLS: A scalable crowdsourcing approach and dataset for nucleus classification and segmentation in breast cancer.

BACKGROUND: Deep learning enables accurate high-resolution mapping of cells and tissue structures that can serve as the foundation of interpretable machine-learning models for computational pathology. However, generating adequate labels for these structures is a critical barrier, given the time and effort required from pathologists. RESULTS: This article describes a novel collaborative framework for engaging crowds of medical students and pathologists to produce quality labels for cell nuclei. We used this approach to produce the NuCLS dataset, containing >220,000 annotations of cell nuclei in breast cancers. This builds on prior work labeling tissue regions to produce an integrated tissue region- and cell-level annotation dataset for training that is the largest such resource for multi-scale analysis of breast cancer histology. This article presents data and analysis results for single and multi-rater annotations from both non-experts and pathologists. We present a novel workflow that uses algorithmic suggestions to collect accurate segmentation data without the need for laborious manual tracing of nuclei. Our results indicate that even noisy algorithmic suggestions do not adversely affect pathologist accuracy and can help non-experts improve annotation quality. We also present a new approach for inferring truth from multiple raters and show that non-experts can produce accurate annotations for visually distinctive classes. CONCLUSIONS: This study is the most extensive systematic exploration of the large-scale use of wisdom-of-the-crowd approaches to generate data for computational pathology applications.

A Diagnosis of Choriocarcinoma in a Parturient Presenting With Intracranial Hemorrhage.

Choriocarcinoma in a viable pregnancy is uncommon. The diagnosis can easily be missed when there is an explanation for the clinical symptoms that the cancer can mimic. We present the case of a primigravid patient whose choriocarcinoma was initially missed as a result of seemingly obvious explanations for her atypical history and disease manifestation. The patient is a Caucasian female at 30 weeks and 5 days of gestation who presented with persistent headaches and new-onset tonic-clonic seizures found on brain magnetic resonance imaging (MRI) to have a left intracranial hematoma, a 5 mm midline shift, and multiple foci of restricted diffusion. Cerebral angiogram demonstrated arteriovenous malformations (AVMs). The fetus was emergently delivered 1 week into hospitalization for non-reassuring fetal heart tracings in the setting of maternal lethargy secondary to continued AVM hemorrhage. The patient's hospital course was complicated by four episodes of intracranial bleeding and edema requiring neurosurgical intervention. Three weeks after hospitalization she was discharged to a rehabilitation center, shortly after which placental biopsy demonstrated choriocarcinoma. MRI after readmission demonstrated extensive metastatic disease and human chorionic gonadotropin (hCG) levels were greater than 225,000 mIU/mL. Despite two additional neurosurgical procedures and extensive chemotherapy the patient died 3 months after initial presentation. Choriocarcinoma is extremely rare in viable pregnancies, but it should be considered when a parturient presents with intracranial bleeding. A high level of suspicion and serial serum hCG levels may lead to early and potentially life-saving multidrug chemotherapy. With a broader differential, earlier hCG measurement, and earlier treatment, our patient may have survived.

Explainable nucleus classification using Decision Tree Approximation of Learned Embeddings.

MOTIVATION: Nucleus detection, segmentation and classification are fundamental to high-resolution mapping of the tumor microenvironment using whole-slide histopathology images. The growing interest in leveraging the power of deep learning to achieve state-of-the-art performance often comes at the cost of explainability, yet there is general consensus that explainability is critical for trustworthiness and widespread clinical adoption. Unfortunately, current explainability paradigms that rely on pixel saliency heatmaps or superpixel importance scores are not well-suited for nucleus classification. Techniques like Grad-CAM or LIME provide explanations that are indirect, qualitative and/or nonintuitive to pathologists. RESULTS: In this article, we present techniques to enable scalable nuclear detection, segmentation and explainable classification. First, we show how modifications to the widely used Mask R-CNN architecture, including decoupling the detection and classification tasks, improves accuracy and enables learning from hybrid annotation datasets like NuCLS, which contain mixtures of bounding boxes and segmentation boundaries. Second, we introduce an explainability method called Decision Tree Approximation of Learned Embeddings (DTALE), which provides explanations for classification model behavior globally, as well as for individual nuclear predictions. DTALE explanations are simple, quantitative, and can flexibly use any measurable morphological features that make sense to practicing pathologists, without sacrificing model accuracy. Together, these techniques present a step toward realizing the promise of computational pathology in computer-aided diagnosis and discovery of morphologic biomarkers. AVAILABILITY AND IMPLEMENTATION: Relevant code can be found at github.com/CancerDataScience/NuCLS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Paravertebral anesthetic nerve block for pain control after peroral endoscopic myotomy.

BACKGROUND: Excess post-operative opioid medication use can delay recovery and is associated with long-term misuse, addiction, and overdose. We aimed to explore the effect of pre-procedural thoracic paravertebral nerve block (PNB) on pain-related outcomes after POEM. METHODS: In this retrospective cohort study, consecutive patients who did and did not receive a PNB prior to POEM were compared. The outcomes were peak and cumulative pain scores, total opioid use during hospitalization, and length of stay. After adjusting for confounders, the associations between nerve block and the outcomes of interest were explored. RESULTS: Forty-nine consecutive patients were enrolled; 25 patients received a block whereas the subsequent 24 did not. There were no differences in baseline characteristics between the study groups. In unadjusted analyses, there was no significant difference between patients who did and did not undergo PNB in peak pain score (7.8 vs. 8.7, p=0.14), cumulative pain score in the first 12 hours (area under curve 66.5 vs. 75.8, p=0.22), median total opioid use (38.9 mg morphine equivalent dosing vs. 42, p=1.00), and median length of hospitalization (26.5 hours vs. 24, p=0.35). In multivariable regression models, PNB was not associated with a reduction in pain scores, opioid use, or hospitalization. There were no adverse events related to the block. CONCLUSIONS: In this exploratory, observational study, paravertebral nerve block immediately before POEM did not result in a statistically significant reduction in pain-related outcomes or hospitalization. Additional observational studies may elucidate whether higher anesthetic doses or longer acting formulations would be of value.

Neuromorphological changes following selection for tameness and aggression in the Russian fox-farm experiment.

The Russian fox-farm experiment is an unusually long-running and well-controlled study designed to replicate wolf-to-dog domestication. As such, it offers an unprecedented window onto the neural mechanisms governing the evolution of behavior. Here we report evolved changes to gray matter morphology resulting from selection for tameness vs. aggressive responses toward humans in a sample of 30 male fox brains. Contrasting with standing ideas on the effects of domestication on brain size, tame foxes did not show reduced brain volume. Rather, gray matter volume in both the tame and aggressive strains was increased relative to conventional farm foxes bred without deliberate selection on behavior. Furthermore, tame- and aggressive-enlarged regions overlapped substantially, including portions of motor, somatosensory, and prefrontal cortex, amygdala, hippocampus, and cerebellum. We also observed differential morphological covariation across distributed gray matter networks. In one prefrontal-cerebellum network, this covariation differentiated the three populations along the tame-aggressive behavioral axis. Surprisingly, a prefrontal-hypothalamic network differentiated the tame and aggressive foxes together from the conventional strain. These findings indicate that selection for opposite behaviors can influence brain morphology in a similar way.SIGNIFICANCE STATEMENTDomestication represents one of the largest and most rapid evolutionary shifts of life on earth. However, its neural correlates are largely unknown. Here we report the neuroanatomical consequences of selective breeding for tameness or aggression in the seminal Russian fox-farm experiment. Compared to a population of conventional farm-bred control foxes, tame foxes show neuroanatomical changes in the prefrontal cortex and hypothalamus, paralleling wolf-to-dog shifts. Surprisingly, though, aggressive foxes also show similar changes. Moreover, both strains show increased gray matter volume relative to controls. These results indicate that similar brain adaptations can result from selection for opposite behavior, that existing ideas of brain changes in domestication may need revision, and that significant neuroanatomical change can evolve very quickly - within the span of less than a hundred generations.

Interactive Classification of Whole-Slide Imaging Data for Cancer Researchers.

Whole-slide histology images contain information that is valuable for clinical and basic science investigations of cancer but extracting quantitative measurements from these images is challenging for researchers who are not image analysis specialists. In this article, we describe HistomicsML2, a software tool for learn-by-example training of machine learning classifiers for histologic patterns in whole-slide images. This tool improves training efficiency and classifier performance by guiding users to the most informative training examples for labeling and can be used to develop classifiers for prospective application or as a rapid annotation tool that is adaptable to different cancer types. HistomicsML2 runs as a containerized server application that provides web-based user interfaces for classifier training, validation, exporting inference results, and collaborative review, and that can be deployed on GPU servers or cloud platforms. We demonstrate the utility of this tool by using it to classify tumor-infiltrating lymphocytes in breast carcinoma and cutaneous melanoma. SIGNIFICANCE: An interactive machine learning tool for analyzing digital pathology images enables cancer researchers to apply this tool to measure histologic patterns for clinical and basic science studies.

Prophylactic Physostigmine for Extreme and Refractory Adult Emergence Delirium, Aimed at Increasing Patient Safety and Reducing Health Care Workplace Violence: A Case Report.

Emergence delirium is a well-known phenomenon that may be encountered after general anesthesia. A common approach to this issue is to risk stratify patients preoperatively and treat them postoperatively if emergence delirium occurs. We present the case of a patient with Barrett esophagus and a history of severe and refractory emergence delirium, who was successfully treated prophylactically with physostigmine, resulting in decreased risk of harm to the patient, trauma to the perioperative staff, and a safer and more positive recovery.

Validation of machine learning models to detect amyloid pathologies across institutions.

Semi-quantitative scoring schemes like the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) are the most commonly used method in Alzheimer's disease (AD) neuropathology practice. Computational approaches based on machine learning have recently generated quantitative scores for whole slide images (WSIs) that are highly correlated with human derived semi-quantitative scores, such as those of CERAD, for Alzheimer's disease pathology. However, the robustness of such models have yet to be tested in different cohorts. To validate previously published machine learning algorithms using convolutional neural networks (CNNs) and determine if pathological heterogeneity may alter algorithm derived measures, 40 cases from the Goizueta Emory Alzheimer's Disease Center brain bank displaying an array of pathological diagnoses (including AD with and without Lewy body disease (LBD), and / or TDP-43-positive inclusions) and levels of Aß pathologies were evaluated. Furthermore, to provide deeper phenotyping, amyloid burden in gray matter vs whole tissue were compared, and quantitative CNN scores for both correlated significantly to CERAD-like scores. Quantitative scores also show clear stratification based on AD pathologies with or without additional diagnoses (including LBD and TDP-43 inclusions) vs cases with no significant neurodegeneration (control cases) as well as NIA Reagan scoring criteria. Specifically, the concomitant diagnosis group of AD + TDP-43 showed significantly greater CNN-score for cored plaques than the AD group. Finally, we report that whole tissue computational scores correlate better with CERAD-like categories than focusing on computational scores from a field of view with densest pathology, which is the standard of practice in neuropathological assessment per CERAD guidelines. Together these findings validate and expand CNN models to be robust to cohort variations and provide additional proof-of-concept for future studies to incorporate machine learning algorithms into neuropathological practice.

A Challenging Cesarean Delivery and Perioperative Course in a Former Polysubstance Abuser Without the Use of Traditional Opioids.

Substance abuse is a major challenge in the United States. According to the Human Resources and Services Administration, we are in an opioid crisis with over 130 people a day dying from opioid-related drug overdoses. As awareness of this epidemic has grown, there has been an increase in patients coming in for surgery requesting a narcotic-free anesthetic. This presents both a challenge and an opportunity for the anesthesiologist who has a duty to respect the patient's autonomy while simultaneously achieving the appropriate perioperative outcome. The considerations are especially important in the vulnerable population of pregnant women.