A case-based narrative review of pregnancy-associated atypical hemolytic uremic syndrome/complement-mediated thrombotic microangiopathy.

Atypical hemolytic uremic syndrome is a complement-mediated thrombotic microangiopathy caused by uncontrolled activation of the alternative complement pathway in the setting of autoantibodies to or rare pathogenic genetic variants in complement proteins. Pregnancy may serve as a trigger and unmask atypical hemolytic uremic syndrome/complement-mediated thrombotic microangiopathy (aHUS/CM-TMA), which has severe, life-threatening consequences. It can be difficult to diagnose aHUS/CM-TMA in pregnancy due to overlapping clinical features with other thrombotic microangiopathy syndromes including hypertensive disorders of pregnancy. However, the distinction among thrombotic microangiopathy etiologies in pregnancy is important because each syndrome has specific disease management and treatment. In this narrative review, we discuss 2 cases to illustrate the diagnostic challenges and evolving approach in the management of pregnancy-associated aHUS/CM-TMA. The first case involves a 30-year-old woman presenting in the first trimester who was diagnosed with aHUS/CM-TMA and treated with eculizumab from 19 weeks' gestation. Genetic testing revealed a likely pathogenic variant in CFI. She successfully delivered a healthy infant at 30 weeks' gestation. In the second case, a 22-year-old woman developed severe postpartum HELLP syndrome, requiring hemodialysis. Her condition improved with supportive management, yet investigations assessing for aHUS/CM-TMA remained abnormal 6 months postpartum consistent with persistent complement activation but negative genetic testing. Through detailed case discussion describing tests assessing for placental health, fetal anatomy, complement activation, autoantibodies to complement regulatory proteins, and genetic testing for aHUS/CM-TMA, we describe how these results aided in the clinical diagnosis of pregnancy-associated aHUS/CM-TMA and assisted in guiding patient management, including the use of anticomplement therapy.

Testing of rapid evaporative mass spectrometry for histological tissue classification and molecular diagnostics in a multi-site study.

BACKGROUND: While REIMS technology has successfully been demonstrated for the histological identification of ex-vivo breast tumor tissues, questions regarding the robustness of the approach and the possibility of tumor molecular diagnostics still remain unanswered. In the current study, we set out to determine whether it is possible to acquire cross-comparable REIMS datasets at multiple sites for the identification of breast tumors and subtypes. METHODS: A consortium of four sites with three of them having access to fresh surgical tissue samples performed tissue analysis using identical REIMS setups and protocols. Overall, 21 breast cancer specimens containing pathology-validated tumor and adipose tissues were analyzed and results were compared using uni- and multivariate statistics on normal, WT and PIK3CA mutant ductal carcinomas. RESULTS: Statistical analysis of data from standards showed significant differences between sites and individual users. However, the multivariate classification models created from breast cancer data elicited 97.1% and 98.6% correct classification for leave-one-site-out and leave-one-patient-out cross validation. Molecular subtypes represented by PIK3CA mutation gave consistent results across sites. CONCLUSIONS: The results clearly demonstrate the feasibility of creating and using global classification models for a REIMS-based margin assessment tool, supporting the clinical translatability of the approach.

3D printer platform and conductance feedback loop for automated imaging of uneven surfaces by liquid microjunction-surface sampling probe mass spectrometry.

RATIONALE: Molecular imaging of samples using mass spectrometric techniques, such as matrix-assisted laser desorption ionization or desorption electrospray ionization, requires the sample surface to be even/flat and sliced into thin sections (c. 10 µm). Furthermore, sample preparation steps can alter the analyte composition of the sample. The liquid microjunction-surface sampling probe (LMJ-SSP) is a robust sampling interface that enables surface profiling with minimal sample preparation. In conjunction with a conductance feedback system, the LMJ-SSP can be used to automatically sample uneven specimens. METHODS: A sampling stage was built with a modified 3D printer where the LMJ-SSP is attached to the printing head. This setup can scan across flat and even surfaces in a predefined pattern ("static sampling mode"). Uneven samples are automatically probed in "conductance sampling mode" where an electric potential is applied and measured at the probe. When the probe contacts the electrically grounded sample, the potential at the probe drops, which is used as a feedback signal to determine the optimal position of the probe for sampling each location. RESULTS: The applicability of the probe/sensing system was demonstrated by first examining the strawberry tissue using the "static sampling mode." Second, porcine tissue samples were profiled using the "conductance sampling mode." With minimal sample preparation, an area of 11 × 15 mm was profiled in less than 2 h. From the obtained results, adipose areas could be distinguished from non-adipose parts. The versatility of the approach was further demonstrated by directly sampling the bacteria colonies on agar and resected human kidney (intratumoral hemorrhage) specimens with thicknesses ranging from 1 to 4 mm. CONCLUSION: The LMJ-SSP in conjunction with a conductive feedback system is a powerful tool that allows for fast, reproducible, and automated assessment of uneven surfaces with minimal sample preparation. This setup could be used for perioperative assessment of tissue samples, food screening, and natural product discovery, among others.

Deep-Learning-Driven Quantification of Interstitial Fibrosis in Digitized Kidney Biopsies.

Interstitial fibrosis and tubular atrophy (IFTA) on a renal biopsy are strong indicators of disease chronicity and prognosis. Techniques that are typically used for IFTA grading remain manual, leading to variability among pathologists. Accurate IFTA estimation using computational techniques can reduce this variability and provide quantitative assessment. Using trichrome-stained whole-slide images (WSIs) processed from human renal biopsies, we developed a deep-learning framework that captured finer pathologic structures at high resolution and overall context at the WSI level to predict IFTA grade. WSIs (n = 67) were obtained from The Ohio State University Wexner Medical Center. Five nephropathologists independently reviewed them and provided fibrosis scores that were converted to IFTA grades: ≤10% (none or minimal), 11% to 25% (mild), 26% to 50% (moderate), and >50% (severe). The model was developed by associating the WSIs with the IFTA grade determined by majority voting (reference estimate). Model performance was evaluated on WSIs (n = 28) obtained from the Kidney Precision Medicine Project. There was good agreement on the IFTA grading between the pathologists and the reference estimate (κ = 0.622 ± 0.071). The accuracy of the deep-learning model was 71.8% ± 5.3% on The Ohio State University Wexner Medical Center and 65.0% ± 4.2% on Kidney Precision Medicine Project data sets. Our approach to analyzing microscopic- and WSI-level changes in renal biopsies attempts to mimic the pathologist and provides a regional and contextual estimation of IFTA. Such methods can assist clinicopathologic diagnosis.

A flexible ChIP-sequencing simulation toolkit.

BACKGROUND: A major challenge in evaluating quantitative ChIP-seq analyses, such as peak calling and differential binding, is a lack of reliable ground truth data. Accurate simulation of ChIP-seq data can mitigate this challenge, but existing frameworks are either too cumbersome to apply genome-wide or unable to model a number of important experimental conditions in ChIP-seq. RESULTS: We present ChIPs, a toolkit for rapidly simulating ChIP-seq data using statistical models of key experimental steps. We demonstrate how ChIPs can be used for a range of applications, including benchmarking analysis tools and evaluating the impact of various experimental parameters. ChIPs is implemented as a standalone command-line program written in C++ and is available from https://github.com/gymreklab/chips . CONCLUSIONS: ChIPs is an efficient ChIP-seq simulation framework that generates realistic datasets over a flexible range of experimental conditions. It can serve as an important component in various ChIP-seq analyses where ground truth data are needed.

Do Patients Respond to Posted Emergency Department Wait Times: Time-Series Evidence From the Implementation of a Wait Time Publication System in Hamilton, Canada.

STUDY OBJECTIVE: One proposed solution to prolonged emergency department (ED) wait times is a publicly available website that displays estimated ED wait times. This could provide information to patients so that they may choose sites with low wait times, which has the potential to smooth the overall wait times in EDs across a health system. We describe the effect of a novel city-wide ED wait time website on patient volume distributions throughout the city of Hamilton, Ontario, Canada. METHODS: We compared the number of new patients arriving every 15 minutes during 2 separate time periods-before and after a publicly viewable wait time website was made available. For each ED site, the effect of the posted wait time was measured by assessing its association with the total number of patient arrivals in the subsequent hour at the same site and at all other sites in Hamilton. RESULTS: Linear models showed clinically modest changes in patient volumes when wait times changed. However, nonlinear models showed that a 60-minute increase in wait time at a site was associated with 10% fewer patients presenting over the next hour. Larger negative associations were observed at community hospitals and urgent care centers. Increases in wait times at a given site were also associated with increased patient volumes at other sites in the system. CONCLUSION: After the implementation of a public wait time website, elevated wait times led to fewer patients at the same site but more patient visits at other sites. This may be consistent with the wait time tracker inducing patients to avoid sites with high wait times and instead visit alternate sites in Hamilton, but only when wait times were very high.

Reliable identification of prostate cancer using mass spectrometry metabolomic imaging in needle core biopsies.

Metabolomic profiling can aid in understanding crucial biological processes in cancer development and progression and can also yield diagnostic biomarkers. Desorption electrospray ionization coupled to mass spectrometry imaging (DESI-MSI) has been proposed as a potential adjunct to diagnostic surgical pathology, particularly for prostate cancer. However, due to low resolution sampling, small numbers of mass spectra, and little validation, published studies have yet to test whether this method is sufficiently robust to merit clinical translation. We used over 900 spatially resolved DESI-MSI spectra to establish an accurate, high-resolution metabolic profile of prostate cancer. We identified 25 differentially abundant metabolites, with cancer tissue showing increased fatty acids (FAs) and phospholipids, along with utilization of the Krebs cycle, and benign tissue showing increased levels of lyso-phosphatidylethanolamine (PE). Additionally, we identified, for the first time, two lyso-PEs with abundance that decreased with cancer grade and two phosphatidylcholines (PChs) with increased abundance with increasing cancer grade. Importantly, we developed and internally validated a multivariate metabolomic classifier for prostate cancer using 534 spatial regions of interest (ROIs) in the training cohort and 430 ROIs in the test cohort. With excellent statistical power, the training cohort achieved a balanced accuracy of 97% and validation on testing data set demonstrated 85% balanced accuracy. Given the validated accuracy of this classifier and the correlation of differentially abundant metabolites with established patterns of prostate cancer cell metabolism, we conclude that DESI-MSI is an effective tool for characterizing prostate cancer metabolism with the potential for clinical translation.

Renal Manifestations of IgG4-Related Disease: A Concise Review.

IgG4-related disease (IgG4-RD) is an immune-mediated disorder marked by fibro-inflammatory masses that can infiltrate multiple organ systems. Due to its relatively recent discovery and limited understanding of its pathophysiology, IgG4-related disease may be difficult to recognize and is consequently potentially underdiagnosed. Renal involvement is becoming regarded as one of the key features of this disease. To date, the most well-recognized renal complication of IgG4-related disease is tubulointerstitial nephritis, but membranous glomerulonephritis, renal masses, and retroperitoneal fibrosis have also been reported. This concise review has two objectives. First, it will briefly encapsulate the history, epidemiology, and presentation of IgG4-related disease. Second, it will examine the reported renal manifestations of IgG4-related disease, exploring the relevant histology, imaging, clinical features, and treatment considerations. This synthesis will be highly relevant for nephrologists, rheumatologists, general internists, and renal pathologists to raise awareness and help improve early recognition of IgG4-related kidney disease (IgG4-RKD).

ImSpect: Image-driven self-supervised learning for surgical margin evaluation with mass spectrometry.

PURPOSE: Real-time assessment of surgical margins is critical for favorable outcomes in cancer patients. The iKnife is a mass spectrometry device that has demonstrated potential for margin detection in cancer surgery. Previous studies have shown that using deep learning on iKnife data can facilitate real-time tissue characterization. However, none of the existing literature on the iKnife facilitate the use of publicly available, state-of-the-art pretrained networks or datasets that have been used in computer vision and other domains. METHODS: In a new framework we call ImSpect, we convert 1D iKnife data, captured during basal cell carcinoma (BCC) surgery, into 2D images in order to capitalize on state-of-the-art image classification networks. We also use self-supervision to leverage large amounts of unlabeled, intraoperative data to accommodate the data requirements of these networks. RESULTS: Through extensive ablation studies, we show that we can surpass previous benchmarks of margin evaluation in BCC surgery using iKnife data, achieving an area under the receiver operating characteristic curve (AUC) of 81%. We also depict the attention maps of the developed DL models to evaluate the biological relevance of the embedding space CONCLUSIONS: We propose a new method for characterizing tissue at the surgical margins, using mass spectrometry data from cancer surgery.

Characterization of Membranous Nephropathy with Microspherular Deposits.

Introduction: Membranous nephropathy (MN) is a common cause of adult nephrotic syndrome in the USA. The typical ultrastructural finding is of global uniformly dense subepithelial electron-dense immune complex deposits along glomerular basement membranes. However, early reports described deposits with a unique microspherular substructure. There was variability in what was identified as microspherular, sometimes overlapping with other entities such as podocyte infolding glomerulopathy. Currently, the nature, composition, and clinical significance of these microspherular deposits (MSDs) remain unknown. Method: We report the clinicopathologic features of a series of MN cases with MSD, with detailed ultrastructural characterization as well as PLA2R and THSD7A immunohistochemical and IgG subclass-staining characteristics. The proportion of MSD to overall deposits is segregated into two groups: global MSD with >50% MSD (n = 14) and segmental MSD with <50% (n = 5). Results: The size and appearance of the microspherules were nearly identical in global and segmental MSD groups (mean diameter of 77.9 nm and 77.2 nm, respectively), with subepithelial (n = 19) or intramembranous (n = 12) distributions in all cases. Mesangial MSDs (n = 5) were only found in the global MSD group. The majority of biopsies (86% of global MSD and 100% of segmental MSD) were Ehrenreich-Churg stage 2 or above; early stage 1 was only observed in the global MSD group. All but 3 cases were PLA2R/THSD7A double negative; 1 THSD7A positive in global MSD and 2 PLA2R positive in segmental MSD. IgG1 was the dominant subclass in the global MSD group, and IgG4 was dominant in the segmental MSD group, including the 2 PLA2R-positive cases. Conclusion: The findings suggest that MSDs are more commonly associated with secondary MN. This case series is the largest to date, and the findings may yield etiologic and prognostic information on this rare but unique subset of MN and provide a well-characterized cohort of cases for future studies.

Metabolically Active Zones Involving Fatty Acid Elongation Delineated by DESI-MSI Correlate with Pathological and Prognostic Features of Colorectal Cancer.

Colorectal cancer (CRC) is the second leading cause of cancer deaths. Despite recent advances, five-year survival rates remain largely unchanged. Desorption electrospray ionization mass spectrometry imaging (DESI) is an emerging nondestructive metabolomics-based method that retains the spatial orientation of small-molecule profiles on tissue sections, which may be validated by 'gold standard' histopathology. In this study, CRC samples were analyzed by DESI from 10 patients undergoing surgery at Kingston Health Sciences Center. The spatial correlation of the mass spectral profiles was compared with histopathological annotations and prognostic biomarkers. Fresh frozen sections of representative colorectal cross sections and simulated endoscopic biopsy samples containing tumour and non-neoplastic mucosa for each patient were generated and analyzed by DESI in a blinded fashion. Sections were then hematoxylin and eosin (H and E) stained, annotated by two independent pathologists, and analyzed. Using PCA/LDA-based models, DESI profiles of the cross sections and biopsies achieved 97% and 75% accuracies in identifying the presence of adenocarcinoma, using leave-one-patient-out cross validation. Among the m/z ratios exhibiting the greatest differential abundance in adenocarcinoma were a series of eight long-chain or very-long-chain fatty acids, consistent with molecular and targeted metabolomics indicators of de novo lipogenesis in CRC tissue. Sample stratification based on the presence of lympovascular invasion (LVI), a poor CRC prognostic indicator, revealed the abundance of oxidized phospholipids, suggestive of pro-apoptotic mechanisms, was increased in LVI-negative compared to LVI-positive patients. This study provides evidence of the potential clinical utility of spatially-resolved DESI profiles to enhance the information available to clinicians for CRC diagnosis and prognosis.

C3 Glomerulopathy: A Review with Emphasis on Ultrastructural Features.

C3 glomerulopathy (C3G) is a rare disease resulting from dysregulation of the alternative complement pathway, resulting in the deposition of complement component 3 (C3) in the kidney. It encompasses two major subgroups: dense deposit disease and C3 glomerulonephritis (C3GN). Although the alternative complement pathway is typically a very tightly controlled system, dysregulation can be a result of genetic mutations in the fluid phase or membrane-bound inhibitors or accelerators. In addition, de novo/acquired autoantibodies against any of the regulatory proteins can alter complement activation either by negating an inhibitor or activating an accelerator. Triggering events can be complex; however, the final pathway is characterized by the uncontrolled deposition of C3 in glomeruli and the formation of the membrane attack complex. Light microscopic findings can be quite heterogeneous with a membranoproliferative pattern most commonly encountered. Diagnostic confirmation of C3G is based on a characteristic pattern of glomerular immunofluorescence staining, with C3-dominant deposits that are at least 2 orders of intensity greater than staining for any immunoglobulin (Ig) or C1q. Electron microscopy is necessary for diagnosing DDD in particular, but can also help to distinguish C3GN from other glomerular disease mimickers.

SC2Net: A Novel Segmentation-Based Classification Network for Detection of COVID-19 in Chest X-Ray Images.

The pandemic of COVID-19 has become a global crisis in public health, which has led to a massive number of deaths and severe economic degradation. To suppress the spread of COVID-19, accurate diagnosis at an early stage is crucial. As the popularly used real-time reverse transcriptase polymerase chain reaction (RT-PCR) swab test can be lengthy and inaccurate, chest screening with radiography imaging is still preferred. However, due to limited image data and the difficulty of the early-stage diagnosis, existing models suffer from ineffective feature extraction and poor network convergence and optimisation. To tackle these issues, a segmentation-based COVID-19 classification network, namely SC2Net, is proposed for effective detection of the COVID-19 from chest x-ray (CXR) images. The SC2Net consists of two subnets: a COVID-19 lung segmentation network (CLSeg), and a spatial attention network (SANet). In order to supress the interference from the background, the CLSeg is first applied to segment the lung region from the CXR. The segmented lung region is then fed to the SANet for classification and diagnosis of the COVID-19. As a shallow yet effective classifier, SANet takes the ResNet-18 as the feature extractor and enhances high-level feature via the proposed spatial attention module. For performance evaluation, the COVIDGR 1.0 dataset is used, which is a high-quality dataset with various severity levels of the COVID-19. Experimental results have shown that, our SC2Net has an average accuracy of 84.23% and an average F1 score of 81.31% in detection of COVID-19, outperforming several state-of-the-art approaches.

Application of Intraoperative Mass Spectrometry and Data Analytics for Oncological Margin Detection, A Review.

OBJECTIVE: A common phase of early-stage oncological treatment is the surgical resection of cancerous tissue. The presence of cancer cells on the resection margin, referred to as positive margin, is correlated with the recurrence of cancer and may require re-operation, negatively impacting many facets of patient outcomes. There exists a significant gap in the surgeon's ability to intraoperatively delineate between tissues. Mass spectrometry methods have shown considerable promise as intraoperative tissue profiling tools that can assist with the complete resection of cancer. To do so, the vastness of the information collected through these modalities must be digested, relying on robust and efficient extraction of insights through data analysis pipelines. METHODS: We review clinical mass spectrometry literature and prioritize intraoperatively applied modalities. We also survey the data analysis methods employed in these studies. RESULTS: Our review outlines the advantages and shortcomings of mass spectrometry imaging and point-based tissue probing methods. For each modality, we identify statistical, linear transformation and machine learning techniques that demonstrate high performance in classifying cancerous tissues across several organ systems. A limited number of studies presented results captured intraoperatively. CONCLUSION: Through continued research of data centric techniques, like mass spectrometry, and the development of robust analysis approaches, intraoperative margin assessment is becoming feasible. SIGNIFICANCE: By establishing the relatively short history of mass spectrometry techniques applied to surgical studies, we hope to inform future applications and aid in the selection of suitable data analysis frameworks for the development of intraoperative margin detection technologies.

Uncertainty estimation for margin detection in cancer surgery using mass spectrometry.

PURPOSE: Rapid evaporative ionization mass spectrometry (REIMS) is an emerging technology for clinical margin detection. Deployment of REIMS depends on construction of reliable deep learning models that can categorize tissue according to its metabolomic signature. Challenges associated with developing these models include the presence of noise during data acquisition and the variance in tissue signatures between patients. In this study, we propose integration of uncertainty estimation in deep models to factor predictive confidence into margin detection in cancer surgery. METHODS: iKnife is used to collect 693 spectra of cancer and healthy samples acquired from 91 patients during basal cell carcinoma resection. A Bayesian neural network and two baseline models are trained on these data to perform classification as well as uncertainty estimation. The samples with high estimated uncertainty are then removed, and new models are trained using the clean data. The performance of proposed and baseline models, with different ratios of filtered data, is then compared. RESULTS: The data filtering does not improve the performance of the baseline models as they cannot provide reliable estimations of uncertainty. In comparison, the proposed model demonstrates a statistically significant improvement in average balanced accuracy (75.2%), sensitivity (74.1%) and AUC (82.1%) after removing uncertain training samples. We also demonstrate that if highly uncertain samples are predicted and removed from the test data, sensitivity further improves to 88.2%. CONCLUSIONS: This is the first study that applies uncertainty estimation to inform model training and deployment for tissue recognition in cancer surgery. Uncertainty estimation is leveraged in two ways: by factoring a measure of input noise in training the models and by including predictive confidence in reporting the outputs. We empirically show that considering uncertainty for model development can help improve the overall accuracy of a margin detection system using REIMS.

Diagnostic and prognostic implications of a three-antibody molecular subtyping algorithm for non-muscle invasive bladder cancer.

Intrinsic molecular subtypes may explain marked variation between bladder cancer patients in prognosis and response to therapy. Complex testing algorithms and little attention to more prevalent, early-stage (non-muscle invasive) bladder cancers (NMIBCs) have hindered implementation of subtyping in clinical practice. Here, using a three-antibody immunohistochemistry (IHC) algorithm, we identify the diagnostic and prognostic associations of well-validated proteomic features of basal and luminal subtypes in NMIBC. By IHC, we divided 481 NMIBCs into basal (GATA3- /KRT5+ ) and luminal (GATA3+ /KRT5 variable) subtypes. We further divided the luminal subtype into URO (p16 low), URO-KRT5+ (KRT5+ ), and genomically unstable (GU) (p16 high) subtypes. Expression thresholds were confirmed using unsupervised hierarchical clustering. Subtypes were correlated with pathology and outcomes. All NMIBC cases clustered into the basal/squamous (basal) or one of the three luminal (URO, URO-KRT5+ , and GU) subtypes. Although uncommon in this NMIBC cohort, basal tumors (3%, n = 16) had dramatically higher grade (100%, n = 16, odds ratio [OR] = 13, relative risk = 3.25) and stage, and rapid progression to muscle invasion (median progression-free survival = 35.4 months, p = 0.0001). URO, the most common subtype (46%, n = 220), showed rapid recurrence (median recurrence-free survival [RFS] = 11.5 months, p = 0.039) compared to its GU counterpart (29%, n = 137, median RFS = 16.9 months), even in patients who received intravesical immunotherapy (p = 0.049). URO-KRT5+ tumors (22%, n = 108) were typically low grade (66%, n = 71, OR = 3.7) and recurred slowly (median RFS = 38.7 months). Therefore, a simple immunohistochemical algorithm can identify clinically relevant molecular subtypes of NMIBC. In routine clinical practice, this three-antibody algorithm may help clarify diagnostic dilemmas and optimize surveillance and treatment strategies for patients.

Delayed-Type Hypersensitivity Reaction to Red Tattoo Ink Triggered by Ledipasvir/Sofosbuvir for Hepatitis C: A Case Report.

Tattoos have become increasingly popular worldwide making adverse effects from tattoos a growing concern. In our report, we present a 51-year-old man who developed an unusual allergic reaction to the red ink portions of his tattoos that coincided with the initiation of ledipasvir/sofosbuvir treatment for his hepatitis C. Clinical and histological features were consistent with a delayed-type hypersensitivity reaction to red ink.

The Continuing Need for Electron Microscopy in Examination of Medical Renal Biopsies: Examples in Practice.

Background: For the better part of the past 6 decades, transmission electron microscopy (EM), together with routine light microscopy and immunofluorescence and/or immunohistochemistry (IHC), has been an essential component of the diagnostic workup of medical renal biopsies, particularly native renal biopsies, with increasing frequency in renal allograft biopsies as well. Studies performed prior to the year 2000 have indeed shown that a substantial fraction of renal biopsies cannot be accurately diagnosed without EM. Still, EM remains costly and labor-intensive, and with increasing pressure to reduce healthcare costs, some centers are de-emphasizing diagnostic EM. This trend has been coupled with advances in IHC and other methods in renal biopsy diagnosis over the past 2-3 decades. Summary: Nonetheless, it has been our experience that the diagnostic value of EM in the comprehensive evaluation of renal biopsies remains similar to what it was 20-30 years ago. In this review, we provide several key examples from our practice where EM was essential in making the correct renal biopsy diagnosis, ranging from relatively common glomerular lesions to rare diseases. Key Messages: EM remains an important component of the diagnostic evaluation of medical renal biopsies. Failure to perform EM in certain cases will result in an incorrect diagnosis, with possible clinical consequences. We strongly recommend that tissue for EM be taken and stored in an appropriate fixative and ultrastructural studies be performed for all native renal biopsies, as well as appropriate renal allograft biopsies as recommended by the Banff consortium.

Metabolomics patterns of breast cancer tumors using mass spectrometry imaging.

PURPOSE: Intraoperative assessment of surgical margins is important for reducing the rate of revisions in breast conserving surgery for palpable malignant tumors. The hypothesis was that metabolomics methods, based on mass spectrometry, could find patterns of relative abundances of molecules that distinguish clusters of benign tissue and cancer in surgical resections. METHODS: Excisions from 8 patients were used to acquire 112,317 mass spectrometry signals by desorption electrospray ionization. A process of nonnegative matrix factorization and graph decomposition produced clusters that were approximated as affine spaces. Each signal's distance to the affine space of a cluster was used to visualize the clustering. RESULTS: The distance maps were superior to binary clustering in identifying cancer regions. They were particularly effective at finding cancer regions that were discontinuously distributed within benign tissue. CONCLUSIONS: Desorption electrospray ionization mass spectrometry, which has been shown to be useful intraoperatively, can acquire signals that distinguish malignant from benign breast tissue in surgically excised tumors. The method may be suitable for real-time surgical decisions based on cancer margins.

Combined Mass Spectrometry and Histopathology Imaging for Perioperative Tissue Assessment in Cancer Surgery.

Mass spectrometry is an effective imaging tool for evaluating biological tissue to detect cancer. With the assistance of deep learning, this technology can be used as a perioperative tissue assessment tool that will facilitate informed surgical decisions. To achieve such a system requires the development of a database of mass spectrometry signals and their corresponding pathology labels. Assigning correct labels, in turn, necessitates precise spatial registration of histopathology and mass spectrometry data. This is a challenging task due to the domain differences and noisy nature of images. In this study, we create a registration framework for mass spectrometry and pathology images as a contribution to the development of perioperative tissue assessment. In doing so, we explore two opportunities in deep learning for medical image registration, namely, unsupervised, multi-modal deformable image registration and evaluation of the registration. We test this system on prostate needle biopsy cores that were imaged with desorption electrospray ionization mass spectrometry (DESI) and show that we can successfully register DESI and histology images to achieve accurate alignment and, consequently, labelling for future training. This automation is expected to improve the efficiency and development of a deep learning architecture that will benefit the use of mass spectrometry imaging for cancer diagnosis.