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.

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.

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.

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.

Domain adaptation and self-supervised learning for surgical margin detection.

PURPOSE: One in five women who undergo breast conserving surgery will need a second revision surgery due to remaining tumor. The iKnife is a mass spectrometry modality that produces real-time margin information based on the metabolite signatures in surgical smoke. Using this modality and real-time tissue classification, surgeons could remove all cancerous tissue during the initial surgery, improving many facets of patient outcomes. An obstacle in developing a iKnife breast cancer recognition model is the destructive, time-consuming and sensitive nature of the data collection that limits the size of the datasets. METHODS: We address these challenges by first, building a self-supervised learning model from limited, weakly labeled data. By doing so, the model can learn to contextualize the general features of iKnife data from a more accessible cancer type. Second, the trained model can then be applied to a cancer classification task on breast data. This domain adaptation allows for the transfer of learnt weights from models of one tissue type to another. RESULTS: Our datasets contained 320 skin burns (129 tumor burns, 191 normal burns) from 51 patients and 144 breast tissue burns (41 tumor and 103 normal) from 11 patients. We investigate the effect of different hyper-parameters on the performance of the final classifier. The proposed two-step method performed statistically significantly better than a baseline model (p-value < 0.0001), by achieving an accuracy, sensitivity and specificity of 92%, 88% and 92%, respectively. CONCLUSION: This is the first application of domain transfer for iKnife REIMS data. We showed that having a limited number of breast data samples for training a classifier can be compensated by self-supervised learning and domain adaption on a set of unlabeled skin data. We plan to confirm this performance by collecting new breast samples and extending it to incorporate other cancer tissues.

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.

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.