Background: A definitive diagnosis of prostate cancer requires a biopsy to obtain tissue for pathologic analysis, but this is an invasive procedure and is associated with complications. Purpose: To develop an artificial intelligence (AI)-based model (named AI-biopsy) for the early diagnosis of prostate cancer using magnetic resonance (MR) images labeled with histopathology information. Study Type: Retrospective. Population: Magnetic resonance imaging (MRI) data sets from 400 patients with suspected prostate cancer and with histological data (228 acquired in-house and 172 from external publicly available databases). Field Strength/Sequence: 1.5 to 3.0 Tesla, T2-weighted image pulse sequences. Assessment: MR images reviewed and selected by two radiologists (with 6 and 17 years of experience). The patient images were labeled with prostate biopsy including Gleason Score (6 to 10) or Grade Group (1 to 5) and reviewed by one pathologist (with 15 years of experience). Deep learning models were developed to distinguish 1) benign from cancerous tumor and 2) high-risk tumor from low-risk tumor. Statistical Tests: To evaluate our models, we calculated negative predictive value, positive predictive value, specificity, sensitivity, and accuracy. We also calculated areas under the receiver operating characteristic (ROC) curves (AUCs) and Cohen's kappa. Results: Our computational method (https://github.com/ih-lab/AI-biopsy) achieved AUCs of 0.89 (95% confidence interval [CI]: [0.86-0.92]) and 0.78 (95% CI: [0.74-0.82]) to classify cancer vs. benign and high-vs. low-risk of prostate disease, respectively. Data Conclusion: AI-biopsy provided a data-driven and reproducible way to assess cancer risk from MR images and a personalized strategy to potentially reduce the number of unnecessary biopsies. AI-biopsy highlighted the regions of MR images that contained the predictive features the algorithm used for diagnosis using the class activation map method. It is a fully automatic method with a drag-and-drop web interface (https://ai-biopsy.eipm-research.org) that allows radiologists to review AI-assessed MR images in real time. Level of Evidence: 1 Technical Efficacy Stage: 2

BackgroundNormothermic machine perfusion (NMP) allows viability assessment and potential resuscitation of donor livers prior to transplantation. The immunological effect of NMP on liver allografts is undetermined, with potential implications on allograft function, rejection outcomes and overall survival. In this study we define the changes in immune profile of human livers during NMP.MethodsSix human livers were placed on a NMP device. Tissue and perfusate samples were obtained during cold storage prior to perfusion and at 1, 3, and 6 hours of perfusion. Flow cytometry, immunohistochemistry, and bead-based immunoassays were used to measure leukocyte composition and cytokines in the perfusate and within the liver tissue. Mean values between baseline and time points were compared by Student’s t-test.ResultsWithin circulating perfusate, significantly increased frequencies of CD4 T cells, B cells and eosinophils were detectable by 1 hour of NMP and continued to increase at 6 hours of perfusion. On the other hand, NK cell frequency significantly decreased by 1 hour of NMP and remained decreased for the duration of perfusion. Within the liver tissue there was significantly increased B cell frequency but decreased neutrophils detectable at 6 hours of NMP. A transient decrease in intermediate monocyte frequency was detectable in liver tissue during the middle of the perfusion run. Overall, no significant differences were detectable in tissue resident T regulatory cells during NMP. Significantly increased levels of pro-inflammatory and anti-inflammatory cytokines were seen following initiation of NMP that continued to rise throughout duration of perfusion.ConclusionsTime-dependent dynamic changes are seen in individual leukocyte cell-types within both perfusate and tissue compartments of donor livers during NMP. This suggests a potential role of NMP in altering the immunogenicity of donor livers prior to transplant. These data also provide insights for future work to recondition the intrinsic immune profile of donor livers during NMP prior to transplantation.

Background As the COVID-19 pandemic moves into the survivorship phase, questions regarding long-term lung damage remain unanswered. Previous histopathological studies are limited to autopsy reports. We studied lung specimens from COVID-19 survivors who underwent elective lung resections to determine whether post-acute histopathological changes are present. Methods In this multicenter observational study, we included adult COVID-19 survivors (n=11) who had recovered but subsequently underwent unrelated elective lung resection for indeterminate lung nodules or lung cancer. We compared these to an age- and procedure-matched control group who never contracted COVID-19 (n=5), and an end-stage COVID-19 group (n=3). A blinded pulmonary pathologist examined the lung parenchyma focusing on four compartments: airways, alveoli, interstitium, and vasculature. Results Eleven COVID-19 survivors with asymptomatic (n=4), moderate (n=4), and severe (n=3) COVID-19 infections underwent elective lung resection at a median 68.5 days (range 24-142) after COVID-19 diagnosis. The most common operation was lobectomy (75%). On histopathological examination, no differences were identified between the lung parenchyma of COVID-19 survivors and controls across all compartments examined. Conversely, patients in the end-stage COVID-19 group showed fibrotic diffuse alveolar damage with intra-alveolar macrophages, organizing pneumonia, and focal interstitial emphysema. Conclusions In this first study to examine the lung parenchyma of COVID-19 survivors, we did not find distinct post-acute histopathological changes to suggest permanent pulmonary damage. These results are reassuring for COVID-19 survivors who recover and become asymptomatic.

Background: Ex vivo lung perfusion (EVLP) is used to assess and preserve lungs prior to transplantation. However, its inherent immunomodulatory effects are not completely understood. We examine perfusate and tissue compartments to determine the change in immune cell composition in human lungs maintained on EVLP. Methods: Six human lungs unsuitable for transplantation underwent EVLP. Tissue and perfusate samples were obtained during cold storage and at 1-, 3-and 6-h during perfusion. Flow cytometry, immunohistochemistry, and bead-based immunoassays were used to measure leukocyte composition and cytokines. Mean values between baseline and time points were compared by Student's t test. Results: During the 1st hour of perfusion, perfusate neutrophils increased (+22.2 ± 13.5%, p < 0.05), monocytes decreased (−77.5 ± 8.6%, p < 0.01) and NK cells decreased (−61.5 ± 22.6%, p < 0.01) compared to cold storage. In contrast, tissue neutrophils decreased (−22.1 ± 12.2%, p < 0.05) with no change in monocytes and NK cells. By 6 h, perfusate neutrophils, NK cells, and tissue neutrophils were similar to baseline. Perfusate monocytes remained decreased, while tissue monocytes remained unchanged. There was no significant change in B cells or T cell subsets. Pro-inflammatory cytokines (IL-1b, G-CSF, IFN-gamma, CXCL2, CXCL1 granzyme A, and granzyme B) and lymphocyte activating cytokines (IL-2, IL-4, IL-6, IL-8) increased during perfusion. Conclusions: Early mobilization of innate immune cells occurs in both perfusate and tissue compartments during EVLP, with neutrophils and NK cells returning to baseline and monocytes remaining depleted after 6 h. The immunomodulatory effect of EVLP may provide a therapeutic window to decrease the immunogenicity of lungs prior to transplantation.

Influenza A virus belongs to the Orthomyxoviridae family and, to date, is one of the most important pathogens causing acute respiratory infections, such as the recent pandemic of 2009. Hemagglutinin (HA) is one of the surface proteins of the virus that allow it to interact with cellular molecules. Due to the fact that it is the most abundant protein in the virus capsule, it is the best target in the detection of the Influenza A H1N1 virus through biosensing devices. Our aim is to develop an electrochemical biosensor to detect H1 by modifying carbon screen-printed electrodes (CSPE) with gold nanoparticles and to add further functionalization with monoclonal antibodies that are specific to this protein. The electrodes were characterized by the means of cyclic voltammetry, differential pulse voltammetry and electrochemical impedance spectroscopy. Our preliminary results suggest that the selected monoclonal antibodies have acceptable affinity and bind effectively to the H1 protein and that the electrodes have a wide potential window in the presence of [Fe(CN)6]3−/4−. In the future, we will continue to develop this biosensor in hope that it will be commercialized and be common in medical procedures during flu seasons and future influenza pandemics.