Toll-like receptor 4 mutation mitigates gut microbiota-mediated hypertensive kidney injury.

Hypertension-associated dysbiosis is linked to several clinical complications, including inflammation and possible kidney dysfunction. Inflammation and TLR4 activation during hypertension result from gut dysbiosis-related impairment of intestinal integrity. However, the contribution of TLR4 in kidney dysfunction during hypertension-induced gut dysbiosis is unclear. We designed this study to address this knowledge gap by utilizing TLR4 normal (TLR4N) and TLR4 mutant (TLR4M) mice. These mice were infused with high doses of Angiotensin-II for four weeks to induce hypertension. Results suggest that Ang-II significantly increased renal arterial resistive index (RI), decreased renal vascularity, and renal function (GFR) in TLR4N mice compared to TLR4M. 16 S rRNA sequencing analysis of gut microbiome revealed that Ang-II-induced hypertension resulted in alteration of Firmicutes: Bacteroidetes ratio in the gut of both TLR4N and TLR4M mice; however, it was not comparably rather differentially. Additionally, Ang-II-hypertension decreased the expression of tight junction proteins and increased gut permeability, which were more prominent in TLR4N mice than in TLR4M mice. Concomitant with gut hyperpermeability, an increased bacterial component translocation to the kidney was observed in TLR4N mice treated with Ang-II compared to TLR4N plus saline. Interestingly, microbiota translocation was mitigated in Ang-II-hypertensive TLR4M mice. Furthermore, Ang-II altered the expression of inflammatory (IL-1ß, IL-6) and anti-inflammatory IL-10) markers, and extracellular matrix proteins, including MMP-2, -9, -14, and TIMP-2 in the kidney of TLR4N mice, which were blunted in TLR4M mice. Our data demonstrate that ablation of TLR4 attenuates hypertension-induced gut dysbiosis resulting in preventing gut hyperpermeability, bacterial translocation, mitigation of renal inflammation and alleviation of kidney dysfunction.

AI in Pathology: What could possibly go wrong?

The field of medicine is undergoing rapid digital transformation. Pathologists are now striving to digitize their data, workflows, and interpretations, assisted by the enabling development of whole-slide imaging. Going digital means that the analog process of human diagnosis can be augmented or even replaced by rapidly evolving AI approaches, which are just now entering into clinical practice. But with such progress comes challenges that reflect a variety of stressors, including the impact of unrepresentative training data with accompanying implicit bias, data privacy concerns, and fragility of algorithm performance. Beyond such core digital aspects, considerations arise related to difficulties presented by changing disease presentations, diagnostic approaches, and therapeutic options. While some tools such as data federation can help with broadening data diversity while preserving expertise and local control, they may not be the full answer to some of these issues. The impact of AI in pathology on the field's human practitioners is still very much unknown: installation of unconscious bias and deference to AI guidance need to be understood and addressed. If AI is widely adopted, it may remove many inefficiencies in daily practice and compensate for staff shortages. It may also cause practitioner deskilling, dethrilling, and burnout. We discuss the technological, clinical, legal, and sociological factors that will influence the adoption of AI in pathology, and its eventual impact for good or ill.

#InSituPathologists: how the #USCAP2015 meeting went viral on Twitter and founded the social media movement for the United States and Canadian Academy of Pathology.

Professional medical conferences over the past five years have seen an enormous increase in the use of Twitter in real-time, also known as "live-tweeting". At the United States and Canadian Academy of Pathology (USCAP) 2015 annual meeting, 24 attendees (the authors) volunteered to participate in a live-tweet group, the #InSituPathologists. This group, along with other attendees, kept the world updated via Twitter about the happenings at the annual meeting. There were 6,524 #USCAP2015 tweets made by 662 individual Twitter users; these generated 5,869,323 unique impressions (potential tweet-views) over a 13-day time span encompassing the dates of the annual meeting. Herein we document the successful implementation of the first official USCAP annual meeting live-tweet group, including the pros/cons of live-tweeting and other experiences of the original #InSituPathologists group members. No prior peer-reviewed publications to our knowledge have described in depth the use of an organized group to "live-tweet" a pathology meeting. We believe our group to be the first of its kind in the field of pathology.

Standardized Reporting of Microscopic Renal Tumor Margins: Introduction of the Renal Tumor Capsule Invasion Scoring System.

PURPOSE: Renal tumor enucleation allows for maximal parenchymal preservation. Identifying pseudocapsule integrity is critically important in nephron sparing surgery by enucleation. Tumor invasion into and through the capsule may have clinical implications, although it is not routinely commented on in standard pathological reporting. We describe a system to standardize the varying degrees of pseudocapsule invasion and identify predictors of invasion. MATERIALS AND METHODS: We performed a multicenter retrospective review between 2002 and 2014 at Indiana University Hospital and Loyola University Medical Center. A total of 327 tumors were evaluated following removal via radical nephrectomy, standard margin partial nephrectomy or enucleation partial nephrectomy. Pathologists scored tumors using our i-Cap (invasion of pseudocapsule) scoring system. Multivariate analysis was done to determine predictors of higher score tumors. RESULTS: Tumor characteristics were similar among surgical resection groups. Enucleated tumors tended to have thinner pseudocapsule rims but not higher i-Cap scores. Rates of complete capsular invasion, scored as i-Cap 3, were similar among the surgical techniques, comprising 22% of the overall cohort. Papillary histology along with increasing tumor grade was predictive of an i-Cap 3 score. CONCLUSIONS: A capsule invasion scoring system is useful to classify renal cell carcinoma pseudocapsule integrity. i-Cap scores appear to be independent of surgical technique. Complete capsular invasion is most common in papillary and high grade tumors. Further work is warranted regarding the relevance of capsular invasion depth as it relates to the oncologic outcome for local recurrence and disease specific survival.

Spontaneous resolution of inflammatory myofibroblastic tumor of the kidney.

Inflammatory myofibroblastic tumor (IMT) of the kidney is a rare and benign condition often confused with renal malignancy based on clinical presentation and radiologic evaluation that has commonly been treated with nephrectomy. Utilizing renal mass biopsy to help diagnose and guide therapeutic intervention is increasing but has not been universally adopted to this point. We present a case of an incidentally found atypical renal mass in a 71-year-old female diagnosed as inflammatory myofibroblastic tumor of the kidney after core needle biopsy. This tumor was managed conservatively without surgical intervention and resolved spontaneously.

Precise grading of non-muscle invasive bladder cancer with multi-scale pyramidal CNN.

The grading of non-muscle invasive bladder cancer (NMIBC) continues to face challenges due to subjective interpretations, which affect the assessment of its severity. To address this challenge, we are developing an innovative artificial intelligence (AI) system aimed at objectively grading NMIBC. This system uses a novel convolutional neural network (CNN) architecture called the multi-scale pyramidal pretrained CNN to analyze both local and global pathology markers extracted from digital pathology images. The proposed CNN structure takes as input three levels of patches, ranging from small patches (e.g., 128 × 128 ) to the largest size patches ( 512 × 512 ). These levels are then fused by random forest (RF) to estimate the severity grade of NMIBC. The optimal patch sizes and other model hyperparameters are determined using a grid search algorithm. For each patch size, the proposed system has been trained on 32K patches (comprising 16K low-grade and 16K high-grade samples) and subsequently tested on 8K patches (consisting of 4K low-grade and 4K high-grade samples), all annotated by two pathologists. Incorporating light and efficient processing, defining new benchmarks in the application of AI to histopathology, the ShuffleNet-based AI system achieved notable metrics on the testing data, including 94.25% ± 0.70% accuracy, 94.47% ± 0.93% sensitivity, 94.03% ± 0.95% specificity, and a 94.29% ± 0.70% F1-score. These results highlight its superior performance over traditional models like ResNet-18. The proposed system's robustness in accurately grading pathology demonstrates its potential as an advanced AI tool for diagnosing human diseases in the domain of digital pathology.

Obesogenic polystyrene microplastic exposures disrupt the gut-liver-adipose axis.

Microplastics (MP) derived from the weathering of polymers, or synthesized in this size range, have become widespread environmental contaminants and have found their way into water supplies and the food chain. Despite this awareness, little is known about the health consequences of MP ingestion. We have previously shown that the consumption of polystyrene (PS) beads was associated with intestinal dysbiosis and diabetes and obesity in mice. To further evaluate the systemic metabolic effects of PS on the gut-liver-adipose tissue axis, we supplied C57BL/6J mice with normal water or that containing 2 sizes of PS beads (0.5 and 5 µm) at a concentration of 1 µg/ml. After 13 weeks, we evaluated indices of metabolism and liver function. As observed previously, mice drinking the PS-containing water had a potentiated weight gain and adipose expansion. Here we found that this was associated with an increased abundance of adipose F4/80+ macrophages. These exposures did not cause nonalcoholic fatty liver disease but were associated with decreased liver:body weight ratios and an enrichment in hepatic farnesoid X receptor and liver X receptor signaling. PS also increased hepatic cholesterol and altered both hepatic and cecal bile acids. Mice consuming PS beads and treated with the berry anthocyanin, delphinidin, demonstrated an attenuated weight gain compared with those mice receiving a control intervention and also exhibited a downregulation of cyclic adenosine monophosphate (cAMP) and peroxisome proliferator-activated receptor (PPAR) signaling pathways. This study highlights the obesogenic role of PS in perturbing the gut-liver-adipose axis and altering nuclear receptor signaling and intermediary metabolism. Dietary interventions may limit the adverse metabolic effects of PS consumption.

Artificial intelligence-based multi-class histopathologic classification of kidney neoplasms.

Artificial intelligence (AI)-based techniques are increasingly being explored as an emerging ancillary technique for improving accuracy and reproducibility of histopathological diagnosis. Renal cell carcinoma (RCC) is a malignancy responsible for 2% of cancer deaths worldwide. Given that RCC is a heterogenous disease, accurate histopathological classification is essential to separate aggressive subtypes from indolent ones and benign mimickers. There are early promising results using AI for RCC classification to distinguish between 2 and 3 subtypes of RCC. However, it is not clear how an AI-based model designed for multiple subtypes of RCCs, and benign mimickers would perform which is a scenario closer to the real practice of pathology. A computational model was created using 252 whole slide images (WSI) (clear cell RCC: 56, papillary RCC: 81, chromophobe RCC: 51, clear cell papillary RCC: 39, and, metanephric adenoma: 6). 298,071 patches were used to develop the AI-based image classifier. 298,071 patches (350 × 350-pixel) were used to develop the AI-based image classifier. The model was applied to a secondary dataset and demonstrated that 47/55 (85%) WSIs were correctly classified. This computational model showed excellent results except to distinguish clear cell RCC from clear cell papillary RCC. Further validation using multi-institutional large datasets and prospective studies are needed to determine the potential to translation to clinical practice.

Diabetic Nephropathy and Gaseous Modulators.

Diabetic nephropathy (DN) remains the leading cause of vascular morbidity and mortality in diabetes patients. Despite the progress in understanding the diabetic disease process and advanced management of nephropathy, a number of patients still progress to end-stage renal disease (ESRD). The underlying mechanism still needs to be clarified. Gaseous signaling molecules, so-called gasotransmitters, such as nitric oxide (NO), carbon monoxide (CO), and hydrogen sulfide (H2S), have been shown to play an essential role in the development, progression, and ramification of DN depending on their availability and physiological actions. Although the studies on gasotransmitter regulations of DN are still emerging, the evidence revealed an aberrant level of gasotransmitters in patients with diabetes. In studies, different gasotransmitter donors have been implicated in ameliorating diabetic renal dysfunction. In this perspective, we summarized an overview of the recent advances in the physiological relevance of the gaseous molecules and their multifaceted interaction with other potential factors, such as extracellular matrix (ECM), in the severity modulation of DN. Moreover, the perspective of the present review highlights the possible therapeutic interventions of gasotransmitters in ameliorating this dreaded disease.

Temporal increase in the incidence of anal squamous cell carcinoma in Kentucky and factors associated with adverse outcomes.

BACKGROUND: Anal squamous cell cancer (ASCC) incidence in Kentucky is increasing at an alarming rate. In 2009, the incidence surpassed the US national average (2.66 vs. 1.77/100,000 people), and currently, Kentucky ranks second by state per capita. The reasons for this rise are unclear. We hypothesize individuals with ASCC in Kentucky have some unique risk factors associated with worse outcomes. METHODS: Individuals with ASCC in a population-level state database (1995-2016), as well as those treated at two urban university-affiliated tertiary care centers (2011-2018), were included and analyzed separately. We evaluated patient-level factors including demographics, tobacco use, stage of disease, HIV-status, and HPV-type. We evaluated factors associated with treatment and survival using univariable and multivariable survival analyses. RESULTS: There were 1698 individuals in state data and 101 in urban center data. In the urban cohort, 77% of patients were ever-smokers. Eighty-four percent of patients had positive HPV testing, and 58% were positive for HPV 16. Seventy-two percent of patients were positive for p16. Neither smoking, HPV, nor p16 status were associated with disease persistence, recurrence-free survival, or overall survival (all p > 0.05). Poorly controlled HIV (CD4 count <500) at time of ASCC diagnosis was associated disease persistence (p = 0.032). Stage III disease (adjusted HR = 5.25, p = 0.025) and local excision (relative to chemoradiation; aHR = 0.19, p = 0.017) were significantly associated with reduced recurrence-free survival. CONCLUSIONS: The rate of ASCC in Kentucky has doubled over the last 10 years, which is outpacing anal SCC rates in the US with the most dramatic rates seen in Kentucky women. The underlying reasons for this are unclear and require further study. There may be other risk factors unique to Kentucky.

Biomarkers for Kidney-Transplant Rejection: A Short Review Study.

Kidney transplantation is the preferred treatment for end-stage renal failure, but the limited availability of donors and the risk of immune rejection pose significant challenges. Early detection of acute renal rejection is a critical step to increasing the lifespan of the transplanted kidney. Investigating the clinical, genetic, and histopathological markers correlated to acute renal rejection, as well as finding noninvasive markers for early detection, is urgently needed. It is also crucial to identify which markers are associated with different types of acute renal rejection to manage treatment effectively. This short review summarizes recent studies that investigated various markers, including genomics, histopathology, and clinical markers, to differentiate between different types of acute kidney rejection. Our review identifies the markers that can aid in the early detection of acute renal rejection, potentially leading to better treatment and prognosis for renal-transplant patients.

Conditional GANs based system for fibrosis detection and quantification in Hematoxylin and Eosin whole slide images.

Assessing the degree of liver fibrosis is fundamental for the management of patients with chronic liver disease, in liver transplants procedures, and in general liver disease research. The fibrosis stage is best assessed by histopathologic evaluation, and Masson's Trichrome stain (MT) is the stain of choice for this task in many laboratories around the world. However, the most used stain in histopathology is Hematoxylin Eosin (HE) which is cheaper, has a faster turn-around time and is the primary stain routinely used for evaluation of liver specimens. In this paper, we propose a novel digital pathology system that accurately detects and quantifies the footprint of fibrous tissue in HE whole slide images (WSI). The proposed system produces virtual MT images from HE using a deep learning model that learns deep texture patterns associated with collagen fibers. The training pipeline is based on conditional generative adversarial networks (cGAN), which can achieve accurate pixel-level transformation. Our comprehensive training pipeline features an automatic WSI registration algorithm, which qualifies the HE/MT training slides for the cGAN model. Using liver specimens collected during liver transplantation procedures, we conducted a range of experiments to evaluate the detected footprint of selected anatomical features. Our evaluation includes both image similarity and semantic segmentation metrics. The proposed system achieved enhanced results in the experiments with significant improvement over the state-of-the-art CycleGAN learning style, and over direct prediction of fibrosis in HE without having the virtual MT step.

Infliximab-Induced Vanishing Bile Duct Syndrome.

Drug-induced liver injury (DILI) is a spectrum of pathology that can be classified by mechanism of injury or by type of observed hepatotoxicity. Vanishing bile duct syndrome (VBDS) is a group of acquired and genetic disorders that cause the destruction and disappearance of intrahepatic bile ducts, and cholestasis. VBDS typically presents with severe cholestatic hepatitis and can have immunoallergic features. Infliximab has been reported to rarely cause a cholestatic pattern of liver injury due to ductopenia characteristic of VBDS. Herein we present a clinical case of infliximab-induced DILI resulting in VBDS.

A deep learning framework for automated classification of histopathological kidney whole-slide images.

Background: Renal cell carcinoma is the most common type of malignant kidney tumor and is responsible for 14,830 deaths per year in the United States. Among the four most common subtypes of renal cell carcinoma, clear cell renal cell carcinoma has the worst prognosis and clear cell papillary renal cell carcinoma appears to have no malignant potential. Distinction between these two subtypes can be difficult due to morphologic overlap on examination of histopathological preparation stained with hematoxylin and eosin. Ancillary techniques, such as immunohistochemistry, can be helpful, but they are not universally available. We propose and evaluate a new deep learning framework for tumor classification tasks to distinguish clear cell renal cell carcinoma from papillary renal cell carcinoma. Methods: Our deep learning framework is composed of three convolutional neural networks. We divided whole-slide kidney images into patches with three different sizes where each network processes a specific patch size. Our framework provides patchwise and pixelwise classification. The histopathological kidney data is composed of 64 image slides that belong to 4 categories: fat, parenchyma, clear cell renal cell carcinoma, and clear cell papillary renal cell carcinoma. The final output of our framework is an image map where each pixel is classified into one class. To maintain consistency, we processed the map with Gauss-Markov random field smoothing. Results: Our framework succeeded in classifying the four classes and showed superior performance compared to well-established state-of-the-art methods (pixel accuracy: 0.89 ResNet18, 0.92 proposed). Conclusions: Deep learning techniques have a significant potential for cancer diagnosis.

A pyramidal deep learning pipeline for kidney whole-slide histology images classification.

Renal cell carcinoma is the most common type of kidney cancer. There are several subtypes of renal cell carcinoma with distinct clinicopathologic features. Among the subtypes, clear cell renal cell carcinoma is the most common and tends to portend poor prognosis. In contrast, clear cell papillary renal cell carcinoma has an excellent prognosis. These two subtypes are primarily classified based on the histopathologic features. However, a subset of cases can a have a significant degree of histopathologic overlap. In cases with ambiguous histologic features, the correct diagnosis is dependent on the pathologist's experience and usage of immunohistochemistry. We propose a new method to address this diagnostic task based on a deep learning pipeline for automated classification. The model can detect tumor and non-tumoral portions of kidney and classify the tumor as either clear cell renal cell carcinoma or clear cell papillary renal cell carcinoma. Our framework consists of three convolutional neural networks and the whole slide images of kidney which were divided into patches of three different sizes for input into the networks. Our approach can provide patchwise and pixelwise classification. The kidney histology images consist of 64 whole slide images. Our framework results in an image map that classifies the slide image on the pixel-level. Furthermore, we applied generalized Gauss-Markov random field smoothing to maintain consistency in the map. Our approach classified the four classes accurately and surpassed other state-of-the-art methods, such as ResNet (pixel accuracy: 0.89 Resnet18, 0.92 proposed). We conclude that deep learning has the potential to augment the pathologist's capabilities by providing automated classification for histopathological images.

Diagnostic approach in TFE3-rearranged renal cell carcinoma: a multi-institutional international survey.

Transcription factor E3-rearranged renal cell carcinoma (TFE3-RCC) has heterogenous morphologic and immunohistochemical (IHC) features.131 pathologists with genitourinary expertise were invited in an online survey containing 23 questions assessing their experience on TFE3-RCC diagnostic work-up.Fifty (38%) participants completed the survey. 46 of 50 participants reported multiple patterns, most commonly papillary pattern (almost always 9/46, 19.5%; frequently 29/46, 63%). Large epithelioid cells with abundant cytoplasm were the most encountered cytologic feature, with either clear (almost always 10/50, 20%; frequently 34/50, 68%) or eosinophilic (almost always 4/49, 8%; frequently 28/49, 57%) cytology. Strong (3+) or diffuse (>75% of tumour cells) nuclear TFE3 IHC expression was considered diagnostic by 13/46 (28%) and 12/47 (26%) participants, respectively. Main TFE3 IHC issues were the low specificity (16/42, 38%), unreliable staining performance (15/42, 36%) and background staining (12/42, 29%). Most preferred IHC assays other than TFE3, cathepsin K and pancytokeratin were melan A (44/50, 88%), HMB45 (43/50, 86%), carbonic anhydrase IX (41/50, 82%) and CK7 (32/50, 64%). Cut-off for positive TFE3 fluorescent in situ hybridisation (FISH) was preferably 10% (9/50, 18%), although significant variation in cut-off values was present. 23/48 (48%) participants required TFE3 FISH testing to confirm TFE3-RCC regardless of the histomorphologic and IHC assessment. 28/50 (56%) participants would request additional molecular studies other than FISH assay in selected cases, whereas 3/50 participants use additional molecular cases in all cases when TFE3-RCC is in the differential.Optimal diagnostic approach on TFE3-RCC is impacted by IHC and/or FISH assay preferences as well as their conflicting interpretation methods.

Isocitrate dehydrogenase 1 mutant glioblastomas demonstrate a decreased rate of pseudoprogression: a multi-institutional experience.

BACKGROUND: Pseudoprogression (psPD) represents false radiologic evidence of tumor progression and is observed in some glioblastoma (GBM) patients after postoperative chemoradiation (CRT) with temozolomide (TMZ). The ambiguity of the psPD diagnosis confounds identification of true progression and may lead to unnecessary interventions. The association between psPD and isocitrate dehydrogenase 1 (IDH1) mutational (mut) status is understudied, and its incidence may alter clinical decision making. METHODS: We retrospectively evaluated 120 patients with IDH1-mut (n = 60) and IDH1-wild-type (IDH-WT; [n = 60]) GBMs who received postoperative CRT with TMZ at 4 academic institutions. Response Assessment in Neuro-Oncology criteria were used to identify psPD rates in routine brain MRIs performed up to 90 days after CRT completion. RESULTS: Within 90 days of completing CRT, 9 GBM patients (1 [1.7%] IDH1-mut and 8 [13.3%] IDH1-WTs) demonstrated true progression, whereas 17 patients (3 [5%] IDH1-muts and 14 [23.3%] IDH1-WTs) demonstrated psPD (P = .004). IDH1-mut GBMs had a lower probability of psPD (hazard ratio: 0.173, 95% CI, 0.047-0.638, P = .008). Among the patients with radiologic signs suggestive of progression (n = 26), psPD was found to be the cause in 3 of 4 (75.0%) of the IDH1-mut GBMs and 14 of 22 (63.6%) of the IDH1-WT GBMs (P = .496). Median overall survival for IDH1-mut and IDH1-WT GBM patients was 40.3 and 23.0 months, respectively (P < .001). CONCLUSIONS: IDH1-mut GBM patients demonstrate lower absolute rates of psPD expression. Irrespective of GBM subtype, psPD expression was more likely than true progression within 90 days of completing CRT. Continuing adjuvant treatment for IDH1-mut GBMs is suggested if radiologic progression is suspected during this time interval.

Determining IDH-Mutational Status in Gliomas Using IDH1-R132H Antibody and Polymerase Chain Reaction.

Determination of the isocitrate dehydrogenase (IDH) mutation status, presence or absence of mutation in IDH genes (IDH1 or IDH2), has become one of the most important molecular features taken into account in the management of patients with diffuse gliomas. Tumors that are IDH-mutant have a better prognosis than their counterparts with similar histologic grade and IDH-wildtype phenotype. IDH1-R132H is the most common IDH mutation, present in ~90% of IDH-mutant cases. This mutation yields an altered protein that can be detected by immunohistochemistry. We evaluated the IDH1-R132H antibody (clone H09) to determine IDH mutation status as the first line test and compared with the results of polymerase chain reaction (PCR) testing that can detect more types of mutations in IDH1 or IDH2. A total of 62 gliomas were evaluated: 30 glioblastomas (including 3 gliosarcomas), 11 grade III diffuse gliomas, 17 grade II diffuse gliomas, and 4 circumscribed gliomas. Twelve of 62 cases were IDH-mutant by immunohistochemistry and 15 of 62 by PCR. PCR detected the following mutations: IDH1-R132H (11 cases), IDH1-R132C (1 case), IDH2 R172, NOS (1 case), IDH1 R132, NOS (1 case), and IDH2-R172K (1 case). The R132H antibody had high specificity (100%) and sensitivity (80%) to detect IDH mutation status; the discordant results were 3 false-negatives. IDH-R132H immunostain is suitable as a first line test. Nonimmunoreactive cases could be studied by PCR following recommendations of the 2016 World Health Organization guidelines.

Diffuse Lewy Body Disease and Alzheimer Disease: Neuropathologic Phenotype Associated With the PSEN1 p.A396T Mutation.

In sporadic and dominantly inherited Alzheimer disease (AD), aggregation of both tau and α-synuclein may occur in neurons. Aggregates of either protein occur separately or coexist in the same neuron. It is not known whether the coaggregation of tau and α-synuclein in dominantly inherited AD occurs in association with specific mutations of the APP, PSEN1, or PSEN2 genes. The aim of this study was to provide the first characterization of the neuropathologic phenotype associated with the PSEN1 p.A396T mutation in a man who was clinically diagnosed as having AD, but for whom the PSEN1 mutation was found postmortem. The proband, who was 56 years old when cognitive impairment first manifested, died at 67 years of age. Neuropathologically, 3 proteinopathies were present in the brain. Widespread α-synuclein-immunopositive neuronal inclusions suggested a diagnosis of diffuse Lewy body disease (DLBD), while severe and widespread tau and amyloid-ß pathologies confirmed the clinical diagnosis of AD. Immunohistochemistry revealed the coexistence of tau and α-synuclein aggregates in the same neuron. Neuropathologic and molecular studies in brains of carriers of the PSEN1 p.A396T mutation or other PSEN1 or PSEN2 mutations associated with the coexistence of DLBD and AD are needed to clarify whether tau and α-synuclein proteinopathies occur independently or whether a relationship exists between α-synuclein and tau that might explain the mechanisms of coaggregation.