VERU-111, an Orally Available Tubulin Inhibitor, Suppresses Ovarian Tumor Growth and Metastasis.

Ovarian cancer is the most lethal gynecological malignancy, with a 5-year survival rate of approximately 50%. The dismal prognosis is due in part to metastatic disease and acquired drug resistance to conventional chemotherapies such as taxanes. Colchicine binding site inhibitors (CBSIs) are attractive alternatives to taxanes because they could potentially achieve oral bioavailability and overcome drug resistance associated with the prolonged use of taxanes. VERU-111 is one of the most advanced CBSIs that is orally available, potent, well-tolerated, and has shown good efficacy in several preclinical solid tumor models. Here, we demonstrate for the first time the in vitro potency of VERU-111 as well as its efficacy at inhibiting tumor growth and metastasis in an orthotopic ovarian cancer mouse model. VERU-111 has nanomolar potency against ovarian cancer cell lines and strongly inhibits colony formation, proliferation, invasion, and migration. VERU-111 disrupts microtubule formation to induce mitotic catastrophe and, ultimately, apoptosis in a concentration-dependent manner. The efficacy of VERU-111 was comparable with standard chemotherapy paclitaxel, the current first-line treatment for ovarian cancer, with no observed synergy with combination paclitaxel + VERU-111 treatment. In vivo, VERU-111 markedly suppressed ovarian tumor growth and completely suppressed distant organ metastasis. Together, these results support VERU-111 for its potential as a novel therapy for ovarian cancer, particularly for late-stage metastatic disease. Significance Statement VERU-111 is an investigational new drug and has comparable efficacy as paclitaxel in suppressing tumor cell proliferation, colony formation, and migration in ovarian cancer models in vitro and has potent in vivo anti-tumor and anti-metastatic activity in an orthotopic ovarian cancer mouse model. VERU-111 has low systemic toxicity and, unlike paclitaxel, is orally bioavailable and is not a substrate for the major drug efflux transporters, making it a promising and attractive alternative to taxane-based therapy.

Performance of an Artificial Intelligence Model for Recognition and Quantitation of Histologic Features of Eosinophilic Esophagitis on Biopsy Samples.

We have developed an artificial intelligence (AI)-based digital pathology model for the evaluation of histologic features related to eosinophilic esophagitis (EoE). In this study, we evaluated the performance of our AI model in a cohort of pediatric and adult patients for histologic features included in the Eosinophilic Esophagitis Histologic Scoring System (EoEHSS). We collected a total of 203 esophageal biopsy samples from patients with mucosal eosinophilia of any degree (91 adult and 112 pediatric patients) and 10 normal controls from a prospectively maintained database. All cases were assessed by a specialized gastrointestinal (GI) pathologist for features in the EoEHSS at the time of original diagnosis and rescored by a central GI pathologist (R.K.M.). We subsequently analyzed whole-slide image digital slides using a supervised AI model operating in a cloud-based, deep learning AI platform (Aiforia Technologies) for peak eosinophil count (PEC) and several histopathologic features in the EoEHSS. The correlation and interobserver agreement between the AI model and pathologists (Pearson correlation coefficient [rs] = 0.89 and intraclass correlation coefficient [ICC] = 0.87 vs original pathologist; rs = 0.91 and ICC = 0.83 vs central pathologist) were similar to the correlation and interobserver agreement between pathologists for PEC (rs = 0.88 and ICC = 0.91) and broadly similar to those for most other histologic features in the EoEHSS. The AI model also accurately identified PEC of >15 eosinophils/high-power field by the original pathologist (area under the curve [AUC] = 0.98) and central pathologist (AUC = 0.98) and had similar AUCs for the presence of EoE-related endoscopic features to pathologists' assessment. Average eosinophils per epithelial unit area had similar performance compared to AI high-power field-based analysis. Our newly developed AI model can accurately identify, quantify, and score several of the main histopathologic features in the EoE spectrum, with agreement regarding EoEHSS scoring which was similar to that seen among GI pathologists.

Villin-1 and Gelsolin Regulate Changes in Actin Dynamics That Affect Cell Survival Signaling Pathways and Intestinal Inflammation.

BACKGROUND & AIMS: Cell stress signaling pathways result in phosphorylation of the eukaryotic translation initiation factor 2 subunit alpha (EIF2S1 or EIF2A), which affects regulation of protein translation. Translation reprogramming mitigates stress by activating pathways that result in autophagy and cell death, to eliminate damaged cells. Actin is modified during stress and EIF2A is dephosphorylated to restore homeostasis. It is not clear how actin affects EIF2A signaling. We studied the actin-binding proteins villin 1 (VIL1) and gelsolin (GSN) in intestinal epithelial cells (IECs) to determine whether they respond to cell stress response and affect signaling pathways. METHODS: We performed studies with mice with disruptions in Vil1 and Gsn (double-knockout mice). Wild-type (WT) mice either were or were not (controls) exposed to cell stressors such as tumor necrosis factor and adherent-invasive Escherichia coli. Distal ileum tissues were collected from mice; IECs and enteroids were cultured and analyzed by histology, immunoblots, phalloidin staining, immunohistochemistry, electron microscopy, and flow cytometry. HT-29 cells were incubated with cell stressors such as DTT, IFN, and adherent-invasive E coli or control agents; cells were analyzed by immunoblots and quantitative polymerase chain reaction. Green fluorescent protein and green fluorescent protein tagged mutant EIF2A were expressed from a lentiviral vector. The mouse immunity-related GTPase (IRGM1) was overexpressed in embryonic fibroblasts from dynamin1 like (DNM1L) protein-knockout mice or their WT littermates. IRGM1 was overexpressed in embryonic fibroblasts from receptor interacting serine/threonine kinase 1-knockout mice or their WT littermates. Human IRGM was overexpressed in human epithelial cell lines incubated with the DNM1L-specific inhibitor Mdivi-1. Mitochondria were analyzed by semi-quantitative confocal imaging. We performed immunohistochemical analyses of distal ileum tissues from 6-8 patients with Crohn's disease (CD) and 6-8 individuals without CD (controls). RESULTS: In IECs exposed to cell stressors, EIF2A signaling reduced expression of VIL1 and GSN. However, VIL1 and GSN were required for dephosphorylation of EIF2A and recovery from cell stress. In mouse and human IECs, prolonged, unresolved stress was accompanied by continued down-regulation of VIL1 and GSN, resulting in constitutive phosphorylation of EIF2A and overexpression of IRGM1 (or IRGM), which regulates autophagy. Overexpression of IRGM1 (or IRGM) induced cell death by necroptosis, accompanied by release of damage-associated molecular patterns (DAMPs). In double-knockout mice, constitutive phosphorylation of EIF2A and over-expression of IRGM1 resulted in spontaneous ileitis that resembled human CD in symptoms and histology. Distal ileum tissues from patients with CD had lower levels of VIL1 and GSN, increased phosphorylation of EIF2A, increased levels of IRGM and necroptosis, and increased release of nuclear DAMPs compared with controls. CONCLUSIONS: In studies of intestinal epithelial tissues from patients with CD and embryonic fibroblasts from mice, along with enteroids and human IEC lines, we found that induction of cell stress alters the cytoskeleton in IECs via changes in the actin-binding proteins VIL1 and GSN. Acute changes in actin dynamics increase IEC survival, whereas long-term changes in actin dynamics lead to IEC death and intestinal inflammation. IRGM regulates necroptosis and release of DAMPs to induce gastrointestinal inflammation, linking IRGM activity with CD.

Multi-Scale Analysis of the Damage Evolution of Coal Gangue Coarse Aggregate Concrete after Freeze-Thaw Cycle Based on CT Technology.

This study utilized X-ray computed tomography (CT) technology to analyze the meso-structure of concrete at different replacement rates, using a coal gangue coarse aggregate, after experiencing various freeze-thaw cycles (F-Ts). A predictive model for the degradation of the elastic modulus of Coal Gangue coarse aggregate Concrete (CGC), based on mesoscopic damage, was established to provide an interpretation of the macroscopic mechanical behavior of CGC after F-Ts damage at a mesoscopic scale. It was found that after F-Ts, the compressive strength of concrete, with coal gangue replacement rates of 30%, 60%, and 100%, respectively, decreased by 33.76%, 34.89%, and 42.05% compared with unfrozen specimens. The results indicate that an increase in the coal gangue replacement rate exacerbates the degradation of concrete performance during the F-Ts process. Furthermore, the established predictive formula for elastic modulus degradation closely matches the experimental data, offering a reliable theoretical basis for the durability design of CGC in F-Ts environments.

Gold Nanoparticle Transport in the Injured Kidneys with Elevated Renal Function Biomarkers.

Renal function biomarkers such as serum blood urea nitrogen (BUN) and creatinine (Cr) serve as key indicators for guiding clinical decisions before administering kidney-excreted small-molecule agents. With engineered nanoparticles increasingly designed to be renally clearable to expedite their clinical translation, understanding the relationship between renal function biomarkers and nanoparticle transport in diseased kidneys becomes crucial to their biosafety in future clinical applications. In this study, renal-clearable gold nanoparticles (AuNPs) are used as X-ray contrast agents to noninvasively track their transport and retention in cisplatin-injured kidneys with varying BUN and Cr levels. The findings reveal that AuNP transport is significantly slowed in the medulla of severely injured kidneys, with BUN and Cr levels elevated to 10 times normal. In mildly injured kidneys, where BUN and Cr levels only four to five times higher than normal, AuNP transport and retention are not predictable by BUN and Cr levels but correlate strongly with the degree of tubular injury due to the formation of gold-protein casts in the Henle's loop of the medulla. These results underscore the need for caution when employing renal-clearable nanomedicines in compromised kidneys and highlight the potential of renal-clearable AuNPs as X-ray probes for assessing kidney injuries noninvasively.

Cross-scale multi-instance learning for pathological image diagnosis.

Analyzing high resolution whole slide images (WSIs) with regard to information across multiple scales poses a significant challenge in digital pathology. Multi-instance learning (MIL) is a common solution for working with high resolution images by classifying bags of objects (i.e. sets of smaller image patches). However, such processing is typically performed at a single scale (e.g., 20× magnification) of WSIs, disregarding the vital inter-scale information that is key to diagnoses by human pathologists. In this study, we propose a novel cross-scale MIL algorithm to explicitly aggregate inter-scale relationships into a single MIL network for pathological image diagnosis. The contribution of this paper is three-fold: (1) A novel cross-scale MIL (CS-MIL) algorithm that integrates the multi-scale information and the inter-scale relationships is proposed; (2) A toy dataset with scale-specific morphological features is created and released to examine and visualize differential cross-scale attention; (3) Superior performance on both in-house and public datasets is demonstrated by our simple cross-scale MIL strategy. The official implementation is publicly available at https://github.com/hrlblab/CS-MIL.

Alleviating tiling effect by random walk sliding window in high-resolution histological whole slide image synthesis.

Multiplex immunofluorescence (MxIF) is an advanced molecular imaging technique that can simultaneously provide biologists with multiple (i.e., more than 20) molecular markers on a single histological tissue section. Unfortunately, due to imaging restrictions, the more routinely used hematoxylin and eosin (H&E) stain is typically unavailable with MxIF on the same tissue section. As biological H&E staining is not feasible, previous efforts have been made to obtain H&E whole slide image (WSI) from MxIF via deep learning empowered virtual staining. However, the tiling effect is a long-lasting problem in high-resolution WSI-wise synthesis. The MxIF to H&E synthesis is no exception. Limited by computational resources, the cross-stain image synthesis is typically performed at the patch-level. Thus, discontinuous intensities might be visually identified along with the patch boundaries assembling all individual patches back to a WSI. In this work, we propose a deep learning based unpaired high-resolution image synthesis method to obtain virtual H&E WSIs from MxIF WSIs (each with 27 markers/stains) with reduced tiling effects. Briefly, we first extend the CycleGAN framework by adding simultaneous nuclei and mucin segmentation supervision as spatial constraints. Then, we introduce a random walk sliding window shifting strategy during the optimized inference stage, to alleviate the tiling effects. The validation results show that our spatially constrained synthesis method achieves a 56% performance gain for the downstream cell segmentation task. The proposed inference method reduces the tiling effects by using 50% fewer computation resources without compromising performance. The proposed random sliding window inference method is a plug-and-play module, which can be generalized for other high-resolution WSI image synthesis applications. The source code with our proposed model are available at https://github.com/MASILab/RandomWalkSlidingWindow.git.

High-yield porphyrin production through metabolic engineering and biocatalysis.

Porphyrins and their derivatives find extensive applications in medicine, food, energy and materials. In this study, we produced porphyrin compounds by combining Rhodobacter sphaeroides as an efficient cell factory with enzymatic catalysis. Genome-wide CRISPRi-based screening in R. sphaeroides identifies hemN as a target for improved coproporphyrin III (CPIII) production, and exploiting phosphorylation of PrrA further improves the production of bioactive CPIII to 16.5 g L-1 by fed-batch fermentation. Subsequent screening and engineering high-activity metal chelatases and coproheme decarboxylase results in the synthesis of various metalloporphyrins, including heme and the anti-tumor agent zincphyrin. After pilot-scale fermentation (200 L) and setting up the purification process for CPIII (purity >95%), we scaled up the production of heme and zincphyrin through enzymatic catalysis in a 5-L bioreactor, with CPIII achieving respective enzyme conversion rates of 63% and 98% and yielding 10.8 g L-1 and 21.3 g L-1, respectively. Our strategy offers a solution for high-yield bioproduction of heme and other valuable porphyrins with substantial industrial and medical applications.

Deep multimodal fusion of image and non-image data in disease diagnosis and prognosis: a review.

The rapid development of diagnostic technologies in healthcare is leading to higher requirements for physicians to handle and integrate the heterogeneous, yet complementary data that are produced during routine practice. For instance, the personalized diagnosis and treatment planning for a single cancer patient relies on various images (e.g. radiology, pathology and camera images) and non-image data (e.g. clinical data and genomic data). However, such decision-making procedures can be subjective, qualitative, and have large inter-subject variabilities. With the recent advances in multimodal deep learning technologies, an increasingly large number of efforts have been devoted to a key question: how do we extract and aggregate multimodal information to ultimately provide more objective, quantitative computer-aided clinical decision making? This paper reviews the recent studies on dealing with such a question. Briefly, this review will include the (a) overview of current multimodal learning workflows, (b) summarization of multimodal fusion methods, (c) discussion of the performance, (d) applications in disease diagnosis and prognosis, and (e) challenges and future directions.

Influence of Cell-type Ratio on Spatially Resolved Single-cell Transcriptomes using the Tangram Algorithm: Based on Implementation on Single-Cell and MxIF Data.

The Tangram algorithm is a benchmarking method of aligning single-cell (sc/snRNA-seq) data to various forms of spatial data collected from the same region. With this data alignment, the annotation of the single-cell data can be projected to spatial data. However, the cell composition (cell-type ratio) of the single-cell data and spatial data might be different because of heterogeneous cell distribution. Whether the Tangram algorithm can be adapted when the two data have different cell-type ratios has not been discussed in previous works. In our practical application that maps the cell-type classification results of single-cell data to the Multiplex immunofluorescence (MxIF) spatial data, cell-type ratios were different, though they were sampled from adjacent areas. In this work, both simulation and empirical validation were conducted to quantitatively explore the impact of the mismatched cell-type ratio on the Tangram mapping in different situations. Results show that the cell-type difference has a negative influence on classification accuracy.

Democratizing Pathological Image Segmentation with Lay Annotators via Molecular-empowered Learning.

Multi-class cell segmentation in high-resolution Giga-pixel whole slide images (WSI) is critical for various clinical applications. Training such an AI model typically requires labor-intensive pixel-wise manual annotation from experienced domain experts (e.g., pathologists). Moreover, such annotation is error-prone when differentiating fine-grained cell types (e.g., podocyte and mesangial cells) via the naked human eye. In this study, we assess the feasibility of democratizing pathological AI deployment by only using lay annotators (annotators without medical domain knowledge). The contribution of this paper is threefold: (1) We proposed a molecular-empowered learning scheme for multi-class cell segmentation using partial labels from lay annotators; (2) The proposed method integrated Giga-pixel level molecular-morphology cross-modality registration, molecular-informed annotation, and molecular-oriented segmentation model, so as to achieve significantly superior performance via 3 lay annotators as compared with 2 experienced pathologists; (3) A deep corrective learning (learning with imperfect label) method is proposed to further improve the segmentation performance using partially annotated noisy data. From the experimental results, our learning method achieved F1 = 0.8496 using molecular-informed annotations from lay annotators, which is better than conventional morphology-based annotations (F1 = 0.7015) from experienced pathologists. Our method democratizes the development of a pathological segmentation deep model to the lay annotator level, which consequently scales up the learning process similar to a non-medical computer vision task. The official implementation and cell annotations are publicly available at https://github.com/hrlblab/MolecularEL.

Feasibility of Universal Anomaly Detection without Knowing the Abnormality in Medical Images.

Many anomaly detection approaches, especially deep learning methods, have been recently developed to identify abnormal image morphology by only employing normal images during training. Unfortunately, many prior anomaly detection methods were optimized for a specific "known" abnormality (e.g., brain tumor, bone fraction, cell types). Moreover, even though only the normal images were used in the training process, the abnormal images were often employed during the validation process (e.g., epoch selection, hyper-parameter tuning), which might leak the supposed "unknown" abnormality unintentionally. In this study, we investigated these two essential aspects regarding universal anomaly detection in medical images by (1) comparing various anomaly detection methods across four medical datasets, (2) investigating the inevitable but often neglected issues on how to unbiasedly select the optimal anomaly detection model during the validation phase using only normal images, and (3) proposing a simple decision-level ensemble method to leverage the advantage of different kinds of anomaly detection without knowing the abnormality. The results of our experiments indicate that none of the evaluated methods consistently achieved the best performance across all datasets. Our proposed method enhanced the robustness of performance in general (average AUC 0.956).

Tumor microenvironment in pancreatic ductal adenocarcinoma: Implications in immunotherapy.

Pancreatic ductal adenocarcinoma is one of the most aggressive and lethal cancers. Surgical resection is the only curable treatment option, but it is available for only a small fraction of patients at the time of diagnosis. With current therapeutic regimens, the average 5-year survival rate is less than 10% in pancreatic cancer patients. Immunotherapy has emerged as one of the most promising treatment options for multiple solid tumors of advanced stage. However, its clinical efficacy is suboptimal in most clinical trials on pancreatic cancer. Current studies have suggested that the tumor microenvironment is likely the underlying barrier affecting immunotherapy drug efficacy in pancreatic cancer. In this review, we discuss the role of the tumor microenvironment in pancreatic cancer and the latest advances in immunotherapy on pancreatic cancer.

Cryptotanshinone inhibits ovarian tumor growth and metastasis by degrading c-Myc and attenuating the FAK signaling pathway.

Cryptotanshinone (CT), a natural compound derived from Salvia miltiorrhiza Bunge that is also known as the traditional Chinese medicine Danshen, exhibits antitumor activity in various cancers. However, it remains unclear whether CT has a potential therapeutic benefit against ovarian cancers. The aim of this study was to test the efficacy of CT in ovarian cancer cells in vitro and using a xenograft model in NSG mice orthotopically implanted with HEY A8 human ovarian cancer cells and to explore the molecular mechanism(s) underlying CT's antitumor effects. We found that CT inhibited the proliferation, migration, and invasion of OVCAR3 and HEY A8 cells, while sensitizing the cell responses to the chemotherapy drugs paclitaxel and cisplatin. CT also suppressed ovarian tumor growth and metastasis in immunocompromised mice orthotopically inoculated with HEY A8 cells. Mechanistically, CT degraded the protein encoded by the oncogene c-Myc by promoting its ubiquitination and disrupting the interaction with its partner protein Max. CT also attenuated signaling via the nuclear focal adhesion kinase (FAK) pathway and degraded FAK protein in both cell lines. Knockdown of c-Myc using lentiviral CRISPR/Cas9 nickase resulted in reduction of FAK expression, which phenocopies the effects of CT and the c-Myc/Max inhibitor 10058-F4. Taken together, our studies demonstrate that CT inhibits primary ovarian tumor growth and metastasis by degrading c-Myc and FAK and attenuating the FAK signaling pathway.

Histological assessment of new cholangioscopy-guided forceps in ERCP biliary stricture sampling: a blinded comparative study.

Background and study aims Obtaining quality tissue during ERCP biliary stricture sampling is of paramount importance for a timely diagnosis. While single-operator cholangioscopy (SOC)-guided biopsies have been suggested to be the superior biliary tissue acquisition modality given direct tissue visualization, less is known about the specimen histological quality. We aimed to analyze the specimen quality of SOC biopsies and compare the new generation forceps with prior "legacy" forceps. Patients and methods Patients who underwent SOC from January 2017-August 2021 for biliary sampling were reviewed. In February 2020, the SOC-guided biopsy forceps were changed from legacy SpyBite to the SpyBite Max forceps (max). Specimens were assessed by blinded pathologists for crush artifact (none, mild, or severe) and gross size (greatest dimension in mm). Crush artifact and gross size were compared between the two groups. The diagnostic performance characteristics for cholangiocarcinoma (CCA), were assessed in an exploratory fashion. Results Eighty-one patients (max = 27, legacy = 54) with similar baseline characteristics were included in this study. On blinded pathological assessment, 58 % had crush artifact, without significant differences between the two groups (Max 63 % vs. Legacy 56 %; P  = 0.64). A similar mean specimen size was found (max 3 mm vs. legacy 3.2 mm; P  = 0.24). The overall prevalence of CCA was 40 %. The sensitivity, specificity, positive predictive value, and negative predictive value of the entire cohort using a combination of cytology, fluorescence in situ hybridization, and SOC-guided biopsies were 78.1 %, 91.8 %, 86.2 %, and 86.5 %, respectively. No difference between legacy or max groups was found. Conclusions A high rate of crush artifact was found in SOC-guided biopsy specimens. Further investigation regarding proper biopsy technique and handling is necessary to increase the diagnostic yield with SOC-guided biopsies.

Cross-scale Attention Guided Multi-instance Learning for Crohn's Disease Diagnosis with Pathological Images.

Multi-instance learning (MIL) is widely used in the computer-aided interpretation of pathological Whole Slide Images (WSIs) to solve the lack of pixel-wise or patch-wise annotations. Often, this approach directly applies "natural image driven" MIL algorithms which overlook the multi-scale (i.e. pyramidal) nature of WSIs. Off-the-shelf MIL algorithms are typically deployed on a single-scale of WSIs (e.g., 20× magnification), while human pathologists usually aggregate the global and local patterns in a multi-scale manner (e.g., by zooming in and out between different magnifications). In this study, we propose a novel cross-scale attention mechanism to explicitly aggregate inter-scale interactions into a single MIL network for Crohn's Disease (CD), which is a form of inflammatory bowel disease. The contribution of this paper is two-fold: (1) a cross-scale attention mechanism is proposed to aggregate features from different resolutions with multi-scale interaction; and (2) differential multi-scale attention visualizations are generated to localize explainable lesion patterns. By training ~250,000 H&E-stained Ascending Colon (AC) patches from 20 CD patient and 30 healthy control samples at different scales, our approach achieved a superior Area under the Curve (AUC) score of 0.8924 compared with baseline models. The official implementation is publicly available at https://github.com/hrlblab/CS-MIL.

Development and technical validation of an artificial intelligence model for quantitative analysis of histopathologic features of eosinophilic esophagitis.

Background: In an attempt to provide quantitative, reproducible, and standardized analyses in cases of eosinophilic esophagitis (EoE), we have developed an artificial intelligence (AI) digital pathology model for the evaluation of histologic features in the EoE/esophageal eosinophilia spectrum. Here, we describe the development and technical validation of this novel AI tool. Methods: A total of 10 726 objects and 56.2 mm2 of semantic segmentation areas were annotated on whole-slide images, utilizing a cloud-based, deep learning artificial intelligence platform (Aiforia Technologies, Helsinki, Finland). Our training set consisted of 40 carefully selected digitized esophageal biopsy slides which contained the full spectrum of changes typically seen in the setting of esophageal eosinophilia, ranging from normal mucosa to severe abnormalities with regard to each specific features included in our model. A subset of cases was reserved as independent "test sets" in order to assess the validity of the AI model outside the training set. Five specialized experienced gastrointestinal pathologists scored each feature blindly and independently of each other and of AI model results. Results: The performance of the AI model for all cell type features was similar/non-inferior to that of our group of GI pathologists (F1-scores: 94.5-94.8 for AI vs human and 92.6-96.0 for human vs human). Segmentation area features were rated for accuracy using the following scale: 1. "perfect or nearly perfect" (95%-100%, no significant errors), 2. "very good" (80%-95%, only minor errors), 3. "good" (70%-80%, significant errors but still captures the feature well), 4. "insufficient" (less than 70%, significant errors compromising feature recognition). Rating scores for tissue (1.01), spongiosis (1.15), basal layer (1.05), surface layer (1.04), lamina propria (1.15), and collagen (1.11) were in the "very good" to "perfect or nearly perfect" range, while degranulation (2.23) was rated between "good" and "very good". Conclusion: Our newly developed AI-based tool showed an excellent performance (non-inferior to a group of experienced GI pathologists) for the recognition of various histologic features in the EoE/esophageal mucosal eosinophilia spectrum. This tool represents an important step in creating an accurate and reproducible method for semi-automated quantitative analysis to be used in the evaluation of esophageal biopsies in this clinical context.

An Adolescent with Chanarin-Dorfman Syndrome Presenting with Ichthyosis and Hepatic Steatosis.

Chanarin-Dorfman syndrome also known as neutral lipid storage disease is a rare multisystemic autosomal recessive disorder. It is mostly encountered in patients of Mediterranean and Middle Eastern origin. Most patients are brought to medical attention secondary to dermatological manifestations namely ichthyosis. Here, we report a 10-year-old Kurdish male patient with ichthyosis, who was referred to pediatric liver clinic for transaminase elevation of unknown etiology despite elaborate workup. Histological findings on liver biopsy were consistent with nonalcoholic steatohepatitis. Genetic testing identified homozygous mutation C.776G>A (p.G259D) in the Abhydrolase domain containing 5 gene on chromosome 3 described in patients with Chanarin-Dorfman syndrome. After the initiation of a diet with high medium chain triglycerides/long chain triglycerides ratio, aerobic exercise, and vitamin E, the patient liver enzymes improved. Due to debilitating ichthyosis, he was started on acitretin therapy that was discontinued due to transaminases elevation. Patient is currently stable and doing well.

Adipose-Derived Stem Cells Facilitate Ovarian Tumor Growth and Metastasis by Promoting Epithelial to Mesenchymal Transition Through Activating the TGF-ß Pathway.

Adipose-derived stem cells (ADSC) are multipotent mesenchymal stem cells derived from adipose tissues and are capable of differentiating into multiple cell types in the tumor microenvironment (TME). The roles of ADSC in ovarian cancer (OC) metastasis are still not well defined. To understand whether ADSC contributes to ovarian tumor metastasis, we examined epithelial to mesenchymal transition (EMT) markers in OC cells following the treatment of the ADSC-conditioned medium (ADSC-CM). ADSC-CM promotes EMT in OC cells. Functionally, ADSC-CM promotes OC cell proliferation, survival, migration, and invasion. We further demonstrated that ADSC-CM induced EMT via TGF-ß growth factor secretion from ADSC and the ensuing activation of the TGF-ß pathway. ADSC-CM-induced EMT in OC cells was reversible by the TGF-ß inhibitor SB431542 treatment. Using an orthotopic OC mouse model, we also provide the experimental evidence that ADSC contributes to ovarian tumor growth and metastasis by promoting EMT through activating the TGF-ß pathway. Taken together, our data indicate that targeting ADSC using the TGF-ß inhibitor has the therapeutic potential in blocking the EMT and OC metastasis.