5-HT4R agonism reduces L-DOPA-induced dyskinesia via striatopallidal neurons in unilaterally 6-OHDA lesioned mice.

Parkinson's disease is caused by a selective vulnerability and cell loss of dopaminergic neurons of the Substantia Nigra pars compacta and, consequently, striatal dopamine depletion. In Parkinson's disease therapy, dopamine loss is counteracted by the administration of L-DOPA, which is initially effective in ameliorating motor symptoms, but over time leads to a burdening side effect of uncontrollable jerky movements, termed L-DOPA-induced dyskinesia. To date, no efficient treatment for dyskinesia exists. The dopaminergic and serotonergic systems are intrinsically linked, and in recent years, a role has been established for pre-synaptic 5-HT1a/b receptors in L-DOPA-induced dyskinesia. We hypothesized that post-synaptic serotonin receptors may have a role and investigated the effect of modulation of 5-HT4 receptor on motor symptoms and L-DOPA-induced dyskinesia in the unilateral 6-OHDA mouse model of Parkinson's disease. Administration of RS 67333, a 5-HT4 receptor partial agonist, reduces L-DOPA-induced dyskinesia without altering L-DOPA's pro-kinetic effect. In the dorsolateral striatum, we find 5-HT4 receptor to be predominantly expressed in D2R-containing medium spiny neurons, and its expression is altered by dopamine depletion and L-DOPA treatment. We further show that 5-HT4 receptor agonism not only reduces L-DOPA-induced dyskinesia, but also enhances the activation of the cAMP-PKA pathway in striatopallidal medium spiny neurons. Taken together, our findings suggest that agonism of the post-synaptic serotonin receptor 5-HT4 may be a novel therapeutic approach to reduce L-DOPA-induced dyskinesia.

Enhancing Prostate Cancer Diagnosis: AI-Driven Virtual Biopsy for Optimal MRI-Targeted Biopsy Approach and Gleason Grading Strategy.

An optimal approach to MRI fusion targeted prostate biopsy (PBx) remains unclear (number of cores, inter-core distance, Gleason grading (GG) principle). The aim of this study was to develop a precise pixel-wise segmentation diagnostic AI algorithm for tumor detection and GG as well as an algorithm for virtual prostate biopsy that are used together to systematically investigate and find an optimal approach to targeted PBx. Pixel-wise AI algorithms for tumor detection and GG were developed using a high-quality, manually annotated dataset (slides n=442) after fast-track annotation transfer into segmentation style. To this end, a virtual biopsy algorithm was developed that can perform random biopsies from tumor regions in whole-mount whole-slide images with pre-defined parameters. A cohort of 115 radical prostatectomy (RP) patient cases with clinically significant, MRI-visible tumors (n=121) was used for systematic studies of the optimal biopsy approach. Three expert genitourinary (GU) pathologists participated in the validation. The tumor detection algorithm (aware version sensitivity/specificity 0.99/0.90, balanced version 0.97/0.97) and GG algorithm (quadratic kappa range vs pathologists 0.77-0.78) perform on par with expert GU pathologists. In total, 65,340 virtual biopsies were performed to study different biopsy approaches with the following results: 1) four biopsy cores is the optimal number for a targeted PBx, 2) cumulative GG strategy is superior to using maximal Gleason score for single cores, 3) controlling for minimal inter-core distance does not improve the predictive accuracy for the RP Gleason score, 4) Using tertiary Gleason pattern principle (for AI tool) in cumulative GG strategy might allow better predictions of final RP Gleason score. The AI algorithm (based on cumulative GG strategy) predicted the RP Gleason score of the tumor better than 2 of the 3 expert GU pathologists. In this study, using an original approach of virtual prostate biopsy on the real cohort of patient cases, we find the optimal approach to the biopsy procedure and the subsequent Gleason grading of a targeted PBx. We publicly release two large datasets with associated expert pathologists' GG and our virtual biopsy algorithm.

Fast-Track Development and Multi-Institutional Clinical Validation of an Artificial Intelligence Algorithm for Detection of Lymph Node Metastasis in Colorectal Cancer.

Lymph node metastasis (LNM) detection can be automated using artificial intelligence (AI)-based diagnostic tools. Only limited studies have addressed this task for colorectal cancer (CRC). This study aimed to develop of a clinical-grade digital pathology tool for LNM detection in CRC using the original fast-track framework. The training cohort included 432 slides from one department. A segmentation algorithm detecting 8 relevant tissue classes was trained. The test cohorts consisted of materials from 5 pathology departments digitized by 4 different scanning systems. A high-quality, large training data set was generated within 7 days and a minimal amount of annotation work using fast-track principles. The AI tool showed very high accuracy for LNM detection in all cohorts, with sensitivity, negative predictive value, and specificity ranges of 0.980 to 1.000, 0.997 to 1.000, and 0.913 to 0.990, correspondingly. Only 5 of 14,460 analyzed test slides with tumor cells over all cohorts were classified as false negative (3/5 representing clusters of tumor cells in lymphatic vessels). A clinical-grade tool was trained in a short time using fast-track development principles and validated using the largest international, multi-institutional, multiscanner cohort of cases to date, showing very high precision for LNM detection in CRC. We are releasing a part of the test data sets to facilitate academic research.

Artificial Intelligence-Based Tool for Tumor Detection and Quantitative Tissue Analysis in Colorectal Specimens.

Digital pathology adoption allows for applying computational algorithms to routine pathology tasks. Our study aimed to develop a clinical-grade artificial intelligence (AI) tool for precise multiclass tissue segmentation in colorectal specimens (resections and biopsies) and clinically validate the tool for tumor detection in biopsy specimens. The training data set included 241 precisely manually annotated whole-slide images (WSIs) from multiple institutions. The algorithm was trained for semantic segmentation of 11 tissue classes with an additional module for biopsy WSI classification. Six case cohorts from 5 pathology departments (4 countries) were used for formal and clinical validation, digitized by 4 different scanning systems. The developed algorithm showed high precision of segmentation of different tissue classes in colorectal specimens with composite multiclass Dice score of up to 0.895 and pixel-wise tumor detection specificity and sensitivity of up to 0.958 and 0.987, respectively. In the clinical validation study on multiple external cohorts, the AI tool reached sensitivity of 1.0 and specificity of up to 0.969 for tumor detection in biopsy WSI. The AI tool analyzes most biopsy cases in less than 1 minute, allowing effective integration into clinical routine. We developed and extensively validated a highly accurate, clinical-grade tool for assistive diagnostic processing of colorectal specimens. This tool allows for quantitative deciphering of colorectal cancer tissue for development of prognostic and predictive biomarkers and personalization of oncologic care. This study is a foundation for a SemiCOL computational challenge. We open-source multiple manually annotated and weakly labeled test data sets, representing a significant contribution to the colorectal cancer computational pathology field.