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.

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.