Developing Advanced AI Ecosystems to Enhance Diagnosis and Care for Patients with Depression.

Major Depressive Disorder (MDD) has a significant impact on the daily lives of those affected. This concept paper presents a project that aims at addressing MDD challenges through innovative therapy systems. The project consists of two use cases: a multimodal neurofeedback (NFB) therapy and an AI-based virtual therapy assistant (VTA). The multimodal NFB integrates EEG and fNIRS to comprehensively assess brain function. The goal is to develop an open-source NFB toolbox for EEG-fNIRS integration, augmented by the VTA for optimized efficacy. The VTA will be able to collect behavioral data, provide personalized feedback and support MDD patients in their daily lives. This project aims to improve depression treatment by bringing together digital therapy, AI and mobile apps to potentially improve outcomes and accessibility for people living with depression.

[Family Medicine in the Digital Age: Scenarios and Recommendations]. / Hausärztliche Medizin im digitalen Zeitalter: Szenarien und Empfehlungen.

AIMS: The aim of this study was to examine the situation of family medicine in the digital age in order to discuss future trends and outline recommendations for action. METHODS: We conducted a structured deliberative process employing elements of the scenario method and involving relevant stakeholder perspectives. Based on an empirically informed analysis of current situations and trends, the scenario method allows the formation of practical recommendations. RESULTS: Extrapolating current trends in the medical profession, the patients, the technological development, and the healthcare system, we developed a best case and a worst case scenario of family medicine in the year 2050. From the analysis and discussion of the scenarios, we derived recommendations for practitioners and decision makers. CONCLUSIONS: Based on the developed scenarios, we recommend twelve measures towards a model of future healthcare that is centered on family medicine and enables a comprehensive, digitally supported holistic and patient-oriented service provision.

Structure-Based Design and Preclinical Characterization of Selective and Orally Bioavailable Factor XIa Inhibitors: Demonstrating the Power of an Integrated S1 Protease Family Approach.

The serine protease factor XI (FXI) is a prominent drug target as it holds promise to deliver efficacious anticoagulation without an enhanced risk of major bleeds. Several efforts have been described targeting the active form of the enzyme, FXIa. Herein, we disclose our efforts to identify potent, selective, and orally bioavailable inhibitors of FXIa. Compound 1, identified from a diverse library of internal serine protease inhibitors, was originally designed as a complement factor D inhibitor and exhibited submicromolar FXIa activity and an encouraging absorption, distribution, metabolism, and excretion (ADME) profile while being devoid of a peptidomimetic architecture. Optimization of interactions in the S1, S1ß, and S1' pockets of FXIa through a combination of structure-based drug design and traditional medicinal chemistry led to the discovery of compound 23 with subnanomolar potency on FXIa, enhanced selectivity over other coagulation proteases, and a preclinical pharmacokinetics (PK) profile consistent with bid dosing in patients.