No abstract

Multiple neurocognitive systems contribute simultaneously to learning. For example, dopamine and basal ganglia (BG) systems are thought to support reinforcement learning (RL) by incrementally updating the value of choices, while the prefrontal cortex (PFC) contributes different computations, such as actively maintaining precise information in working memory (WM). It is commonly thought that WM and PFC show more protracted development than RL and BG systems, yet their contributions are rarely assessed in tandem. Here, we used a simple learning task to test how RL and WM contribute to changes in learning across adolescence. We tested 187 subjects ages 8 to 17 and 53 adults (25-30). Participants learned stimulus-action associations from feedback; the learning load was varied to be within or exceed WM capacity. Participants age 8-12 learned slower than participants age 13-17, and were more sensitive to load. We used computational modeling to estimate subjects' use of WM and RL processes. Surprisingly, we found more robust changes in RL than WM during development. RL learning rate increased significantly with age across adolescence and WM parameters showed more subtle changes, many of them early in adolescence. These results underscore the importance of changes in RL processes for the developmental science of learning. Key words (max 6): development, reinforcement learning, working memory, computational modeling, adolescence to this research: boys girlsshould aim to develop experimental paradigms and computational models that more precisely dissociate the use of reinforcement learning, working memory, and episodic memory throughout development.

The use of TLR agonists as an anti-cancer treatment is gaining momentum given their capacity to activate various host cellular responses through the secretion of inflammatory cytokines and type-I interferons. It is now also recognized that the perioperative period is a window of opportunity for various interventions aiming at reducing the risk of cancer metastases – the major cause of cancer related death. However, immune-stimulatory approach has not been used perioperatively given several contraindications to surgery. To overcome these obstacles, in the current study we employed the newly introduced, fully synthetic TLR-4 agonist, Glucopyranosyl Lipid-A (GLA-SE), in various models of cancer metastases, and in the context of acute stress or surgery. Without exerting evident adverse effects, a single systemic administration of GLA-SE rapidly and dose dependently elevated both innate and adaptive immunity in the circulation, lungs, and the lymphatic system. Importantly, GLA-SE treatment led to reduced metastatic development of a mammary adenocarcinoma and a colon carcinoma by approximately 40-75% in F344 rats and BALB/c mice, respectively, at least partly through elevating marginating-pulmonary NK cell cytotoxicity. GLA-SE is safe and well tolerated in humans, and currently is used as an adjuvant in phase-II clinical trials. Given that the TLR-4 receptor and its signaling cascade is highly conserved throughout evolution, our current results suggest that GLA-SE may be a promising immune stimulatory agent in the context of oncological surgeries, aiming to reduce long-term cancer recurrence.

Solid-organ transplantation is a life-saving treatment for end-stage organ disease in highly selected patients. Alongside the tremendous progress in the last several decades, new challenges have emerged. The growing disparity between organ demand and supply requires optimal patient/donor selection and matching. Improvements in long-term graft and patient survival require data-driven diagnosis and management of post-transplant complications. The growing abundance of clinical, genetic, radiologic, and metabolic data in transplantation has led to increasing interest in applying machine-learning (ML) tools that can uncover hidden patterns in large datasets. ML algorithms have been applied in predictive modeling of waitlist mortality, donor–recipient matching, survival prediction, post-transplant complications diagnosis, and prediction, aiming to optimize immunosuppression and management. In this review, we provide insight into the various applications of ML in transplant medicine, why these were used to evaluate a specific clinical question, and the potential of ML to transform the care of transplant recipients. 36 articles were selected after a comprehensive search of the following databases: Ovid MEDLINE; Ovid MEDLINE Epub Ahead of Print and In-Process & Other Non-Indexed Citations; Ovid Embase; Cochrane Database of Systematic Reviews (Ovid); and Cochrane Central Register of Controlled Trials (Ovid). In summary, these studies showed that ML techniques hold great potential to improve the outcome of transplant recipients. Future work is required to improve the interpretability of these algorithms, ensure generalizability through larger-scale external validation, and establishment of infrastructure to permit clinical integration.