The deep fascia is a three‐dimensional continuum of connective tissue surrounding the bones, muscles, nerves and blood vessels throughout our body. Its importance in chronically debilitating conditions has recently been brought to light. This work investigates changes in these tissues in pathological settings. A state‐of‐the‐art review was conducted in PubMed and Google Scholar following a two‐stage process. A first search was performed to identify main types of deep fasciae. A second search was performed to identify studies considering a deep fascia, common pathologies of this deep fascia and the associated alterations in tissue anatomy. We find that five main deep fasciae pathologies are chronic low back pain, chronic neck pain, Dupuytren's disease, plantar fasciitis and iliotibial band syndrome. The corresponding fasciae are respectively the thoracolumbar fascia, the cervical fascia, the palmar fascia, the plantar fascia and the iliotibial tract. Pathological fascia is characterized by increased tissue stiffness along with alterations in myofibroblast activity and the extra‐cellular matrix, both in terms of collagen and Matrix Metalloproteases (MMP) levels. Innervation changes such as increased density and sensitization of nociceptive nerve fibers are observed. Additionally, markers of inflammation such as pro‐inflammatory cytokines and immune cells are documented. Pain originating from the deep fascia likely results from a combination of increased nerve density, sensitization and chronic nociceptive stimulation, whether physical or chemical. The pathological fascia is characterized by changes in innervation, immunology and tissue contracture. Further investigation is required to best benefit both research opportunities and patient care.

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Mechanical ventilation is a key form of life support for patients with pulmonary impairment. Healthcare workers are required to continuously adjust ventilator settings for each patient, a challenging and time consuming task. Hence, it would be beneficial to develop an automated decision support tool to optimize ventilation treatment. We present DeepVent, a Conservative Q-Learning (CQL) based offline Deep Reinforcement Learning (DRL) agent that learns to predict the optimal ventilator parameters for a patient to promote 90 day survival. We design a clinically relevant intermediate reward that encourages continuous improvement of the patient vitals as well as addresses the challenge of sparse reward in RL. We find that DeepVent recommends ventilation parameters within safe ranges, as outlined in recent clinical trials. The CQL algorithm offers additional safety by mitigating the overestimation of the value estimates of out-of-distribution states/actions. We evaluate our agent using Fitted Q Evaluation (FQE) and demonstrate that it outperforms physicians from the MIMIC-III dataset.

Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer-related death globally. Patients typically present at an advanced stage and less than 50% reach the maximum 1-year survival rate, when given as first-line treatment, Sorafenib. This highlights the need for early detection and novel therapeutic targets crucial to increase overall survival (OS) for patients with HCC. Given the important role of angiogenesis in HCC from its early stage and its rich immune composition, anti-angiogenic and immune checkpoint inhibitors (ICI), are two therapeutic approaches when combined marked the first treatment in more than a decade to significantly improve the overall survival and progression-free survival in patients with advanced HCC compared to Sorafenib. While the combination of agents inhibiting angiogenesis and ICI have recently entered the clinic, the interplay between angiogenic factors and immunity in the context of this approach remains poorly understood. Here we focus on understanding the interplay between the vascular state of the tumor and the immune response in HCC. As a first step, we focus on defining the immune and vasculature landscape of the central tumor, peripheral tumor, adjacent liver to the tumor, and distal liver regions of each lesion by immunohistochemistry (IHC). Forty (40) formalin-fixed paraffin-embedded (FFPE) human liver tissue samples containing untreated and non-viral HCC tumors, obtained from the Liver Disease Biobank of the RI-MUHC were used to perform IHC. Images were viewed and scored using the Aperio ImageScope software. With respect to the vasculature, we observe that all tumors have a combination of both angiogenic (CD34/Ki67+ve) and co-optioning (CD31 +ve) features, with no uniform distribution. Our immune markers demonstrate that both adaptive and innate immune cells are present at the interface and different tumors demonstrate different levels of infiltration. Next, to identify the immune subtype populations (ie macrophage M1 vs M2, Treg, etc) present, we will use the NanoString Whole Transcriptome Atlas spatial profiler technology. We will also then link the vasculature to any specific immune profile. For example, it has been shown in other cancer types that macrophages are associated with co-option. This project presents preliminary evidence for the interaction of vascular factors with immune cells, thus providing insight into the biological rationale of why 30% of patients responded to combined angiogenic and immunotherapy treatment. Citation Format: Audrey Kapelanski-Lamoureux, Flemming Kondrup, Lucyna Krzywon, Stephanie K. Petrillo, Anthoula Lazaris, Peter Metrakos. Characterizing the interplay between angiogenic and immunoactive factors of hepatocellular carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5291.