Citations

Abstract Measuring prebiotic activities of foods on human microbiome and providing dietary guidelines on one’s health status are an emerging research topic. However, it is difficult to directly compare different foods using the prebiotic index (PI) proposed in 2003 to determine their prebiotic activities. This ambiguity can be resolved with the gut microbiome health index (GMHI), newly built in 2020, using a consortium of 50 microbial species associated with human health. As a translational application of this index, we used this formula to predict the PI of five commercial prebiotics, fructooligosaccharides (FOS), galactooligosaccharides (GOS), xylooligosaccharides (XOS), inulin (IN), and 2`-fucosyllactose (2FL), using in vitro human fecal fermentation. To calculate the GMHI-based PI values, microbial community changes were analyzed at the species level using shotgun sequencing during the fermentation of the prebiotics. As a result, the five compounds exerted a prebiotic effect, increasing the abundance of health-prevalent species, such as Bifidobacterium adolescentis and decreasing health-scarce species. In this context, the GMHI of prebiotics increased after 24 h, but that of the control group without carbon sources decreased. GMHI was highest in the order of IN (0.48 ± 0.06), FOS (0.47 ± 0.03), XOS (0.33 ± 0.02), GOS (0.26 ± 0.02), and 2FL (0.16 ± 0.06), which more accurately reflects the prebiotic effect than PI. Additionally, GMHI stratified the prebiotics more effectively than ecological metrics analyses, such as Shannon diversity, species richness, species evenness, and the inverse Simpson diversity index. This study demonstrates that GMHI-based PI can be used to measure prebiotic activities of several bifidogenic oligosaccharides and perhaps predict the health-beneficial effects of diets after collecting substantial amounts of population-scale meta-datasets.

Background and Objectives: The number of patients who undergo multiple operations on a knee is increasing. The objective of this study was to develop a deep learning algorithm that could detect 17 different surgical implants on plain knee radiographs. Materials and Methods: An internal dataset consisted of 5206 plain knee antero-posterior X-rays from a single, tertiary institute for model development. An external set contained 238 X-rays from another tertiary institute. A total of 17 different types of implants including total knee arthroplasty, unicompartmental knee arthroplasty, plate, and screw were labeled. The internal dataset was approximately split into a train set, a validation set, and an internal test set at a ratio of 7:1:2. You Only look Once (YOLO) was selected as the detection network. Model performances with the validation set, internal test set, and external test set were compared. Results: Total accuracy, total sensitivity, total specificity value of the validation set, internal test set, and external test set were (0.978, 0.768, 0.999), (0.953, 0.810, 0.990), and (0.956, 0.493, 0.975), respectively. Means ± standard deviations (SDs) of diagonal components of confusion matrix for these three subsets were 0.858 ± 0.242, 0.852 ± 0.182, and 0.576 ± 0.312, respectively. True positive rate of total knee arthroplasty, the most dominant class of the dataset, was higher than 0.99 with internal subsets and 0.96 with an external test set. Conclusion: Implant identification on plain knee radiographs could be automated using a deep learning technique. The detection algorithm dealt with overlapping cases while maintaining high accuracy on total knee arthroplasty. This could be applied in future research that analyzes X-ray images with deep learning, which would help prompt decision-making in clinics.

Abstract BACKGROUND Emavusertib, an oral interleukin-1 receptor-associated kinase 4 (IRAK4) inhibitor, targets toll-like receptor (TLR) and interleukin-1 receptor (IL-1R) signaling pathway in B-cell proliferation. IRAK4 forms a Myddosome complex with MYD88 adaptor protein and drives overactivation of NF-κB, causing inflammation and tumor growth. Emavusertib has been reported to be well tolerated and active as monotherapy in heavily pretreated relapsed/refractory (R/R) non-Hodgkin lymphoma (NHL). In a murine PDX model of primary CNS lymphoma (PCNSL), emavusertib crossed the blood-brain barrier, resulting in tumor response and prolonged survival. In combination with Bruton tyrosine kinase (BTK) inhibitors, emavusertib showed in vivo anti-cancer synergy in B-cell NHL. METHODS This is an ongoing open-label trial (NCT03328078) in patients with R/R NHL. Currently, we are in the dose escalation portion of combination therapy to evaluate safety and efficacy following treatment of emavusertib at dose levels of 200 or 300mg BID with ibrutinib at full prescribed dose. As of May 6th, 2022, 13 patients have been treated with emavusertib+ibrutinib combination therapy. RESULTS Among the 13 patients, two were diagnosed with R/R PCNSL and had several prior lines of anti-cancer therapy. Emavusertib in combination with ibrutinib (560 mg daily) appeared to be well tolerated in these two subjects. One patient experienced Gr3 treatment-related adverse events (thrombocytopenia, muscle weakness, pain). The preliminary efficacy data demonstrated one CR and one SD. The patient who achieved CR after the combination therapy was originally intolerant to high-dose methotrexate based chemoimmunotherapy and did not achieve complete remission after switching to ibrutinib, providing early clinical evidence of CNS penetration and anti-tumor activity of emavusertib. CONCLUSION In R/R PCNSL, these preliminary data suggest that combination therapy has a tolerable safety profile with promising anti-cancer activity and may overcome ibrutinib resistance.

Abstract BACKGROUND PCNSL is an aggressive brain tumor accounting for 3% of all CNS malignancies and is associated with poor prognosis. Standard of care treatment includes induction with high-dose methotrexate based chemoimmunotherapy followed by consolidation with autologous stem cell transplant or whole brain radiation. However, more than half of PCNSL patients cannot tolerate this intensive therapeutic intervention. Novel treatments are urgently needed. Toll-like receptor signaling pathway via MyD88/IRAK-4 signalosome is constitutively active in PCNSL secondary to MYD88 L265P mutation and represents an excellent therapeutic target. Emavusertib (CA-4948) is an oral first-in-class small molecule inhibitor of IRAK-4 that demonstrates clinical activity in patients with systemic Non-Hodgkin’s Lymphoma, however it has not been evaluated in the CNS space. HYPOTHESIS: Emavusertib has anti-tumor activity against preclinical PCNSL. METHODS Using multiparameter immunohistochemistry (IHC) and proteomics analysis, IRAK-1, IRAK-4, and NF-κB pathway activation were examined in patient PCNSL tissues as compared to normal controls. Plasma, cerebrospinal fluid, and brain tissue were assessed via UPLC-MS/MS for emavusertib drug concentration following single oral high dose in a murine model. Survival responses in preclinical PCNSL models were measured in response emavusertib treatment, along with in vivo tumor proliferation and downstream biomarker expression by multi-parameter IHC. RESULTS Our data confirm elevated IRAK-1, IRAK-4, and NF-κB signaling in patient PCNSL. We show that emavusertib can achieve therapeutically relevant concentrations in the brain and CSF. Emavusertib impairs tumor cell proliferation in vitro and in vivo, and shows dose-dependent single-agent activity in preclinical PCNSL models. We confirm decreased ERK1/2, MAPK and NF-κB activation, indicative of downstream IRAK-4 inhibition. CONCLUSIONS Our data supports that emavusertib demonstrates anti-tumor activity in the CNS space, warranting further preclinical and clinical evaluation of this agent for the treatment of PCNSL.