Concordia University Ann Arbor

The convergence of deep learning and big data has spurred significant interest in developing novel hardware that can run large artificial intelligence (AI) workloads more efficiently. Over the last several years, silicon photonics has emerged as a disruptive technology for next-generation accelerators for machine learning (ML). More recently, the heterogeneous integration of III-V compound semiconductors has opened the door to integrating lasers and semiconductor optical amplifiers at wafer-scale, enabling the scaling of the size, density, and complexity of silicon photonic integrated circuits (PICs). Furthermore, using this technology, all of the individual components required to execute the operations within a neural network are available and can be integrated on the same PIC. Here, we review our innovations of an energy-efficient and scalable silicon photonic platform serving as the underlying foundation for next-generation AI accelerator hardware.

As medical research continues to promise the advancement of health equity, it is called to address its incorrect and ongoing use of the term “Caucasian.” The term “Caucasian” has persisted in medical research despite its entanglement with beliefs of race as a biological factor. To continue to advance efforts in addressing health disparities and achieving health equity, researchers are called to use accurate racial and ethnic terminology.

INTRODUCTION Metabolic stressors (obesity, metabolic syndrome, prediabetes, and type 2 diabetes [T2D]) increase the risk of cognitive impairment (CI), including Alzheimer's disease (AD). Immune system dysregulation and inflammation, particularly microglial mediated, may underlie this risk, but mechanisms remain unclear. METHODS Using a high‐fat diet‐fed (HFD) model, we assessed longitudinal metabolism and cognition, and terminal inflammation and brain spatial transcriptomics. Additionally, we performed hippocampal spatial transcriptomics and single‐cell RNA sequencing of post mortem tissue from AD and T2D human subjects versus controls. RESULTS HFD induced progressive metabolic and CI with terminal inflammatory changes, and dysmetabolic, neurodegenerative, and inflammatory gene expression profiles, particularly in microglia. AD and T2D human subjects had similar gene expression changes, including in secreted phosphoprotein 1 (SPP1), a pro‐inflammatory gene associated with AD. DISCUSSION These data show that metabolic stressors cause early and progressive CI, with inflammatory changes that promote disease. They also indicate a role for microglia, particularly microglial SPP1, in CI. Highlights Metabolic stress causes persistent metabolic and cognitive impairments in mice. Murine and human brain spatial transcriptomics align and indicate a pro‐inflammatory milieu. Transcriptomic data indicate a role for microglial‐mediated inflammatory mechanisms. Secreted phosphoprotein 1 emerged as a potential target of interest in metabolically driven cognitive impairment.

Introduction Medicare Advantage (MA) managed care plans, now chosen by 51% of Medicare beneficiaries, are incentivized to constrain healthcare spending and utilization, a shift in financial incentives compared to Traditional Medicare's fee‐for‐service payment model. Beyond its primary beneficiaries, MA's mechanisms to constrain utilization may impact Traditional Medicare beneficiaries with prostate cancer through “spillover” effects on physician behavior. Methods From a 20% sample of Medicare claims, we identified patients diagnosed with prostate cancer from 2016 to 2019. We calculated MA penetration [MA beneficiaries/(Traditional Medicare and MA beneficiaries)] at the practice‐level. We assessed the relationship between practice‐level MA penetration and two measures of quality—potential overtreatment (i.e., treatment among those with > 75% noncancer mortality within 10 years of diagnosis) and confirmatory testing (repeat prostate biopsy, MRI, or genomic test)—using a multilevel logistic regression. We also assessed two measures of utilization, price standardized spending (i.e., global utilization) and overall treatment. Results We identified 41,092 patients. Median practice‐level MA penetration was 33% (IQR 23%–43%). Increasing practice‐level MA penetration was associated with increased odds of overall treatment among all Traditional Medicare beneficiaries (adjusted OR 1.03 (95% CI 1.01–1.05), p = 0.01, per 10% increase in MA penetration). However, MA penetration was not associated with our quality measures, potential overtreatment and confirmatory testing, or price‐standardized spending. Conclusions MA penetration at the urology practice‐level varies considerably. In men with prostate cancer, greater practice‐level MA penetration was associated with increased odds of treatment, but not overall utilization—even where it might influence quality.

Background: The limited diagnostic accuracy of prostate-specific antigen screening for prostate cancer (PCa) has prompted innovative solutions, such as the state-of-the-art 18-gene urine test for clinically-significant PCa (MyProstateScore2.0 (MPS2)). Objective: We aim to develop a non-invasive biomarker test, the simplified MPS2 (sMPS2), which achieves similar state-of-the-art accuracy as MPS2 for predicting high-grade PCa but requires substantially fewer genes than the 18-gene MPS2 to improve its accessibility for routine clinical care. Methods: We grounded the development of sMPS2 in the Predictability, Computability, and Stability (PCS) framework for veridical data science. Under this framework, we stress-tested the development of sMPS2 across various data preprocessing and modeling choices and developed a stability-driven PCS ranking procedure for selecting the most predictive and robust genes for use in sMPS2. Results: The final sMPS2 model consisted of 7 genes and achieved a 0.784 AUROC (95% confidence interval, 0.742–0.825) for predicting high-grade PCa on a blinded external validation cohort. This is only 2.3% lower than the 18-gene MPS2, which is similar in magnitude to the 1–2% in uncertainty induced by different data preprocessing choices. Conclusions: The 7-gene sMPS2 provides a unique opportunity to expand the reach and adoption of non-invasive PCa screening.

A better understanding of the mechanisms regulating CD8⁺ T cell differentiation is essential to develop new strategies to fight infections and cancer. Using genetic mouse models and blocking antibodies, we uncovered cellular and molecular mechanisms by which Notch signaling favors the efficient generation of effector CD8⁺ T cells. Fibroblastic reticular cells from secondary lymphoid organs, but not dendritic cells, were the dominant source of Notch signals in T cells via Delta-like1/4 ligands within the first 3 days of immune responses to vaccination or infection. Using transcriptional and epigenetic studies, we identified a unique Notch-driven T cell–specific signature. Early Notch signals were associated with chromatin opening in regions occupied by bZIP transcription factors, specifically BATF, known to be important for CD8⁺ T cell differentiation. Overall, we show that fibroblastic reticular cell niches control the ultimate molecular and functional fate of CD8⁺ T cells after vaccination or infection through the delivery of early Notch signals.

Despite decades of research, impaired extremity wound healing in type 2 diabetes remains a significant driver of patient morbidity, mortality, and health care costs. Advances in surgical and medical therapies, including the advent of endovascular interventions for peripheral artery disease and topical therapies developed to promote wound healing, have not reduced the frequency of lower leg amputations for nonhealing wounds in type 2 diabetes. This brief report is aimed at reviewing the roles of various cell types in tissue repair and summarizing the known dysfunctions of these cell types in diabetic foot ulcers. Recent advances in our understanding of the epigenetic regulation in immune cells identified to be altered in type 2 diabetes are summarized, and particular attention is paid to the developing research defining the epigenetic regulation of structural cells, including keratinocytes, fibroblasts, and endothelial cells. Gaps in knowledge are highlighted, and potential future directions are suggested based on the current state of the field.

Asparaginase (ASNase)-based chemotherapy regimens significantly improve survival outcomes in children, adolescent and young adult (AYA), and even adults with acute lymphoblastic leukemia/lymphoma (ALL); however, the incidence and severity of ASNase-associated adverse events (AEs) in adults may differ significantly from those reported in children. Strategies to mitigate, monitor for, and manage toxicities that allow adult ALL patients to receive full ASNase courses are needed. A representative 12-member panel of experts who treat AYA and adult ALL patients, incorporate ASNase into their treatment regimens, and conduct related research was assembled to consider opportunities to optimize the use of pediatric-inspired ALL regimens in these adult patients. Following 2 systematic biomedical literature searches from April 2009 through April 2024, a modified Delphi method was used to distill expert opinion into clinical statements that met a standardized definition of consensus. After 2 iterative Delphi method surveys, 23 statements met the standardized definition of consensus, whereas 19 statements did not. Five statements were merged to avoid redundancy. The clinical statements were grouped into 5 distinct categories: 1) hepatotoxicity; 2) hypersensitivity reactions; 3) thromboembolic and coagulopathy complications; 4) pancreatitis and metabolic complications; and 5) dosing. The intent of these statements is to provide health care providers with information that will help them mitigate, monitor for, and manage the most common and/or unique ASNase-induced AEs in adult ALL patients, allowing these patients to receive more or all the planned ASNase doses and thereby improve outcomes.

This study aims to introduce a new index that could become a framework for future modification and improvement, and retrospectively test the predictability of this index collectively and individually for final bone changes by using existing research data pertinent to guided bone regeneration (GBR). Methods The MAPS score was introduced to evaluate the bioMechanical, Aesthetic/Anatomical, Pathophysiologic, and Subject-related parameters for the healing assessment of 20 patients who underwent GBR in the posterior mandible retrospectively. Intraoral photography was taken at 3-, 10-, 21 days, and 5 months, resulting in 80 follow-up visits. Two independent examiners evaluated the photos giving scores for each timepoint and tested against horizontal bone gain (CBCT) for predictability. Results Cohen’s Kappa values showed high intra- and inter-examiner agreement. Pearson’s correlation showed an inverse correlation between baseline bone width and bone changes at a 3 mm level (R² = 0.23). The higher M, A, and P values at any time point were associated with higher bone gain. The 10-day MAPS score turns out the most predictive of bone gain (RMSE 1.32, R² 0.75). In addition, increasing the average P score by 1 point at 10 days is associated with an increase in bone gain of 1.23 (p=.057). Conclusion The MAPS score improves consistently over the 5-month healing period. However, no statistically significant difference is observed between the scores at 21 days and 5 months, reflecting the clinical healing pattern for GBR. The overall MAPS score correlated with bone changes after GBR procedures, indicating its potential for estimating hard tissue regenerative outcomes.

Background Sepsis is a major global health problem. However, it lacks a true reference standard for case identification, complicating epidemiologic surveillance. Consensus definitions have changed multiple times, clinicians struggle to identify sepsis at the bedside, and differing identification algorithms generate wide variation in incidence rates. The two current identification approaches use codes from administrative data, or electronic health record (EHR)-based algorithms such as the Center for Disease Control Adult Sepsis Event (ASE); both have limitations. Here our primary purpose is to report initial steps in developing a novel approach to identifying sepsis using unsupervised clustering methods. Secondarily, we report preliminary analysis of resulting clusters, using identification by ASE criteria as a familiar comparator. Methods This retrospective cohort study used hospital administrative and EHR data on adults admitted to intensive care units (ICUs) at five Canadian medical centres (2015–2017), with split development and validation cohorts. After preprocessing 592 variables (demographics, encounter characteristics, diagnoses, medications, laboratory tests, and clinical management) and applying data reduction, we presented 55 principal components to eight different clustering algorithms. An automated elbow method determined the optimal number of clusters, and the optimal algorithm was selected based on clustering metrics for consistency, separation, distribution and stability. Cluster membership in the validation cohort was assigned using an XGBoost model trained to predict cluster membership in the development cohort. For cluster analysis, we prospectively subdivided clusters by their fractions meeting ASE criteria (≥ 50% ASE-majority clusters vs. ASE-minority clusters), and compared their characteristics. Results There were 3660 patients in the development cohort and 3012 in the validation cohort, of which 21.5% (development) and 19.1% (validation) were ASE (+). The Robust and Sparse K-means Clustering (RSKC) method performed best. In the development cohort, it identified 48 clusters of hospitalizations; 11 ASE-majority clusters contained 22.4% of all patients but 77.8% of all ASE (+) patients. 34.9% of the 209 ASE (−) patients in the ASE-majority clusters met more liberal ASE criteria for sepsis. Findings were consistent in the validation cohort. Conclusions Unsupervised clustering applied to diverse, large-scale medical data offers a promising approach to the identification of sepsis phenotypes for epidemiological surveillance.