Microphysiological systems as models for immunologically ‘cold’ tumors

The tumor microenvironment (TME) is a diverse milieu of cells including cancerous and non-cancerous cells such as fibroblasts, pericytes, endothelial cells and immune cells. The intricate cellular interactions within the TME hold a central role in shaping the dynamics of cancer progression, influencing pivotal aspects such as tumor initiation, growth, invasion, response to therapeutic interventions, and the emergence of drug resistance. In immunologically ‘cold’ tumors, the TME is marked by a scarcity of infiltrating immune cells, limited antigen presentation in the absence of potent immune-stimulating signals, and an abundance of immunosuppressive factors. While strategies targeting the TME as a therapeutic avenue in ‘cold’ tumors have emerged, there is a pressing need for novel approaches that faithfully replicate the complex cellular and non-cellular interactions in order to develop targeted therapies that can effectively stimulate immune responses and improve therapeutic outcomes in patients. Microfluidic devices offer distinct advantages over traditional in vitro 3D co-culture models and in vivo animal models, as they better recapitulate key characteristics of the TME and allow for precise, controlled insights into the dynamic interplay between various immune, stromal and cancerous cell types at any timepoint. This review aims to underscore the pivotal role of microfluidic systems in advancing our understanding of the TME and presents current microfluidic model systems that aim to dissect tumor-stromal, tumor-immune and immune-stromal cellular interactions in various ‘cold’ tumors. Understanding the intricacies of the TME in ‘cold’ tumors is crucial for devising effective targeted therapies to reinvigorate immune responses and overcome the challenges of current immunotherapy approaches

Tumor-on-chip modeling of organ-specific cancer and metastasis

Every year, cancer claims millions of lives around the globe. Unfortunately, model systems that accurately mimic human oncology - a requirement for the development of more effective therapies for these patients - remain elusive. Tumor development is an organ-specific process that involves modification of existing tissue features, recruitment of other cell types, and eventual metastasis to distant organs. Recently, tissue engineered microfluidic devices have emerged as a powerful in vitro tool to model human physiology and pathology with organ-specificity. These organ-on-chip platforms consist of cells cultured in 3D hydrogels and offer precise control over geometry, biological components, and physiochemical properties. Here, we review progress towards organ-specific microfluidic models of the primary and metastatic tumor microenvironments. Despite the field\u27s infancy, these tumor-on-chip models have enabled discoveries about cancer immunobiology and response to therapy. Future work should focus on the development of autologous or multi-organ systems and inclusion of the immune system

Evaluation of different decellularization protocols on the generation of pancreas-derived hydrogels

Different approaches have investigated the effects of different extracellular matrices (ECMs) and three-dimensional (3D) culture on islet function, showing encouraging results. Ideally, the proper scaffold should mimic the biochemical composition of the native tissue as it drives numerous signaling pathways involved in tissue homeostasis and functionality. Tissue-derived decellularized biomaterials can preserve the ECM composition of the native tissue making it an ideal scaffold for 3D tissue engineering applications. However, the decellularization process may affect the retention of specific components, and the choice of a proper detergent is fundamental in preserving the native ECM composition. In this study, we evaluated the effect of different decellularization protocols on the mechanical properties and biochemical composition of pancreatic ECM (pECM) hydrogels. Fresh porcine pancreas tissue was harvested, cut into small pieces, rinsed in water, and treated with two different detergents (sodium dodecyl sulfate [SDS] or Triton X-100) for 1 day followed by 3 days in water. Effective decellularization was confirmed by PicoGreen assay, Hoescht, and H&E staining, showing no differences among groups. Use of a protease inhibitor (PI) was also evaluated. Effective decellularization was confirmed by PicoGreen assay and hematoxylin and eosin (H&E) staining, showing no differences among groups. Triton-treated samples were able to form a firm hydrogel under appropriate conditions, while the use of SDS had detrimental effects on the gelation properties of the hydrogels. ECM biochemical composition was characterized both in the fresh porcine pancreas and all decellularized pECM hydrogels by quantitative mass spectrometry analysis. Fibrillar collagen was the major ECM component in all groups, with all generated hydrogels having a higher amount compared with fresh pancreas. This effect was more pronounced in the SDS-treated hydrogels when compared with the Triton groups, showing very little retention of other ECM molecules. Conversely, basement membrane and matricellular proteins were better retained when the tissue was pretreated with a PI and decellularized in Triton X-100, making the hydrogel more similar to the native tissue. In conclusion, we showed that all the protocols evaluated in the study showed effective tissue decellularization, but only when the tissue was pretreated with a PI and decellularized in Triton detergent, the biochemical composition of the hydrogel was closer to the native tissue ECM. The article compares different methodologies for the generation of a pancreas-derived hydrogel for tissue engineering applications. The biochemical characterization of the newly generated hydrogel shows that the material retains all the extracellular molecules of the native tissue and is capable of sustaining functionality of the encapsulated beta-cells

Unknown Quantum States: The Quantum de Finetti Representation

We present an elementary proof of the quantum de Finetti representation theorem, a quantum analogue of de Finetti's classical theorem on exchangeable probability assignments. This contrasts with the original proof of Hudson and Moody [Z. Wahrschein. verw. Geb. 33, 343 (1976)], which relies on advanced mathematics and does not share the same potential for generalization. The classical de Finetti theorem provides an operational definition of the concept of an unknown probability in Bayesian probability theory, where probabilities are taken to be degrees of belief instead of objective states of nature. The quantum de Finetti theorem, in a closely analogous fashion, deals with exchangeable density-operator assignments and provides an operational definition of the concept of an ``unknown quantum state'' in quantum-state tomography. This result is especially important for information-based interpretations of quantum mechanics, where quantum states, like probabilities, are taken to be states of knowledge rather than states of nature. We further demonstrate that the theorem fails for real Hilbert spaces and discuss the significance of this point.Comment: 30 pages, 2 figure

Arterial pulse wave modelling and analysis for vascular age studies: a review from VascAgeNet

Arterial pulse waves (PWs) such as blood pressure and photoplethysmogram (PPG) signals contain a wealth of information on the cardiovascular (CV) system that can be exploited to assess vascular age and identify individuals at elevated CV risk. We review the possibilities, limitations, complementarity, and differences of reduced-order, biophysical models of arterial PW propagation, as well as theoretical and empirical methods for analyzing PW signals and extracting clinically relevant information for vascular age assessment. We provide detailed mathematical derivations of these models and theoretical methods, showing how they are related to each other. Finally, we outline directions for future research to realize the potential of modeling and analysis of PW signals for accurate assessment of vascular age in both the clinic and in daily life

Honey bee foraging distance depends on month and forage type

To investigate the distances at which honey bee foragers collect nectar and pollen, we analysed 5,484 decoded waggle dances made to natural forage sites to determine monthly foraging distance for each forage type. Firstly, we found significantly fewer overall dances made for pollen (16.8 %) than for non-pollen, presumably nectar (83.2 %; P < 2.2 × 10−23). When we analysed distance against month and forage type, there was a significant interaction between the two factors, which demonstrates that in some months, one forage type is collected at farther distances, but this would reverse in other months. Overall, these data suggest that distance, as a proxy for forage availability, is not significantly and consistently driven by need for one type of forage over the other

Characterization of Major Histocompatibility Complex (MHC) DRB Exon 2 and DRA Exon 3 Fragments in a Primary Terrestrial Rabies Vector (Procyon lotor)

The major histocompatibility complex (MHC) presents a unique system to explore links between genetic diversity and pathogens, as diversity within MHC is maintained in part by pathogen driven selection. While the majority of wildlife MHC studies have investigated species that are of conservation concern, here we characterize MHC variation in a common and broadly distributed species, the North American raccoon (Procyon lotor). Raccoons host an array of broadly distributed wildlife diseases (e.g., canine distemper, parvovirus and raccoon rabies virus) and present important human health risks as they persist in high densities and in close proximity to humans and livestock. To further explore how genetic variation influences the spread and maintenance of disease in raccoons we characterized a fragment of MHC class II DRA exon 3 (250bp) and DRB exon 2 (228 bp). MHC DRA was found to be functionally monomorphic in the 32 individuals screened; whereas DRB exon 2 revealed 66 unique alleles among the 246 individuals screened. Between two and four alleles were observed in each individual suggesting we were amplifying a duplicated DRB locus. Nucleotide differences between DRB alleles ranged from 1 to 36 bp (0.4–15.8% divergence) and translated into 1 to 21 (1.3–27.6% divergence) amino acid differences. We detected a significant excess of nonsynonymous substitutions at the peptide binding region (P = 0.005), indicating that DRB exon 2 in raccoons has been influenced by positive selection. These data will form the basis of continued analyses into the spatial and temporal relationship of the raccoon rabies virus and the immunogenetic response in its primary host

Comparing Breast Cancer Multiparameter Tests in the OPTIMA Prelim Trial: No Test Is More Equal Than the Others

Background: Previous reports identifying discordance between multiparameter tests at the individual patient level have been largely attributed to methodological shortcomings of multiple in silico studies. Comparisons between tests, when performed using actual diagnostic assays, have been predicted to demonstrate high degrees of concordance. OPTIMA prelim compared predicted risk stratification and subtype classification of different multiparameter tests performed directly on the same population. Methods: Three hundred thirteen women with early breast cancer were randomized to standard (chemotherapy and endocrine therapy) or test-directed (chemotherapy if Oncotype DX recurrence score &gt;25) treatment. Risk stratification was also determined with Prosigna (PAM50), MammaPrint, MammaTyper, NexCourse Breast (IHC4-AQUA), and conventional IHC4 (IHC4). Subtype classification was provided by Blueprint, MammaTyper, and Prosigna. Results: Oncotype DX predicted a higher proportion of tumors as low risk (82.1%, 95% confidence interval [CI] = 77.8% to 86.4%) than were predicted low/intermediate risk using Prosigna (65.5%, 95% CI = 60.1% to 70.9%), IHC4 (72.0%, 95% CI = 66.5% to 77.5%), MammaPrint (61.4%, 95% CI = 55.9% to 66.9%), or NexCourse Breast (61.6%, 95% CI = 55.8% to 67.4%). Strikingly, the five tests showed only modest agreement when dichotomizing results between high vs low/intermediate risk. Only 119 (39.4%) tumors were classified uniformly as either low/intermediate risk or high risk, and 183 (60.6%) were assigned to different risk categories by different tests, although 94 (31.1%) showed agreement between four of five tests. All three subtype tests assigned 59.5% to 62.4% of tumors to luminal A subtype, but only 121 (40.1%) were classified as luminal A by all three tests and only 58 (19.2%) were uniformly assigned as nonluminal A. Discordant subtyping was observed in 123 (40.7%) tumors. Conclusions: Existing evidence on the comparative prognostic information provided by different tests suggests that current multiparameter tests provide broadly equivalent risk information for the population of women with estrogen receptor (ER)–positive breast cancers. However, for the individual patient, tests may provide differing risk categorization and subtype information