Paola Dama

Organ-on-chip (OoC) systems are increasingly used to generate human-relevant evidence in drug discovery and preclinical development, yet interpretation can be limited by the interaction of biological variability, device-to-device variability, and technical assay noise. Here, we describe the UstarFlowAI barrier-confidence framework, an in silico assay-readiness approach that quantifies whether a predefined permeability-related effect is resolvable under specified uncertainty. The proposed context of use is decision support for laminar, low-Reynolds-number airway epithelial monolayer barrier OoC assays in which device fabrication, pump performance, flow, wall shear stress, and biological fluctuation may affect permeability-related readouts. Monte Carlo simulations propagate defined manufacturing, biological, systemic, and well-level uncertainty and estimate the minimum detectable shift (MDS) under a prespecified decision rule. In the present proof-of-concept design, the reference workflow was parameterized at 9.4% technical coefficient of variation (CV), whereas the standardized workflow was parameterized at 5.0% CV, corresponding to an approximately 46.8% reduction in the modeled technical-noise component. At n = 48 replicate wells per group and a nominal total assay-noise operating point of 17% CV, a predefined 10% permeability shift exceeded the upper MDS uncertainty bound for the standardized workflow in both best- and worst-case scenarios, whereas it overlapped the 95% MDS interval for the reference workflow and therefore did not meet the prespecified PASS criterion. These findings demonstrate the behavior of the computational framework under the stated assumptions; they do not constitute experimental validation, regulatory qualification, or evidence that a 10% shift is universally meaningful across barrier OoC systems. We therefore propose a prospective wet-lab validation strategy in which model predictions are tested against airway barrier measurements generated under controlled sources of technical and biological variability. The framework is intended to complement, rather than replace, the underlying biological model by making assay uncertainty and decision thresholds explicit and auditable.

Background: The rapid expansion of new approach methodologies (NAMs), organoids, microphysiological systems, multi-omics and computational tools has increased the number of technically plausible preclinical options without necessarily simplifying model selection. A recurring risk is technology-first study design, in which available platforms are selected before the biological or that begins with the question to be answered and progressively structures the biological context, model requirements, experimental environment, readouts, analytical layers and evidence objective. Methods: We describe the public scientific architecture of Program Builder and perform a retrospective construct-level representability and traceability evaluation using a previously reported pancreatic ductal adenocarcinoma (PDAC) mature-tumoroid pharmacology study as an empirical benchmark. Seven prespecified program-design domains were evaluated for structural representability and traceability: decision, biological context, model, environment, readouts, analytics, and evidence objective. We additionally mapped experimentally consequential variables identified in the PDAC study to explicit Program Builder layers. Results: All seven prespecified domains were structurally representable within the Program Builder architecture (7/7 structural coverage). We stress that structural representability is a deliberately low bar: it asks only whether the architecture contains an explicit layer capable of preserving each type of information, not whether the framework selects, weights, or recommends correctly. The retrospective maturation, time-in-3D/ECM context, growth-rate-normalized pharmacology, and mechanistic transporter-Because these four variables were prespecified from the already-published PDAC findings, this analysis tests whether the framework can represent determinants known in hindsight to be consequential; it does not test prospective identification of such variables. The evaluation was performed by a single nonblinded rater who is also the framework's developer, and it does not establish prospective predictive performance, regulatory qualification, cross-user reproducibility, or superiority to expert study design. Conclusions: A decision-led, progressively constrained architecture can make the assumptions linking biological questions, model selection and evidence generation more explicit and reviewable. In a single retrospective oncology (PDAC) case, Program Builder represented the model, environmental, analytical and mechanistic determinants that later proved experimentally consequential, providing construct-level rather than predictive evidence. Because this rests on one self-selected case scored by a single nonindependent rater, the finding should be read as a proof of representability, not of general utility. Prospective external-user studies, blinded inter-rater comparisons and outcome-linked evaluations are required before any claim about reproducibility, recommendation quality or predictive utility can be made

Our study employs pooled CRISPR screens, integrating 2D and 3D culture models, to identify miRNAs critical in Breast Cancer (BC) tumoursphere formation. These screens combine with RNA-seq experiments allowing identification of miRNA signatures and targets essential for tumoursphere growth. miR-4787-3p exhibits significant up-regulation in BC, particularly in basal-like BCs, suggesting its association with aggressive disease. Surprisingly, despite its location within the 5’UTR of a protein coding gene, which defines DROSHA-independent transcription start site (TSS)-miRNAs, we find it dependant on both DROSHA and DICER1 for maturation. Inhibition of miR-4787-3p hinders tumoursphere formation, highlighting its potential as a therapeutic target in BC. Our study proposes elevated miR-4787-3p expression as a potential prognostic biomarker for adverse outcomes in BC. We find that protein-coding genes positively selected in the CRISPR screens are enriched of miR-4787-3p targets. Of these targets, we select ARHGAP17, FOXO3A, and PDCD4 as known tumour suppressors in cancer and experimentally validate the interaction of miR-4787-3p with their 3’UTRs. Our work illuminates the molecular mechanisms underpinning miR-4787-3p’s oncogenic role in BC. These findings advocate for clinical investigations targeting miR-4787-3p and underscore its prognostic significance, offering promising avenues for tailored therapeutic interventions and prognostic assessments in BC.

In recent years, the field of cancer treatment has witnessed remarkable breakthroughs that have revolutionized the landscape of care for cancer patients. While traditional pillars such as surgery, chemotherapy, and radiation therapy have long been available, a cutting-edge therapeutic approach called CAR T-cell therapy has emerged as a game-changer in treating multiple myeloma (MM). This novel treatment method complements options like autologous stem cell transplants and immunomodulatory medications, such as proteasome inhibitors, by utilizing protein complexes or anti-CD38 antibodies with potent complement-dependent cytotoxic effects. Despite the challenges and obstacles associated with these treatments, the recent approval of the second FDA multiple myeloma CAR T-cell therapy has sparked immense promise in the field. Thus far, the results indicate its potential as a highly effective therapeutic solution. Moreover, ongoing preclinical and clinical trials are exploring the capabilities of CAR T-cells in targeting specific antigens on myeloma cells, offering hope for patients with relapsed/refractory MM (RRMM). These advancements have shown the potential for CAR T cell-based medicines or combination therapies to elicit greater treatment responses and minimize side effects. In this context, it is crucial to delve into the history and functions of CAR T-cells while acknowledging their limitations. We can strategize and develop innovative approaches to overcome these barriers by understanding their challenges. This article aims to provide insights into the application of CAR T-cells in treating MM, shedding light on their potential, limitations, and strategies employed to enhance their efficacy.

(1) Background: Mounting evidence supports the idea that one of the most critical agents in controlling gene expression could be long non-coding RNAs (lncRNAs). Upregulation of lncRNA is observed in the different processes related to pathologies, such as tumor occurrence and development. Among the crescent number of lncRNAs discovered, FLVCR1-AS1 and FBXL19-AS1 have been identified as oncogenes in many cancer progression and prognosis types, including cholangiocarcinoma, gastric cancer, glioma and glioblastoma, hepatocellular carcinoma, lung cancer, ovarian cancer, breast cancer, colorectal cancer, and osteosarcoma. Therefore, abnormal FBXL19-AS1 and FLVCR1-AS1 expression affect a variety of cellular activities, including metastasis, aggressiveness, and proliferation; (2) Methods: This study was searched via PubMed and Google Scholar databases until May 2022; (3) Results: FLVCR1-AS1 and FBXL19-AS1 participate in tumorigenesis and have an active role in impacting several signaling pathways that regulate cell proliferation, migration, invasion, metastasis, and EMT; (4) Conclusions: Our review focuses on the possible molecular mechanisms in a variety of cancers regulated by FLVCR1-AS1 and FBXL19-AS1. It is not surprising that there has been significant interest in the possibility that these lncRNAs might be used as biomarkers for diagnosis or as a target to improve a broader range of cancers in the future.

Breast cancer affects millions of women each year. Despite recent advances in targeted treatments breast cancer remains a significant threat to women’s health. In recent years the development of high-throughput sequencing technologies has advanced the field of transcriptomics shedding light on the role of non-coding RNAs (ncRNAs), including long ncRNAs (lncRNAs), in human cellular function and disease. LncRNAs are classified as transcripts longer than 200nt with no coding potential. These transcripts constitute a diverse group of regulatory molecules essential to the modulation of crucial cellular processes, which dysregulation of leads to disease. LncRNAs exert their regulatory functions through their sequences and by forming complex secondary and tertiary structures that interact with other transcripts, chromatin and/or proteins. Numerous studies have provided evidence of the involvement of LncRNAs in tumor development and disease progression. They possess multiple characteristics that make them novel therapeutic and diagnostic targets. Indeed, the discovery of a novel mechanism by which lncRNAs associated with proteins can induce the formation of phase-separated droplets broadens our understanding of the spatiotemporal control of cellular processes and opens up developing a new treatment. Nevertheless, the role and the molecular mechanisms of many lncRNAs in the regulation of cellular processes and cancer still remain elusive. This is due to the absence of a thorough characterization of the regulatory role of their loci and the functional impact of their aberrations in cancer biology. Here, we present some of the latest advances concerning the role of LncRNAs in breast cancer.