Citations

Aging is a major risk factor for osteoarthritis (OA), but the specific mechanisms connecting aging and OA remain unclear. Although chondrocytes rarely divide in adult articular cartilage, they undergo replicative senescence in vitro, offering a model to study aging-related changes under controlled conditions. OA cartilage was obtained from an 80-year-old male and a 72-year-old female, while normal cartilage was sourced from a 26-year-old male. Chondrocyte cultures were established and sub-cultured to their Hayflick limit. Bulk RNA sequencing on early- and late-passage human articular chondrocytes identified transcriptomic changes associated with cellular aging. Early-passage OA chondrocytes already showed senescent phenotypes, unlike normal chondrocytes. All three cultures underwent 30 population doublings before replicative exhaustion, at which point all cells displayed senescence. During this process, cells lost their ability to form cartilaginous pellets. Differential gene expression analysis revealed distinct transcriptomic profiles between early- and late-passage chondrocytes and between normal and OA-derived cells. Genes related to matrix synthesis, degradation, inflammation, and the senescence-associated secretory phenotype (SASP) showed significant expression changes. Despite being a small pilot study, these findings suggest that further research into the molecular and metabolic changes during chondrocyte senescence could provide valuable insights into OA pathobiology.

AbstractBackground and ObjectivesNonmedical use of prescription tranquilizers/sedatives (NMUPTS) is associated with a 15‐times increased likelihood of polysubstance use (PSU). This is correlated with an increased risk for use disorders and death, therefore, to inform prevention efforts, we identified PSU profiles among adults reporting sleep‐related NMUPTS.MethodsNational Survey on Drug Use and Health 2015–2019 participants with past 12‐month sleep‐related NMUPTS and use of an additional drug(s) (n = 2603) were studied. Latent class analyses identified unique latent profiles, and multivariable logistic regressions identified covariates associated with each profile.ResultsAlcohol (90.4%), tobacco (61.5%), and marijuana (55.5%) were most commonly reported, with an average of 3.6 additional substances. Two latent PSU profiles were identified: marijuana/alcohol/tobacco (MAT) (68.3%) and MAT + cocaine/hallucinogens/prescription drugs (MAT + CHPR) (31.7%). In comparison to those likely in the MAT profile, those likely in the MAT + CHPR profile had lower odds of being older than 18–25 years (26–34: adjusted odds ratio [AOR]: 0.5, 95% confidence interval [CI]: 0.3, 0.6; 35–49: AOR: 0.2, 95% CI: 0.1, 0.2; 50+: AOR: 0.1, 95% CI: 0.0, 0.1), female (AOR: 0.4, 95% CI: 0.3, 0.6), non‐Hispanic Black (AOR: 0.5, 95% CI: 0.4, 0.8) or Hispanic in comparison to non‐Hispanic White (AOR: 0.5, 95% CI: 0.4, 0.8), and to not have past 12‐month insurance coverage (AOR: 1.7, 95% CI: 1.3, 2.3).Conclusions and Scientific SignificanceTwo PSU profiles were identified among people with NMUPTS, with noted variations in sociodemographic characteristics. In comparison to previous studies, sleep‐related NMUPTS was associated with higher rates of stimulant use, suggesting stimulant use and sleep‐related NMUPTS may be related.

We demonstrate the fruitfulness of using card sort activities as an engagement method by detailing community consultation for ethical, legal, and social implications of sociogenomics. Readers are provided with a user-guide for card sort engagement through: (1) an overview of the card sort activity and its merits for engagement, (2) detailed methods of sorting for values-elicitation and prioritization goals, and (3) strategies to design this approach for other participatory research designs. Our intent is to add to meaningful exchanges between community engaged researchers and empirical bioethicists.

Introduction While incidentally discovered covert cerebrovascular diseases (id-CCD) are associated with future stroke, it is not known if patients with id-CCD are prescribed statins. Methods Patients age ≥50 with id-CCD on neuroimaging from 2009 to 2019 with no prior ischaemic stroke, transient ischaemic attack or dementia were identified using natural language processing in a large real-world cohort. Robust Poisson multivariable regression was used to assess statin prescription among patients without prior statins. Results Among 2 41 050 patients, 74 975 patients (31.1%; 4.7% with covert brain infarcts (CBI); 29.0% with white matter disease (WMD)) had id-CCD. 53.5% (95% CI 53.2 to 53.9%) were not on statins within 6 months prior to the scan. Of those, 12.0% (95% CI 11.7 to 12.3%) were prescribed statins in the next 6 months compared with 9.3% (95% CI 9.1 to 9.4%) in those without CCD, a 2.7% (95% CI 2.4 to 3.1%) absolute increase in statin prescription for those with id-CCD. In adjusted analyses, the presence of id-CCD was only associated with minor increases in statin prescription (CBI or WMD (risk ratio (RR) 1.09, 95% CI 1.05 to 1.13), CBI alone (RR 1.34, 95% CI 1.21 to 1.47), WMD alone (RR 1.05, 95% CI 1.01 to 1.09), and CBI and WMD (RR 1.23, 95% CI 1.12 to 1.35)). Discussion Identification of id-CCD is not associated with substantial changes in statin prescription in routine clinical practice.

Abstract Clinical management of IDH wildtype glioblastoma (GBM) and tumefactive multiple sclerosis (tMS) is drastically different. GBM requires maximal safe resection followed by chemoradiation, while tMS outcome is worsened by surgery and radiotherapy. Noninvasive methods are needed to help with accurate diagnosis of tumor and non-tumor etiologies. To develop an MRI-based classification model, tMS subjects diagnosed prior to January 1, 2020, were matched to tMS by age at diagnosis, sex, index MRI date, and 2D/3D acquisition. Inclusion criteria included one cm minimal lesion size and pre-operative post-contrast T1 and T2 images available for analysis. A 3D-DenseNet121 was used to develop a classification model using prespecified parameters: 650 epochs, batch size 16, learning rate 10-3, cross-entropy loss, and AdamW optimizer. The stopping rule was defined as three sequential differences in epoch cross-entropy loss <0.02. Models were developed using both T1gd and T2, as well as from only T1gd and only T2. Training included 220 subjects (110 GBM, 110 tMS). A 2-stage validation design was used, which included both retrospective and prospective cohorts. Stage 1 consisted of 272 retrospective GBM (diagnosed prior to January 1, 2020). Stage 2 consisted of 69 and 34 prospective (diagnosed after January 1, 2020) GBM and tMS, respectively. External validation on the 272 retrospective GBM demonstrated accuracy of 91%, 84%, and 78% for T1gd+T2, T1gd only, and T2 only, respectively. External validation on the 69 prospective GBM demonstrated an accuracy of 87%, 64%, and 67% for T1gd+T2, T1gd only, and T2 only, respectively. The 34 prospective tMS demonstrated accuracy of 76%, 76%, and 82% for T1gd+T2, T1gd only, and T2 only, respectively. This shows the feasibility of deep learning to aid in differential diagnosis of brain lesions. Future work entails the integration of germline variants into the classification model, including variants associated with the risk of glioma or MS.

To retrospectively evaluate the trends in utilization and results of computed tomography pulmonary angiography (CTPA study) for detection of acute pulmonary embolism (PE) in the hospital inpatients during different phases of COVID-19 public health emergency. We conducted an Institutional Review Board (IRB)-approved retrospective review of CTPA studies for our hospital inpatients in the years from 2019 to 2023, ranging from the prepandemic year (2019) to the year coinciding with the end of public health declarations (2023). Collected characteristics included patient age, patient sex, and result of the study. The utilization of CTPA studies for inpatients was higher in 2020 to 2023, compared to 2019 (3.8% in 2023 compared to 2.4% in 2019, P < .001). The increase in utilization was also statistically significant in each group, when stratified by age and sex (OR = 1.46 for female, OR = 1.71 for male, OR = 1.93 for elderly, and OR = 1.29 for non-elderly inpatients in 2023, compared to 2019). The positivity rate of acute PE in the inpatients was overall higher in 2021, 2022, and 2023, compared to 2019 (for example, 18.5% in 2023 compared to 14.3% in 2019, P = .01). When stratified by age and sex, only the non-elderly patients continued to have a significantly higher rate of acute PE in 2023, compared to 2019 (OR = 1.39). While studies spanning longer time frames and involving multiple institutions are needed to understand and generalize this conclusion, we conclude that the utilization and positivity rates of CTPA studies in inpatients remains high at the end of the COVID-19 pandemic public health emergency.

Abstract Glioblastoma, the most common malignant primary adult brain tumor, poses significant diagnostic and treatment challenges due to its heterogeneous molecular and micro-environmental profiles. To this end, we organize the BraTS-Path challenge to provide a public benchmarking environment and a comprehensive dataset to develop and validate AI models for identifying distinct histopathologic glioblastoma sub-regions in H&E-stained digitized tissue sections. We identified 188 multi-institutional diagnostic slides of glioblastoma (IDH-wt, Gr.4) cases, from the TCGA-GBM and TCGA-LGG data collections, following their reclassification according to the 2021 WHO classification criteria. Sub-regions were selected according to distinctive morphology of histopathologic features and included aggressive tumor biology and areas consistent with potential treatment effect. Selected sub-region annotations included cellular tumor, geographic necrosis, cortical infiltration, pseudopalisading necrosis, microvascular proliferation, white matter penetration, regions dense with macrophages, leptomeningeal infiltration, and presence of lymphocytes. We obtained 107,340 patches of size 512x512 from the 9 sub-regions. A global network of board-certified expert neuropathologists defined and followed a systematic annotation protocol based on clinical definitions and only delineated sub-regions with high confidence, thus ensuring high-quality standardized data. Each tissue section was assigned to an annotator-approver pair, with the annotator delineating sub-regions and the approver ensuring the consistency of the annotations. By crowdsourcing annotations, the BraTS-Path challenge harnesses the collective expertise of clinical neuropathologists and fosters a collaborative environment to advance the neuro-oncology field. The anticipated developed algorithms are expected to integrate state-of-the-art computational methods, achieving high accuracy in identifying diverse histopathologic features and advancing clinical decision-making processes. The BraTS-Path challenge aims to bridge the gap between research and clinical practice by promoting the development of AI-driven tools for precise tumor characterization. This collaborative effort can significantly enhance our understanding of glioblastoma, improve diagnostic accuracy, and inform treatment strategies, thereby contributing to better patient outcomes.

Abstract The extracellular matrix (ECM) plays a critical role in cancer progression, with its biophysical properties influencing cancer behavior. ECM stiffness can induce long-lasting adaptations in cancer cells, promoting sustained invasion through a process known as “mechanical memory” (MM). Glioblastoma (GBM) exhibits spatial heterogeneity in stiffness, with stiffer ECM found at the tumor periphery where invasive cells reside and promote recurrence. We hypothesize that increased stiffness at the tumor periphery primes GBM cells for sustained invasion through MM. METHODS Cell lines were established from image-guided human GBM samples from tumor margin and core (n=10 patients, 20 cell lines). Cells from random locations were conditioned in physiologically relevant extracellular matrices for two weeks. Human organotypic assay (n=3), Migration Assays, Atomic Force Microscopy (AFM), Immunocytochemistry, Western Blot, and RNA-sequencing (n=6) were performed. RESULTS GBM cells at higher stiff surfaces displayed increased migration (p&lt;0.01). Magnetic Resonance Elastography (n=9) confirmed GBM heterogeneity, with softer cores (0.2-0.5kPa) transitioning to stiffer margins (&gt;2kPa). Organotypic scaffolds mimicking the tumor margin promoted enhanced migration of GFP-labeled GBM cells. Margin cells and cells conditioned on stiffer matrices demonstrated higher migratory potential compared to their core and soft-conditioned counterparts (p&lt;0.05). These trends persist over time and across multiple passages. ECM stiffness exposure causes nanomechanical, cytoskeletal, and transcriptional adaptations. AFM shows that cells from the margin (n=6) are softer (p&lt;0.01) and more deformable (p&lt;0.01). RNAseq identified upregulation of MM-specific genes in GBM margin cells. The mechanosensitive transcription factor YAP1 emerged as a key driver of MM. Notably, YAP1 is targetable by the FDA-approved drug Verteporfin. YAP1 expression was elevated in margin and stiff-conditioned cells (p&lt;0.05). Stiff-conditioned cells and margin cells displayed increased sensitivity to Verteporfin treatment (p&lt;0.05), with treatment significantly reducing margin migration compared to the core (p&lt;0.001). CONCLUSION Stiffer GBM margins promote the formation of aggressive phenotypes through MM. Targeting YAP1 with Verteporfin, a readily available drug, presents a promising strategy to halt GBM invasion and improve patient outcomes.