Citations

Objectives Periodic quality control (QC) testing of ultrasound (US) imaging systems is essential to ensure and maintain image quality and safety. The study aims to analyze QC findings from medical physics annual surveys of modern clinical US systems in a multi‐institutional survey. Methods QC results from annual surveys between 2018 and 2021 were retrospectively collected from 12 medical physicists from 11 institutions or consulting companies (hereafter referred to as sites). QC tests were classified as scanner‐ and transducer‐related tests, where scanner‐related tests also included evaluation of diagnostic workstation display monitors and routine QC programs if applicable. Test methodology and pass/fail criteria were established by each site. QC findings were defined as results requiring follow‐up action by service engineers, vendors, or other personnel. The percentage of annual findings was calculated for each site and averaged across the years of data contributed. Furthermore, findings across all sites were aggregated for an overall analysis. Results QC data from a total of 1069 scanner tests and 4542 transducer tests were collected. The average annual percentage of scanner‐related findings varied from 0 to 40.8% among all sites (first quartile 8.7%, median 18.2%, and third quartile 34.1%), while that of transducer‐related findings ranged between 0.5% and 19.9% (first quartile 3.9%, median 7.2%, and third quartile 14.5%). For scanner‐related tests, the top 3 categories of findings were associated with physical and mechanical integrity (171 findings, 74.7%), quantitative testing of the scanner display monitor (40 findings, 17.5%), and scanner port creating artifacts (10 findings, 4.4%). The top 3 findings for transducers related to uniformity and artifact (227 findings, 79.9%), physical and mechanical integrity (44 findings, 15.5%), and sensitivity in fundamental mode (9 findings, 3.2%). Conclusions Medical physics annual surveys revealed considerable actionable findings of modern US systems, which inform the status of current US QC practices in the United States and may guide future standardization and recommendation of QC tests.

Heterotopic ossification (HO), defined as the formation of extraskeletal bone in muscle and soft tissues, is a diverse pathological process caused by either genetic mutations or inciting trauma. Fibrodysplasia ossificans progressiva (FOP) is a genetic form of HO caused by mutations in the bone morphogenetic protein (BMP) type I receptor gene activin A receptor type 1 ( ACVR1 ). These mutations make ACVR1 hypersensitive to BMP and responsive to activin A. Hedgehog (Hh) signaling also contributes to HO development. However, the exact pathophysiology of how skeletogenic cells contribute to endochondral ossification in FOP remains unknown. Here, we showed that the wild-type or FOP-mutant ACVR1 localized in the cilia of stem cells from human exfoliated deciduous teeth with key FOP signaling components, including activin A receptor type 2A/2B, SMAD family member 1/5, and FK506-binding protein 12kD. Cilia suppression by deletion of intraflagellar transport 88 or ADP ribosylation factor like GTPase 3 effectively inhibited pathological BMP and Hh signaling, subdued aberrant chondro-osteogenic differentiation in primary mouse or human FOP cells, and diminished in vivo extraskeletal ossification in Acvr1 Q207D , Sox2-Cre ; Acvr1 R206H/+ FOP mice and in burn tenotomy–treated wild-type mice. Our results provide a rationale for early and localized suppression of cilia in affected tissues after injury as a therapeutic strategy against either genetic or acquired HO.

Background: Amyloid-β (Aβ) plaques play a pivotal role in Alzheimer’s disease. The current positron emission tomography (PET) is expensive and limited in availability. In contrast, blood-based biomarkers (BBBMs) show potential for characterizing Aβ plaques more affordably. We have previously proposed an MRI-based hippocampal morphometry measure to be an indicator of Aβ plaques. Objective: To develop and validate an integrated model to predict brain amyloid PET positivity combining MRI feature and plasma Aβ42/40 ratio. Methods: We extracted hippocampal multivariate morphometry statistics from MR images and together with plasma Aβ42/40 trained a random forest classifier to perform a binary classification of participant brain amyloid PET positivity. We evaluated the model performance using two distinct cohorts, one from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the other from the Banner Alzheimer’s Institute (BAI), including prediction accuracy, precision, recall rate, F1 score, and AUC score. Results: Results from ADNI (mean age 72.6, Aβ+ rate 49.5%) and BAI (mean age 66.2, Aβ+ rate 36.9%) datasets revealed the integrated multimodal (IMM) model’s superior performance over unimodal models. The IMM model achieved prediction accuracies of 0.86 in ADNI and 0.92 in BAI, surpassing unimodal models based solely on structural MRI (0.81 and 0.87) or plasma Aβ42/40 (0.73 and 0.81) predictors. CONCLUSIONS: Our IMM model, combining MRI and BBBM data, offers a highly accurate approach to predict brain amyloid PET positivity. This innovative multiplex biomarker strategy presents an accessible and cost-effective avenue for advancing Alzheimer’s disease diagnostics, leveraging diverse pathologic features related to Aβ plaques and structural MRI.

Abstract Introduction: Autophagy pathway activation and upregulation is an important mechanism for cancer cell survival upon exposure to chemotherapeutic agents. LC3, a ubiquitin-like protein, plays a key role in the regulation of cargo selection and autophagosome formation. Given small molecule inhibitors have not been well developed to target LC3, we aim to design and synthesize an LC3 binder and evaluate its activity in the inhibition of autophagy pathway in cancer cells. Methods: The crystal structure of LC3 and p62 complex (PDB: 2ZJD) was used to assist our design of helical sulfonyl-γ-AApeptides to the LC3 interacting region of p62. The peptidomimetics were synthesized on the solid phase and purified by HPLC. We used fluorescent polarization assay to assess the binding affinity of the peptidomimetics to LC3. We then utilized an LC3 HiBiT reporter cell line (U2OS) from Promega, which was engineered to have a fused human LC3-HiBiT with a HaloTag in between to study the effect of the lead LC3 binder on autophagic flux. In addition, confocal microscopy enabled direction visualization of the colocalization of FITC-labeled LC3 binder with LC3 and lysosome, respectively. Cytotoxicity of LC3 binder was evaluated in colorectal cancer cell line HCT116. Results: We designed and synthesized 6 helical sulfonyl-γ-AApeptides (JC1-6) to target the binding interface of LC3 and p62. These peptidomimetic compounds were designed to retain essential LC3 and p62 interactions at the W and L regions and yet have different functional groups on the helical scaffold. Compounds JC1 and JC6 have binding affinity of Kd 280 nM and 150 nM with LC3, respectively. FITC-labelled JC6 was able to penetrate U2OS and HCT116 cells. It had intracytoplasmic colocalization with not only lysosomes but also LC3 aggregates. Direct visualization of intracytoplasmic LC3 aggregates showed that JC6 at 50 µM significantly inhibited the autophagic degradation of LC3 aggregates. This effect was augmented by the presence of 2 µM autophagy activator PP242. Lastly, JC6 demonstrated cytotoxicity with IC50 approximately 50 µM in HCT116 cells bearing baseline autophagy activation compared to lack of cytotoxicity in U2OS cells with no baseline autophagy activity. Conclusion: We designed and synthesized novel helical sulfonyl-γ-AApeptide LC3 binders. With the ability of cellular penetration, they demonstrated inhibition of autophagic flux and preliminary cytotoxicity in cancer cells. Future studies may focus on structural activity relationship and further evaluation of its mechanisms of action in cancer cells. Citation Format: Mentalla Mahmoud, Jianyu Chen, Jianfeng Cai, Hao Xie. Discovery of helical sulfonyl-γ-AApeptides targeting LC3 in cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3101.

Accurate prediction of progression in subjects at risk of Alzheimer's disease is crucial for enrolling the right subjects in clinical trials. However, a prospective comparison of state-of-the-art algorithms for predicting disease onset and progression is currently lacking. We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimer's disease. Challenge participants were required to make a prediction, for each month of a 5-year future time period, of three key outcomes: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. The methods used by challenge participants included multivariate linear regression, machine learning methods such as support vector machines and deep neural networks, as well as disease progression models. No single submission was best at predicting all three outcomes. For clinical diagnosis and ventricle volume prediction, the best algorithms strongly outperform simple baselines in predictive ability. However, for ADAS-Cog13 no single submitted prediction method was significantly better than random guesswork. Two ensemble methods based on taking the mean and median over all predictions, obtained top scores on almost all tasks. Better than average performance at diagnosis prediction was generally associated with the additional inclusion of features from cerebrospinal fluid (CSF) samples and diffusion tensor imaging (DTI). On the other hand, better performance at ventricle volume prediction was associated with inclusion of summary statistics, such as the slope or maxima/minima of patient-specific biomarkers. On a limited, cross-sectional subset of the data emulating clinical trials, performance of the best algorithms at predicting clinical diagnosis decreased only slightly (2 percentage points) compared to the full longitudinal dataset. The submission system remains open via the website https://tadpole.grand-challenge.org, while TADPOLE SHARE (https://tadpole-share.github.io/) collates code for submissions. TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease. However, results call into question the usage of cognitive test scores for patient selection and as a primary endpoint in clinical trials.

Background: Alzheimer’s disease (AD) is the most common type of age-related dementia, affecting 6.2 million people aged 65 or older according to CDC data. It is commonly agreed that discovering an effective AD diagnosis biomarker could have enormous public health benefits, potentially preventing or delaying up to 40% of dementia cases. Tau neurofibrillary tangles are the primary driver of downstream neurodegeneration and subsequent cognitive impairment in AD, resulting in structural deformations such as hippocampal atrophy that can be observed in magnetic resonance imaging (MRI) scans. Objective: To build a surface-based model to 1) detect differences between APOE subgroups in patterns of tau deposition and hippocampal atrophy, and 2) use the extracted surface-based features to predict cognitive decline. Methods: Using data obtained from different institutions, we develop a surface-based federated Chow test model to study the synergistic effects of APOE, a previously reported significant risk factor of AD, and tau on hippocampal surface morphometry. Results: We illustrate that the APOE-specific morphometry features correlate with AD progression and better predict future AD conversion than other MRI biomarkers. For example, a strong association between atrophy and abnormal tau was identified in hippocampal subregion cornu ammonis 1 (CA1 subfield) and subiculum in e4 homozygote cohort. Conclusion: Our model allows for identifying MRI biomarkers for AD and cognitive decline prediction and may uncover a corner of the neural mechanism of the influence of APOE and tau deposition on hippocampal morphology.

AbstractIt has been challenging to target mutant KRAS (mKRAS) in colorectal cancer (CRC) and other malignancies. Recent efforts have focused on developing inhibitors blocking molecules essential for KRAS activity. In this regard, SOS1 inhibition has arisen as an attractive approach for mKRAS CRC given its essential role as a guanine nucleotide exchange factor for this GTPase. Here, we demonstrated the translational value of SOS1 blockade in mKRAS CRC. We used CRC patient‐derived organoids (PDOs) as preclinical models to evaluate their sensitivity to SOS1 inhibitor BI3406. A combination of in silico analyses and wet lab techniques was utilized to define potential predictive markers for SOS1 sensitivity and potential mechanisms of resistance in CRC. RNA‐seq analysis of CRC PDOs revealed two groups of CRC PDOs with differential sensitivities to SOS1 inhibitor BI3406. The resistant group was enriched in gene sets involving cholesterol homeostasis, epithelial−mesenchymal transition, and TNF‐α/NFκB signaling. Expression analysis identified a significant correlation between SOS1 and SOS2 mRNA levels (Spearman's ρ 0.56, p < 0.001). SOS1/2 protein expression was universally present with heterogeneous patterns in CRC cells but only minimal to none in surrounding nonmalignant cells. Only SOS1 protein expression was associated with worse survival in patients with RAS/RAF mutant CRC (p = 0.04). We also found that SOS1/SOS2 protein expression ratio >1 by immunohistochemistry (p = 0.03) instead of KRAS mutation (p = 1) was a better predictive marker to BI3406 sensitivity of CRC PDOs, concordant with the significant positive correlation between SOS1/SOS2 protein expression ratio and SOS1 dependency. Finally, we showed that GTP‐bound RAS level underwent rebound even in BI3406‐sensitive PDOs with no change of KRAS downstream effector genes, thus suggesting upregulation of guanine nucleotide exchange factor as potential cellular adaptation mechanisms to SOS1 inhibition. Taken together, our results show that high SOS1/SOS2 protein expression ratio predicts sensitivity to SOS1 inhibition and support further clinical development of SOS1‐targeting agents in CRC.