An NTRK-1-associated soft tissue tumor showing distinctive nodules with peripheral accentuation of cellularity.

Lipofibromatosis-like neural tumor is a recently described entity defined by a low-cellularity spindle cell infiltrate in the subcutaneous fat with admixed inflammatory cells. This tumor is histopathologically similar to lipofibromatosis, but unlike lipofibromatosis shows reactivity for S100 and has an NTRK-1 kinase fusion. These lesions are locally aggressive but appear to have a negligible metastatic potential. Subsequently, a more cellular variant has been described with generally low mitotic rate. This variant also displays S100 reactivity and kinase fusions (typically involving NTRK-1), but it has a low risk of metastasis. In this report, we describe a case that aligns with the more cellular variant of NTRK-1 kinase fusion tumors on histopathologic, immunohistochemical, and molecular grounds, but in addition has distinctive nodules with peripheral accentuation of cellularity, reminiscent of those present in epithelioid malignant peripheral nerve sheath tumors. This latter feature is previously undescribed in NTRK kinase fusion soft tissue tumors and offers further support for the presumed neural differentiation of this neoplasm.

Assessing Heterogeneity in the N-Telopeptides of Type I Collagen by Mass Spectrometry.

Collagen cross-links created by the lysyl oxidase and lysyl hydroxylase families of enzymes are a significant contributing factor to the biomechanical strength and rigidity of tissues, which in turn influence cell signaling and ultimately cell phenotype. In the clinic, the proteolytically liberated N-terminal cross-linked peptide of collagen I (NTX) is used as a biomarker of bone and connective tissue turnover, which is altered in several disease processes. Despite the clinical utility of these collagen breakdown products, the majority of the cross-linked peptide species have not been identified in proteomic datasets. Here we evaluate several parameters for the preparation and identification of these peptides from the collagen I-rich Achilles tendon. Our refined approach involving chemical digestion for protein solubilization coupled with mass spectrometry allows for the identification of the NTX cross-links in a range of modification states. Based on the specificity of the enzymatic cross-linking reaction we utilized follow-up variable modification searches to facilitate identification with a wider range of analytical workflows. We then applied a spectral library approach to identify differences in collagen cross-links in bovine pulmonary hypertension. The presented method offers unique opportunities to understand extracellular matrix remodeling events in development, aging, wound healing, and fibrotic disease that modulate collagen architecture through lysyl-hydroxylase and lysyl-oxidase enzymes.

Predicting suicidal and self-injurious events in a correctional setting using AI algorithms on unstructured medical notes and structured data.

Suicidal and self-injurious incidents in correctional settings deplete the institutional and healthcare resources, create disorder and stress for staff and other inmates. Traditional statistical analyses provide some guidance, but they can only be applied to structured data that are often difficult to collect and their recommendations are often expensive to act upon. This study aims to extract information from medical and mental health progress notes using AI algorithms to make actionable predictions of suicidal and self-injurious events to improve the efficiency of triage for health care services and prevent suicidal and injurious events from happening at California's Orange County Jails. The results showed that the notes data contain more information with respect to suicidal or injurious behaviors than the structured data available in the EHR database at the Orange County Jails. Using the notes data alone (under-sampled to 50%) in a Transformer Encoder model produced an AUC-ROC of 0.862, a Sensitivity of 0.816, and a Specificity of 0.738. Incorporating the information extracted from the notes data into traditional Machine Learning models as a feature alongside structured data (under-sampled to 50%) yielded better performance in terms of Sensitivity (AUC-ROC: 0.77, Sensitivity: 0.89, Specificity: 0.65). In addition, under-sampling is an effective approach to mitigating the impact of the extremely imbalanced classes.

T2*-weighted MRI produces viable fetal "Black-Bone" contrast with significant benefits when compared to current sequences.

OBJECTIVES: Fetal "black bone" MRI could be useful in the diagnosis of various skeletal conditions during pregnancy without exposure to ionizing radiation. Previously suggested susceptibility-weighted imaging (SWI) is not available in the suggested form on all scanners leading to long imaging times that are susceptible to motion artefacts. We aimed to assess if an optimized T2*-weighted GRE sequence can provide viable "black bone" contrast and compared it to other sequences in the literature. METHODS: A retrospective study was conducted on 17 patients who underwent fetal MRI. Patients were imaged with an optimized T2*-weighted GRE sequence, as well as at least one other "black-bone" sequence. Image quality was scored by four blinded observers on a five-point scale. RESULTS: The T2*-weighted GRE sequence offered adequate to excellent image quality in 63% of cases and scored consistently higher than the three other comparison sequences when comparing images from the same patient. Image quality was found to be dependent on gestational age with good image quality achieved on almost all patients after 26 weeks. CONCLUSIONS: T2*-weighted GRE imaging can provide adequate fetal "black bone" contrast and performs at least as well as other sequences in the literature due to good bone to soft tissue contrast and minimal motion artefacts. ADVANCES IN KNOWLEDGE: T2*-weighted fetal "black-bone" imaging can provide excellent bone to soft tissue contrast without using ionizing radiation. It is as good as other "black bone" sequences and may be simpler and more widely implemented, with less motion artefacts.

Genotype tunes pancreatic ductal adenocarcinoma tissue tension to induce matricellular fibrosis and tumor progression.

Fibrosis compromises pancreatic ductal carcinoma (PDAC) treatment and contributes to patient mortality, yet antistromal therapies are controversial. We found that human PDACs with impaired epithelial transforming growth factor-ß (TGF-ß) signaling have high epithelial STAT3 activity and develop stiff, matricellular-enriched fibrosis associated with high epithelial tension and shorter patient survival. In several KRAS-driven mouse models, both the loss of TGF-ß signaling and elevated ß1-integrin mechanosignaling engaged a positive feedback loop whereby STAT3 signaling promotes tumor progression by increasing matricellular fibrosis and tissue tension. In contrast, epithelial STAT3 ablation attenuated tumor progression by reducing the stromal stiffening and epithelial contractility induced by loss of TGF-ß signaling. In PDAC patient biopsies, higher matricellular protein and activated STAT3 were associated with SMAD4 mutation and shorter survival. The findings implicate epithelial tension and matricellular fibrosis in the aggressiveness of SMAD4 mutant pancreatic tumors and highlight STAT3 and mechanics as key drivers of this phenotype.