Citations

Abstract Despite the widespread use of carfilzomib (K) in relapsed/refractory multiple myeloma (RRMM), there is no consensus on optimal K dose in milligrams per square meter (mg/m2) or dosing schedule. We assessed three modern K prescribing patterns in RRMM using a large United States electronic health record-derived database. Our final cohort (n = 486) included 136 patients (28.0%) who received K 56 mg/m2 once weekly (K56-1x), 86 (17.7%) who received 56 mg/m2 twice weekly (K56-2x), and 264 (54.3%) who received 70 mg/m2 once weekly (K70-1x). Between 2016 and 2023, once-weekly dosing became more common: K70-1x proportions changed from 21.1% in 2016 to 50.6% in 2023, K56-1x from 15.8% to 37.0%, and K56-2x from 63.2% to 12.3%. Median real-world progression-free survival was 13.0 months [95% confidence interval (CI) 11.2-20.7] for K56-1x, 13.2 months (95% CI 9.0-28.1 months) for K56-2x, and 10.9 months (95% CI 9.9-15.3 months) for K70-1x; these differences were not statistically significant (log-rank p = 0.46). The prevalence of heart failure was comparable (<5% in all cohorts). In summary, our findings do not support improved outcomes with twice-weekly carfilzomib in RRMM. K56-1x may provide the best balance of efficacy, safety, and avoidance of time toxicity from frequent infusions.

AbstractLiver fibrosis is a pathological process resulting from prolonged exposure to various injury factors. It is characterized by the abnormal proliferation and activation of hepatic stellate cells and excessive deposition of extracellular matrix. If left untreated, it can progress to cirrhosis, liver failure, and even liver cancer. There is currently no efficient and accurate clinical diagnostic method for early liver fibrosis. Therefore, there is an urgent need to address the challenge of accurate staging and early diagnosis of liver fibrosis in clinical practice. Recently, nanomaterials have demonstrated significant potential for enhancing the diagnosis of liver fibrosis. Nanomaterials possess the ability to precisely identify and target the microenvironment associated with liver fibrosis. By enhancing their enrichment in the target area, nanomaterials can improve imaging contrast of fibrosis lesions in the liver, thereby enabling accurate diagnosis of liver fibrosis. Accordingly, this review delves into the latest research and advancements concerning nanomaterials in liver fibrosis diagnosis.

Abstract AIMS Methods for disease monitoring in glioma are currently limited. Liquid biopsies are of increasing interest within the field but have been challenging due to the blood-brain barrier. To bypass this challenge, we aimed to assess the technical and clinical feasibility of obtaining intracranial cerebrospinal fluid (CSF) for disease monitoring via cell-free DNA (cfDNA). METHOD Adults with gliomas were recruited for longitudinal intracranial CSF collection via CSF access devices, including Ommaya reservoirs in four patients and an existing VP shunt in one patient. cfDNA was then extracted and analyzed using two sequencing platforms, PredicineCARE for cancer variant profiling and PredicineSCORE for low-pass whole genome sequencing (LP-WGS). This analysis generated genomic aberration profiles and genome wide copy number burden (CNB) score. RESULTS Across five patients, thirty-five CSF samples were obtained (median volume: 3.80 mL; 0.5-5 mL), with 30 samples (85.7%) yielding sufficient cfDNA. Tumor fraction increased by 6.28x and 2.87x in two patients with radiographic disease progression and decreased by 0.08x and 0.04x in another two after chemoradiation. In another patient with pseudoprogression, tumor fraction decreased from 0.26 after resection to unmeasurable levels. Patient specific tumor-associated variant allelic frequencies (VAFs), including TP53, PTEN, and TERT, decreased within individual patients after resection and chemoradiation. In two patients with IDH-mutant gliomas, IDH1 VAF after resection correlated with decreased CSF D-2-hydroxyglutarate (D-2-HG) levels (0.64x and 0.62x, respectively, for the first patient, and 0.01x and 0.07x for the other patient). Both IDH1 VAF and D-2-HG increased in a patient with radiographic progression (2.56x and 9.21x, respectively). CNB decreased below the limit of quantification during treatment and increased above the limit at progression. CONCLUSION Longitudinal CSF cfDNA can feasibly be obtained via CSF access devices in patients with gliomas during their disease course. Further sample collection and analysis are ongoing to determine the thresholds of change indicative of treatment response and progression.

Abstract Background Fibrinogen plays a critical role in hemostasis and is essential to clot formation. Changes in the quantity or activity of fibrinogen can lead to disorders affecting coagulation. Thus, it is essential to precisely quantify fibrinogen levels to differentiate between hyperfibrinogenemia in hypercoagulable disorders and dysfibrinogenemia, hypo/afibrinogenemia, and irregular bleeding. Current diagnostic tests that use antibodies to detect fibrinogen or activity-based measures are susceptible to interference from heparin contamination, hemolysis, and hyperbilirubinemia. Here, we evaluated the use of targeted proteomics for developing a quantitative assay for fibrinogen in plasma samples. Methods Four peptides each for alpha and gamma chains of fibrinogen were synthesized using FMOC chemistry on a Liberty Blue (CEM Corp) peptide synthesizer. A targeted multiple reaction monitoring (MRM) assay was developed and optimized using Dionex Ultimate 3000 HPLC connected to a TSQ Altis triple quadrupole mass spectrometer (Thermo Scientific). Evaluations were conducted using a range of standards, such as pure protein, WHO plasma and commercially available plasma with known fibrinogen amounts for the accurate quantitation of fibrinogen. Calibration curves were generated using different dilutions in various matrices. The repeatability and precision of the assay were evaluated across five different days. The plasma samples were processed and digested in a 96-well plate and subjected to C18 clean-up using a Bravo automated liquid handler (Agilent). All samples were spiked-in with corresponding heavy-labeled peptides prior to digestion with trypsin. Multiple reaction monitoring assays were performed on tryptic digests of plasma samples. Results Calibration curves using commercial plasma for all peptides showed linearity from 20 mg/dl to 1,308 mg/dl. This covers the entire clinical range including normal (200-350 mg/dl), deficient states (&lt;100 mg/dl) and excess production (&gt;350 mg/dl) that can be used for evaluation of bleeding disorders including consumption coagulopathy and hyperfibrinogenemia. Based on the overall performance in terms of linearity, limit of detection/quantitation, carryover and coefficient of variation (CV) across five different days, two peptides per chain were finally selected for developing the quantitative assay. The intra-assay variability was &lt;5% and intra-day CV was ∼20%. This assay is currently being tested on a variety of clinical samples representing a range of fibrinogen levels. Conclusions Our study focused on assessing whether multiplexed MRM assay could be implemented to measure fibrinogen levels in plasma. Our data indicate the feasibility of MRM-based assays for determining fibrinogen levels as an alternative to existing non-mass spectrometry-based activity or antigen assays.

Abstract Background Laboratory identification of lupus anticoagulant (LAC) is critical for diagnosing antiphospholipid antibody syndrome, a common cause of unprovoked thromboembolic events and pregnancy morbidity. Commonly used clot-based assays for LAC include the dilute Russell’s viper venom time (DRVVT) and activated partial thromboplastin time (APTT), but these are susceptible to anticoagulant interferences. Despite standardization efforts, LAC interpretation remains challenging due to pre- and post-analytical complexity and limited expertise. This study explored the use of deep neural networks (DNN) to facilitate LAC diagnosis. Methods Retrospective data for LAC profiling with 13 features performed at Mayo Clinic between 2021 and 2022 was collected. The profiles of 7,202 patients were randomly divided into training (n=4608), validation (n=1153), and test cohorts (n=1441). Diagnostic outputs including LAC positivity (determined by DRVVT or APTT) and the presence of warfarin (WAR) and heparin (HEP), were confirmed by expert assessment. Three DNN architectures were developed and evaluated (Figure 1): (1) four single output DNN models with a domain knowledge driven selection of input features, (2) four single output DNN models using the same 13 inputs across all models, and (3) a multi-output DNN model. Model performance was evaluated using the F1 score and the index of balanced accuracy (IBA) for each output label. Results Architecture 1 achieved high diagnostic accuracy with IBA values of 0.989-1.000 for all four outputs and F1 scores of 0.945-0.991. Architecture 2 exhibited F1 scores of 0.902-0.973 and IBA values of 0.935-0.992. Architecture 3 achieved F1 scores of 0.933-0.967 and IBA values of 0.941-0.983. Conclusions All three architectures accurately classify LAC and common anticoagulation effects. The comparable performance of architectures 1 and 2 suggests that manual feature extraction is unnecessary. Given its relative simplicity and versatility, architecture 3 is the preferred choice for implementation in a clinical laboratory to standardize LAC diagnosis.