Citations

Abstract Context Due to its rare incidence, molecular features of primary aldosteronism (PA) in young adults are largely unknown. Recently developed targeted mutational analysis identified aldosterone-driver somatic mutations in aldosterone-producing lesions, including aldosterone-producing adenomas (APAs), aldosterone-producing nodules (APNs), and aldosterone-producing micronodules, formerly known as aldosterone-producing cell clusters. Objective To investigate histologic and genetic characteristics of lateralized PA in young adults. Methods Formalin-fixed, paraffin-embedded adrenal tissue sections from 74 young patients with lateralized PA (<35 years old) were used for this study. Immunohistochemistry (IHC) for aldosterone synthase (CYP11B2) was performed to define the histopathologic diagnosis. Somatic mutations in aldosterone-producing lesions were further determined by CYP11B2 IHC-guided DNA sequencing. Results Based on the CYP11B2 IHC results, histopathologic classification was made as follows: 48 APAs, 20 APNs, 2 multiple aldosterone-producing nodules (MAPN), 1 double APN, 1 APA with MAPN, and 2 nonfunctioning adenomas (NFAs). Of 45 APAs with successful sequencing, 43 (96%) had somatic mutations, with KCNJ5 mutations being the most common genetic cause of young-onset APA (35/45, 78%). Of 18 APNs with successful sequencing, all of them harbored somatic mutations, with CACNA1D mutations being the most frequent genetic alteration in young-onset APN (8/18, 44%). Multiple CYP11B2-expressing lesions in patients with MAPN showed several aldosterone-driver mutations. No somatic mutations were identified in NFAs. Conclusion APA is the most common histologic feature of lateralized PA in young adults. Somatic KCNJ5 mutations are common in APAs, whereas CACNA1D mutations are often seen in APNs in this young PA population.

Severe acute respiratory syndrome coronavirus (SARS-CoV-2) is highly contagious and causes coronavirus disease 2019 (COVID-19). Reverse transcription quantitative polymerase chain reaction (RT-qPCR) is the most accurate and reliable molecular assay to detect active SARS-CoV-2 infection. However, a rapid increase in test subjects has created a global bottleneck in testing capacity. Given that efficient nucleic acid extraction greatly affects reliable and accurate testing results, we compared three extraction platforms: MagNA Pure 96 DNA and Viral NA Small Volume kit on MagNA Pure 96 (Roche, Basel, Switzerland), careGENETM Viral/Pathogen HiFi Nucleic Acid Isolation kit (WELLS BIO Inc., Seoul, Korea) on KingFisher Flex (Thermo Fisher Scientific, Rocklin, CA, USA), and SGRespiTM Pure kit (Seegene Inc., Seoul, Korea) on Maelstrom 9600 (Taiwan Advanced Nanotech Inc., Taoyuan, Taiwan). RNA was extracted from 245 residual respiratory specimens from the different types of samples (i.e., NPS, sputum, and saliva) using three different kits. The 95% limits of detection of median tissue culture infectious dose per milliliter (TCID50/mL) for the MagNA Pure 96, KingFisher Flex, and Maelstrom 9600 were 0.37–3.15 × 101, 0.41–3.62 × 101, and 0.33–1.98 × 101, respectively. The KingFisher Flex platform exhibited 99.2% sensitivity and 100% specificity, whereas Maelstrom 9600 exhibited 98.3–100% sensitivity and 100% specificity. Bland–Altman analysis revealed a 95.2% concordance between MagNA Pure 96 and KingFisher Flex and 95.4% concordance between MagNA Pure 96 and Maelstrom 9600, indicating that all three platforms provided statistically reliable results. This suggests that two modifying platforms, KingFisher Flex and Maelstrom 9600, are accurate and scalable extraction platforms for large-scale SARS-CoV-2 clinical detection and could help the management of COVID-19 patients.

Introduction Despite extensive studies on the relationship between diabetes mellitus (DM) and depression, the associations of depressive symptom severity with prevalence, awareness, treatment, and control of diabetes remain unclear. We aimed to investigate changes in these outcomes of diabetes as depressive symptoms aggravate. Research design and methods We conducted a cross-sectional analysis of 14 328 participants in the 2011–2016 National Health and Nutrition Examination Survey. Participants were classified into depressive symptom groups of none, mild, moderate, moderately severe, and severe depending on their Patient Health Questionnaire-9 scores. Multivariate logistic regression analyses were conducted in three models adjusted for expanding confounders to evaluate the associations between severity of depressive symptoms and prevalence, awareness, treatment, and control of DM. Results As depressive symptom severity worsened, both prevalence and awareness of DM increased regardless of models (p value for trend <0.01 in all models for prevalence and awareness; adjusted OR (aOR) 2.14, 95% CI 1.29 to 3.56 for prevalence in the severe group, model 1; aOR 2.43, 95% CI 1.27 to 4.64 for awareness in the moderately severe group, model 1). Notwithstanding higher awareness of diabetes in the moderately severe and severe groups (84.5% and 86.2%, respectively, vs 71.3% in the none group), these groups were treated less (aOR 0.25, 95% CI 0.11 to 0.55 in the severe group, model 3) or inadequately controlled (aOR 0.51, 95% CI 0.27 to 0.98 in the moderately severe group, model 3). Conclusions The gap between patients’ higher awareness and lower treatment rate or control of diabetes among individuals with severe depressive symptoms highlights the unmet needs for postdiagnostic multidisciplinary care for patients with comorbid depression and DM.

Abstract Glioblastoma (GBM) is the most common and malignant form of primary brain tumor. Average survival rate of GBM is 12-15 months from diagnosis. Due to its highly invasive nature, current treatment options have not been able to improve the survival rate in past 20 years. In order to discover GBM therapeutic targets, omics technologies have been widely used in order to identify potential biomarkers. Identification of cancer biomarkers for diagnosis and prognosis continues to evolve as clinical specimen pipelines and technologies are optimized. This research study focused on investigating proteins, metabolites and lipids biomarkers and patterns of human GBM xenografts employing mass spectrometry. Human tumor cell lines GBM 10 and GBM 43 were injected in the right cerebral hemisphere and flank sites in NOD/SCID mice (n = 10 mice per group). After harvesting brain and flank tumors and control brain from mouse models, protein, metabolites, and lipids of tumor samples were collected through one simple extraction procedure, which may solve sample preparation difficulties in omics studies, developed by Dr. Jiri Adamec. These samples were analyzed by reverse phase high performance liquid chromatography - Fourier transform ion cyclotron resonance mass spectrometry (LC-FTMS). FTMS analyzers have the highest resolving power of all MS instruments, which is ideal for complex mixtures such as GBM tissue. Spectra obtained from the FTMS analysis were analyzed using Student t-tests to detect significant differences in tissue profiles at a level of p = 0.05. Compounds below this threshold were identified through a database using the m/z ratio. Lipidomic analysis indicated the possible up and down-regulation of several lipids classes in GBM tissue connected to metabolic pathways, tumor proliferation and immunodepression, such as glycerophospholipids, both up- and down-regulated (e.g. PC38:4 or PG 35:2, respectively), and down-regulated glycosphingolipids (Galβ1-3GlcNAcβ1-3Galβ1-4Glc-Cer, Galβ1-4GlcNAcβ1-3Galβ1-4Glc-Cer), and triglycerides (TG 55:9, 53:4,52:8). We expect that integrated studies of proteomics, metabolomics, and lipidomics increase the confidence of identification of potential biomarkers. Accompanying these studies is a collaborative effort to improve the effectiveness and efficiency of computational pipelines that are imperative to the analytics, visualization, identification, and interpretation of the omics data. Only by carefully integrating the computational pipelines can we successfully perform the types of integrative studies needed to advance the identification of cancer biomarkers for diagnosis and prognosis, and our integrative studies serve as a case study for our pipeline advancement efforts. Citation Information: Mol Cancer Ther 2013;12(11 Suppl):B37. Citation Format: Soo Jung Ha, Gordon Showalter, Jenna Rickus, Shanbao Cai, Haiyan Wang, Wei Michael Liu, Jann N. Sarkaria, John Springer, Jiri Adamec, Karen E. Pollok, Kari L. Clase. Proteomic and metabolomic analyses of glioblastoma using mass spectrometry. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr B37.