Benchmarking the generalizability of brain age models: Challenges posed by scanner variance and prediction bias

Machine learning has been increasingly applied to neuroimaging data to predict age, deriving a personalized biomarker with potential clinical applications. The scientific and clinical value of these models depends on their applicability to independently acquired scans from diverse sources. Accordingly, we evaluated the generalizability of two brain age models that were trained across the lifespan by applying them to three distinct early-life samples with participants aged 8-22 years. These models were chosen based on the size and diversity of their training data, but they also differed greatly in their processing methods and predictive algorithms. Specifically, one brain age model was built by applying gradient tree boosting (GTB) to extracted features of cortical thickness, surface area, and brain volume. The other model applied a 2D convolutional neural network (DBN) to minimally preprocessed slices of T1-weighted scans. Additional model variants were created to understand how generalizability changed when each model was trained with data that became more similar to the test samples in terms of age and acquisition protocols. Our results illustrated numerous trade-offs. The GTB predictions were relatively more accurate overall and yielded more reliable predictions when applied to lower quality scans. In contrast, the DBN displayed the most utility in detecting associations between brain age gaps and cognitive functioning. Broadly speaking, the largest limitations affecting generalizability were acquisition protocol differences and biased brain age estimates. If such confounds could eventually be removed without post-hoc corrections, brain age predictions may have greater utility as personalized biomarkers of healthy aging

A Study of the Residual 39Ar Content in Argon from Underground Sources

The discovery of argon from underground sources with significantly less 39Ar than atmospheric argon was an important step in the development of direct-detection dark matter experiments using argon as the active target. We report on the design and operation of a low background detector with a single phase liquid argon target that was built to study the 39Ar content of the underground argon. Underground argon from the Kinder Morgan CO2 plant in Cortez, Colorado was determined to have less than 0.65% of the 39Ar activity in atmospheric argon.Comment: 21 pages, 10 figure

Presence of factors that activate platelet aggregation in mitral stenotic patients' plasma

BACKGROUND: Although the association between mitral stenosis (MS) and increased coagulation activity is well recognized, it is unclear whether enhanced coagulation remains localized in the left atrium or whether this represents a systemic problem. To assess systemic coagulation parameters and changes in platelet aggregation, we measured fibrinogen levels and performed in vitro platelet function tests in plasma obtained from mitral stenotic patients' and from healthy control subjects' peripheral venous blood. METHODS: Sixteen newly diagnosed patients with rheumatic MS (Group P) and 16 healthy subjects (Group N) were enrolled in the study. Platelet-equalized plasma samples were evaluated to determine in vitro platelet function, using adenosine diphosphate (ADP), collagen and epinephrine in an automated aggregometer. In vitro platelet function tests in group N were performed twice, with and without plasma obtained from group P. RESULTS: There were no significant differences between the groups with respect to demographic variables. Peripheral venous fibrinogen levels in Group P were not significantly different from those in Group N. Adenosine diphosphate, epinephrine and collagen-induced platelet aggregation ratios were significantly higher in Group P than in Group N. When plasma obtained from Group P was added to Group N subjects' platelets, ADP and collagen-induced, but not epinephrine-induced, aggregation ratios were significantly increased compared to baseline levels in Group N. CONCLUSION: Platelet aggregation is increased in patients with MS, while fibrinogen levels remain similar to controls. We conclude that mitral stenotic patients exhibit increased systemic coagulation activity and that plasma extracted from these patients may contain some transferable factors that activate platelet aggregation

Gene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering

Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and SNP data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-driven neuroimaging phenotypes

The Society for Immunotherapy of Cancer statement on best practices for multiplex immunohistochemistry (IHC) and immunofluorescence (IF) staining and validation.

OBJECTIVES: The interaction between the immune system and tumor cells is an important feature for the prognosis and treatment of cancer. Multiplex immunohistochemistry (mIHC) and multiplex immunofluorescence (mIF) analyses are emerging technologies that can be used to help quantify immune cell subsets, their functional state, and their spatial arrangement within the tumor microenvironment. METHODS: The Society for Immunotherapy of Cancer (SITC) convened a task force of pathologists and laboratory leaders from academic centers as well as experts from pharmaceutical and diagnostic companies to develop best practice guidelines for the optimization and validation of mIHC/mIF assays across platforms. RESULTS: Representative outputs and the advantages and disadvantages of mIHC/mIF approaches, such as multiplexed chromogenic IHC, multiplexed immunohistochemical consecutive staining on single slide, mIF (including multispectral approaches), tissue-based mass spectrometry, and digital spatial profiling are discussed. CONCLUSIONS: mIHC/mIF technologies are becoming standard tools for biomarker studies and are likely to enter routine clinical practice in the near future. Careful assay optimization and validation will help ensure outputs are robust and comparable across laboratories as well as potentially across mIHC/mIF platforms. Quantitative image analysis of mIHC/mIF output and data management considerations will be addressed in a complementary manuscript from this task force

AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder:COORDINATE-MDD consortium design and rationale

BACKGROUND: Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. METHODS: We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. RESULTS: We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. CONCLUSION: We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project

Clinical presentation of abdominal tuberculosis in HIV seronegative adults

BACKGROUND: The accurate diagnosis of abdominal tuberculosis usually takes a long time and requires a high index of suspicion in clinic practice. Eighty-eight immune-competent patients with abdominal tuberculosis were grouped according to symptoms at presentation and followed prospectively in order to investigate the effect of symptomatic presentation on clinical diagnosis and prognosis. METHODS: Based upon the clinical presentation, the patients were divided into groups such as non-specific abdominal pain & less prominent in bowel habit, ascites, alteration in bowel habit, acute abdomen and others. Demographic, clinical and laboratory features, coexistence of pulmonary tuberculosis, diagnostic procedures, definitive diagnostic tests, need for surgical therapy, and response to treatment were assessed in each group. RESULTS: According to clinical presentation, five groups were constituted as non-specific abdominal pain (n = 24), ascites (n = 24), bowel habit alteration (n = 22), acute abdomen (n = 9) and others (n = 9). Patients presenting with acute abdomen had significantly higher white blood cell counts (p = 0.002) and abnormalities in abdominal plain radiographs (p = 0.014). Patients presenting with alteration in bowel habit were younger (p = 0.048). The frequency of colonoscopic abnormalities (7.5%), and need for therapeutic surgery (12.5%) were lower in patients with ascites, (p = 0.04) and (p = 0.001), respectively. There was no difference in gender, disease duration, diagnostic modalities, response to treatment, period to initial response, and mortality between groups (p > 0.05). Gastrointestinal tract alone was the most frequently involved part (38.5%), and this was associated with acid-fast bacteria in the sputum (p = 0.003). CONCLUSION: Gastrointestinal tract involvement is frequent in patients with active pulmonary tuberculosis. Although different clinical presentations of patients with abdominal tuberculosis determine diagnostic work up and need for therapeutic surgery, evidence based diagnosis and consequences of the disease does not change