Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis.

As more diagnostic testing options become available to physicians, it becomes more difficult to combine various types of medical information together in order to optimize the overall diagnosis. To improve diagnostic performance, here we introduce an approach to optimize a decision-fusion technique to combine heterogeneous information, such as from different modalities, feature categories, or institutions. For classifier comparison we used two performance metrics: The receiving operator characteristic (ROC) area under the curve [area under the ROC curve (AUC)] and the normalized partial area under the curve (pAUC). This study used four classifiers: Linear discriminant analysis (LDA), artificial neural network (ANN), and two variants of our decision-fusion technique, AUC-optimized (DF-A) and pAUC-optimized (DF-P) decision fusion. We applied each of these classifiers with 100-fold cross-validation to two heterogeneous breast cancer data sets: One of mass lesion features and a much more challenging one of microcalcification lesion features. For the calcification data set, DF-A outperformed the other classifiers in terms of AUC (p < 0.02) and achieved AUC=0.85 +/- 0.01. The DF-P surpassed the other classifiers in terms of pAUC (p < 0.01) and reached pAUC=0.38 +/- 0.02. For the mass data set, DF-A outperformed both the ANN and the LDA (p < 0.04) and achieved AUC=0.94 +/- 0.01. Although for this data set there were no statistically significant differences among the classifiers' pAUC values (pAUC=0.57 +/- 0.07 to 0.67 +/- 0.05, p > 0.10), the DF-P did significantly improve specificity versus the LDA at both 98% and 100% sensitivity (p < 0.04). In conclusion, decision fusion directly optimized clinically significant performance measures, such as AUC and pAUC, and sometimes outperformed two well-known machine-learning techniques when applied to two different breast cancer data sets.

Measurement of the differential cross section for the reaction gamman-->pi- p from deuterium.

We report a measurement of the differential cross section for the gamman-->pi- p process from the CLAS detector at Jefferson Laboratory in Hall B for photon energies between 1.0 and 3.5 GeV and pion center-of-mass (c.m.) angles (thetac.m.) between 50 degrees and 115 degrees. We confirm a previous indication of a broad enhancement around a c.m. energy ([sqrt]s) of 2.1 GeV at thetac.m.=90 degrees in the scaled differential cross section s7dsigma/dt and a rapid falloff in a center-of-mass energy region of about 400 MeV following the enhancement. Our data show an angular dependence of this enhancement as the suggested scaling region is approached for thetac.m. from 70 degrees to 105 degrees.

Differential inhibition of human immunodeficiency virus type 1 in peripheral blood mononuclear cells and TZM-bl cells by endotoxin-mediated chemokine and gamma interferon production.

Bacterial lipopolysaccharide (endotoxin) is a frequent contaminant of biological specimens and is also known to be a potent inducer of beta-chemokines and other soluble factors that inhibit HIV-1 infection in vitro. Though lipopolysaccharide (LPS) has been shown to stimulate the production of soluble HIV-1 inhibitors in cultures of monocyte-derived macrophages, the ability of LPS to induce similar inhibitors in other cell types is poorly characterized. Here we show that LPS exhibits potent anti-HIV activity in phytohemagglutinin-stimulated peripheral blood mononuclear cells (PBMCs) but has no detectable anti-HIV-1 activity in TZM-bl cells. The anti-HIV-1 activity of LPS in PBMCs was strongly associated with the production of beta-chemokines from CD14-positive monocytes. Culture supernatants from LPS-stimulated PBMCs exhibited potent anti-HIV-1 activity when added to TZM-bl cells but, in this case, the antiviral activity appeared to be related to IFN-gamma rather than to beta-chemokines. These observations indicate that LPS stimulates PBMCs to produce a complex array of soluble HIV-1 inhibitors, including beta-chemokines and IFN-gamma, that differentially inhibit HIV-1 depending on the target cell type. The results also highlight the need to use endotoxin-free specimens to avoid artifacts when assessing HIV-1-specific neutralizing antibodies in PBMC-based assays.

Diagnosis of partial body radiation exposure in mice using peripheral blood gene expression profiles.

In the event of a terrorist-mediated attack in the United States using radiological or improvised nuclear weapons, it is expected that hundreds of thousands of people could be exposed to life-threatening levels of ionizing radiation. We have recently shown that genome-wide expression analysis of the peripheral blood (PB) can generate gene expression profiles that can predict radiation exposure and distinguish the dose level of exposure following total body irradiation (TBI). However, in the event a radiation-mass casualty scenario, many victims will have heterogeneous exposure due to partial shielding and it is unknown whether PB gene expression profiles would be useful in predicting the status of partially irradiated individuals. Here, we identified gene expression profiles in the PB that were characteristic of anterior hemibody-, posterior hemibody- and single limb-irradiation at 0.5 Gy, 2 Gy and 10 Gy in C57Bl6 mice. These PB signatures predicted the radiation status of partially irradiated mice with a high level of accuracy (range 79-100%) compared to non-irradiated mice. Interestingly, PB signatures of partial body irradiation were poorly predictive of radiation status by site of injury (range 16-43%), suggesting that the PB molecular response to partial body irradiation was anatomic site specific. Importantly, PB gene signatures generated from TBI-treated mice failed completely to predict the radiation status of partially irradiated animals or non-irradiated controls. These data demonstrate that partial body irradiation, even to a single limb, generates a characteristic PB signature of radiation injury and thus may necessitate the use of multiple signatures, both partial body and total body, to accurately assess the status of an individual exposed to radiation.

Differential mechanisms of morphine antinociceptive tolerance revealed in (beta)arrestin-2 knock-out mice.

Morphine induces antinociception by activating mu opioid receptors (muORs) in spinal and supraspinal regions of the CNS. (Beta)arrestin-2 (beta)arr2), a G-protein-coupled receptor-regulating protein, regulates the muOR in vivo. We have shown previously that mice lacking (beta)arr2 experience enhanced morphine-induced analgesia and do not become tolerant to morphine as determined in the hot-plate test, a paradigm that primarily assesses supraspinal pain responsiveness. To determine the general applicability of the (beta)arr2-muOR interaction in other neuronal systems, we have, in the present study, tested (beta)arr2 knock-out ((beta)arr2-KO) mice using the warm water tail-immersion paradigm, which primarily assesses spinal reflexes to painful thermal stimuli. In this test, the (beta)arr2-KO mice have greater basal nociceptive thresholds and markedly enhanced sensitivity to morphine. Interestingly, however, after a delayed onset, they do ultimately develop morphine tolerance, although to a lesser degree than the wild-type (WT) controls. In the (beta)arr2-KO but not WT mice, morphine tolerance can be completely reversed with a low dose of the classical protein kinase C (PKC) inhibitor chelerythrine. These findings provide in vivo evidence that the muOR is differentially regulated in diverse regions of the CNS. Furthermore, although (beta)arr2 appears to be the most prominent and proximal determinant of muOR desensitization and morphine tolerance, in the absence of this mechanism, the contributions of a PKC-dependent regulatory system become readily apparent.

Overexpression of the cardiac beta(2)-adrenergic receptor and expression of a beta-adrenergic receptor kinase-1 (betaARK1) inhibitor both increase myocardial contractility but have differential effects on susceptibility to ischemic injury.

Cardiac beta(2)-adrenergic receptor (beta(2)AR) overexpression is a potential contractile therapy for heart failure. Cardiac contractility was elevated in mice overexpressing beta(2)ARs (TG4s) with no adverse effects under normal conditions. To assess the consequences of beta(2)AR overexpression during ischemia, perfused hearts from TG4 and wild-type mice were subjected to 20-minute ischemia and 40-minute reperfusion. During ischemia, ATP and pH fell lower in TG4 hearts than wild type. Ischemic injury was greater in TG4 hearts, as indicated by lower postischemic recoveries of contractile function, ATP, and phosphocreatine. Because beta(2)ARs, unlike beta(1)ARs, couple to G(i) as well as G(s), we pretreated mice with the G(i) inhibitor pertussis toxin (PTX). PTX treatment increased basal contractility in TG4 hearts and abolished the contractile resistance to isoproterenol. During ischemia, ATP fell lower in TG4+PTX than in TG4 hearts. Recoveries of contractile function and ATP were lower in TG4+PTX than in TG4 hearts. We also studied mice that overexpressed either betaARK1 (TGbetaARK1) or a betaARK1 inhibitor (TGbetaARKct). Recoveries of function, ATP, and phosphocreatine were higher in TGbetaARK1 hearts than in wild-type hearts. Despite basal contractility being elevated in TGbetaARKct hearts to the same level as that of TG4s, ischemic injury was not increased. In summary, beta(2)AR overexpression increased ischemic injury, whereas betaARK1 overexpression was protective. Ischemic injury in the beta(2)AR overexpressors was exacerbated by PTX treatment, implying that it was G(s) not G(i) activity that enhanced injury. Unlike beta(2)AR overexpression, basal contractility was increased by betaARK1 inhibitor expression without increasing ischemic injury, thus implicating a safer potential therapy for heart failure.

An approach to the study of G-protein-coupled receptor kinases: an in vitro-purified membrane assay reveals differential receptor specificity and regulation by G beta gamma subunits.

Phosphorylation of GTP-binding-regulatory (G)-protein-coupled receptors by specific G-protein-coupled receptor kinases (GRKs) is a major mechanism responsible for agonist-mediated desensitization of signal transduction processes. However, to date, studies of the specificity of these enzymes have been hampered by the difficulty of preparing the purified and reconstituted receptor preparations required as substrates. Here we describe an approach that obviates this problem by utilizing highly purified membrane preparations from Sf9 and 293 cells overexpressing G-protein-coupled receptors. We use this technique to demonstrate specificity of several GRKs with respect to both receptor substrates and the enhancing effects of G-protein beta gamma subunits on phosphorylation. Enriched membrane preparations of the beta 2- and alpha 2-C2-adrenergic receptors (ARs, where alpha 2-C2-AR refers to the AR whose gene is located on human chromosome 2) prepared by sucrose density gradient centrifugation from Sf9 or 293 cells contain the receptor at 100-300 pmol/mg of protein and serve as efficient substrates for agonist-dependent phosphorylation by beta-AR kinase 1 (GRK2), beta-AR kinase 2 (GRK3), or GRK5. Stoichiometries of agonist-mediated phosphorylation of the receptors by GRK2 (beta-AR kinase 1), in the absence and presence of G beta gamma, are 1 and 3 mol/mol, respectively. The rate of phosphorylation of the membrane receptors is 3 times faster than that of purified and reconstituted receptors. While phosphorylation of the beta 2-AR by GRK2, -3, and -5 is similar, the activity of GRK2 and -3 is enhanced by G beta gamma whereas that of GRK5 is not. In contrast, whereas GRK2 and -3 efficiently phosphorylate alpha 2-C2-AR, GRK5 is quite weak. The availability of a simple direct phosphorylation assay applicable to any cloned G-protein-coupled receptor should greatly facilitate elucidation of the mechanisms of regulation of these receptors by the expanding family of GRKs.

Differential Apaf-1 levels allow cytochrome c to induce apoptosis in brain tumors but not in normal neural tissues.

Brain tumors are typically resistant to conventional chemotherapeutics, most of which initiate apoptosis upstream of mitochondrial cytochrome c release. In this study, we demonstrate that directly activating apoptosis downstream of the mitochondria, with cytosolic cytochrome c, kills brain tumor cells but not normal brain tissue. Specifically, cytosolic cytochrome c is sufficient to induce apoptosis in glioblastoma and medulloblastoma cell lines. In contrast, primary neurons from the cerebellum and cortex are remarkably resistant to cytosolic cytochrome c. Importantly, tumor tissue from mouse models of both high-grade astrocytoma and medulloblastoma display hypersensitivity to cytochrome c when compared with surrounding brain tissue. This differential sensitivity to cytochrome c is attributed to high Apaf-1 levels in the tumor tissue compared with low Apaf-1 levels in the adjacent brain tissue. These differences in Apaf-1 abundance correlate with differences in the levels of E2F1, a previously identified activator of Apaf-1 transcription. ChIP assays reveal that E2F1 binds the Apaf-1 promoter specifically in tumor tissue, suggesting that E2F1 contributes to the expression of Apaf-1 in brain tumors. Together, these results demonstrate an unexpected sensitivity of brain tumors to postmitochondrial induction of apoptosis. Moreover, they raise the possibility that this phenomenon could be exploited therapeutically to selectively kill brain cancer cells while sparing the surrounding brain parenchyma.

Convergent differential regulation of parvalbumin in the brains of vocal learners.

Spoken language and learned song are complex communication behaviors found in only a few species, including humans and three groups of distantly related birds--songbirds, parrots, and hummingbirds. Despite their large phylogenetic distances, these vocal learners show convergent behaviors and associated brain pathways for vocal communication. However, it is not clear whether this behavioral and anatomical convergence is associated with molecular convergence. Here we used oligo microarrays to screen for genes differentially regulated in brain nuclei necessary for producing learned vocalizations relative to adjacent brain areas that control other behaviors in avian vocal learners versus vocal non-learners. A top candidate gene in our screen was a calcium-binding protein, parvalbumin (PV). In situ hybridization verification revealed that PV was expressed significantly higher throughout the song motor pathway, including brainstem vocal motor neurons relative to the surrounding brain regions of all distantly related avian vocal learners. This differential expression was specific to PV and vocal learners, as it was not found in avian vocal non-learners nor for control genes in learners and non-learners. Similar to the vocal learning birds, higher PV up-regulation was found in the brainstem tongue motor neurons used for speech production in humans relative to a non-human primate, macaques. These results suggest repeated convergent evolution of differential PV up-regulation in the brains of vocal learners separated by more than 65-300 million years from a common ancestor and that the specialized behaviors of learned song and speech may require extra calcium buffering and signaling.

Differential predictability of four dimensions of affect intensity.

Individual differences in affect intensity are typically assessed with the Affect Intensity Measure (AIM). Previous factor analyses suggest that the AIM is comprised of four weakly correlated factors: Positive Affectivity, Negative Reactivity, Negative Intensity and Positive Intensity or Serenity. However, little data exist to show whether its four factors relate to other measures differently enough to preclude use of the total scale score. The present study replicated the four-factor solution and found that subscales derived from the four factors correlated differently with criterion variables that assess personality domains, affective dispositions, and cognitive patterns that are associated with emotional reactions. The results show that use of the total AIM score can obscure relationships between specific features of affect intensity and other variables and suggest that researchers should examine the individual AIM subscales.

A memory-based model of posttraumatic stress disorder: evaluating basic assumptions underlying the PTSD diagnosis.

In the mnemonic model of posttraumatic stress disorder (PTSD), the current memory of a negative event, not the event itself, determines symptoms. The model is an alternative to the current event-based etiology of PTSD represented in the Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; American Psychiatric Association, 2000). The model accounts for important and reliable findings that are often inconsistent with the current diagnostic view and that have been neglected by theoretical accounts of the disorder, including the following observations. The diagnosis needs objective information about the trauma and peritraumatic emotions but uses retrospective memory reports that can have substantial biases. Negative events and emotions that do not satisfy the current diagnostic criteria for a trauma can be followed by symptoms that would otherwise qualify for PTSD. Predisposing factors that affect the current memory have large effects on symptoms. The inability-to-recall-an-important-aspect-of-the-trauma symptom does not correlate with other symptoms. Loss or enhancement of the trauma memory affects PTSD symptoms in predictable ways. Special mechanisms that apply only to traumatic memories are not needed, increasing parsimony and the knowledge that can be applied to understanding PTSD.

Cost-effectiveness analysis of the diagnosis of meniscus tears.

BACKGROUND: Diagnostic imaging represents the fastest growing segment of costs in the US health system. This study investigated the cost-effectiveness of alternative diagnostic approaches to meniscus tears of the knee, a highly prevalent disease that traditionally relies on MRI as part of the diagnostic strategy. PURPOSE: To identify the most efficient strategy for the diagnosis of meniscus tears. STUDY DESIGN: Economic and decision analysis; Level of evidence, 1. METHODS: A simple-decision model run as a cost-utility analysis was constructed to assess the value added by MRI in various combinations with patient history and physical examination (H&P). The model examined traumatic and degenerative tears in 2 distinct settings: primary care and orthopaedic sports medicine clinic. Strategies were compared using the incremental cost-effectiveness ratio (ICER). RESULTS: In both practice settings, H&P alone was widely preferred for degenerative meniscus tears. Performing MRI to confirm a positive H&P was preferred for traumatic tears in both practice settings, with a willingness to pay of less than US$50,000 per quality-adjusted life-year. Performing an MRI for all patients was not preferred in any reasonable clinical scenario. The prevalence of a meniscus tear in a clinician's patient population was influential. For traumatic tears, MRI to confirm a positive H&P was preferred when prevalence was less than 46.7%, with H&P preferred above that. For degenerative tears, H&P was preferred until the prevalence reaches 74.2%, and then MRI to confirm a negative was the preferred strategy. In both settings, MRI to confirm positive physical examination led to more than a 10-fold lower rate of unnecessary surgeries than did any other strategy, while MRI to confirm negative physical examination led to a 2.08 and 2.26 higher rate than H&P alone in primary care and orthopaedic clinics, respectively. CONCLUSION: For all practitioners, H&P is the preferred strategy for the suspected degenerative meniscus tear. An MRI to confirm a positive H&P is preferred for traumatic tears for all practitioners. Consideration should be given to implementing alternative diagnostic strategies as well as enhancing provider education in physical examination skills to improve the reliability of H&P as a diagnostic test. CLINICAL RELEVANCE: Alternative diagnostic strategies that do not include the use of MRI may result in decreased health care costs without harm to the patient and could possibly reduce unnecessary procedures.

Micro-Anatomical Quantitative Imaging Towards Enabling Automated Diagnosis of Thick Tissues at the Point of Care

Histopathology is the clinical standard for tissue diagnosis. However, histopathology has several limitations including that it requires tissue processing, which can take 30 minutes or more, and requires a highly trained pathologist to diagnose the tissue. Additionally, the diagnosis is qualitative, and the lack of quantitation leads to possible observer-specific diagnosis. Taken together, it is difficult to diagnose tissue at the point of care using histopathology.

Several clinical situations could benefit from more rapid and automated histological processing, which could reduce the time and the number of steps required between obtaining a fresh tissue specimen and rendering a diagnosis. For example, there is need for rapid detection of residual cancer on the surface of tumor resection specimens during excisional surgeries, which is known as intraoperative tumor margin assessment. Additionally, rapid assessment of biopsy specimens at the point-of-care could enable clinicians to confirm that a suspicious lesion is successfully sampled, thus preventing an unnecessary repeat biopsy procedure. Rapid and low cost histological processing could also be potentially useful in settings lacking the human resources and equipment necessary to perform standard histologic assessment. Lastly, automated interpretation of tissue samples could potentially reduce inter-observer error, particularly in the diagnosis of borderline lesions.

To address these needs, high quality microscopic images of the tissue must be obtained in rapid timeframes, in order for a pathologic assessment to be useful for guiding the intervention. Optical microscopy is a powerful technique to obtain high-resolution images of tissue morphology in real-time at the point of care, without the need for tissue processing. In particular, a number of groups have combined fluorescence microscopy with vital fluorescent stains to visualize micro-anatomical features of thick (i.e. unsectioned or unprocessed) tissue. However, robust methods for segmentation and quantitative analysis of heterogeneous images are essential to enable automated diagnosis. Thus, the goal of this work was to obtain high resolution imaging of tissue morphology through employing fluorescence microscopy and vital fluorescent stains and to develop a quantitative strategy to segment and quantify tissue features in heterogeneous images, such as nuclei and the surrounding stroma, which will enable automated diagnosis of thick tissues.

To achieve these goals, three specific aims were proposed. The first aim was to develop an image processing method that can differentiate nuclei from background tissue heterogeneity and enable automated diagnosis of thick tissue at the point of care. A computational technique called sparse component analysis (SCA) was adapted to isolate features of interest, such as nuclei, from the background. SCA has been used previously in the image processing community for image compression, enhancement, and restoration, but has never been applied to separate distinct tissue types in a heterogeneous image. In combination with a high resolution fluorescence microendoscope (HRME) and a contrast agent acriflavine, the utility of this technique was demonstrated through imaging preclinical sarcoma tumor margins. Acriflavine localizes to the nuclei of cells where it reversibly associates with RNA and DNA. Additionally, acriflavine shows some affinity for collagen and muscle. SCA was adapted to isolate acriflavine positive features or APFs (which correspond to RNA and DNA) from background tissue heterogeneity. The circle transform (CT) was applied to the SCA output to quantify the size and density of overlapping APFs. The sensitivity of the SCA+CT approach to variations in APF size, density and background heterogeneity was demonstrated through simulations. Specifically, SCA+CT achieved the lowest errors for higher contrast ratios and larger APF sizes. When applied to tissue images of excised sarcoma margins, SCA+CT correctly isolated APFs and showed consistently increased density in tumor and tumor + muscle images compared to images containing muscle. Next, variables were quantified from images of resected primary sarcomas and used to optimize a multivariate model. The sensitivity and specificity for differentiating positive from negative ex vivo resected tumor margins was 82% and 75%. The utility of this approach was further tested by imaging the in vivo tumor cavities from 34 mice after resection of a sarcoma with local recurrence as a bench mark. When applied prospectively to images from the tumor cavity, the sensitivity and specificity for differentiating local recurrence was 78% and 82%. The results indicate that SCA+CT can accurately delineate APFs in heterogeneous tissue, which is essential to enable automated and rapid surveillance of tissue pathology.

Two primary challenges were identified in the work in aim 1. First, while SCA can be used to isolate features, such as APFs, from heterogeneous images, its performance is limited by the contrast between APFs and the background. Second, while it is feasible to create mosaics by scanning a sarcoma tumor bed in a mouse, which is on the order of 3-7 mm in any one dimension, it is not feasible to evaluate an entire human surgical margin. Thus, improvements to the microscopic imaging system were made to (1) improve image contrast through rejecting out-of-focus background fluorescence and to (2) increase the field of view (FOV) while maintaining the sub-cellular resolution needed for delineation of nuclei. To address these challenges, a technique called structured illumination microscopy (SIM) was employed in which the entire FOV is illuminated with a defined spatial pattern rather than scanning a focal spot, such as in confocal microscopy.

Thus, the second aim was to improve image contrast and increase the FOV through employing wide-field, non-contact structured illumination microscopy and optimize the segmentation algorithm for new imaging modality. Both image contrast and FOV were increased through the development of a wide-field fluorescence SIM system. Clear improvement in image contrast was seen in structured illumination images compared to uniform illumination images. Additionally, the FOV is over 13X larger than the fluorescence microendoscope used in aim 1. Initial segmentation results of SIM images revealed that SCA is unable to segment large numbers of APFs in the tumor images. Because the FOV of the SIM system is over 13X larger than the FOV of the fluorescence microendoscope, dense collections of APFs commonly seen in tumor images could no longer be sparsely represented, and the fundamental sparsity assumption associated with SCA was no longer met. Thus, an algorithm called maximally stable extremal regions (MSER) was investigated as an alternative approach for APF segmentation in SIM images. MSER was able to accurately segment large numbers of APFs in SIM images of tumor tissue. In addition to optimizing MSER for SIM image segmentation, an optimal frequency of the illumination pattern used in SIM was carefully selected because the image signal to noise ratio (SNR) is dependent on the grid frequency. A grid frequency of 31.7 mm-1 led to the highest SNR and lowest percent error associated with MSER segmentation.

Once MSER was optimized for SIM image segmentation and the optimal grid frequency was selected, a quantitative model was developed to diagnose mouse sarcoma tumor margins that were imaged ex vivo with SIM. Tumor margins were stained with acridine orange (AO) in aim 2 because AO was found to stain the sarcoma tissue more brightly than acriflavine. Both acriflavine and AO are intravital dyes, which have been shown to stain nuclei, skeletal muscle, and collagenous stroma. A tissue-type classification model was developed to differentiate localized regions (75x75 µm) of tumor from skeletal muscle and adipose tissue based on the MSER segmentation output. Specifically, a logistic regression model was used to classify each localized region. The logistic regression model yielded an output in terms of probability (0-100%) that tumor was located within each 75x75 µm region. The model performance was tested using a receiver operator characteristic (ROC) curve analysis that revealed 77% sensitivity and 81% specificity. For margin classification, the whole margin image was divided into localized regions and this tissue-type classification model was applied. In a subset of 6 margins (3 negative, 3 positive), it was shown that with a tumor probability threshold of 50%, 8% of all regions from negative margins exceeded this threshold, while over 17% of all regions exceeded the threshold in the positive margins. Thus, 8% of regions in negative margins were considered false positives. These false positive regions are likely due to the high density of APFs present in normal tissues, which clearly demonstrates a challenge in implementing this automatic algorithm based on AO staining alone.

Thus, the third aim was to improve the specificity of the diagnostic model through leveraging other sources of contrast. Modifications were made to the SIM system to enable fluorescence imaging at a variety of wavelengths. Specifically, the SIM system was modified to enabling imaging of red fluorescent protein (RFP) expressing sarcomas, which were used to delineate the location of tumor cells within each image. Initial analysis of AO stained panels confirmed that there was room for improvement in tumor detection, particularly in regards to false positive regions that were negative for RFP. One approach for improving the specificity of the diagnostic model was to investigate using a fluorophore that was more specific to staining tumor. Specifically, tetracycline was selected because it appeared to specifically stain freshly excised tumor tissue in a matter of minutes, and was non-toxic and stable in solution. Results indicated that tetracycline staining has promise for increasing the specificity of tumor detection in SIM images of a preclinical sarcoma model and further investigation is warranted.

In conclusion, this work presents the development of a combination of tools that is capable of automated segmentation and quantification of micro-anatomical images of thick tissue. When compared to the fluorescence microendoscope, wide-field multispectral fluorescence SIM imaging provided improved image contrast, a larger FOV with comparable resolution, and the ability to image a variety of fluorophores. MSER was an appropriate and rapid approach to segment dense collections of APFs from wide-field SIM images. Variables that reflect the morphology of the tissue, such as the density, size, and shape of nuclei and nucleoli, can be used to automatically diagnose SIM images. The clinical utility of SIM imaging and MSER segmentation to detect microscopic residual disease has been demonstrated by imaging excised preclinical sarcoma margins. Ultimately, this work demonstrates that fluorescence imaging of tissue micro-anatomy combined with a specialized algorithm for delineation and quantification of features is a means for rapid, non-destructive and automated detection of microscopic disease, which could improve cancer management in a variety of clinical scenarios.

Challenges in the diagnosis and treatment of depression in autism spectrum disorders across the lifespan.

Diagnosis and treatment of comorbid neuropsychiatric illness is often a secondary focus of treatment in individuals with autism spectrum disorder (ASD), given that substantial impairment may be caused by core symptoms of ASD itself. However, psychiatric comorbidities, including depressive disorders, are common and frequently result in additional functional impairment, treatment costs, and burden on caregivers. Clinicians may struggle to appropriately diagnose depression in ASD due to communication deficits, atypical presentation of depression in ASD, and lack of standardized diagnostic tools. Specific risk and resilience factors for depression in ASD across the lifespan, including level of functioning, age, family history, and coping style, have been suggested, but require further study. Treatment with medications or psychotherapy may be beneficial, though more research is required to establish guidelines for management of symptoms. This review will describe typical presentations of depression in individuals with ASD, review current information on the prevalence, assessment, and treatment of comorbid depression in individuals with ASD, and identify important research gaps.