Biphenotypic Sinonasal Sarcoma-Case Report and Review of Clinicopathological Features and Diagnostic Modalities.

Background Biphenotypic sinonasal sarcoma is a recently described malignancy showing dual differentiation with both myogenic and neural elements. Due to its histologic similarities to other sinonasal malignancies, it is a diagnostic challenge. Objective The main purpose of this article is to report a case of biphenotypic sinonasal sarcoma and to consolidate data and provide a comprehensive review regarding pathological differences between biphenotypic sarcoma and other sinonasal malignancies and diagnostic modalities used for biphenotypic sarcoma. Material and Methods A systematic review of all cases of biphenotypic sinonasal sarcoma was performed using electronic databases (PubMed and Medline). Data collected included age, gender, symptoms, sub-site of origin, immunophenotyping, metastasis, recurrence, treatment, duration of follow-up, and survival outcomes. Results Ninety-five cases of biphenotypic sarcoma were found with mean age at diagnosis of 52.36 years (range, 24-87 years). Female to male ratio was 2.27:1. Extra-sinonasal extension was present in 28%. Immunophenotyping revealed that S-100 and SMA (smooth muscle actin) were consistently positive, while SOX-10 was consistently negative. PAX3-MAML3 fusion [t (2; 4) (q35; q31.1)] was the most common genetic rearrangement. Surgical excision with or without adjuvant radiotherapy was the most frequent treatment modality used. Recurrence was observed in 32% of cases with follow-up. None of the cases reported metastasis. Three patients had died at the time of publication that included one case with intracranial extension. Conclusion Biphenotypic sarcoma is distinct sinonasal malignancy with unique clinicopathological features. Testing involving a battery of myogenic and neural immunomarkers is essential for diagnostic confirmation and is a clinically useful endeavor when clinical suspicion is high. © 2019 Georg Thieme Verlag KG Stuttgart. New York

Development of mental health quality indicators (MHQIs) for inpatient psychiatry based on the interRAI mental health assessment

Abstract Background Outcome quality indicators are rarely used to evaluate mental health services because most jurisdictions lack clinical data systems to construct indicators in a meaningful way across mental health providers. As a result, important information about the effectiveness of health services remains unknown. This study examined the feasibility of developing mental health quality indicators (MHQIs) using the Resident Assessment Instrument - Mental Health (RAI-MH), a clinical assessment system mandated for use in Ontario, Canada as well as many other jurisdictions internationally. Methods Retrospective analyses were performed on two datasets containing RAI-MH assessments for 1,056 patients from 7 facilities and 34,788 patients from 70 facilities in Ontario, Canada. The RAI-MH was completed by clinical staff of each facility at admission and follow-up, typically at discharge. The RAI-MH includes a breadth of information on symptoms, functioning, socio-demographics, and service utilization. Potential MHQIs were derived by examining the empirical patterns of improvement and incidence in depressive symptoms and cognitive performance across facilities in both sets of data. A prevalence indicator was also constructed to compare restraint use. Logistic regression was used to evaluate risk adjustment of MHQIs using patient case-mix index scores derived from the RAI-MH System for Classification of Inpatient Psychiatry. Results Subscales from the RAI-MH, the Depression Severity Index (DSI) and Cognitive Performance Scale (CPS), were found to have good reliability and strong convergent validity. Unadjusted rates of five MHQIs based on the DSI, CPS, and restraints showed substantial variation among facilities in both sets of data. For instance, there was a 29.3% difference between the first and third quartile facility rates of improvement in cognitive performance. The case-mix index score was significantly related to MHQIs for cognitive performance and restraints but had a relatively small impact on adjusted rates/prevalence. Conclusions The RAI-MH is a feasible assessment system for deriving MHQIs. Given the breadth of clinical content on the RAI-MH there is an opportunity to expand the number of MHQIs beyond indicators of depression, cognitive performance, and restraints. Further research is needed to improve risk adjustment of the MHQIs for their use in mental health services report card and benchmarking activities.http://deepblue.lib.umich.edu/bitstream/2027.42/112590/1/12913_2012_Article_2419.pd

The inflammatory response of human pancreatic cancer samples compared to normal controls.

Pancreatic ductal adenocarcinoma (PDAC) is a poor prognosis cancer with an aggressive growth profile that is often diagnosed at late stage and that has few curative or therapeutic options. PDAC growth has been linked to alterations in the pancreas microbiome, which could include the presence of the fungus Malassezia. We used RNA-sequencing to compare 14 matched tumor and normal (tumor adjacent) pancreatic cancer samples and found Malassezia RNA in both the PDAC and normal tissues. Although the presence of Malassezia was not correlated with tumor growth, a set of immune- and inflammatory-related genes were up-regulated in the PDAC compared to the normal samples, suggesting that they are involved in tumor progression. Gene set enrichment analysis suggests that activation of the complement cascade pathway and inflammation could be involved in pro PDAC growth

Epstein–Barr virus-associated inflammatory pseudotumor of the spleen: report of two cases and review of the literature

We report two rare examples of Epstein–Barr virus (EBV)-associated inflammatory pseudotumor of the spleen. One patient presented with night sweats, abdominal pain, and weight loss and was found to have a splenic mass on CT scan suspected of lymphoma. The splenic mass in second patient was found incidentally at the time of work up for kidney stones. The pathologic examination of these splenectomy specimens showed similar histologic features. However, the spindle cells were composed of EBV-infected follicular dendritic cells in one case whereas the second case lacked significant follicular dendritic cell proliferation and showed only focal EBV-infected cells suggesting that these proliferations are heterogenous in nature

Meta-learning of Sequential Strategies

In this report we review memory-based meta-learning as a tool for building sample-efficient strategies that learn from past experience to adapt to any task within a target class. Our goal is to equip the reader with the conceptual foundations of this tool for building new, scalable agents that operate on broad domains. To do so, we present basic algorithmic templates for building near-optimal predictors and reinforcement learners which behave as if they had a probabilistic model that allowed them to efficiently exploit task structure. Furthermore, we recast memory-based meta-learning within a Bayesian framework, showing that the meta-learned strategies are near-optimal because they amortize Bayes-filtered data, where the adaptation is implemented in the memory dynamics as a state-machine of sufficient statistics. Essentially, memory-based meta-learning translates the hard problem of probabilistic sequential inference into a regression problem.Comment: DeepMind Technical Report (15 pages, 6 figures

Communications Biophysics

Contains research objectives and summary of research on five research projects, with ten sub-topics.National Institutes of Health (Grant 1 RO1 NS10916-01)National Institutes of Health (Grant 5 RO1 NS11000-03)National Institutes of Health (Grant 1 RO1 NS11153-01)Harvard-M.I.T. Rehabilitation Engineering CenterU. S. Department of Health, Education, and Welfare (Grant 23-P-55854)National Institutes of Health (Grant 1 RO1 NS11680-01)National Institutes of Health (Grant 5 ROI NS11080-02)M.I.T. Health Sciences FundNational Aeronautics and Space Administration (Grant NSG-2032)National Institutes of Health (Grant 5 TO1 GM01555-09)Massachusetts General Hospital Purchase Order F63853Boston City Hospital Purchase Order 4338-7543

Predicting inpatient violence using an extended version of the Brøset-Violence-Checklist: instrument development and clinical application

BACKGROUND: Patient aggression is a common problem in acute psychiatric wards and calls for preventive measures. The timely use of preventive measures presupposes a preceded risk assessment. The Norwegian Brøset-Violence-Checklist (BVC) is one of the few instruments suited for short-time prediction of violence of psychiatric inpatients in routine care. Aims of our study were to improve the accuracy of the short-term prediction of violence in acute inpatient settings by combining the Brøset-Violence-Checklist (BVC) with an overall subjective clinical risk-assessment and to test the application of the combined measure in daily practice. METHOD: We conducted a prospective cohort study with two samples of newly admitted psychiatric patients for instrument development (219 patients) and clinical application (300 patients). Risk of physical attacks was assessed by combining the 6-item BVC and a 6-point score derived from a Visual Analog Scale. Incidents were registered with the Staff Observation of Aggression Scale-Revised SOAS-R. Test accuracy was described as the area under the receiver operating characteristic curve (AUC(ROC)). RESULTS: The AUC(ROC )of the new VAS-complemented BVC-version (BVC-VAS) was 0.95 in and 0.89 in the derivation and validation study respectively. CONCLUSION: The BVC-VAS is an easy to use and accurate instrument for systematic short-term prediction of violent attacks in acute psychiatric wards. The inclusion of the VAS-derived data did not change the accuracy of the original BVC

Semiautomated Device for Batch Extraction of Metabolites from Tissue Samples

ABSTRACT: Metabolomics has become a mainstream analytical strategy for investigating metabolism. The quality of data derived from these studies is proportional to the consistency of the sample preparation. Although considerable research has been devoted to finding optimal extraction protocols, most of the established methods require extensive sample handling. Manual sample preparation can be highly effective in the hands of skilled technicians, but an automated tool for purifying metabolites from complex biological tissues would be of obvious utility to the field. Here, we introduce the semiautomated metabolite batch extraction device (SAMBED), a new tool designed to simplify metabolomics sample preparation. We discuss SAMBED’s design and show that SAMBED-based extractions are of comparable quality to extracts produced through traditional methods (13 % mean coefficient of variation from SAMBED versus 16 % from manual extractions). Moreover, we show that aqueous SAMBED-based methods ca

Communications Biophysics

Contains research objectives and summary of research on nine research projects split into four sections.National Institutes of Health (Grant 5 ROI NS11000-03)National Institutes of Health (Grant 1 P01 NS13126-01)National Institutes of Health (Grant 1 RO1 NS11153-01)National Institutes of Health (Grant 2 R01 NS10916-02)Harvard-M.I.T. Rehabilitation Engineering CenterU. S. Department of Health, Education, and Welfare (Grant 23-P-55854)National Institutes of Health (Grant 1 ROl NS11680-01)National Institutes of Health (Grant 5 ROI NS11080-03)M.I.T. Health Sciences Fund (Grant 76-07)National Institutes of Health (Grant 5 T32 GM07301-02)National Institutes of Health (Grant 5 TO1 GM01555-10