Clinical Trials Related To The Spine & Shoulder/elbow: Rates, Predictors, & Reasons For Termination

Clinical trials are key to the advancement of products and procedures related to musculoskeletal conditions. Unfortunately, many trials are terminated prior to completion. ClinicalTrials.gov is a registry and results database maintained by the National Library of Medicine that catalogs trial characteristics and tracks overall recruitment status (e.g., ongoing, completed, terminated) for each study as well as reasons for termination. Reasons for termination have not been specifically evaluated for musculoskeletal trials and those related to the spine and shoulder/elbow were selected for characterization and assessment of independent predictors of termination by comparing characteristics of completed and terminated trials. The ClinicalTrials.gov database was queried for all completed and terminated interventional studies using search terms built into the ClinicalTrials.gov search engine related to the spine on June 20, 2021 and those related to the shoulder/elbow on August 6, 2021, respectively. Trial characteristics and reasons for termination were abstracted. Univariate and multivariate analyses were performed to determine independent predictors of trial termination. For clinical trials related to the spine, a total of 969 were identified and characterized, of which 136 (14%) were terminated. Insufficient rate of participant accrual was the most frequently reported reason for trial termination, accounting for 33.8% of terminated trials. Multivariate analysis demonstrated increased odds of trial termination for industry-sponsorship (odds ratio [OR] = 1.59) relative to sponsorship from local groups, device studies (OR = 2.18) relative to investigations of drug or biological product(s), and phase II (OR = 3.07) relative to phase III studies (p \u3c 0.05 for each). For clinical trials related to the shoulder, a total of 662 were identified and characterized, of which 51 (8%) were terminated. Difficulties with participant recruitment and/or retention was the individual reason most frequently reported for trial termination, accounting for 51% of terminated clinical trials related to the shoulder. For clinical trials related to the shoulder, multivariate analysis of primary trial characteristics demonstrated increased odds of trial termination for industry-sponsorship (OR = 4.2, p = 0.001) relative to sponsorship from local groups, and blinded studies (OR = 45.8, p = 0.0003) relative to studies that did not implement any form of blinding. For clinical trials related to the elbow, a total of 126 were identified and characterized, of which 16 (13%) were terminated. Difficulties with participant recruitment and/or retention was the individual reason most frequently reported for trial termination, accounting for 38% of terminated clinical trials related to the elbow. For clinical trials related to the elbow, logistic regression did not reveal any of the primary trial characteristics evaluated to be correlated with odds of termination. Clinical trials related to the spine, shoulder, and elbow were terminated at a rate of 14%, 8%, and 13%, respectively. Overall, difficulties in the recruitment and/or retention of trial participants was the reason most frequently reported for trial termination. With significant resources put into clinical trials and the need to advance scientific objectives, independent predictors and reasons for trial termination should be considered and addressed, when possible, to optimize the completion rate of trials that are initiated

Photovoltaic Cell: Optimum Photon Utilisation

In the 21st century, global energy consumption has increased exponentially and hence, sustainable energy sources are essential to accommodate for this. Advancements within photovoltaics, in regards to light trapping, has demonstrated to be a promising field of dramatically improving the efficiency of solar cells. This improvement is done by using different nanostructures, which enables solar cells to use the light spectrum emitted more efficiently. The purpose of this meta study is to investigate irreversible entropic losses related to light trapping. In this respect, the observation is aimed at how nanostructures on a silicon substrate captures high energy incident photons. Furthermore, different types of nanostructures are then investigated and compared, using the étendue ratio during light trapping. It is predicted that étendue mismatching is a parasitic entropy generation variable, and that the matching has an effect on the open circuit voltage of the solar cell. Although solar cells do have their limiting efficiencies, according to the Shockley-Queisser theory and Yablonovitch limit, with careful engineering and manufacturing practices, these irreversible entropic losses could be minimized. Further research in energy losses, due to entropy generation, may guide nanostructures and photonics in exceeding past these limits.Keywords: Photovoltaic cell; Shockley-Queisser; Solar cell nanostructures; Solar cell intrinsic and extrinsic losses; entropy; étendue; light trapping; Shockley Queisser; Geometry; Meta-stud

Pan-cancer classifications of tumor histological images using deep learning

Histopathological images are essential for the diagnosis of cancer type and selection of optimal treatment. However, the current clinical process of manual inspection of images is time consuming and prone to intra- and inter-observer variability. Here we show that key aspects of cancer image analysis can be performed by deep convolutional neural networks (CNNs) across a wide spectrum of cancer types. In particular, we implement CNN architectures based on Google Inception v3 transfer learning to analyze 27815 H&E slides from 23 cohorts in The Cancer Genome Atlas in studies of tumor/normal status, cancer subtype, and mutation status. For 19 solid cancer types we are able to classify tumor/normal status of whole slide images with extremely high AUCs (0.995±0.008). We are also able to classify cancer subtypes within 10 tissue types with AUC values well above random expectations (micro-average 0.87±0.1). We then perform a cross-classification analysis of tumor/normal status across tumor types. We find that classifiers trained on one type are often effective in distinguishing tumor from normal in other cancer types, with the relationships among classifiers matching known cancer tissue relationships. For the more challenging problem of mutational status, we are able to classify TP53 mutations in three cancer types with AUCs from 0.65-0.80 using a fully-trained CNN, and with similar cross-classification accuracy across tissues. These studies demonstrate the power of CNNs for not only classifying histopathological images in diverse cancer types, but also for revealing shared biology between tumors. We have made software available at: https://github.com/javadnoorb/HistCNNFirst author draf

The European Federation of Organisations for Medical Physics policy statement no 14 : the role of the medical physicist in the management of safety within the magnetic resonance imaging environment : EFOMP recommendations

This European Federation of Organisations for Medical Physics (EFOMP) Policy Statement outlines the way in which a Safety Management System can be developed for MRI units. The Policy Statement can help eliminate or at least minimize accidents or incidents in the magnetic resonance environment and is recommended as a step towards harmonisation of safety of workers, patients, and the general public regarding the use of magnetic resonance imaging systems in diagnostic and interventional procedures.peer-reviewe

Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images.

Histopathological images are a rich but incompletely explored data type for studying cancer. Manual inspection is time consuming, making it challenging to use for image data mining. Here we show that convolutional neural networks (CNNs) can be systematically applied across cancer types, enabling comparisons to reveal shared spatial behaviors. We develop CNN architectures to analyze 27,815 hematoxylin and eosin scanned images from The Cancer Genome Atlas for tumor/normal, cancer subtype, and mutation classification. Our CNNs are able to classify TCGA pathologist-annotated tumor/normal status of whole slide images (WSIs) in 19 cancer types with consistently high AUCs (0.995 ± 0.008), as well as subtypes with lower but significant accuracy (AUC 0.87 ± 0.1). Remarkably, tumor/normal CNNs trained on one tissue are effective in others (AUC 0.88 ± 0.11), with classifier relationships also recapitulating known adenocarcinoma, carcinoma, and developmental biology. Moreover, classifier comparisons reveal intra-slide spatial similarities, with an average tile-level correlation of 0.45 ± 0.16 between classifier pairs. Breast cancers, bladder cancers, and uterine cancers have spatial patterns that are particularly easy to detect, suggesting these cancers can be canonical types for image analysis. Patterns for TP53 mutations can also be detected, with WSI self- and cross-tissue AUCs ranging from 0.65-0.80. Finally, we comparatively evaluate CNNs on 170 breast and colon cancer images with pathologist-annotated nuclei, finding that both cellular and intercellular regions contribute to CNN accuracy. These results demonstrate the power of CNNs not only for histopathological classification, but also for cross-comparisons to reveal conserved spatial behaviors across tumors

Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images

Histopathological images are a rich but incompletely explored data type for studying cancer. Manual inspection is time consuming, making it challenging to use for image data mining. Here we show that convolutional neural networks (CNNs) can be systematically applied across cancer types, enabling comparisons to reveal shared spatial behaviors. We develop CNN architectures to analyze 27,815 hematoxylin and eosin scanned images from The Cancer Genome Atlas for tumor/normal, cancer subtype, and mutation classification. Our CNNs are able to classify TCGA pathologist-annotated tumor/normal status of whole slide images (WSIs) in 19 cancer types with consistently high AUCs (0.995 ± 0.008), as well as subtypes with lower but significant accuracy (AUC 0.87 ± 0.1). Remarkably, tumor/normal CNNs trained on one tissue are effective in others (AUC 0.88 ± 0.11), with classifier relationships also recapitulating known adenocarcinoma, carcinoma, and developmental biology. Moreover, classifier comparisons reveal intra-slide spatial similarities, with an average tile-level correlation of 0.45 ± 0.16 between classifier pairs. Breast cancers, bladder cancers, and uterine cancers have spatial patterns that are particularly easy to detect, suggesting these cancers can be canonical types for image analysis. Patterns for TP53 mutations can also be detected, with WSI self- and cross-tissue AUCs ranging from 0.65-0.80. Finally, we comparatively evaluate CNNs on 170 breast and colon cancer images with pathologist-annotated nuclei, finding that both cellular and intercellular regions contribute to CNN accuracy. These results demonstrate the power of CNNs not only for histopathological classification, but also for cross-comparisons to reveal conserved spatial behaviors across tumors.R01 CA230031 - NCI NIH HHSPublished versio

Iraq/Afghanistan war lung injury reflects burn pits exposure

This descriptive case series retrospectively reviewed medical records from thirty-one previously healthy, war-fighting veterans who self-reported exposure to airborne hazards while serving in Iraq and Afghanistan between 2003 and the present. They all noted new-onset dyspnea, which began during deployment or as a military contractor. Twenty-one subjects underwent non-invasive pulmonary diagnostic testing, including maximum expiratory pressure (MEP) and impulse oscillometry (IOS). In addition, five soldiers received a lung biopsy; tissue results were compared to a previously published sample from a soldier in our Iraq Afghanistan War Lung Injury database and others in our database with similar exposures, including burn pits. We also reviewed civilian control samples (5) from the Stony Brook University database. Military personnel were referred to our International Center of Excellence in Deployment Health and Medical Geosciences, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell under the auspices of Northwell IRB: 17-0140-FIMR Feinstein Institution for Medical Research "Clinicopathologic characteristics of Iraq Afghanistan War Lung Injury." We retrospectively examined medical records, including exposure data, radiologic imaging, and non-invasive pulmonary function testing (MGC Diagnostic Platinum Elite Plethysmograph) using the American Thoracic Society (ATS) standard interpretation based on Morgan et al., and for a limited cohort, biopsy data. Lung tissue, when available, was examined for carbonaceous particles, polycyclic aromatic hydrocarbons (Raman spectroscopy), metals, titanium connected to iron (Brookhaven National Laboratory, National Synchrotron Light Source II, Beamline 5-ID), oxidized metals, combustion temperature, inflammatory cell accumulation and fibrosis, neutrophil extracellular traps, Sirius red, Prussian Blue, as well as polarizable crystals/particulate matter/dust. Among twenty-one previously healthy, deployable soldiers with non-invasive pulmonary diagnostic tests, post-deployment, all had severely decreased MEP values, averaging 42% predicted. These same patients concurrently demonstrated abnormal airways reactance (X5Hz) and peripheral/distal airways resistance (D5-D20%) via IOS, averaging - 1369% and 23% predicted, respectively. These tests support the concept of airways hyperresponsiveness and distal airways narrowing, respectively. Among the five soldiers biopsied, all had constrictive bronchiolitis. We detected the presence of polycyclic aromatic hydrocarbons (PAH)-which are products of incomplete combustion-in the lung tissue of all five warfighters. All also had detectable titanium and iron in the lungs. Metals were all oxidized, supporting the concept of inhaling burned metals. Combustion temperature was consistent with that of burned petrol rather than higher temperatures noted with cigarettes. All were nonsmokers. Neutrophil extracellular traps were reported in two biopsies. Compared to our prior biopsies in our Middle East deployment database, these histopathologic results are similar, since all database biopsies have constrictive bronchiolitis, one has lung fibrosis with titanium bound to iron in fixed mathematical ratios of 1:7 and demonstrated polarizable crystals. These results, particularly constrictive bronchiolitis and polarizable crystals, support the prior data of King et al. (N. Engl. J. Med. 365:222-230, 2011) Soldiers in this cohort deployed to Iraq and Afghanistan since 2003, with exposure to airborne hazards, including sandstorms, burn pits, and improvised explosive devices, are at high risk for developing chronic clinical respiratory problems, including: (1) reduction in respiratory muscle strength; (2) airways hyperresponsiveness; and (3) distal airway narrowing, which may be associated with histopathologic evidence of lung damage, reflecting inhalation of burned particles from burn pits along with particulate matter/dust. Non-invasive pulmonary diagnostic tests are a predictor of burn pit-induced lung injury

Relevance of Norepinephrine–Dopamine Interactions in the Treatment of Major Depressive Disorder

Central dopaminergic and noradrenergic systems play essential roles in controlling several forebrain functions. Consequently, perturbations of these neurotransmissions may contribute to the pathophysiology of neuropsychiatric disorders. For many years, there was a focus on the serotonin (5-HT) system because of the efficacy of selective serotonin reuptake inhibitors (SSRIs), the most prescribed antidepressants in the treatment of major depressive disorder (MDD). Given the interconnectivity within the monoaminergic network, any action on one system may reverberate in the other systems. Analysis of this network and its dysfunctions suggests that drugs with selective or multiple modes of action on dopamine (DA) and norepinephrine (NE) may have robust therapeutic effects. This review focuses on NE-DA interactions as demonstrated in electrophysiological and neurochemical studies, as well as on the mechanisms of action of agents with either selective or dual actions on DA and NE. Understanding the mode of action of drugs targeting these catecholaminergic neurotransmitters can improve their utilization in monotherapy and in combination with other compounds particularly the SSRIs. The elucidation of such relationships can help design new treatment strategies for MDD, especially treatment-resistant depression

Elective surgery cancellations due to the COVID-19 pandemic: global predictive modelling to inform surgical recovery plans.

BACKGROUND: The COVID-19 pandemic has disrupted routine hospital services globally. This study estimated the total number of adult elective operations that would be cancelled worldwide during the 12 weeks of peak disruption due to COVID-19. METHODS: A global expert response study was conducted to elicit projections for the proportion of elective surgery that would be cancelled or postponed during the 12 weeks of peak disruption. A Bayesian β-regression model was used to estimate 12-week cancellation rates for 190 countries. Elective surgical case-mix data, stratified by specialty and indication (surgery for cancer versus benign disease), were determined. This case mix was applied to country-level surgical volumes. The 12-week cancellation rates were then applied to these figures to calculate the total number of cancelled operations. RESULTS: The best estimate was that 28 404 603 operations would be cancelled or postponed during the peak 12 weeks of disruption due to COVID-19 (2 367 050 operations per week). Most would be operations for benign disease (90·2 per cent, 25 638 922 of 28 404 603). The overall 12-week cancellation rate would be 72·3 per cent. Globally, 81·7 per cent of operations for benign conditions (25 638 922 of 31 378 062), 37·7 per cent of cancer operations (2 324 070 of 6 162 311) and 25·4 per cent of elective caesarean sections (441 611 of 1 735 483) would be cancelled or postponed. If countries increased their normal surgical volume by 20 per cent after the pandemic, it would take a median of 45 weeks to clear the backlog of operations resulting from COVID-19 disruption. CONCLUSION: A very large number of operations will be cancelled or postponed owing to disruption caused by COVID-19. Governments should mitigate against this major burden on patients by developing recovery plans and implementing strategies to restore surgical activity safely

Global wealth disparities drive adherence to COVID-safe pathways in head and neck cancer surgery

Peer reviewe