Advances in the synthesis of acyclic peroxides

Peroxide-containing compounds are an attractive synthetic target, given their widespread abundance in nature, with many displaying potent antimalarial and antimicrobial properties. This review summarises the many developments in the synthesis of acyclic peroxides, with a particular focus on the past 20 years, and seeks to update organic chemists about these new approaches. The synthetic methodologies have been subdivided into metal-catalysed reactions, organocatalytic reactions, direct oxidation reactions, miscellaneous reactions and enzymatic routes to acyclic peroxides

Tumor Susceptibility Gene 101 (TSG101) Is a Novel Binding-Partner for the Class II Rab11-FIPs

The Rab11-FIPs (Rab11-family interacting proteins; henceforth, FIPs) are a family of Rab11a/Rab11b/Rab25 GTPase effector proteins implicated in an assortment of intracellular trafficking processes. Through proteomic screening, we have identified TSG101 (tumor susceptibility gene 101), a component of the ESCRT-I (endosomal sorting complex required for transport) complex, as a novel FIP4-binding protein, which we find can also bind FIP3. We show that α-helical coiled-coil regions of both TSG101 and FIP4 mediate the interaction with the cognate protein, and that point mutations in the coiled-coil regions of both TSG101 and FIP4 abrogate the interaction. We find that expression of TSG101 and FIP4 mutants cause cytokinesis defects, but that the TSG101-FIP4 interaction is not required for localisation of TSG101 to the midbody/Flemming body during abscission. Together, these data suggest functional overlap between Rab11-controlled processes and components of the ESCRT pathway

Synthetic white balancing for intra-operative hyperspectral imaging

Hyperspectral imaging shows promise for surgical applications to non-invasively provide spatially-resolved, spectral information. For calibration purposes, a white reference image of a highly-reflective Lambertian surface should be obtained under the same imaging conditions. Standard white references are not sterilizable, and so are unsuitable for surgical environments. We demonstrate the necessity for in situ white references and address this by proposing a novel, sterile, synthetic reference construction algorithm. The use of references obtained at different distances and lighting conditions to the subject were examined. Spectral and color reconstructions were compared with standard measurements qualitatively and quantitatively, using $\Delta E$ and normalised RMSE respectively. The algorithm forms a composite image from a video of a standard sterile ruler, whose imperfect reflectivity is compensated for. The reference is modelled as the product of independent spatial and spectral components, and a scalar factor accounting for gain, exposure, and light intensity. Evaluation of synthetic references against ideal but non-sterile references is performed using the same metrics alongside pixel-by-pixel errors. Finally, intraoperative integration is assessed though cadaveric experiments. Improper white balancing leads to increases in all quantitative and qualitative errors. Synthetic references achieve median pixel-by-pixel errors lower than 6.5% and produce similar reconstructions and errors to an ideal reference. The algorithm integrated well into surgical workflow, achieving median pixel-by-pixel errors of 4.77%, while maintaining good spectral and color reconstruction.Comment: 22 pages, 10 figure

Lightfield hyperspectral imaging in neuro-oncology surgery: an IDEAL 0 and 1 study

IntroductionHyperspectral imaging (HSI) has shown promise in the field of intra-operative imaging and tissue differentiation as it carries the capability to provide real-time information invisible to the naked eye whilst remaining label free. Previous iterations of intra-operative HSI systems have shown limitations, either due to carrying a large footprint limiting ease of use within the confines of a neurosurgical theater environment, having a slow image acquisition time, or by compromising spatial/spectral resolution in favor of improvements to the surgical workflow. Lightfield hyperspectral imaging is a novel technique that has the potential to facilitate video rate image acquisition whilst maintaining a high spectral resolution. Our pre-clinical and first-in-human studies (IDEAL 0 and 1, respectively) demonstrate the necessary steps leading to the first in-vivo use of a real-time lightfield hyperspectral system in neuro-oncology surgery.MethodsA lightfield hyperspectral camera (Cubert Ultris ×50) was integrated in a bespoke imaging system setup so that it could be safely adopted into the open neurosurgical workflow whilst maintaining sterility. Our system allowed the surgeon to capture in-vivo hyperspectral data (155 bands, 350–1,000 nm) at 1.5 Hz. Following successful implementation in a pre-clinical setup (IDEAL 0), our system was evaluated during brain tumor surgery in a single patient to remove a posterior fossa meningioma (IDEAL 1). Feedback from the theater team was analyzed and incorporated in a follow-up design aimed at implementing an IDEAL 2a study.ResultsFocusing on our IDEAL 1 study results, hyperspectral information was acquired from the cerebellum and associated meningioma with minimal disruption to the neurosurgical workflow. To the best of our knowledge, this is the first demonstration of HSI acquisition with 100+ spectral bands at a frame rate over 1Hz in surgery.DiscussionThis work demonstrated that a lightfield hyperspectral imaging system not only meets the design criteria and specifications outlined in an IDEAL-0 (pre-clinical) study, but also that it can translate into clinical practice as illustrated by a successful first in human study (IDEAL 1). This opens doors for further development and optimisation, given the increasing evidence that hyperspectral imaging can provide live, wide-field, and label-free intra-operative imaging and tissue differentiation

Impact of COVID-19 on cardiovascular testing in the United States versus the rest of the world

Objectives: This study sought to quantify and compare the decline in volumes of cardiovascular procedures between the United States and non-US institutions during the early phase of the coronavirus disease-2019 (COVID-19) pandemic. Background: The COVID-19 pandemic has disrupted the care of many non-COVID-19 illnesses. Reductions in diagnostic cardiovascular testing around the world have led to concerns over the implications of reduced testing for cardiovascular disease (CVD) morbidity and mortality. Methods: Data were submitted to the INCAPS-COVID (International Atomic Energy Agency Non-Invasive Cardiology Protocols Study of COVID-19), a multinational registry comprising 909 institutions in 108 countries (including 155 facilities in 40 U.S. states), assessing the impact of the COVID-19 pandemic on volumes of diagnostic cardiovascular procedures. Data were obtained for April 2020 and compared with volumes of baseline procedures from March 2019. We compared laboratory characteristics, practices, and procedure volumes between U.S. and non-U.S. facilities and between U.S. geographic regions and identified factors associated with volume reduction in the United States. Results: Reductions in the volumes of procedures in the United States were similar to those in non-U.S. facilities (68% vs. 63%, respectively; p = 0.237), although U.S. facilities reported greater reductions in invasive coronary angiography (69% vs. 53%, respectively; p < 0.001). Significantly more U.S. facilities reported increased use of telehealth and patient screening measures than non-U.S. facilities, such as temperature checks, symptom screenings, and COVID-19 testing. Reductions in volumes of procedures differed between U.S. regions, with larger declines observed in the Northeast (76%) and Midwest (74%) than in the South (62%) and West (44%). Prevalence of COVID-19, staff redeployments, outpatient centers, and urban centers were associated with greater reductions in volume in U.S. facilities in a multivariable analysis. Conclusions: We observed marked reductions in U.S. cardiovascular testing in the early phase of the pandemic and significant variability between U.S. regions. The association between reductions of volumes and COVID-19 prevalence in the United States highlighted the need for proactive efforts to maintain access to cardiovascular testing in areas most affected by outbreaks of COVID-19 infection

High-throughput molecular imaging via deep learning enabled Raman spectroscopy

Raman spectroscopy enables non-destructive, label-free imaging with unprecedented molecular contrast but is limited by slow data acquisition, largely preventing high-throughput imaging applications. Here, we present a comprehensive framework for higher-throughput molecular imaging via deep learning enabled Raman spectroscopy, termed DeepeR, trained on a large dataset of hyperspectral Raman images, with over 1.5 million spectra (400 hours of acquisition) in total. We firstly perform denoising and reconstruction of low signal-to-noise ratio Raman molecular signatures via deep learning, with a 9x improvement in mean squared error over state-of-the-art Raman filtering methods. Next, we develop a neural network for robust 2-4x super-resolution of hyperspectral Raman images that preserves molecular cellular information. Combining these approaches, we achieve Raman imaging speed-ups of up to 160x, enabling high resolution, high signal-to-noise ratio cellular imaging in under one minute. Finally, transfer learning is applied to extend DeepeR from cell to tissue-scale imaging. DeepeR provides a foundation that will enable a host of higher-throughput Raman spectroscopy and molecular imaging applications across biomedicine

Hybrid confocal Raman endomicroscopy for morpho-chemical tissue characterization

Confocal laser endomicroscopy (CLE) offers imaging of tissue microarchitecture and has emerged as a promising tool for in vivo clinical diagnosis of cancer across many organs. CLE, however, can show high inter-observer dependency and does not provide information about tissue molecular composition. In contrast, Raman spectroscopy is a label-free optical technique that provides detailed biomolecular compositional information but offers limited or no morphological information. Here we present a novel hybrid fiber-optic confocal Raman endomicroscopy system for morpho-chemical tissue imaging and analysis. The developed confocal endomicroscopy system is based on a novel detection scheme for rejecting Raman silica fiber interference permitting simultaneous CLE imaging and Raman spectral acquisition of tissues through a coherent fiber bundle. We show that this technique enables real-time microscopic visualization of tissue architecture as well as simultaneous pointwise label-free biomolecular characterization and fingerprinting of tissue paving the way for multimodal diagnostics at endoscopy

Tuning the mechanical and morphological properties of self-assembled peptide hydrogels via control over the gelation mechanism through regulation of ionic strength and the rate of pH change

Hydrogels formed by the self-assembly of peptides are promising biomaterials. The bioactive and biocompatible molecule Fmoc-FRGDF has been shown to be an efficient hydrogelator via a &pi;-&beta; self-assembly mechanism. Herein, we show that the mechanical properties and morphology of Fmoc-FRGDF hydrogels can be effectively and easily manipulated by tuning both the final ionic strength and the rate of pH change. The increase of ionic strength, and consequent increase in rate of gelation and stiffness, does not interfere with the underlying &pi;-&beta; assembly of this Fmoc-protected peptide. However, by tuning the changing rate of the system\u27s pH through the use of glucono-&delta;-lactone to form a hydrogel, as opposed to the previously reported HCl methodology, the morphology (nano- and microscale) of the scaffold can be manipulated