N95 vs Half-face Respirator Wear in Surgical Trainees: Physiologic and Psychological Effects of Prolonged Use

Objectives: As specialists of the upper airway, otolaryngologists are at high risk for COVID-19 transmission. N95 and half-face respirator (HFR) masks are commonly worn, each with advantages in functionality and comfort. In this study, physiologic and psychological parameters of prolonged N95 vs HFR wear were compared. Study Design: Prospective crossover cohort study. Setting: Single academic tertiary care hospital. Methods: A prospective crossover cohort study was performed. Healthy otolaryngology trainees and medical students (N = 23) participated and wore N95 and HFR masks continuously for 3 hours each on separate days. Various measures were analyzed: vitals, spirometry variables, scores on the State-Trait Anxiety Inventory and HIT-6 (Headache Impact Test–6), distress, and “difficulty being understood.” Results: The average age was 26.3 years (SD, 3.42). There were no significant differences in vital signs and spirometry variables between N95 and HFR wear. N95 wear was associated with decreases in oxygen saturation of approximately 1.09% more than with HFRs (95% CI, 0.105-2.077). State-Trait Anxiety Inventory scores increased more with HFR wear when compared with mean changes with N95 wear (95% CI, 1.350-8.741). There were no significant differences in HIT-6 scores or distress levels between masks. The proportions of participants reporting difficulty being understood was significantly higher with HFRs. Conclusions: Oxygen saturation decreases with prolonged N95 wear, but anxiety and difficulty being understood are greater with HFR wear. Although HFRs have less resistance to gas exchange, N95 respirators may produce less anxiety and distress in clinical situations. Further studies are warranted to evaluate the clinical significance of these differences. Level of Evidence: 2

Fingerprint multiplex CARS at high speed based on supercontinuum generation in bulk media and deep learning spectral denoising

We introduce a broadband coherent anti-Stokes Raman scattering (CARS) microscope based on a 2-MHz repetition rate ytterbium laser generating 1035-nm high-energy (≈µJ level) femtosecond pulses. These features of the driving laser allow producing broadband red-shifted Stokes pulses, covering the whole fingerprint region (400-1800 cm-1), employing supercontinuum generation in a bulk crystal. Our system reaches state-of-the-art acquisition speed (<1 ms/pixel) and unprecedented sensitivity of ≈14.1 mmol/L when detecting dimethyl sulfoxide in water. To further improve the performance of the system and to enhance the signal-to-noise ratio of the CARS spectra, we designed a convolutional neural network for spectral denoising, coupled with a post-processing pipeline to distinguish different chemical species of biological tissues

Label-free multimodal nonlinear optical microscopy reveals features of bone composition in pathophysiological conditions

Bone tissue features a complex microarchitecture and biomolecular composition, which determine biomechanical properties. In addition to state-of-the-art technologies, innovative optical approaches allowing the characterization of the bone in native, label-free conditions can provide new, multi-level insight into this inherently challenging tissue. Here, we exploited multimodal nonlinear optical (NLO) microscopy, including co-registered stimulated Raman scattering, two-photon excited fluorescence, and second-harmonic generation, to image entire vertebrae of murine spine sections. The quantitative nature of these nonlinear interactions allowed us to extract accurate biochemical, morphological, and topological information on the bone tissue and to highlight differences between normal and pathologic samples. Indeed, in a murine model showing bone loss, we observed increased collagen and lipid content as compared to the wild type, along with a decreased craniocaudal alignment of bone collagen fibres. We propose that NLO microscopy can be implemented in standard histopathological analysis of bone in preclinical studies, with the ambitious future perspective to introduce this technique in the clinical practice for the analysis of larger tissue sections

Seismotectonics related to the Azores – Gibraltar Fracture Zone: Analysis of the February 12th 2007 earthquake, SW Gorringe Bank

This work deals with the analysis of the seismicity and tectonic evolution of the eastern end of the Azores – Gibraltar Fracture Zone. The location of the main seismogenetic areas in this region is related to the complex geometry of the boundary between the Iberian and African lithospheric plates. To the west of the San Vicente Cape the seismicity can be related to a local compression at the Gorringe Bank. A detailed seismotectonic analysis allows the geological interpretation of the position of the hypocenter for the 12th February 2007 earthquake. It had Mw 6.0 and was placed on a fault having a NNE-SSW strike and a high dip to NW. The fault shows an oblique displacement (sinistral) and locates in the margin of the Horseshoe abyssal basin. Displacement along this fault is here tentatively related to complex deformation in the outer swell of an incipient downgoing plate. This can be the first indication of the beginning of subduction of the northern part of the African plate under the continental margin of Iberia. On the other hand, an analysis of the location of earthquakes having Mw >6.0 generated to the SW of the San Vicente Cape and the estimated isoseismal map has been made. It can be noted the importance of the uppermost crustal materials on the seismic intensity measurement. Sea waves resulting from these earthquakes and measured in the littoral of the Huelva province have an average velocity of propagation of approximately 600 km/

Umbilical cord mesenchymal stem cells for COVID-19 acute respiratory distress syndrome: A double-blind, phase 1/2a, randomized controlled trial

Acute respiratory distress syndrome (ARDS) in COVID-19 is associated with high mortality. Mesenchymal stem cells are known to exert immunomodulatory and anti-inflammatory effects and could yield beneficial effects in COVID-19 ARDS. The objective of this study was to determine safety and explore efficacy of umbilical cord mesenchymal stem cell (UC-MSC) infusions in subjects with COVID-19 ARDS. A double-blind, phase 1/2a, randomized, controlled trial was performed. Randomization and stratification by ARDS severity was used to foster balance among groups. All subjects were analyzed under intention to treat design. Twenty-four subjects were randomized 1:1 to either UC-MSC treatment (n = 12) or the control group (n = 12). Subjects in the UC-MSC treatment group received two intravenous infusions (at day 0 and 3) of 100 ± 20 × 106 UC-MSCs; controls received two infusions of vehicle solution. Both groups received best standard of care. Primary endpoint was safety (adverse events [AEs]) within 6 hours; cardiac arrest or death within 24 hours postinfusion). Secondary endpoints included patient survival at 31 days after the first infusion and time to recovery. No difference was observed between groups in infusion-associated AEs. No serious adverse events (SAEs) were observed related to UC-MSC infusions. UC-MSC infusions in COVID-19 ARDS were found to be safe. Inflammatory cytokines were significantly decreased in UC-MSC-treated subjects at day 6. Treatment was associated with significantly improved patient survival (91% vs 42%, P =.015), SAE-free survival (P =.008), and time to recovery (P =.03). UC-MSC infusions are safe and could be beneficial in treating subjects with COVID-19 ARDS

Unsupervised Random Forests and Target Outcome Relationship Exploration

Random forest is a machine learning algorithm that has been applied to a variety of problems, though mostly in the supervised setting. Here, a new method of applying random forests to the unsupervised setting will be introduced, which we call sidClustering. sidClustering first involves what is called sidification of the features by: first, staggering the features so they have mutually exclusive ranges; and secondly, forming all pairwise interactions from these shifted variables. Sidification results in what are called the SID main features and the SID interaction features, respectively. Then a multivariate random forest (MVRF) from the randomForestSRC R-package (whose splitting rules can handle both continuous and categorical target variables at the same time) is used to predict the SID main features. Sidification in conjunction with MVRF provides a better way to carve out the data space which results in better measures of distance between observations. sidClustering’s advantages are that it is adept at finding clusters arising from categorical and/or continuous variables, requires minimal tuning (just like random forests), and retains all of the advantages of random forests regarding computational scaling for big data without distributional and specification assumptions. Later, we will discuss the development of a rule generator and method for estimating k (the number of clusters to be determined). The idea is that after the clusters have been determined, we need some way of describing the clusters with human readable output. This is done by first discretizing all the continuous features by utilizing the splits from a random forest and then create rules based on a small set of features that most drive the clusters. This method can also be applied to the semisupervised setting and gives us a method of peaking into the random forest model. As for the estimation of k, we take advantage of the existence of the OOB set in the random forest algorithm and test for the k that brings out the most stable clusters. The idea is that the correct k value should bring about the most consistent clusters. Lastly, we will go over covariate adjusted random forest statistics. These take advantage of the multivariate random forest framework to determine estimates of the variability and covariance between outcomes. The idea is that by utilizing random forest weights we are able to develop weighted versions of these statistics for individual observations.</p

Unsupervised random forests

sidClustering is a new random forests unsupervised machine learning algorithm. The first step in sidClustering involves what is called sidification of the features: staggering the features to have mutually exclusive ranges (called the staggered interaction data [SID] main features) and then forming all pairwise interactions (called the SID interaction features). Then a multivariate random forest (able to handle both continuous and categorical variables) is used to predict the SID main features. We establish uniqueness of sidification and show how multivariate impurity splitting is able to identify clusters. The proposed sidClustering method is adept at finding clusters arising from categorical and continuous variables and retains all the important advantages of random forests. The method is illustrated using simulated and real data as well as two in depth case studies, one from a large multi‐institutional study of esophageal cancer, and the other involving hospital charges for cardiovascular patients

Conditional uncertainty: Misinterpretations of “significant” p values

Misapplication of p values can lead to very misleading conclusions. The frequent insignificance of a “significant” p value 1provides examples illustrating situations where p values are misused in interpretation and describes alternatives to p values to consider. The concern about p values, and their potential shortcomings and misinterpretations, has been widely discussed and has received a lot of debate from the statistical community. While we agree with much of the content presented in the article, the issue fundamentally represents a misinterpretation of conditional probability, misapplication of measures regarding diagnostic accuracy, and consequently violations of assumptions inherent in experimental design and the validity of many approaches to statistical analysis and inference. One way this can be remedied is through team science, and collaboration between clinical and biostatistical scientists in research

Contextualizing COVID-19 spread: a county level analysis, urban versus rural, and implications for preparing for the next wave [version 1; peer review: 1 approved]

BACKGROUND: Contextual determinants of health including social, environmental, healthcare and others, are a so-called deck of cards one is dealt. The ability to modify health outcomes varies then based upon how one’s hand is played. It is thus of great interest to understand how these determinants associate with the emerging pandemic coronavirus disease 2019 (COVID-19). : METHODS: To this end, we conducted a deep-dive analysis into this problem using a recently curated public dataset on COVID-19 that connects infection spread over time to a rich collection of contextual determinants for all counties of the U.S and Washington, D.C. : RESULTS: Using random forest machine learning methodology, we identified a relevant constellation of contextual factors of disease spread which manifest differently for urban and rural counties. : CONCLUSIONS: The findings also have clear implications for better preparing for the next wave of disease