Modelling the Physics of Bubble Nucleation in Histotripsy

This work aims to establish a theoretical framework for the modeling of bubble nucleation in histotripsy. A phenomenological version of the classical nucleation theory was parametrized with histotripsy experimental data, fitting a temperature-dependent activity factor that harmonizes theoretical predictions and experimental data for bubble nucleation at both high and low temperatures. Simulations of histotripsy pressure and temperature fields are then used in order to understand spatial and temporal properties of bubble nucleation at varying sonication conditions. This modeling framework offers a thermodynamic understanding on the role of the ultrasound frequency, waveforms, peak focal pressures, and duty cycle on patterns of ultrasound-induced bubble nucleation. It was found that at temperatures lower than 50 °C, nucleation rates are more appreciable at very large negative pressures such as -30 MPa. For focal peak-negative pressures of -15 MPa, characteristic of boiling histotripsy, nucleation rates grow by 20 orders of magnitude in the temperature interval 60 °C-100 °C

Potential associations between behavior change techniques and engagement with mobile health apps: a systematic review

Introduction: Lack of engagement is a common challenge for digital health interventions. To achieve their potential, it is necessary to understand how best to support users’ engagement with interventions and target health behaviors. The aim of this systematic review was to identify the behavioral theories and behavior change techniques being incorporated into mobile health apps and how they are associated with the different components of engagement. Methods: The review was structured using the PRISMA and PICOS frameworks and searched six databases in July 2022: PubMed, Embase, CINAHL, APA PsycArticles, ScienceDirect, and Web of Science. Risk of bias was evaluated using the Cochrane Collaboration Risk of Bias 2 and the Mixed Methods Appraisal Tools. Analysis: A descriptive analysis provided an overview of study and app characteristics and evidence for potential associations between Behavior Change Techniques (BCTs) and engagement was examined. Results: The final analysis included 28 studies. Six BCTs were repeatedly associated with user engagement: goal setting, self-monitoring of behavior, feedback on behavior, prompts/cues, rewards, and social support. There was insufficient data reported to examine associations with specific components of engagement, but the analysis indicated that the different components were being captured by various measures. Conclusion: This review provides further evidence supporting the use of common BCTs in mobile health apps. To enable developers to leverage BCTs and other app features to optimize engagement in specific contexts and individual characteristics, we need a better understanding of how BCTs are associated with different components of engagement. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42022312596

Incidence of accidental awareness during general anaesthesia in obstetrics: a multicentre, prospective cohort study

General anaesthesia for obstetric surgery has distinct characteristics that may contribute towards a higher risk of accidental awareness during general anaesthesia. The primary aim of this study was to investigate the incidence, experience and psychological implications of unintended conscious awareness during general anaesthesia in obstetric patients. From May 2017 to August 2018, 3115 consenting patients receiving general anaesthesia for obstetric surgery in 72 hospitals in England were recruited to the study. Patients received three repetitions of standardised questioning over 30 days, with responses indicating memories during general anaesthesia that were verified using interviews and record interrogation. A total of 12 patients had certain/probable or possible awareness, an incidence of 1 in 256 (95%CI 149–500) for all obstetric surgery. The incidence was 1 in 212 (95%CI 122–417) for caesarean section surgery. Distressing experiences were reported by seven (58.3%) patients, paralysis by five (41.7%) and paralysis with pain by two (16.7%). Accidental awareness occurred during induction and emergence in nine (75%) of the patients who reported awareness. Factors associated with accidental awareness during general anaesthesia were: high BMI (25–30 kg.m-2); low BMI (<18.5 kg.m-2); out-of-hours surgery; and use of ketamine or thiopental for induction. Standardised psychological impact scores at 30 days were significantly higher in awareness patients (median (IQR [range]) 15 (2.7–52.0 [2–56]) than in patients without awareness 3 (1–9 [0–64]), p = 0.010. Four patients had a provisional diagnosis of post-traumatic stress disorder. We conclude that direct postoperative questioning reveals high rates of accidental awareness during general anaesthesia for obstetric surgery, which has implications for anaesthetic practice, consent and follow-up

Incidence of accidental awareness during general anaesthesia in obstetrics: a multi-centre, prospective cohort study

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Fracturing ranked surfaces

Discretized landscapes can be mapped onto ranked surfaces, where every element (site or bond) has a unique rank associated with its corresponding relative height. By sequentially allocating these elements according to their ranks and systematically preventing the occupation of bridges, namely elements that, if occupied, would provide global connectivity, we disclose that bridges hide a new tricritical point at an occupation fraction $p=p_{c}$, where $p_{c}$ is the percolation threshold of random percolation. For any value of $p$ in the interval $p_{c}< p \leq 1$, our results show that the set of bridges has a fractal dimension $d_{BB} \approx 1.22$ in two dimensions. In the limit $p \rightarrow 1$, a self-similar fracture is revealed as a singly connected line that divides the system in two domains. We then unveil how several seemingly unrelated physical models tumble into the same universality class and also present results for higher dimensions

Highly Sensitive Detection of Individual HEAT and ARM Repeats with HHpred and COACH

BACKGROUND:HEAT and ARM repeats occur in a large number of eukaryotic proteins. As these repeats are often highly diverged, the prediction of HEAT or ARM domains can be challenging. Except for the most clear-cut cases, identification at the individual repeat level is indispensable, in particular for determining domain boundaries. However, methods using single sequence queries do not have the sensitivity required to deal with more divergent repeats and, when applied to proteins with known structures, in some cases failed to detect a single repeat. METHODOLOGY AND PRINCIPAL FINDINGS:Testing algorithms which use multiple sequence alignments as queries, we found two of them, HHpred and COACH, to detect HEAT and ARM repeats with greatly enhanced sensitivity. Calibration against experimentally determined structures suggests the use of three score classes with increasing confidence in the prediction, and prediction thresholds for each method. When we applied a new protocol using both HHpred and COACH to these structures, it detected 82% of HEAT repeats and 90% of ARM repeats, with the minimum for a given protein of 57% for HEAT repeats and 60% for ARM repeats. Application to bona fide HEAT and ARM proteins or domains indicated that similar numbers can be expected for the full complement of HEAT/ARM proteins. A systematic screen of the Protein Data Bank for false positive hits revealed their number to be low, in particular for ARM repeats. Double false positive hits for a given protein were rare for HEAT and not at all observed for ARM repeats. In combination with fold prediction and consistency checking (multiple sequence alignments, secondary structure prediction, and position analysis), repeat prediction with the new HHpred/COACH protocol dramatically improves prediction in the twilight zone of fold prediction methods, as well as the delineation of HEAT/ARM domain boundaries. SIGNIFICANCE:A protocol is presented for the identification of individual HEAT or ARM repeats which is straightforward to implement. It provides high sensitivity at a low false positive rate and will therefore greatly enhance the accuracy of predictions of HEAT and ARM domains

Benznidazole biotransformation and multiple targets in <i>Trypanosoma</i> cruzi revealed by metabolomics

&lt;b&gt;Background&lt;/b&gt;&lt;p&gt;&lt;/p&gt; The first line treatment for Chagas disease, a neglected tropical disease caused by the protozoan parasite Trypanosoma cruzi, involves administration of benznidazole (Bzn). Bzn is a 2-nitroimidazole pro-drug which requires nitroreduction to become active, although its mode of action is not fully understood. In the present work we used a non-targeted MS-based metabolomics approach to study the metabolic response of T. cruzi to Bzn.&lt;p&gt;&lt;/p&gt; &lt;b&gt;Methodology/Principal findings&lt;/b&gt;&lt;p&gt;&lt;/p&gt; Parasites treated with Bzn were minimally altered compared to untreated trypanosomes, although the redox active thiols trypanothione, homotrypanothione and cysteine were significantly diminished in abundance post-treatment. In addition, multiple Bzn-derived metabolites were detected after treatment. These metabolites included reduction products, fragments and covalent adducts of reduced Bzn linked to each of the major low molecular weight thiols: trypanothione, glutathione, γ-glutamylcysteine, glutathionylspermidine, cysteine and ovothiol A. Bzn products known to be generated in vitro by the unusual trypanosomal nitroreductase, TcNTRI, were found within the parasites, but low molecular weight adducts of glyoxal, a proposed toxic end-product of NTRI Bzn metabolism, were not detected.&lt;p&gt;&lt;/p&gt; &lt;b&gt;Conclusions/significance&lt;/b&gt;&lt;p&gt;&lt;/p&gt; Our data is indicative of a major role of the thiol binding capacity of Bzn reduction products in the mechanism of Bzn toxicity against T. cruzi

The AFLOW Fleet for Materials Discovery

The traditional paradigm for materials discovery has been recently expanded to incorporate substantial data driven research. With the intent to accelerate the development and the deployment of new technologies, the AFLOW Fleet for computational materials design automates high-throughput first principles calculations, and provides tools for data verification and dissemination for a broad community of users. AFLOW incorporates different computational modules to robustly determine thermodynamic stability, electronic band structures, vibrational dispersions, thermo-mechanical properties and more. The AFLOW data repository is publicly accessible online at aflow.org, with more than 1.7 million materials entries and a panoply of queryable computed properties. Tools to programmatically search and process the data, as well as to perform online machine learning predictions, are also available.Comment: 14 pages, 8 figure