Nevin Manimala

Eur J Trauma Emerg Surg. 2023 Apr 8. doi: 10.1007/s00068-023-02267-z. Online ahead of print.

ABSTRACT

PURPOSE: As blunt thoracic aortic injury (BTAI) treatment has shifted from open to thoracic endovascular aortic repair (TEVAR), logistical challenges exist in creating and maintaining inventories of appropriately sized stent-grafts, including storage demands, shelf-life management and cost. We hypothesized that most injured aortas can be successfully repaired with a narrow range of stent-graft sizes and present a value-based anatomic approach to optimizing inventory.

METHODS: CT-scans of all patients with BTAI admitted to our Level I trauma center from Apr 2010-Dec 2018 were reviewed. Patients with anatomy incompatible with TEVAR were excluded. For each patient, after aortic sizing a set of two stent-grafts most likely to be utilized was selected from a list of twenty commercially available GORE conformable TAG endografts based on manufacturer instructions. Stent-graft sizes were then ranked based on the number of cases they would be suitable for. MATLAB was utilized to determine the combinations of stent-grafts which would cover the most patients.

RESULTS: Twenty-eight patients with BTAI were identified and three were excluded based on iliac diameter. Most patients were male (68%), mean age 42.3 ± 20.2 years, mean ISS 37.0 ± 9.8. Overall mortality was 25%. Of the 20 available stent-graft options, a combination of four stent-grafts would successfully treat 100% of the patients in this series.

CONCLUSIONS: Based on actual CT-scan aortic measurements, we demonstrated that an inventory of four sent-graft sizes was sufficient to treat 100% of patients with BTAI. These data can be utilized as a value-based anatomic approach to aortic stent-graft institutional inventory creation and maintenance.

PMID:37029792 | DOI:10.1007/s00068-023-02267-z

Diabet Med. 2023 Apr 8:e15108. doi: 10.1111/dme.15108. Online ahead of print.

ABSTRACT

AIMS: The contemporary prescription patterns of antidiabetic drugs following guideline changes recommending metformin as first-line gestational diabetes (GDM) pharmacotherapy is underexplored. We aimed to examined use of metformin and insulin during pregnancy among women with GDM over 20 years in the United Kingdom.

METHODS: We conducted a population-based cohort study using linked data from the Clinical Practice Research Datalink, its pregnancy register, and Hospital Episode Statistics from 1998-2017. We included pregnancies of women without prior diabetes history who received GDM diagnosis or initiated an antidiabetic drug after 20 weeks gestation. Patient-level and practice-level characteristics were compared between metformin initiators and insulin initiators. We described trends of initiating metformin as first-line treatment and described time to initiation of rescue insulin overall, and by body mass index among metformin initiators.

RESULTS: Our cohort included 5,633 pregnancies from 5,393 women with GDM, of whom 39.6% initiated pharmacotherapy (41% insulin, 59% metformin). Metformin prescriptions (as opposed to insulin) increased substantially, from < 5% of pregnancies before 2007 to 42.5%. Since 2008. Over 85% of pregnancies that were prescribed a pharmacotherapy were prescribed metformin as first-line treatment in 2015. Among metformin initiators, 16% initiated rescue insulin, typically occurring within 40 days of metformin initiation. Choice of GDM pharmacotherapy varied by characteristics, including smoking, obesity, race/ethnicity, and general practice regions.

CONCLUSIONS: Metformin was the most prescribed medication for GDM, with large increases over the past 2 decades. The increasing use of oral-antidiabetic drugs during pregnancy, consistent with other regions, highlights the need for future studies examining effectiveness and safety of antidiabetic drug use during pregnancy.

PMID:37029772 | DOI:10.1111/dme.15108

Photochem Photobiol. 2023 Apr 8. doi: 10.1111/php.13805. Online ahead of print.

ABSTRACT

Far-UVC, from filtered Krypton-Chloride lamps, is promising for reducing airborne transmission of disease. Whilst significant research has been undertaken to investigate skin safety of these lamps, less work has been undertaken on eye safety. There is limited data on human eye safety or discomfort from the deployment of this germicidal technology. In this pilot study, immediate and delayed eye discomfort were assessed in a simulated office environment with deployment of Krypton-Chloride lamps, located on the ceiling and directed downwards into the occupied room. Discomfort was assessed immediately post-exposure and several days after exposure using validated, Standard Patient Evaluation Eye Dryness (SPEED) and Ocular Surface Disease Index (OSDI) questionnaires. Our results show no significant eye discomfort or adverse effects from the deployment of Far-UVC in this simulated office environment, even when lamps were operated continuously with participants receiving head exposures of up to 50 mJcm^-2 . In addition, a statistically significant reduction in bacteria and fungi of 52% was observed. Far-UVC in this simulated office environment did not cause any clinically significant eye discomfort and was effective at reducing pathogens in the room. These results contribute an important step to further investigation of the interaction of Far-UVC with the human eye.

PMID:37029739 | DOI:10.1111/php.13805

Med Phys. 2023 Apr 8. doi: 10.1002/mp.16404. Online ahead of print.

ABSTRACT

BACKGROUND: Age-Related macular degeneration (AMD) is a major cause of irreversible central vision loss. The main reason for lost vision due to AMD is choroidal neovascularization (CNV). In the clinic, current treatments for CNV include photodynamic therapy, laser photocoagulation, and anti-vascular endothelial growth factor (VEGF) therapy.

PURPOSE: This study evaluates a novel treatment technique combining synchronized nanosecond laser pulses and ultrasound bursts, namely photo-mediated ultrasound therapy (PUT) as a potential treatment method for CNV, for its efficacy and safety in treatment of CNV via the experiments in a clinically-relevant rabbit model in vivo.

METHODS: CNV was created by subretinal injection of Matrigel and vascular endothelial growth factor (M&V) in 10 New Zealand white rabbits. Six rabbits were used in the PUT group. In the control groups, two rabbits were treated by laser-only, and two rabbits were treated by ultrasound-only. The treatment efficacy was evaluated through fundus photography and fluorescein angiography (FA) longitudinally for up to 4 weeks. Rabbits were sacrificed for histopathology at 3 months after treatment to examine the safety of PUT.

RESULTS: The fluorescein leakage on FA was quantified to longitudinally evaluate treatment outcome. Compared with baseline, the relative intensity index was reduced by 26.57% ± 8.66% at 3 days after treatment, 27.24% ± 6.21% at 1 week after treatment, 27.79% ± 2.61% at 2 weeks after treatment, and 32.12% ± 3.23% at 4 weeks after treatment, all with a statistically significant difference of P < 0.01. The comparison between the relative intensity indexes from the two control groups (laser-only treatment and ultrasound-only treatment) did not show any statistically significant difference at all time points. Safety evaluation at 3 months with histopathology demonstrated that the PUT did not result in morphologic changes to the neurosensory retina.

CONCLUSIONS: This study introduces PUT for the first time for the treatment of CNV. The results demonstrated good efficacy and safety of PUT to treat CNV in a clinically-relevant rabbit model. With a single session of treatment, PUT can safely reduce the leakage of CNV for at least 1 month after treatment. This article is protected by copyright. All rights reserved.

PMID:37029733 | DOI:10.1002/mp.16404

Am J Biol Anthropol. 2023 Apr 8. doi: 10.1002/ajpa.24746. Online ahead of print.

ABSTRACT

OBJECTIVES: The level of fluctuating asymmetry is suggested as a putative signal of developmental stability, thus according to this theoretical framework more symmetric individuals should be in better biological condition and have greater reproductive potential. Here we hypothesize that women with more symmetric faces have more successful reproduction.

METHODS: Data were collected from 164 postmenopausal Polish women. Facial photographs were taken and the overall facial asymmetry (OFA) was calculated. The associations between the OFA and reproductive parameters were analyzed using multiple regression models. Furthermore, the mediation analysis was conducted to test for the indirect effects of the OFA on reproductive success.

RESULTS: There was a statistically significant relationship between the OFA and the number of children born, which was mediated by the age at first reproduction (p = 0.03), however, the size of the effect was rather low. Women with more symmetric faces had an earlier age at first reproduction and, in consequence, a greater number of children.

DISCUSSION: As fluctuating asymmetry is suggested to be established in utero, these findings shed light on the possible life-long importance of developmental conditions in shaping women’s reproductive potential and performance.

PMID:37029695 | DOI:10.1002/ajpa.24746

IEEE Trans Pattern Anal Mach Intell. 2023 Mar 6;PP. doi: 10.1109/TPAMI.2023.3253390. Online ahead of print.

ABSTRACT

Highly realistic imaging and video synthesis have become possible and relatively simple tasks with the rapid growth of generative adversarial networks (GANs). GAN-related applications, such as DeepFake image and video manipulation and adversarial attacks, have been used to disrupt and confound the truth in images and videos over social media. DeepFake technology aims to synthesize high visual quality image content that can mislead the human vision system, while the adversarial perturbation attempts to mislead the deep neural networks to a wrong prediction. Defense strategy becomes difficult when adversarial perturbation and DeepFake are combined. This study examined a novel deceptive mechanism based on statistical hypothesis testing against DeepFake manipulation and adversarial attacks. Firstly, a deceptive model based on two isolated sub-networks was designed to generate two-dimensional random variables with a specific distribution for detecting the DeepFake image and video. This research proposes a maximum likelihood loss for training the deceptive model with two isolated sub-networks. Afterward, a novel hypothesis was proposed for a testing scheme to detect the DeepFake video and images with a well-trained deceptive model. The comprehensive experiments demonstrated that the proposed decoy mechanism could be generalized to compressed and unseen manipulation methods for both DeepFake and attack detection.

PMID:37028044 | DOI:10.1109/TPAMI.2023.3253390

IEEE Trans Neural Syst Rehabil Eng. 2023 Mar 6;PP. doi: 10.1109/TNSRE.2023.3253051. Online ahead of print.

ABSTRACT

Complementary Linear Filter (CLF) is a common techinque employed for estimating the ground projection of body Centre of Mass starting from ground reaction forces. This method fuses centre of pressure position and double integration of horizontal forces, selecting best cut-off frequencies for low-pass and high-pass filters. Classical Kalman filter is a substantially equivalent approach, as both methods rely on an overall quantification of error/noise and don’t analyze its origin and time-dependence. In order to overcome such limitations, a Time-Varying Kalman Filter (TVKF) is proposed in this paper: the effect of unknown variables is directly taken into account by employing a statistical description which is obtained from experimental data. To this end, in this paper we have employed a dataset of 8 walking healthy subjects: beside supplying gait cycles at different speeds, it deals with subjects in age of development and provides a wide range of body sizes, allowing therefore to assess the observers’ behaviour under different conditions. The comparison carried out between CLF and TVKF appears to highlight several advantages of the latter method in terms of better average performance and smaller variability. Results presented in this paper suggest that a strategy which incorporates a statistical description of unknown variables and a time-varying structure can yield a more reliable observer. The demonstrated methodology sets a tool that can undergo a broader investigation to be carried out including more subjects and different walking styles.

PMID:37028027 | DOI:10.1109/TNSRE.2023.3253051

IEEE Trans Biomed Eng. 2023 Mar 6;PP. doi: 10.1109/TBME.2023.3250650. Online ahead of print.

ABSTRACT

OBJECTIVE: electrical impedance tomography (EIT) is a promising technique for rapid and continuous bedside monitoring of lung function. Accurate and reliable EIT reconstruction of ventilation requires patient-specific shape information. However, this shape information is often not available and current EIT reconstruction methods typically have limited spatial fidelity. This study sought to develop a statistical shape model (SSM) of the torso and lungs and evaluate whether patient-specific predictions of torso and lung shape could enhance EIT reconstructions in a Bayesian framework.

METHODS: torso and lung finite element surface meshes were fitted to computed tomography data from 81 participants, and a SSM was generated using principal component analysis and regression analyses. Predicted shapes were implemented in a Bayesian EIT framework and were quantitatively compared to generic reconstruction methods.

RESULTS: Five principal shape modes explained 38% of the cohort variance in lung and torso geometry, and regression analysis yielded nine total anthropometrics and pulmonary function metrics that significantly predicted these shape modes. Incorporation of SSM-derived structural information enhanced the accuracy and reliability of the EIT reconstruction as compared to generic reconstructions, demonstrated by reduced relative error, total variation, and Mahalanobis distance.

CONCLUSION: As compared to deterministic approaches, Bayesian EIT afforded more reliable quantitative and visual interpretation of the reconstructed ventilation distribution. However, no conclusive improvement of reconstruction performance using patient specific structural information was observed as compared to the mean shape of the SSM.

SIGNIFICANCE: The presented Bayesian framework builds towards a more accurate and reliable method for ventilation monitoring via EIT.

PMID:37028024 | DOI:10.1109/TBME.2023.3250650

IEEE Trans Biomed Eng. 2023 Mar 6;PP. doi: 10.1109/TBME.2023.3253381. Online ahead of print.

ABSTRACT

OBJECTIVE: Development of a contact microphone-driven screening framework for the diagnosis of coexisting valvular heart diseases (VHDs).

METHODS: A sensitive accelerometer contact microphone (ACM) is employed to capture heart-induced acoustic components on the chest wall. Inspired by the human auditory system, ACM recordings are initially transformed into Mel-frequency cepstral coefficients (MFCCs) and their first and second derivatives, resulting in 3-channel images. An image-to-sequence translation network based on the convolution-meets-transformer (CMT) architecture is then applied to each image to find local and global dependencies in images, and predict a 5-digit binary sequence, where each digit corresponds to the presence of a specific type of VHD. The performance of the proposed framework is evaluated on 58 VHD patients and 52 healthy individuals using a 10-fold leave-subject-out cross-validation (10-LSOCV) approach.

RESULTS: Statistical analyses suggest an average sensitivity, specificity, accuracy, positive predictive value, and F1 score of 93.28%, 98.07%, 96.87%, 92.97%, and 92.4% respectively, for the detection of coexisting VHDs. Furthermore, areas under the curve (AUC) of 0.99 and 0.98 are respectively reported for the validation and test sets.

CONCLUSION: The high performances achieved prove that local and global features of ACM recordings effectively characterize heart murmurs associated with valvular abnormalities.

SIGNIFICANCE: Limited access of primary care physicians to echocardiography machines has resulted in a low sensitivity of 44% when using a stethoscope for the identification of heart murmurs. The proposed framework provides accurate decision-making on the presence of VHDs, thus reducing the number of undetected VHD patients in primary care settings.

PMID:37028021 | DOI:10.1109/TBME.2023.3253381

IEEE J Biomed Health Inform. 2023 Mar 2;PP. doi: 10.1109/JBHI.2023.3250429. Online ahead of print.

ABSTRACT

Deep learning approaches have shown great success in myocardium region segmentation in Cardiac MR (CMR) images. However, most of these often ignore irregularities such as protrusions, breaks in contour, etc. As a result, the common practice by clinicians is to manually correct the obtained outputs for the evaluation of myocardium condition. This paper aims to make the deep learning systems capable of handling the aforementioned irregularities and satisfy desired clinical constraints, necessary for various downstream clinical analysis. We propose a refinement model which imposes structural constraints on the outputs of the existing deep learning-based myocardium segmentation methods. The complete system is a pipeline of deep neural networks where an initial network performs myocardium segmentation as accurate as possible and the refinement network removes defects from the initial output to make it suitable for clinical decision support systems. We experiment with datasets collected from four different sources and observe consistent final segmentation outputs with improvement up to 8% in Dice Coefficient and up to 18 pixels in Hausdorff Distance due to the proposed refinement model. The proposed refinement strategy leads to qualitative and quantitative improvements in the performances of all the considered segmentation networks. Our work is an important step towards the development of a fully automatic myocardium segmentation system. It can also be generalized for other tasks where the object of interest has regular structure and the defects can be modelled statistically.

PMID:37028020 | DOI:10.1109/JBHI.2023.3250429