The era of nano-bionic: 2D materials for wearable and implantable body sensors

Nano-bionics have the potential of revolutionizing modern medicine. Among nano-bionic devices, body sensors allow to monitor in real-time the health of patients, to achieve personalized medicine, and even to restore or enhance human functions. The advent of two-dimensional (2D) materials is facilitating the manufacturing of miniaturized and ultrathin bioelectronics, that can be easily integrated in the human body. Their unique electronic properties allow to efficiently transduce physical and chemical stimuli into electric current. Their flexibility and nanometric thickness facilitate the adaption and adhesion to human body. The low opacity permits to obtain transparent devices. The good cellular adhesion and reduced cytotoxicity are advantageous for the integration of the devices in vivo. Herein we review the latest and more significant examples of 2D material-based sensors for health monitoring, describing their architectures, sensing mechanisms, advantages and, as well, the challenges and drawbacks that hampers their translation into commercial clinical devices

A priori choice of neuraxial labour analgesia and breastfeeding initiation success: A community-based cohort study in an Italian baby-friendly hospital

Objective To investigate whether the nature of the decision about receiving neuraxial labour analgesia is associated with breastfeeding initiation success (BIS), defined as exclusive breastfeeding until discharge associated with postnatal weight loss <7% at 60 hours from birth. Design Single-centre community-based cohort study. Setting An Italian baby-friendly hospital, from 1 July 2011 to 22 September 2015. Participants Inclusion criteria: women vaginally delivering singleton cephalic newborns and willing to breastfeed. Exclusion criteria: women who delivered in uterus-dead fetuses, were single or requested but did not receive neuraxial analgesia. Overall, 775 out of the 3628 enrolled women received neuraxial analgesia. Results Compared with women who tried to cope with labour pain, those who decided a priori to receive neuraxial analgesia had less BIS (planned vaginal birth: 2121/3421 (62.0%), vs 102/207 (49.3%; p<0.001; risk difference (RD), 12.7%); actual vaginal birth: 1924/2994 (64.3%), vs 93/189 (49.2%; p<0.001; RD, 15.1%)). Multivariable analyses with antelabour-only confounders confirmed both associations (planned vaginal birth: relative risk (RR), 0.65; 95% CI, 0.48 to 0.87; actual vaginal birth: RR, 0.59; 95% CI, 0.43 to 0.80). Although women who requested analgesia as a last resort had less BIS than did those successfully coping with labour pain in the bivariable analyses (planned vaginal birth: 1804/2853 (63.2%), vs 317/568 (55.8%; p=0.001; RD, 7.4%); actual vaginal birth: 1665/2546 (65.4%), vs 259/448 (57.8%; p=0.002; RD, 7.6%)), multivariable analyses with either antelabour-only or peripartum confounders did not confirm these associations (planned vaginal birth: RR, 0.99; 95% CI, 0.80 to 1.23; actual vaginal birth: RR, 0.90; 95% CI, 0.69 to 1.16). Conclusions Compared with trying to cope with labour pain, a priori choice of neuraxial analgesia is negatively associated with BIS. Conversely, compared with having successfully coped with pain, requesting neuraxial analgesia as a last resort is not negatively associated with BIS

Introduction of a cascaded segmentation pipeline for parametric T1 mapping in cardiovascular magnetic resonance to improve segmentation performance

The manual and often time-consuming segmentation of the myocardium in cardiovascular magnetic resonance is increasingly automated using convolutional neural networks (CNNs). This study proposes a cascaded segmentation (CASEG) approach to improve automatic image segmentation quality. First, an object detection algorithm predicts a bounding box (BB) for the left ventricular myocardium whose 1.5 times enlargement defines the region of interest (ROI). Then, the ROI image section is fed into a U-Net based segmentation. Two CASEG variants were evaluated: one using the ROI cropped image solely (cropU) and the other using a 2-channel-image additionally containing the original BB image section (crinU). Both were compared to a classical U-Net segmentation (refU). All networks share the same hyperparameters and were tested on basal and midventricular slices of native and contrast enhanced (CE) MOLLI T1 maps. Dice Similarity Coefficient improved significantly (p < 0.05) in cropU and crinU compared to refU (81.06%, 81.22%, 72.79% for native and 80.70%, 79.18%, 71.41% for CE data), while no significant improvement (p < 0.05) was achieved in the mean absolute error of the T1 time (11.94 ms, 12.45 ms, 14.22 ms for native and 5.32 ms, 6.07 ms, 5.89 ms for CE data). In conclusion, CASEG provides an improved geometric concordance but needs further improvement in the quantitative outcome

Lazy Luna: extendible software for multilevel reader comparison in cardiovascular magnetic resonance imaging

BACKGROUND AND OBJECTIVES: Cardiovascular Magnetic Resonance (CMR) imaging is a growing field with increasing diagnostic utility in clinical routine. Quantitative diagnostic parameters are typically calculated based on contours or points provided by readers, e.g. natural intelligences (NI) such as clinicians or researchers, and artificial intelligences (AI). As clinical applications multiply, evaluating the precision and reproducibility of quantitative parameters becomes increasingly important. Although segmentation challenges for AIs and guidelines for clinicians provide quality assessments and regulation, the methods ought to be combined and streamlined for clinical applications. The goal of the developed software, Lazy Luna (LL), is to offer a flexible evaluation tool that is readily extendible to new sequences and scientific endeavours. METHODS: An interface was designed for LL, which allows for comparing annotated CMR images. Geometric objects ensure precise calculations of metric values and clinical results regardless of whether annotations originate from AIs or NIs. A graphical user interface (GUI) is provided to make the software available to non-programmers. The GUI allows for an interactive inspection of image datasets as well as implementing tracing procedures, which follow statistical reader differences in clinical results to their origins in individual image contours. The backend software builds on a set of meta-classes, which can be extended to new imaging sequences and clinical parameters. Following an agile development procedure with clinical feedback allows for a quick implementation of new classes, figures and tables for evaluation. RESULTS: Two application cases present LL's extendibility to clinical evaluation and AI development contexts. The first concerns T1 parametric mapping images segmented by two expert readers. Quantitative result differences are traced to reveal typical segmentation dissimilarities from which these differences originate. The meta-classes are extended to this new application scenario. The second applies to the open source Late Gadolinium Enhancement (LGE) quantification challenge for AI developers “Emidec”, which illustrates LL's usability as open source software. CONCLUSION: The presented software Lazy Luna allows for an automated multilevel comparison of readers as well as identifying qualitative reasons for statistical reader differences. The open source software LL can be extended to new application cases in the future

Introduction of Lazy Luna an automatic software-driven multilevel comparison of ventricular function quantification in cardiovascular magnetic resonance imaging

Cardiovascular magnetic resonance imaging is the gold standard for cardiac function assessment. Quantification of clinical results (CR) requires precise segmentation. Clinicians statistically compare CRs to ensure reproducibility. Convolutional Neural Network developers compare their results via metrics. Aim: Introducing software capable of automatic multilevel comparison. A multilevel analysis covering segmentations and CRs builds on a generic software backend. Metrics and CRs are calculated with geometric accuracy. Segmentations and CRs are connected to track errors and their effects. An interactive GUI makes the software accessible to different users. The software's multilevel comparison was tested on a use case based on cardiac function assessment. The software shows good reader agreement in CRs and segmentation metrics (Dice > 90%). Decomposing differences by cardiac position revealed excellent agreement in midventricular slices: > 90% but poorer segmentations in apical (> 71%) and basal slices (> 74%). Further decomposition by contour type locates the largest millilitre differences in the basal right cavity (> 3 ml). Visual inspection shows these differences being caused by different basal slice choices. The software illuminated reader differences on several levels. Producing spreadsheets and figures concerning metric values and CR differences was automated. A multilevel reader comparison is feasible and extendable to other cardiac structures in the future

Simultaneous multi slice (SMS) balanced steady state free precession first-pass myocardial perfusion cardiovascular magnetic resonance with iterative reconstruction at 1.5T

Background: Simultaneous-Multi-Slice (SMS) perfusion imaging has the potential to acquire multiple slices, increasing myocardial coverage without sacrificing in-plane spatial resolution. To maximise signal-to-noise ratio (SNR), SMS can be combined with a balanced steady state free precession (bSSFP) readout. Furthermore, application of gradient-controlled local Larmor adjustment (GC-LOLA) can ensure robustness against off-resonance artifacts and SNR loss can be mitigated by applying iterative reconstruction with spatial and temporal regularisation. The objective of this study was to compare cardiovascular magnetic resonance (CMR) myocardial perfusion imaging using SMS bSSFP imaging with GC-LOLA and iterative reconstruction to 3 slice bSSFP. Methods: Two contrast-enhanced rest perfusion sequences were acquired in random order in 8 patients: 6-slice SMS bSSFP and 3 slice bSSFP. All images were reconstructed with TGRAPPA. SMS images were also reconstructed using a non-linear iterative reconstruction with L1 regularisation in wavelet space (SMS-iter) with 7 different combinations for spatial (λσ) and temporal (λτ) regularisation parameters. Qualitative ratings of overall image quality (0 = poor image quality, 1 = major artifact, 2 = minor artifact, 3 = excellent), perceived SNR (0 = poor SNR, 1 = major noise, 2 = minor noise, 3 = high SNR), frequency of sequence related artifacts and patient related artifacts were undertaken. Quantitative analysis of contrast ratio (CR) and percentage of dark rim artifact (DRA) was performed. Results: Among all SMS-iter reconstructions, SMS-iter 6 (λσ 0.001 λτ 0.005) was identified as the optimal reconstruction with the highest overall image quality, least sequence related artifact and higher perceived SNR. SMS-iter 6 had superior overall image quality (2.50 ± 0.53 vs 1.50 ± 0.53, p = 0.005) and perceived SNR (2.25 ± 0.46 vs 0.75 ± 0.46, p = 0.010) compared to 3 slice bSSFP. There were no significant differences in sequence related artifact, CR (3.62 ± 0.39 vs 3.66 ± 0.65, p = 0.88) or percentage of DRA (5.25 ± 6.56 vs 4.25 ± 4.30, p = 0.64) with SMS-iter 6 compared to 3 slice bSSFP. Conclusions: SMS bSSFP with GC-LOLA and iterative reconstruction improved image quality compared to a 3 slice bSSFP with doubled spatial coverage and preserved in-plane spatial resolution. Future evaluation in patients with coronary artery disease is warranted

Electrochemiluminescent immunoassay enhancement driven by carbon nanotubes

Electrochemiluminescence (ECL) is a leading analytical technique for clinical monitoring and early disease diagnosis. Carbon nanotubes are used as efficient nanomaterials for ECL signal enhancement providing new insights into the mechanism for the ECL generation but also affording application in bead-based immunoassay and ECL microscopy-based bioimaging