Early Features Research Articles

External validation of a prognostic model to improve prediction of psychosis: a retrospective cohort study in primary care.

Early detection could reduce the duration of untreated psychosis. GPs are a vital part of the psychosis care pathway, but find it difficult to detect the early features. An accurate risk prediction tool, P Risk, was developed to detect these. To externally validate P Risk. This retrospective cohort study used a validation dataset of 1 647 934 UK Clinical Practice Research Datalink (CPRD) primary care records linked to secondary care records. The same predictors (age; sex; ethnicity; social deprivation; consultations for suicidal behaviour, depression/anxiety, and substance misuse; history of consultations for suicidal behaviour; smoking history; substance misuse; prescribed medications for depression/anxiety/post-traumatic stress disorder/obsessive compulsive disorder; and total number of consultations) were used as for the development of P Risk. Predictive risk, sensitivity, specificity, and likelihood ratios were calculated for various risk thresholds. Discrimination (Harrell's C-index) and calibration were calculated. Results were compared between the development (CPRD GOLD) and validation (CPRD Aurum) datasets. Psychosis risk increased with values of the P Risk prognostic index. Incidence was highest in younger age groups and, in the main, higher in males. Harrell's C was 0.79 (95% confidence interval = 0.78 to 0.79) in the validation dataset and 0.77 in the development dataset. A risk threshold of 1.0% gave sensitivity of 65.9% and specificity of 86.6%. Further testing is required, but P Risk has the potential to be used in primary care to detect future risk of psychosis.

Clinical signatures of genetic epilepsies precede diagnosis in electronic medical records of 32,000 individuals

PurposeAn early genetic diagnosis can guide the time-sensitive treatment of individuals with genetic epilepsies. However, most genetic diagnoses occur long after disease onset. We aimed to identify early clinical features suggestive of genetic diagnoses in individuals with epilepsy through large-scale analysis of full-text electronic medical records. MethodsWe extracted 89 million time-stamped standardized clinical annotations using Natural Language Processing from 4,572,783 clinical notes from 32,112 individuals with childhood epilepsy, including 1925 individuals with known or presumed genetic epilepsies. We applied these features to train random forest models to predict SCN1A-related disorders and any genetic diagnosis. ResultsWe identified 47,774 age-dependent associations of clinical features with genetic etiologies a median of 3.6 years before molecular diagnosis. Across all 710 genetic etiologies identified in our cohort, neurodevelopmental differences between 6 to 9 months increased the likelihood of a later molecular diagnosis 5-fold (P < .0001, 95% CI = 3.55-7.42). A later diagnosis of SCN1A-related disorders (area under the curve [AUC] = 0.91) or an overall positive genetic diagnosis (AUC = 0.82) could be reliably predicted using random forest models. ConclusionClinical features predictive of genetic epilepsies precede molecular diagnoses by up to several years in conditions with known precision treatments. An earlier diagnosis facilitated by automated electronic medical records analysis has the potential for earlier targeted therapeutic strategies in the genetic epilepsies.

Early identification of high attention content for online mental health community users based on multi-level fusion model

PurposeThe overload of user-generated content in online mental health community makes the focus and resonance tendencies of the participating groups less clear. Thus, the purpose of this paper is to build an early identification mechanism for users' high attention content to promote early intervention and effective dissemination of professional medical guidance.Design/methodology/approachWe decouple the identification mechanism from two processes: early feature combing and algorithmic model construction. Firstly, based on the differentiated needs and concerns of the participant groups, the multiple features of “information content + source users” are refined. Secondly, a multi-level fusion model is constructed for features processing. Specifically, Bidirectional Encoder Representation from Transformers (BERT)-Bi-directional Long-Short Term Memory (BiLSTM)-Linear are used to refine the semantic features, while Graph Attention Networks (GAT) is used to capture the entity attributes and relation features. Finally, the Convolutional Neural Network (CNN) is used to optimize the multi-level fusion features.FindingsThe results show that the ACC of the multi-level fusion model is 84.42%, F1 is 79.43% and R is 76.71%. Compared with other baseline models and single feature elements, the ACC and F1 values are improved to different degrees.Originality/valueThe originality of this paper lies in analyzing multiple features based on early stages and constructing a new multi-level fusion model for processing. Further, the study is valuable for the orientation of psychological patients' needs and early guidance of professional medical care.

The Pathobiology of Behavioral Changes in Multiple System Atrophy: An Update.

While cognitive impairment, which was previously considered a red flag against the clinical diagnosis of multiple system atrophy (MSA), is a common symptom of this rare neurodegenerative disorder, behavioral disorders are reported in 30 to 70% of MSA patients. They include anxiety, apathy, impaired attention, compulsive and REM sleep behavior disorders (RBD), and these conditions, like depression, are early and pervasive features in MSA, which may contribute to disease progression. Despite changing concepts of behavioral changes in this synucleinopathy, the underlying pathophysiological and biochemical mechanisms are poorly understood. While specific neuropathological data are unavailable, neuroimaging studies related anxiety disorders to changes in the cortico-limbic system, apathy (and depression) to dysfunction of prefrontal-subcortical circuits, and compulsive behaviors to impairment of basal ganglia networks and involvement of orbito-frontal circuits. Anxiety has also been related to α-synuclein (αSyn) pathology in the amygdala, RBD to striatal monoaminergic deficit, and compulsive behavior in response to dopamine agonist therapy in MSA, while the basic mechanisms of the other behavioral disorders and their relations to other non-motor dysfunctions in MSA are unknown. In view of the scarcity of functional and biochemical findings in MSA with behavioral symptoms, further neuroimaging and biochemical studies are warranted in order to obtain better insight into their pathogenesis as a basis for the development of diagnostic biomarkers and future adequate treatment modalities of these debilitating comorbidities.

Multi-stage coarse-to-fine progressive enhancement network for single-image HDR reconstruction

Compared with traditional imaging, high dynamic range (HDR) imaging technology can record scene information more accurately, thereby providing users higher quality of visual experience. Inverse tone mapping is a direct and effective way to realize single-image HDR reconstruction, but it usually suffers from some problems such as detail loss, color deviation and artifacts. To solve the problems, this paper proposes a multi-stage coarse-to-fine progressive enhancement network (named MSPENet) for single-image HDR reconstruction. The entire multi-stage network architecture is designed in a progressive manner to obtain higher-quality HDR images from coarse-to-fine, where a mask mechanism is used to eliminate the effects of over-exposure regions. Specifically, in the first two stages, two asymmetric U-Nets are constructed to learn the multi-scale information of input image and perform coarse reconstruction. In the third stage, a residual network with channel attention mechanism is constructed to learn the fusion of progressively transferred multi-level features and perform fine reconstruction. In addition, a multi-stage progressive detail enhancement mechanism is designed, including progressive gated recurrent unit fusion mechanism and multi-stage feature transfer mechanism. The former fuses the progressively transferred features with coarse HDR features to reduce the error stacking effect caused by multi-stage networks. Meanwhile, the latter fuses early features to supplement the lost information during each stage of feature delivery and combines features from different stages. Extensive experimental results show that the proposed method can reconstruct higher quality HDR images and effectively recover texture and color information in over-exposure regions compared to the state-of-the-art methods.

Leverage Effective Deep Learning Searching Method for Forensic Age Estimation.

Dental age estimation is extensively employed in forensic medicine practice. However, the accuracy of conventional methods fails to satisfy the need for precision, particularly when estimating the age of adults. Herein, we propose an approach for age estimation utilizing orthopantomograms (OPGs). We propose a new dental dataset comprising OPGs of 27,957 individuals (16,383 females and 11,574 males), covering an age range from newborn to 93 years. The age annotations were meticulously verified using ID card details. Considering the distinct nature of dental data, we analyzed various neural network components to accurately estimate age, such as optimal network depth, convolution kernel size, multi-branch architecture, and early layer feature reuse. Building upon the exploration of distinctive characteristics, we further employed the widely recognized method to identify models for dental age prediction. Consequently, we discovered two sets of models: one exhibiting superior performance, and the other being lightweight. The proposed approaches, namely AGENet and AGE-SPOS, demonstrated remarkable superiority and effectiveness in our experimental results. The proposed models, AGENet and AGE-SPOS, showed exceptional effectiveness in our experiments. AGENet outperformed other CNN models significantly by achieving outstanding results. Compared to Inception-v4, with the mean absolute error (MAE) of 1.70 and 20.46 B FLOPs, our AGENet reduced the FLOPs by 2.7×. The lightweight model, AGE-SPOS, achieved an MAE of 1.80 years with only 0.95 B FLOPs, surpassing MobileNetV2 by 0.18 years while utilizing fewer computational operations. In summary, we employed an effective DNN searching method for forensic age estimation, and our methodology and findings hold significant implications for age estimation with oral imaging.

Pricing airbag option via first passage time approach

Airbag option is a new structured product that has been highly favored by investors in the over-the-counter derivatives market. It provides protections against the downside loss to investors like the ‘airbag’ in a car crash. In contrast with its popularity, there is little literature on the pricing of airbag options, especially from the mathematical finance point of view. In this paper, we study the pricing of airbag options using the first passage time approach. We propose two innovative new types of airbag options, namely airbag-TB and airbag-ER, which enhance the downside loss protections by integrating a time-varying barrier and early redemption features respectively in the payoffs. Explicit and closed-form pricing formulas for the original and the two new types of airbag options are derived under the mixed-exponential jump diffusion (MEJD) model, double exponential jump diffusion (DEJD) model and Black-Scholes (BS) model. In doing so, we significantly improve the Hybrid model proposed by Jaimungal and Sigloch (Incorporating risk and ambiguity aversion into a hybrid model of default. Math. Finance, 2012, 22, 57–81) by incorporating a stochastic redemption intensity model and more general underlying asset dynamics. We further demonstrate the performance of our pricing formulas and the two new types of airbag options with numerical examples based on Monte Carlo simulation, which verifies the accuracy and efficiency of our pricing formulas.

Sequence of Morphological Changes Preceding Atrophy in Intermediate AMD Using Deep Learning.

Investigating the sequence of morphological changes preceding outer plexiform layer (OPL) subsidence, a marker preceding geographic atrophy, in intermediate AMD (iAMD) using high-precision artificial intelligence (AI) quantifications on optical coherence tomography imaging. In this longitudinal observational study, individuals with bilateral iAMD participating in a multicenter clinical trial were screened for OPL subsidence and RPE and outer retinal atrophy. OPL subsidence was segmented on an A-scan basis in optical coherence tomography volumes, obtained 6-monthly with 36months follow-up. AI-based quantification of photoreceptor (PR) and outer nuclear layer (ONL) thickness, drusen height and choroidal hypertransmission (HT) was performed. Changes were compared between topographic areas of OPL subsidence (AS), drusen (AD), and reference (AR). Of 280 eyes of 140 individuals, OPL subsidence occurred in 53 eyes from 43 individuals. Thirty-six eyes developed RPE and outer retinal atrophy subsequently. In the cohort of 53 eyes showing OPL subsidence, PR and ONL thicknesses were significantly decreased in AS compared with AD and AR 12 and 18months before OPL subsidence occurred, respectively (PR: 20µm vs. 23µm and 27µm [P < 0.009]; ONL, 84µm vs. 94µm and 98µm [P < 0.008]). Accelerated thinning of PR (0.6µm/month; P < 0.001) and ONL (0.8µm/month; P < 0.001) was observed in AS compared with AD and AR. Concomitant drusen regression and hypertransmission increase at the occurrence of OPL subsidence underline the atrophic progress in areas affected by OPL subsidence. PR and ONL thinning are early subclinical features associated with subsequent OPL subsidence, an indicator of progression toward geographic atrophy. AI algorithms are able to predict and quantify morphological precursors of iAMD conversion and allow personalized risk stratification.

Blood-based molecular and cellular biomarkers of early response to neoadjuvant PD-1 blockade in patients with non-small cell lung cancer

BackgroundDespite the improved survival observed in PD-1/PD-L1 blockade therapy, a substantial proportion of cancer patients, including those with non-small cell lung cancer (NSCLC), still lack a response.MethodsTranscriptomic profiling was conducted on a discovery cohort comprising 100 whole blood samples, as collected multiple times from 48 healthy controls (including 43 published data) and 31 NSCLC patients that under treatment with a combination of anti-PD-1 Tislelizumab and chemotherapy. Differentially expressed genes (DEGs), simulated immune cell subsets, and germline DNA mutational markers were identified from patients achieved a pathological complete response during the early treatment cycles. The predictive values of mutational markers were further validated in an independent immunotherapy cohort of 1661 subjects, and then confirmed in genetically matched lung cancer cell lines by a co-culturing model.ResultsThe gene expression of hundreds of DEGs (FDR p < 0.05, fold change < -2 or > 2) distinguished responders from healthy controls, indicating the potential to stratify patients utilizing early on-treatment features from blood. PD-1-mediated cell abundance changes in memory CD4 + and regulatory T cell subset were more significant or exclusively observed in responders. A panel of top-ranked genetic alterations showed significant associations with improved survival (p < 0.05) and heightened responsiveness to anti-PD-1 treatment in patient cohort and co-cultured cell lines.ConclusionThis study discovered and validated peripheral blood-based biomarkers with evident predictive efficacy for early therapy response and patient stratification before treatment for neoadjuvant PD-1 blockade in NSCLC patients.

Oligocene magmato-tectonic events influential in the development of Pb-Zn, Ag, and Au mineralization at the Plomosas deposit, southwestern Sierra Madre Occidental, Mexico

ABSTRACT The Plomosas deposit is the product of a sequence of overlapping events of Pb–Zn, Ag, and Au low sulfidation mineralization that occurred in Mexico’s southwestern Sierra Madre Occidental (SMO). Field observations, detailed petrography, and U–Pb age dating of the entire stratigraphic sequence were used to characterize the magmatic evolution of the district and to obtain new insights into the evolution of the SMO. The stratigraphy at Plomosas is distinct from other areas to the north as it lacks Jurassic marine units, Cretaceous continental volcanic successions as well as Cretaceous and Palaeocene batholiths. The local basement is composed of undeformed Late Jurassic volcaniclastic units and coeval felsic intrusions. Early to late Oligocene silicic ignimbrites, intermediate lava flows, and occasional basaltic flows unconformably overlie the Jurassic basement. Several Oligocene intermediate to felsic porphyry bodies and rhyolitic domes crosscut the stratigraphy. The upper volcanic sequence is separated into two packages by an angular unconformity. Mineralization produced an early Pb–Zn-mineralized hydrothermal fault breccia, followed by Cu–Ag veins and late Au veins. The Pb–Zn mineralized breccia is hosted by ~30 Ma Oligocene volcanic units that form the basal sequence. The Cu–Ag and Au-rich veins overlap and crosscut the early hydrothermal features and cut the late Oligocene volcanic cover that unconformably overlie the basal sequence. Our results highlight the importance of episodes of Oligocene magmatism to the genesis of mineralization in the southwestern SMO specifically through a genetic link between Pb–Zn veins and intermediate Oligocene intrusive bodies, and of Ag and Au veins with late Oligocene volcanism.

Fire-RPG: An Urban Fire Detection Network Providing Warnings in Advance

Urban fires are characterized by concealed ignition points and rapid escalation, making the traditional methods of detecting early stage fire accidents inefficient. Thus, we focused on the features of early stage fire accidents, such as faint flames and thin smoke, and established a dataset. We found that these features are mostly medium-sized and small-sized objects. We proposed a model based on YOLOv8s, Fire-RPG. Firstly, we introduced an extra very small object detection layer to enhance the detection performance for early fire features. Next, we optimized the model structure with the bottleneck in GhostV2Net, which reduced the computational time and the parameters. The Wise-IoUv3 loss function was utilized to decrease the harmful effects of low-quality data in the dataset. Finally, we integrated the low-cost yet high-performance RepVGG block and the CBAM attention mechanism to enhance learning capabilities. The RepVGG block enhances the extraction ability of the backbone and neck structures, while CBAM focuses the attention of the model on specific size objects. Our experiments showed that Fire-RPG achieved an mAP of 81.3%, an improvement of 2.2%. In addition, Fire-RPG maintained high detection performance across various fire scenarios. Therefore, our model can provide timely warnings and accurate detection services.

An Efficient Hypertuned DNN Based Approach for Pneumonia Detection

Objectives: To create a deep learning system for pneumonia detection that is both effective and gradually optimized. Methods: A customized CNN is used with an incremental approach for pneumonia classification and detection. Starting with a baseline model, hypertuning parameters such as four convolution layers with filters of 16, 32, 64, and 128 sizes, a dropout layer with values of 0.3, 0.5, and 0.7, four batch normalization layers, and an Adam optimizer are added. A total images of 5,863 for training, 624 for testing, and 16 for validation from the Paul Mooney dataset were used to test the suggested model. Findings: The study recorded a test accuracy of 94% for the customized CNN followed by ResNet50 at 79.9%, VGG16 at 90.14%, VGG19 at 82.21%, InceptionV3 at 74.51%, and EfficientNetB1 at 83.17%. Recall of 98.20%, accuracy of 85.55%, AUC of 93.52%, and F1_score of 92.45% obtained were all fairly excellent for the customized CNN. 15 epochs, a learning rate of 0.0001, callbacks with a patience of 3, and an early stopping feature were applied to the training model. Novelty: Five convolution blocks, two separable convolution layers, one batch normalization layer, one maxpooling layer, and a fully connected layer with an Adam optimizer were all included in the customized CNN that was developed to identify and categorize pneumonia. With Explainable AI's GradCAM technology, pneumonia-infected areas on chest X-rays were highlighted and the sickness was seen. Keywords: Customized CNN, VGG16, VGG19, ResNet50, Explainable AI

Investigating the Involvement of C-X-C Motif Chemokine 5 and P2X7 Purinoceptor in Ectopic Calcification in Mouse Models of Duchenne Muscular Dystrophy.

Ectopic calcification of myofibers is an early pathogenic feature in patients and animal models of Duchenne muscular dystrophy (DMD). In previous studies using the Dmdmdx-βgeo mouse model, we found that the dystrophin-null phenotype exacerbates this abnormality and that mineralised myofibers are surrounded by macrophages. Furthermore, the P2X7 purinoceptor, functioning in immune cells offers protection against dystrophic calcification. In the present study, by exploring transcriptomic data from Dmdmdx mice, we hypothesised these effects to be mediated by C-X-C motif chemokine 5 (CXCL5) downstream of P2X7 activation. We found that CXCL5 is upregulated in the quadriceps muscles of Dmdmdx-βgeo mice compared to wild-type controls. In contrast, at the cell level, dystrophic (SC5) skeletal muscle cells secreted less CXCL5 chemokine than wild-type (IMO) controls. Although release from IMO cells was increased by P2X7 activation, this could not explain the elevated CXCL5 levels observed in dystrophic muscle tissue. Instead, we found that CXCL5 is released by dystrophin-null macrophages in response to P2X7 activation, suggesting that macrophages are the source of CXCL5 in dystrophic muscles. The effects of CXCL5 upon mineralisation were investigated using the Alizarin Red assay to quantify calcium deposition in vitro. In basal (low phosphate) media, CXCL5 increased calcification in IMO but not SC5 myoblasts. However, in cultures treated in high phosphate media, to mimic dysregulated phosphate metabolism occurring in DMD, CXCL5 decreased calcification in both IMO and SC5 cells. These data indicate that CXCL5 is part of a homoeostatic mechanism regulating intracellular calcium, that CXCL5 can be released by macrophages in response to the extracellular ATP damage-associated signal, and that CXCL5 can be part of a damage response to protect against ectopic calcification. This mechanism is affected by DMD gene mutations.

Visco-elastohydrodynamic lubrication and wear model amended by deformation velocity

The deformation velocity caused by viscoelasticity is one of the drives of the Lubrication flow field and significantly influences the mixed lubrication and wear characteristics of journal bearings under dynamic loads. However, research considering the deformation velocity has yet to be reported. To address this research gap, we defined three-dimensional (3D) deformation velocities to amend the bearing surface velocity and proposed its calculation method based on the derivative function of the temporal association deformation equation and the finite element method. Therefore, a new mixed visco-elastohydrodynamic lubrication model amended by 3D deformation velocities was built and a nested solution method was proposed in which the sub-iterative process for deformation velocities was embedded into the oil film pressure iterative process. Furthermore, the lubrication model was coupled with the Archard model to predict wear. The viscoelastic parameter of the bearing was experimentally measured, and numerical simulations of lubrication and wear were performed for an ordinary bearing under a simple dynamic load and for the crankshaft main bearing of an internal combustion engine (ICE) under impact. The simulation results indicate that the deformation velocities reduce the extrusion effect and increase the contracting effect in the lubrication flow field. This enables the new model to identify the micro asperity contact, impact, and early wear features of bearings more accurately than the traditional mixed lubrication model. In addition, an increase in the ICE load intensifies the early wear of the bearing.

A Robust Numerical Simulation of a Fractional Black–Scholes Equation for Pricing American Options

After the discovery of fractal structures of financial markets, fractional partial differential equations (fPDEs) became very popular in studying dynamics of financial markets. Available research results involves two key modelling aspects; firstly, derivation of tractable asset pricing models, those that closely reflects the actual dynamics of financial markets. Secondly, the development of robust numerical solution methods. Often times, most the effective models are of a nonlinear nature, and as such, reliable analytical solution methods are seldomly available. On the other hand, the accurate value of American options strongly lies on the unknown free boundaries associated with these types of derivative contracts. The free boundaries emanates from the flexibility of the early exercise features with American options. To the best of our knowledge, the approach of pricing American options under the fractional calculus framework has not been extensively explored in literature, and an obvious wider research gap still exist on the design of robust solution methods for pricing American option problems formulated under the fractional calculus framework. Therefore, this paper serve to propose a robust numerical scheme for solving time-fractional Black–Scholes PDEs for pricing American put option problems. The proposed scheme is based on the front-fixing algorithm, under which the early exercise boundaries are transformed into fixed boundaries, allowing for a simultaneous computation of optimal exercise boundaries and their corresponding fair premiums. Results herein indicate that, the proposed numerical scheme is consistent, stable, convergent with order O(h2,k)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {O}}(h^2,k)$$\\end{document}, and also does guarantee positivity of solutions under all possible market conditions.

Genotype-specific spinal cord damage in spinocerebellar ataxias: an ENIGMA-Ataxia study

BackgroundSpinal cord damage is a feature of many spinocerebellar ataxias (SCAs), but well-powered in vivo studies are lacking and links with disease severity and progression remain unclear. Here we characterise...

Oxidative Stress in Prediabetic Young Adults.

Background Malondialdehyde (MDA) and nitric oxide (NO) are considered specific biomarkers for oxidative stress. Oxidative stress in prediabetics with an augmented potential for the onset of diabetes is at least partly responsible for the various complications of diabetes. Evidence shows that the early features of cell injury are due to transient acute elevations in blood glucose. This study aims to determine whether oxidative stress in prediabetic young adults increases the risk of developing diabetes. Aim and objectives We envisaged a study to determine whether the parameters representing oxidative stress are deranged in prediabetics. Materials and methods The study was conducted on prediabetic young individuals from 18 to 35 years, screened from the tertiary-level hospital, and a similar group of non-prediabetic young individuals identified from the same in a tertiary-level hospital in India. Results We observed significant elevations inprediabetics in the following oxidative stress parameters: MDA (P= <0.001), and NO (P= <0.001); indicating that these parameters were significantly higher among the prediabetics than the controls. We also observed significantly greater body weight, waist circumference, and BMI among the prediabetics than the controls. Conclusion Early identification and appropriate treatment of hyperglycemia in prediabetics is essential, as impairments in pancreatic beta-cell functioning and resistance to insulin are already present before the onset of type 2 diabetes mellitus (T2DM). Owing to the high potential for mortality and morbidity due to cardiovascular diseases (CVDs) as a complication of diabetes, treatment plans must be put in place early enough so that complications can be prevented. Inflammation and oxidative stress may be viewed as valuable targets to hinder the evolution of T2DM from prediabetes.

Optical Coherence Tomography in the Assessment and Management of Cardiac Allograft Vasculopathy.

Cardiac Allograft vasculopathy (CAV) is a major barrier to improving outcomes after heart transplantation. Coronary angiography has very low sensitivity to detect early CAV and intravascular ultrasound (IVUS) only improves it to some extent. In this article, we detail the current evidence surrounding use of Optical Coherence tomography (OCT) in patients with CAV. OCT has the ability to recognize CAV at earlier stages with intimal thickness < 150μm, can characterize CAV in almost pathologic / microscopic detail - plaque characteristics are better visualized and novel early features such as layered fibrotic plaques and microchannels have been identified. Progression of CAV can be monitored also, with promise shown in automated serial measurements also. OCT has significantly advanced our understanding of the pathophysiology-as well as permits precise monitoring and surveillance of the disease. Potential treatment options could also be evaluated using OCT.

Association of EEG characteristics with outcomes following pediatric ICU cardiac arrest: A secondary analysis of the ICU-RESUScitation trial

Background and ObjectivesThere are limited tools available following cardiac arrest to prognosticate neurologic outcomes. Prior retrospective and single center studies have demonstrated early EEG features are associated with neurologic outcome. This study aimed to evaluate the prognostic value of EEG for pediatric in-hospital cardiac arrest (IHCA) in a prospective, multicenter study. MethodsThis cohort study is a secondary analysis of the ICU-Resuscitation trial, a multicenter randomized interventional trial conducted at 18 pediatric and pediatric cardiac ICUs in the United States. Patients who achieved return of circulation (ROC) and had post-ROC EEG monitoring were eligible for inclusion. Patients < 90 days old and those with pre-arrest Pediatric Cerebral Performance Category (PCPC) scores > 3 were excluded. EEG features of interest included EEG Background Category, and presence of focal abnormalities, sleep spindles, variability, reactivity, periodic and rhythmic patterns, and seizures. The primary outcome was survival to hospital discharge with favorable neurologic outcome. Associations between EEG features and outcomes were assessed with multivariable logistic regression. Prediction models with and without EEG Background Category were developed and receiver operator characteristic curves compared. ResultsOf the 1129 patients with an index cardiac arrest who achieved ROC in the parent study, 261 had EEG within 24 h of ROC, of which 151 were evaluable. The cohort included 57% males with a median age of 1.1 years (IQR 0.4, 6.8). EEG features including EEG Background Category, sleep spindles, variability, and reactivity were associated with survival with favorable outcome and survival, (all p < 0.001). The addition of EEG Background Category to clinical models including age category, illness category, PRISM score, duration of CPR, first documented rhythm, highest early post-arrest arterial lactate improved the prediction accuracy achieving an AUROC of 0.84 (CI 0.77–0.92), compared to AUROC of 0.76 (CI 0.67–0.85) (p = 0.005) without EEG Background Category. ConclusionThis multicenter study demonstrates the value of EEG, in the first 24 h following ROC, for predicting survival with favorable outcome after a pediatric IHCA.

Delivery Room Lung Ultrasound-Feasibility, Normal Patterns, and Predictive Value for Respiratory Support in Term and Near-Term Neonates: A Monocentric Study.

our study aimed to characterize the lung ultrasound (LUS) patterns noted immediately after delivery in term and near-term neonates, and to investigate whether the LUS scores or patterns observed at that point could anticipate the need for respiratory support in the sample of patients studied. We performed two ultrasound examinations: one in the delivery room and the second at one hour of age. The anterior and lateral regions of both lungs were examined. We assessed the correlation between the LUS scores or patterns and the gestational age, umbilical arterial blood gases, the need for respiratory support (CPAP or mechanical ventilation), the presence of respiratory distress, and the need for the administration of oxygen. LUS scores were significantly higher in the delivery room examination (8.05 ± 1.95) than at 1 h of age (6.4 ± 1.75) (p < 0.001). There were also statistically significant differences between the LUS patterns observed in different lung regions between the delivery room exam and the exam performed at 1 h of age (p values between 0.001 and 0.017). There were also differences noted regarding the LUS patterns between different lung regions at the exam in the delivery room (the right anterior region LUS patterns were significantly worse than the right lateral LUS patterns (p < 0.004), left anterior LUS patterns (p < 0.001), and left lateral LUS patterns (p < 0.001)). A statistically significant correlation was found between LUS scores and the gestational age of the patients (r = 0.568, p < 0.001-delivery room; r = 4.0443, p < 0.001-one hour of age). There were statistically significant associations between LUS scores, patterns at delivery (p < 0.001) and 1 h of age (p < 0.001), and the need for respiratory support (CPAP or mechanical ventilation). LUS in the delivery room offers important information regarding lung fluid elimination and aeration of the lungs, and early LUS features are significantly associated with the risk of respiratory distress and the need for respiratory support.