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1/2 and 0 vs. Merbarone 2 were 0.7668 and 0.8593, respectively. Similar accuracy of image-based pain classifier was found using VGG16 and InceptionV1. The accuracy of the video-based pain classifier to classify 0 vs. 1/2 and 0 vs. 2 was approximately 0.81 and 0.88, respectively. We further tested the performance of established classifiers without reference, mimicking clinical scenarios with a new patient, and found the performance remained high.

The present study demonstrates the practical application of deep learning-based automated pain assessment in critically ill patients, and more studies are warranted to validate our findings.

The present study demonstrates the practical application of deep learning-based automated pain assessment in critically ill patients, and more studies are warranted to validate our findings.

Several cases of adverse reactions following vaccination for coronavirus disease 2019 (COVID-19) with adenoviral vector vaccines or mRNA-based vaccines have been reported to date. The underlying syndrome has been named "vaccine-induced immune thrombotic thrombocytopenia" (VITT) or "thrombosis with thrombocytopenia syndrome (TTS)" with different clinical manifestations.

We report the clinical course of five patients who had severe adverse reactions to COVID-19 vaccines, either with VITT/TTS, abdominal or pulmonary thrombosis after adenoviral vaccines, or Stevens' Johnson syndrome because of mRNA vaccination, all of whom required admission to the intensive care unit (ICU).

All patients with severe or life-threatening suspected reaction to different types of COVID-19 vaccination required ICU admission. A prompt evaluation of early symptoms and individualized clinical management is needed to improve outcomes.

All patients with severe or life-threatening suspected reaction to different types of COVID-19 vaccination required ICU admission. A prompt evaluation of early symptoms and individualized clinical management is needed to improve outcomes.

To evaluate peripapillary vascular reactivity in primary open-angle glaucoma (POAG) with and without high myopia (HM) by using optical coherence tomography angiography (OCTA).

This prospective study enrolled 48 eyes with POAG, including 16 and 32 eyes with and without HM, respectively. The retinal peripapillary vessel density (VD) was repeatedly assessed using OCTA at baseline and after a hyperoxia test (breathing 80% oxygen). The VD changes between different oxygenation conditions were calculated to reflect the vasoreactivity. Linear regression was performed to determine the relationship between myopia and retinal vascular reactivity in patients with POAG. Systemic hemodynamic characteristics were also evaluated under both conditions.

The VD was significantly reduced after hyperoxia in the whole image (baseline and hyperoxia 41.4 ± 4.5 and 38.8 ± 4.4, respectively,

< 0.001) and in the peripapillary regions (44.3 ± 5.7 and 41.1 ± 5.4, respectively,

< 0.001) in POAG eyes without HM. However, hat in POAG eyes without HM. A lower peripapillary vascular response was significantly associated with worse SE and elongated AL.

Multimorbidity has an effect on life expectancy, while its effect on healthy life years is unclear. This study aims to investigate the associations between healthy life years lost due to multimorbidity and living risk.

The participants of The China Health and Retirement Longitudinal Study (CHARLS) were assessed at four visits between 2011 (baseline) and 2018. At baseline, 13,949 individuals were administered surveys. A combined score based on seven health-related factors was calculated, and the participants were classified into 3 groups based on living risk. We used the adjusted Cox regression methods to examine the associations between living risk groups and multimorbidity. We estimated the healthy life years lost due to multimorbidity using the Sullivan method.

A total of 9,091 adults aged 45 years or older (mean age of 59.55 ± 9.50 years with one disease, 52.60% women) were analyzed in the CHARLS. The probability of no multimorbidity over 7 years decreased from 0.9947 to 0.9697 in the low-risk group,e years under multimorbidity. The probability of multimorbidity in women and in urban areas was high. Hypertension was correlated with the hazard risk of multimorbidity.Multiple Sclerosis (MS) is a demyelinating disease of the central nervous system that affects nearly 1 million adults in the United States. Magnetic Resonance Imaging (MRI) plays a vital role in diagnosis and treatment monitoring in MS patients. In particular, follow-up MRI with T2-FLAIR images of the brain, depicting white matter lesions, is the mainstay for monitoring disease activity and making treatment decisions. In this article, we present a computational approach that has been deployed and integrated into a real-world routine clinical workflow, focusing on two tasks (a) detecting new disease activity in MS patients, and (b) determining the necessity for injecting Gadolinium Based Contract Agents (GBCAs). This computer-aided detection (CAD) software has been utilized for the former task on more than 19, 000 patients over the course of 10 years, while its added function of identifying patients who need GBCA injection, has been operative for the past 3 years, with > 85% sensitivity. The benefits of this approach are summarized in (1) offering a reproducible and accurate clinical assessment of MS lesion patients, (2) reducing the adverse effects of GBCAs (and the deposition of GBCAs to the patient's brain) by identifying the patients who may benefit from injection, and (3) reducing healthcare costs, patients' discomfort, and caregivers' workload.

COVID-19 has been associated with an increased risk of incident dementia (post-COVID dementia). Establishing additional risk markers may help identify at-risk individuals and guide clinical decision-making.

We investigated pre-COVID psychotropic medication use (exposure) and 1-year incidence of dementia (outcome) in 1,755 patients (≥65 years) hospitalized with COVID-19. Logistic regression models were used to examine the association, adjusting for demographic and clinical variables. For further confirmation, we applied the Least Absolute Shrinkage and Selection Operator (LASSO) regression and a machine learning (Random Forest) algorithm.

One-year incidence rate of post-COVID dementia was 12.7% (

= 223). Pre-COVID psychotropic medications (OR = 2.7, 95% CI 1.8-4.0,

< 0.001) and delirium (OR = 3.0, 95% CI 1.9-4.6,

< 0.001) were significantly associated with greater 1-year incidence of post-COVID dementia. The association between psychotropic medications and incident dementia remained robust zation. Pre-COVID psychotropic medications were associated with higher risk of incident dementia. Psychotropic medications may be risk markers that signify neuropsychiatric symptoms during prodromal dementia, and not mutually exclusive, contribute to post-COVID dementia.

Glaucoma is the main cause of irreversible blindness worldwide. However, the diagnosis and treatment of glaucoma remain difficult because of the lack of an effective glaucoma grading measure. In this study, we aimed to propose an artificial intelligence system to provide adequate assessment of glaucoma patients.

A total of 16,356 visual fields (VFs) measured by Octopus perimeters and Humphrey Field Analyzer (HFA) were collected, from three hospitals in China and the public Harvard database. We developed a fine-grained grading deep learning system, named FGGDL, to evaluate the VF loss, compared to ophthalmologists. Subsequently, we discuss the relationship between structural and functional damage for the comprehensive evaluation of glaucoma level. In addition, we developed an interactive interface and performed a cross-validation study to test its auxiliary ability. The performance was valued by F1 score, overall accuracy and area under the curve (AUC).

The FGGDL achieved a high accuracy of 85 and 90%, and AUC of 0.93 and 0.90 for HFA and Octopus data, respectively. It was significantly superior (

< 0.01) to that of medical students and nearly equal (

= 0.614) to that of ophthalmic clinicians. For the cross-validation study, the diagnosis accuracy was almost improved (

< 0.05).

We proposed a deep learning system to grade VF of glaucoma with a high detection accuracy, for effective and adequate assessment for glaucoma patients. Besides, with the convenient and credible interface, this system can promote telemedicine and be used as a self-assessment tool for patients with long-duration diseases.

We proposed a deep learning system to grade VF of glaucoma with a high detection accuracy, for effective and adequate assessment for glaucoma patients. Besides, with the convenient and credible interface, this system can promote telemedicine and be used as a self-assessment tool for patients with long-duration diseases.Specific pillow use is a seldom studied or controlled factor in the setting of sleep disordered breathing. The aim of this study was to investigate the effect of different pillows [own pillow (OP), memory foam pillow (MFP), generic laboratory pillow (LP)] on polysomnography (PSG)-derived parameters in patients with Obstructive Sleep Apnea Syndrome (OSAS). Thirty-two consecutive patients with OSAS were randomly allocated into two groups with randomized pillow usage [Group A 3 h with LP and 3 h with OP (Age 53.8 ± 12.5 years, BMI 32.1 ± 4.6 kg/m2); Group B 3 h with LP and 3 h with MFP (Age 52.0 ± 6.3 years, BMI 30.6 ± 2.2 kg/m2)]. Statistically significant differences between pillow types were detected in desaturation index and heart rate. In Group B (with MFP), a statistically significant decrease of 47.0 ± 15.9% was observed in snoring events (p 0.05) compared to LP. These findings indicate that pillow type and usage, often uncontrolled in OSAS studies (contribution to the field), may impact several PSG parameters and are related to a snoring subtype of the syndrome. Secondly, they indicate that a focus on the treatment of the snoring OSAS subtype warrants further dedicated investigation.

This study aimed to assess the predictive ability of 18F-FDG PET/CT radiomic features for MYCN, 1p and 11q abnormalities in NB.

One hundred and twenty-two pediatric patients (median age 3. 2 years, range, 0.2-9.8 years) with NB were retrospectively enrolled. Significant features by multivariable logistic regression were retained to establish a clinical model (C_model), which included clinical characteristics. 18F-FDG PET/CT radiomic features were extracted by Computational Environment for Radiological Research. The least absolute shrinkage and selection operator (LASSO) regression was used to select radiomic features and build models (R-model). The predictive performance of models constructed by clinical characteristic (C_model), radiomic signature (R_model), and their combinations (CR_model) were compared using receiver operating curves (ROCs). Nomograms based on the radiomic score (rad-score) and clinical parameters were developed.

The patients were classified into a training set (

= 86) and a test set (

= 36). Accordingly, 6, 8, and 7 radiomic features were selected to establish R_models for predicting MYCN, 1p and 11q status. The R_models showed a strong power for identifying these aberrations, with area under ROC curves (AUCs) of 0.96, 0.89, and 0.89 in the training set and 0.92, 0.85, and 0.84 in the test set. When combining clinical characteristics and radiomic signature, the AUCs increased to 0.98, 0.91, and 0.93 in the training set and 0.96, 0.88, and 0.89 in the test set. The CR_models had the greatest performance for MYCN, 1p and 11q predictions (

< 0.05).

The pre-therapy 18F-FDG PET/CT radiomics is able to predict MYCN amplification and 1p and 11 aberrations in pediatric NB, thus aiding tumor stage, risk stratification and disease management in the clinical practice.

The pre-therapy 18F-FDG PET/CT radiomics is able to predict MYCN amplification and 1p and 11 aberrations in pediatric NB, thus aiding tumor stage, risk stratification and disease management in the clinical practice.