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Changes in and around anatomical structures such as blood vessels, optic disc, fovea, and macula can lead to ophthalmological diseases such as diabetic retinopathy, glaucoma, age-related macular degeneration (AMD), myopia, hypertension, and cataracts. If these diseases are not diagnosed early, they may cause partial or complete loss of vision in patients. Fundus imaging is the primary method used to diagnose ophthalmologic diseases. In this study, a powerful R-CNN+LSTM-based approach is proposed that automatically detects eight different ophthalmologic diseases from fundus images. Deep features were extracted from fundus images with the proposed R-CNN+LSTM structure. Among the deep features extracted, those with high representative power were selected with an approach called NCAR, which is a multilevel feature selection algorithm. In the classification phase, the SVM algorithm, which is a powerful classifier, was used. The proposed approach is evaluated on the eight-class ODIR dataset. The accuracy (main metric), sensitivity, specificity, and precision metrics were used for the performance evaluation of the proposed approach. Besides, the performance of the proposed approach was compared with the existing approaches using the ODIR dataset.(1) Introduction According to recent studies, the ratio of C-reactive-protein to lymphocyte is more sensitive and specific than other biomarkers associated to systemic inflammatory processes. This study aimed to determine the prognostic value of CLR on COVID-19 severity and mortality at emergency department (ED) admission. (2) Methods Between 1 March and 30 April 2020, we carried out a multicenter and retrospective study in six major hospitals of northeast France. The cohort was composed of patients hospitalized for a confirmed diagnosis of moderate to severe COVID-19. (3) Results A total of 1,035 patients were included in this study. selleck chemical Factors associated with infection severity were the CLR (OR 1.001, CI 95% (1.000-1.002), p = 0.012), and the lymphocyte level (OR 1.951, CI 95% (1.024-3.717), p = 0.042). link2 In multivariate analysis, the only biochemical factor significantly associated with mortality was lymphocyte rate (OR 2.308, CI 95% (1.286-4.141), p = 0.005). The best threshold of CLR to predict the severity of infection was 78.3 (sensitivity 79%; specificity 47%), and to predict mortality, was 159.5 (sensitivity 48%; specificity 70%). link3 (4) Conclusion The CLR at admission to the ED could be a helpful prognostic biomarker in the early screening and prediction of the severity and mortality associated with SARS-CoV-2 infection.The purpose of this study was to summarize and evaluate evidence on the effectiveness of perioperative magnesium as an adjuvant for postoperative analgesia. We conducted an umbrella review of the evidence across systematic reviews and meta-analyses of randomized controlled trials (RCTs) on the effect of perioperative magnesium on pain after surgical procedures. Two independent investigators retrieved pain-related outcomes and assessed the methodological quality of the evidence of included studies using the A MeaSurement Tool to Assess systematic Reviews (AMSTAR) tool, and the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) system. In addition, an updated meta-analysis of postoperative pain-related outcomes with a trial sequential analysis (TSA) was conducted. Of the 773 articles initially identified, 17 systematic reviews and meta-analyses of 258 RCTs were included in the current umbrella review. Based on the AMSTAR tool, the overall confidence of the included systematic reviews was deemed critically low to low. Pain score, analgesic consumption, time to first analgesic request, and incidence of analgesic request were examined as pain-related outcomes. According to the GRADE system, the overall quality of evidence ranged from very low to moderate. While the updated meta-analysis showed the beneficial effect of perioperative magnesium on postoperative analgesia, and TSA appeared to suggest sufficient existing evidence, the heterogeneity was substantial for every outcome. Although the majority of included systematic reviews and updated meta-analysis showed a significant improvement in outcomes related to pain after surgery when magnesium was administered during the perioperative period, the evidence reveals a limited confidence in the beneficial effect of perioperative magnesium on postoperative pain.

Due to the vulnerability to protective and risk factors during adolescence, there is a growing interest in the study of health-related quality of life (HRQoL) at this stage. The CHU9D is a generic and practical HRQoL instrument that provides values on all dimensions of self-perceived health, in addition to providing utilities and a cost-utility assessment fee, unlike other instruments. This study was conducted with an adolescent population in Peru. The main objective of this article is to report the normative values of the CHU9D questionnaire in Peruvian adolescents.

The CHU9D questionnaire was administered to Peruvian adolescent students. A total of 1229 young people participated in the survey. The CHU9D score was reflected as a function of gender, age, weight, height, and educational level.

The mean CHU9D utility index for the total sample was 0.890; this rating was significantly better for boys with 0.887 and girls with 0.867. The ceiling effect was higher for male adolescents with 32.6 than for female adolescents.

The results of the present study show that adolescents in school show a positive perception of HRQoL. It is also concluded that the CHU9D instrument can be effectively applied to economic evaluations for interventions to improve the quality of life of adolescents.

The results of the present study show that adolescents in school show a positive perception of HRQoL. It is also concluded that the CHU9D instrument can be effectively applied to economic evaluations for interventions to improve the quality of life of adolescents.

Several prediction models have been proposed for preoperative risk stratification for mortality. However, few studies have investigated postoperative risk factors, which have a significant influence on survival after surgery. This study aimed to develop prediction models using routine immediate postoperative laboratory values for predicting postoperative mortality.

Two tertiary hospital databases were used in this research one for model development and another for external validation of the resulting models. The following algorithms were utilized for model development LASSO logistic regression, random forest, deep neural network, and XGBoost. We built the models on the lab values from immediate postoperative blood tests and compared them with the SASA scoring system to demonstrate their efficacy.

There were 3817 patients who had immediate postoperative blood test values. All models trained on immediate postoperative lab values outperformed the SASA model. Furthermore, the developed random forest model had the best AUROC of 0.82 and AUPRC of 0.13, and the phosphorus level contributed the most to the random forest model.

Machine learning models trained on routine immediate postoperative laboratory values outperformed previously published approaches in predicting 30-day postoperative mortality, indicating that they may be beneficial in identifying patients at increased risk of postoperative death.

Machine learning models trained on routine immediate postoperative laboratory values outperformed previously published approaches in predicting 30-day postoperative mortality, indicating that they may be beneficial in identifying patients at increased risk of postoperative death.We have previously shown that ablative radiotherapy (A-RT) with a biologically effective dose (BED10) ≥ 80.5 Gy for patients with unresectable intrahepatic cholangiocarcinoma (ICC) is associated with longer survival. Despite recent large-scale sequencing efforts in ICC, outcomes following RT based on genetic alterations have not been described. We reviewed records of 156 consecutive patients treated with A-RT for unresectable ICC from 2008 to 2020. For 114 patients (73%), next-generation sequencing provided molecular profiles. The overall survival (OS), local control (LC), and distant metastasis-free survival (DMFS) were estimated using the Kaplan-Meier method. Univariate and multivariable Cox analyses were used to determine the associations with the outcomes. The median tumor size was 7.3 (range 2.2-18.2) cm. The portal vein thrombus (PVT) was present in 10%. The RT median BED10 was 98 Gy (range 81-144 Gy). The median (95% confidence interval) follow-up was 58 (42-104) months from diagnosis and 39 (33-74) months from RT. The median OS was 32 (29-35) months after diagnosis and 20 (16-24) months after RT. The one-year OS, LC, and intrahepatic DMFS were 73% (65-80%), 81% (73-87%), and 34% (26-42%). The most common mutations were in IDH1 (25%), TP53 (22%), ARID1A (19%), and FGFR2 (13%). Upon multivariable analysis, the factors associated with death included worse performance status, larger tumor, metastatic disease, higher CA 19-9, PVT, satellitosis, and IDH1 and PIK3CA mutations. TP53 mutation was associated with local failure. Further investigation into the prognostic value of individual mutations and combinations thereof is warranted.

Traumatic hollow viscus injury (THVI) is one of the most difficult challenges in the trauma setting. Computed tomography (CT) is the most common modality used to diagnose THVI; however, various performance outcomes of CT have been reported. We conducted a systematic review and meta-analysis to analyze how precise and reliable CT is as a tool for the assessment of THVI.

A systematic review and meta-analysis were conducted on studies on the use of CT to diagnose THVI. Publications were retrieved by performing structured searches in databases, review articles and major textbooks. For the statistical analysis, summary receiver operating characteristic (SROC) curves were constructed using hierarchical models.

Sixteen studies enrolling 12,514 patients were eligible for the final analysis. The summary sensitivity and specificity of CT for the diagnosis of THVI were 0.678 (95% CI 0.501-0.809) and 0.969 (95% CI 0.920-0.989), respectively. The summary false positive rate was 0.031 (95% CI 0.011-0.071).

In this meta-analysis, we found that CT had indeterminate sensitivity and excellent specificity for the diagnosis of THVI.

In this meta-analysis, we found that CT had indeterminate sensitivity and excellent specificity for the diagnosis of THVI.

We tested the hypothesis that artificial intelligence (AI)-powered algorithms applied to cardiac magnetic resonance (CMR) images could be able to detect the potential patterns of cardiac amyloidosis (CA). Readers in CMR centers with a low volume of referrals for the detection of myocardial storage diseases or a low volume of CMRs, in general, may overlook CA. In light of the growing prevalence of the disease and emerging therapeutic options, there is an urgent need to avoid misdiagnoses.

Using CMR data from 502 patients (CA

= 82), we trained convolutional neural networks (CNNs) to automatically diagnose patients with CA. We compared the diagnostic accuracy of different state-of-the-art deep learning techniques on common CMR imaging protocols in detecting imaging patterns associated with CA. As a result of a 10-fold cross-validated evaluation, the best-performing fine-tuned CNN achieved an average ROC AUC score of 0.96, resulting in a diagnostic accuracy of 94% sensitivity and 90% specificity.

Applying AI to CMR to diagnose CA may set a remarkable milestone in an attempt to establish a fully computational diagnostic path for the diagnosis of CA, in order to support the complex diagnostic work-up requiring a profound knowledge of experts from different disciplines.