Despite the fact that health facilities in Ethiopia are being built closer to communities in all regions, the proportion of home deliveries remains high, and there are no studies being conducted to identify low birth weight (LBW) and premature newborn babies using simple, best, alternative, and appropriate anthropometric measurement in the study area. The objective of the present study was to find the simple, best, and alternative anthropometric measurement and identified its cut-off points for detecting LBW and premature newborn babies. A health facility-based cross-sectional study was conducted in the Dire Dawa city administration, Eastern Ethiopia. The study included 385 women who gave birth in health facility. To evaluate the overall accuracy of the anthropometric measurements, a non-parametric receiver operating characteristic curve was used. Chest circumference (AUC = 0⋅95) with 29⋅4 cm and mean upper arm circumference (AUC = 0⋅93) with 7⋅9 cm proved to be the best anthropometric diagnostic measure for LBW and gestational age, respectively. Also, both anthropometric measuring tools are achieved the highest correlation (r = 0⋅62) for LBW and gestational age. Foot length had a higher sensitivity (94⋅8 %) in detecting LBW than other measurements, with a higher negative predictive value (NPV) (98⋅4 %) and a higher positive predictive value (PPV) (54⋅8 %). Chest circumference and mid-upper arm circumference were found to be better surrogate measurements for identifying LBW and premature babies in need of special care. More research is needed to identify better diagnostic interventions in situations like the study area, which has limited resources and a high proportion of home deliveries.

UNASSIGNED: Selecting region of interest (ROI) for left atrial appendage (LAA) filling defects assessment can be time consuming and prone to subjectivity. This study aimed to develop and validate a novel artificial intelligence (AI), deep learning (DL) based framework for automatic filling defects assessment on CT images for clinical and subclinical atrial fibrillation (AF) patients.
UNASSIGNED: A total of 443,053 CT images were used for DL model development and testing. Images were analyzed by the AI framework and expert cardiologists/radiologists. The LAA segmentation performance was evaluated using Dice coefficient. The agreement between manual and automatic LAA ROI selections was evaluated using intraclass correlation coefficient (ICC) analysis. Receiver operating characteristic (ROC) curve analysis was used to assess filling defects based on the computed LAA to ascending aorta Hounsfield unit (HU) ratios.
UNASSIGNED: A total of 210 patients (Group 1: subclinical AF, n = 105; Group 2: clinical AF with stroke, n = 35; Group 3: AF for catheter ablation, n = 70) were enrolled. The LAA volume segmentation achieved 0.931-0.945 Dice scores. The LAA ROI selection demonstrated excellent agreement (ICC ≥0.895, p < 0.001) with manual selection on the test sets. The automatic framework achieved an excellent AUC score of 0.979 in filling defects assessment. The ROC-derived optimal HU ratio threshold for filling defects detection was 0.561.
UNASSIGNED: The novel AI-based framework could accurately segment the LAA region and select ROIs while effectively avoiding trabeculae for filling defects assessment, achieving close-to-expert performance. This technique may help preemptively detect the potential thromboembolic risk for AF patients.

UNASSIGNED: To assess the potential of radiomic features in comparison to dual-energy CT (DECT) material decomposition to objectively stratify abdominal lymph node metastases.
UNASSIGNED: In this retrospective study, we included 81 patients (m, 57; median age, 65 (interquartile range, 58.7-73.3) years) with either lymph node metastases (n = 36) or benign lymph nodes (n = 45) who underwent contrast-enhanced abdominal DECT between 06/2015-07/2019. All malignant lymph nodes were classified as unequivocal according to RECIST criteria and confirmed by histopathology, PET-CT or follow-up imaging. Three investigators segmented lymph nodes to extract DECT and radiomics features. Intra-class correlation analysis was applied to stratify a robust feature subset with further feature reduction by Pearson correlation analysis and LASSO. Independent training and testing datasets were applied on four different machine learning models. We calculated the performance metrics and permutation-based feature importance values to increase interpretability of the models. DeLong test was used to compare the top performing models.
UNASSIGNED: Distance matrices and t-SNE plots revealed clearer clusters using a combination of DECT and radiomic features compared to DECT features only. Feature reduction by LASSO excluded all DECT features of the combined feature cohort. The top performing radiomic features model (AUC = 1.000; F1 = 1.000; precision = 1.000; Random Forest) was significantly superior to the top performing DECT features model (AUC = 0.942; F1 = 0.762; precision = 0.800; Stochastic Gradient Boosting) (DeLong < 0.001).
UNASSIGNED: Imaging biomarkers have the potential to stratify unequivocal lymph node metastases. Radiomics models were superior to DECT material decomposition and may serve as a support tool to facilitate stratification of abdominal lymph node metastases.

Centrosome and spindle pole-associated protein (CSPP1) is a centrosome and microtubule-binding protein that plays a role in cell cycle-dependent cytoskeleton organization and cilia formation. Previous studies have suggested that CSPP1 plays a role in tumorigenesis; however, no pan-cancer analysis has been performed. This study systematically investigates the expression of CSPP1 and its potential clinical outcomes associated with diagnosis, prognosis, and therapy. CSPP1 is widely present in tissues and cells and its aberrant expression serves as a diagnostic biomarker for cancer. CSPP1 dysregulation is driven by multi-dimensional mechanisms involving genetic alterations, DNA methylation, and miRNAs. Phosphorylation of CSPP1 at specific sites may play a role in tumorigenesis. In addition, CSPP1 correlates with clinical features and outcomes in multiple cancers. Take brain low-grade gliomas (LGG) with a poor prognosis as an example, functional enrichment analysis implies that CSPP1 may play a role in ferroptosis and tumor microenvironment (TME), including regulating epithelial-mesenchymal transition, stromal response, and immune response. Further analysis confirms that CSPP1 dysregulates ferroptosis in LGG and other cancers, making it possible for ferroptosis-based drugs to be used in the treatment of these cancers. Importantly, CSPP1-associated tumors are infiltrated in different TMEs, rendering immune checkpoint blockade therapy beneficial for these cancer patients. Our study is the first to demonstrate that CSPP1 is a potential diagnostic and prognostic biomarker associated with ferroptosis and TME, providing a new target for drug therapy and immunotherapy in specific cancers.

UNASSIGNED: Differentiating Warthin tumor (WT) from pleomorphic adenoma (PA) is of primary importance due to differences in patient management, treatment and outcome. We sought to evaluate the performance of MRI-based radiomic features in discriminating PA from WT in the preoperative setting.
UNASSIGNED: We retrospectively evaluated 81 parotid gland lesions (48 PA and 33 WT) on T2-weighted (T2w) images and 52 of them on post-contrast fat-suppressed T1-weighted (pcfsT1w) images. All MRI examinations were carried out on a 1.5-Tesla MRI scanner, and images were segmented manually using the software ITK-SNAP (www.itk-snap.org).
UNASSIGNED: The most discriminative feature on pcfsT1w images was GLCM_InverseVariance, yielding area under the curve (AUC), sensitivity and specificity of 0.9, 86 % and 87 %, respectively. Skewness was the feature extracted from T2w images with the highest specificity (88 %) in discriminating WT from PA.
UNASSIGNED: Radiomic analysis could be an important tool to improve diagnostic accuracy in differentiating PA from WT.

OBJECTIVE: This study aims to determine if the presence of specific clinical and computed tomography (CT) patterns are associated with epidermal growth factor receptor (EGFR) mutation in patients with non-small cell lung cancer.
METHODS: A systematic literature review and meta-analysis was carried out in 6 databases between January 2002 and July 2021. The relationship between clinical and CT patterns to detect EGFR mutation was measured and pooled using odds ratios (OR). These results were used to build several mathematical models to predict EGFR mutation.
RESULTS: 34 retrospective diagnostic accuracy studies met the inclusion and exclusion criteria. The results showed that ground-glass opacities (GGO) have an OR of 1.86 (95%CI 1.34 -2.57), air bronchogram OR 1.60 (95%CI 1.38 - 1.85), vascular convergence OR 1.39 (95%CI 1.12 - 1.74), pleural retraction OR 1.99 (95%CI 1.72 - 2.31), spiculation OR 1.42 (95%CI 1.19 - 1.70), cavitation OR 0.70 (95%CI 0.57 - 0.86), early disease stage OR 1.58 (95%CI 1.14 - 2.18), non-smoker status OR 2.79 (95%CI 2.34 - 3.31), female gender OR 2.33 (95%CI 1.97 - 2.75). A mathematical model was built, including all clinical and CT patterns assessed, showing an area under the curve (AUC) of 0.81.
CONCLUSIONS: GGO, air bronchogram, vascular convergence, pleural retraction, spiculated margins, early disease stage, female gender, and non-smoking status are significant risk factors for EGFR mutation. At the same time, cavitation is a protective factor for EGFR mutation. The mathematical model built acts as a good predictor for EGFR mutation in patients with lung adenocarcinoma.

UNASSIGNED: The aim of the study was to investigate the potential of electrocardiogram (ECG)-gated computed tomography pulmonary angiography (CTPA) as a predictor of disease severity in patients with chronic thromboembolic pulmonary hypertension (CTEPH).
UNASSIGNED: Forty-five CTEPH patients with a mean age of 63.8 years±12.7 y (±standard deviation) who had undergone ECG-gated CTPA and right heart catheterization (RHC) were included in the study. Right ventricular to left ventricular volume ratio (RVV/LVV), diameter ratio on 4-chamber view (RVD4CH/LVD4CH), pulmonary trunk (PT) diameter, PT to aortic diameter ratio (PT/A), and septal angle were correlated to mean pulmonary artery pressure (mPAP). Moreover, RVV/LVV and RVD4CH/LVD4CH were adjusted to pulmonary diameter index (PADi) and PT/A index. Areas under the curve (AUC) for predicting mPAP above 40 mmHg, 35 mmHg, and 30 mmHg were calculated.
UNASSIGNED: RVD4CH/LVD4CH revealed the strongest correlation to mPAP before (r = 0.6507) and after (r = 0.7650; p < 0.0001) PT/A adjustment. The AUCs for predicting pH with mPAP over 40 mmHg and 30 mmHg were 0.9229 and 0.864, respectively. A cutoff value of 1.298 enabled prediction of pH with mPAP over 40 mmHg with a sensitivity, specificity, positive predictive, and negative predictive value of 80.00 %, 95.83 %, 88.46 %, and 94.12 %, respectively. Intra- and interobserver variability were excellent for all parameters.
UNASSIGNED: Combining different and easily evaluable ECG-gated CTPA parameters enables excellent prediction of pulmonary hemodynamics in CTEPH patients. Ventricular diameter ratio on 4-chamber view adjusted by the PT/A ratio yielded the best correlation to mPAP.

UNASSIGNED: Alzheimer\'s Disease (AD) is a complex and multifactorial disease and novel approaches are needed to illuminate the underlying pathology. Metabolites comprise the end-product of genes, transcripts, and protein regulations and might reflect disease pathogenesis. Blood is a common biofluid used in metabolomics; however, since extracellular vesicles (EVs) hold cell-specific biological material and can cross the blood-brain barrier, their utilization as biological material warrants further investigation. We aimed to investigate blood- and EV-derived metabolites to add insigts to the pathological mechanisms of AD.
UNASSIGNED: Blood samples were collected from 10 AD and 10 Mild Cognitive Impairment (MCI) patients, and 10 healthy controls. EVs were enriched from plasma using 100,000×g, 1 h, 4 °C with a wash. Metabolites from serum and EVs were measured using liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) spectroscopy. Multivariate and univariate analyses were employed to identify altered metabolites in cognitively impaired individuals.
UNASSIGNED: While no significant EV-derived metabolites were found differentiating patients from healthy individuals, six serum metabolites were found important; valine (p = 0.001, fold change, FC = 0.8), histidine (p = 0.001, FC = 0.9), allopurinol riboside (p = 0.002, FC = 0.2), inosine (p = 0.002, FC = 0.3), 4-pyridoxic acid (p = 0.006, FC = 1.6), and guanosine (p = 0.004, FC = 0.3). Pathway analysis revealed branched-chain amino acids, purine and histidine metabolisms to be downregulated, and vitamin B6 metabolism upregulated in patients compared to controls.
UNASSIGNED: Using a combination of LC-MS and NMR methodologies we identified several altered mechanisms possibly related to AD pathology. EVs require additional optimization prior to their possible utilization as a biological material for AD-related metabolomics studies.

UNASSIGNED: To investigate the diagnostic yield of low to ultra-high b-values for the differentiation of benign from malignant vertebral fractures using a state-of-the-art single-shot zonal-oblique-multislice spin-echo echo-planar diffusion-weighted imaging sequence (SShot ZOOM SE-EPI DWI).
UNASSIGNED: 66 patients (34 malignant, 32 benign) were examined on 1.5 T MR scanners. ADC maps were generated from b-values of 0,400; 0,1000 and 0,2000s/mm2. ROIs were placed into the fracture of interest on ADC maps and trace images and into adjacent normal vertebral bodies on trace images. The ADC of fractures and the Signal-Intensity-Ratio (SIR) of fractures relative to normal vertebral bodies on trace images were considered quantitative metrics. The appearance of the fracture of interest was graded qualitatively as iso-, hypo-, or hyperintense relative to normal vertebrae.
UNASSIGNED: ADC achieved an area under the curve (AUC) of 0.785/0.698/0.592 for b = 0,400/0,1000/0,2000s/mm2 ADC maps respectively. SIR achieved an AUC of 0.841/0.919/0.917 for b = 400/1000/2000s/mm2 trace images respectively. In qualitative analyses, only b = 2000s/mm2 trace images were diagnostically valuable (sensitivity:1, specificity:0.794). Machine learning models incorporating all qualitative and quantitative metrics achieved an AUC of 0.95/0.98/0.98 for b-values of 400/1000/2000s/mm2 respectively. The model incorporating only qualitative metrics from b = 2000s/mm2 achieved an AUC of 0.97.
UNASSIGNED: By using quantitative and qualitative metrics from SShot ZOOM SE-EPI DWI, benign and malignant vertebral fractures can be differentiated with high diagnostic accuracy. Importantly qualitative analysis of ultra-high b-value images may suffice for differentiation as well.

Hepatitis B e antigen (HBeAg) seroconversion is an important intermediate outcome in HBeAg-positive chronic hepatitis B patients. This study aimed to explore whether hepatitis B virus (HBV) RNA serum levels can predict HBeAg seroconversion treated with entecavir.
Serum samples from HBeAg-positive children previously treated with entecavir were retrospectively analyzed. HBV RNA levels were measured at baseline, weeks 12, 24, 48, 72 of therapy. Ability of individual biomarkers to predict HBeAg seroconversion was evaluated using receiver operating characteristics (ROC) analyzes.
Serum HBV RNA was detectable in 51 children with a median of 6.05 (4.04-8.29) log10 IU/mL at baseline. Patients with subsequent HBeAg seroconversion showed a significantly larger decline in median HBV RNA levels during treatment from baseline to week 12 of 1.96 (0.30-3.38) and to week 24 of 2.27 (1.20-3.38) log10 IU/mL, respectively, in comparison to HBeAg-positive patients without HBeAg seroconversion (P < 0.001). Levels of HBV RNA at treatment weeks 12 and 24 showed good ability to predict HBeAg seroconversion (area under ROC scores > 0.85, P < 0.001).
On-treatment HBV RNA dynamic predicts entecavir-induced HBeAg seroconversion in children with chronic hepatitis B living in China.