The beneficial effect of nanomethionine supplementation on growth performance, gene expression profile, and histopathology of heat-stressed broiler chicken.

This study investigated the effects of nanomethionine (nano-meth) on performance, antioxidants, and gene expression of HSP70, HSP90 and Heat Shock factor-1 (HSF-1) from the liver, and TLR4 from the jejunum, of broiler chickens reared under normal temperatures or under heat stress. Three hundred 1-day-old chicks were randomly assigned to 5 treatment groups. Group 1 served as control. Under normal temperature, birds in group 2 received nano-meth (10 mL/L of drinking water) from d1 until the experiment ended. Group 3 birds were heat-stressed (HS) and did not receive any supplementation. Group 4 received nano-meth in the same dose from d1 old until experiment ended, and the birds were exposed to HS. Group 5 birds were HS and received supplementation of nano-meth during the HS period only. Nano-meth improved (P < 0.0001) final body weight, weight gain, feed conversion ratio, and also decreased (P < 0.0001) the effect of HS on growth performance. Reduction (P < 0.0001) in malondialdehyde and changes in antioxidant enzymes GPX and CAT activity indicated the antioxidant effect of nano-meth. Nano-meth supplementation caused an increase in the expression of HSP70 , HSP90 and HSF1, and a downregulation of TLR4 gene expression. Additionally, nano-meth-supplemented groups showed marked improvement in the histological liver structure, intestinal morphology and villus height compared to control or HS groups.

The incidence of retinopathy of prematurity in neonates in Germany in 2019; a nationwide epidemiological cohort study.

Retinopathy of prematurity (ROP) is a leading cause of childhood blindness in preterm infants. The incidence of ROP varies widely across countries, with rates as high as 30% in some regions. This study investigated the incidence, risk factors, treatment, and mortality of ROP patients in Germany. Data were extracted from the German Federal Statistical Office (Destatis) diagnosis-related group (DRG) and Institute for the Remuneration System in Hospitals (InEK) databases. Patients with a secondary diagnosis of ROP (ICD-10 code H35.1) in the first 28 days of life were included. Data were extracted for patients admitted between January 1, 2019 and December 31, 2019. The diagnoses and procedures were determined using the German version of the International Classification of Diseases (ICD-10-GM) and the German procedure coding system (OPS). The codes 5-154.xx, 5-155.xx, 8-020.xx, 5-156.9, 6-003.(c&d), 6-007.(2&8) were utilised to denote different ocular treatments. Patient Clinical Complexity Levels were extracted and used to compare ROP with non-ROP patients. A total of 1326 patients with ROP were identified. The incidence of ROP is estimated to be 17.04 per 10,000 live births. The incidence was highest in infants with birth weights less than 500 g and decreased with increasing birth weight. The most common risk factors for ROP were low birth weight, male sex, and prematurity. Of the infants with ROP, 7.2% required ocular treatment. The most common treatment was intraocular injections, followed by photocoagulation. No surgical treatment was required for any of the infants during the study period. The mortality rate for infants with ROP was 60.33 per 10,000. This is higher than the overall neonatal death rate of 24.2 per 10,000. CONCLUSIONS: This study found that the incidence of ROP in Germany is similar to that in other developed countries. The study also found that the mortality rate for infants with ROP is higher than the overall neonatal death rate. These findings highlight the importance of early detection and treatment of ROP in preterm infants. WHAT IS KNOWN: • ROP is a severe eye condition often affecting preterm infants. • Previous data are limited in scope and generalizability. WHAT IS NEW: • Based on a national database, our study found ROP incidence to be 17.04 per 10,000 new births, higher in males (17.71) than in females (16.34). • 7.2% of ROP cases required ocular treatment, inversely correlated with birth weight. • High rates of multimorbidity such as neonatal jaundice (84.69%), respiratory distress syndrome (80.84%), and apnea (78.88%) were observed.

A computer-aided diagnosis system for detecting various diabetic retinopathy grades based on a hybrid deep learning technique.

Diabetic retinopathy (DR) is a serious disease that may cause vision loss unawares without any alarm. Therefore, it is essential to scan and audit the DR progress continuously. In this respect, deep learning techniques achieved great success in medical image analysis. Deep convolution neural network (CNN) architectures are widely used in multi-label (ML) classification. It helps in diagnosing normal and various DR grades: mild, moderate, and severe non-proliferative DR (NPDR) and proliferative DR (PDR). DR grades are formulated by appearing multiple DR lesions simultaneously on the color retinal fundus images. Many lesion types have various features that are difficult to segment and distinguished by utilizing conventional and hand-crafted methods. Therefore, the practical solution is to utilize an effective CNN model. In this paper, we present a novel hybrid, deep learning technique, which is called E-DenseNet. We integrated EyeNet and DenseNet models based on transfer learning. We customized the traditional EyeNet by inserting the dense blocks and optimized the resulting hybrid E-DensNet model's hyperparameters. The proposed system based on the E-DenseNet model can accurately diagnose healthy and different DR grades from various small and large ML color fundus images. We trained and tested our model on four different datasets that were published from 2006 to 2019. The proposed system achieved an average accuracy (ACC), sensitivity (SEN), specificity (SPE), Dice similarity coefficient (DSC), the quadratic Kappa score (QKS), and the calculation time (T) in minutes (m) equal [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], 0.883, and 3.5m respectively. The experiments show promising results as compared with other systems.

A comprehensive diagnosis system for early signs and different diabetic retinopathy grades using fundus retinal images based on pathological changes detection.

Multi-label classification (MLC) is deemed as an effective and dynamic research topic in the medical image analysis field. For ophthalmologists, MLC benefits can be utilized to detect early diabetic retinopathy (DR) signs, as well as its different grades. This paper proposes a comprehensive computer-aided diagnostic (CAD) system that exploits the MLC of DR grades using colored fundus photography. The proposed system detects and analyzes various retina pathological changes accompanying DR development. We extracted some significant features to differentiate healthy from DR cases as well as differentiate various DR grades. First, we preprocessed the retinal images to eliminate noise and enhance the image quality by using histogram equalization for brightness preservation based on dynamic stretching technique. Second, the images were segmented to extract four pathology variations, which are blood vessels, exudates, microaneurysms, and hemorrhages. Next, six various features were extracted using a gray level co-occurrence matrix, the four extracting areas, and blood-vessel bifurcation points. Finally, the features were supplied to a support vector machine (SVM) classifier to distinguish normal and different DR grades. To train and test the proposed system, we utilized four benchmark datasets (two of them are multi-label datasets) using six performance metrics. The proposed system achieved an average accuracy of 89.2%, sensitivity of 85.1%, specificity of 85.2%, positive predictive value of 92.8%, area under the curve of 85.2%, and Disc similarity coefficient (DSC) of 88.7%. The experiments show promising results as compared with other systems.

Interferon-gamma serum level and immunohistochemical expression of CD8 cells in tissue biopsies in patients with alopecia areata in correlation with trichoscopic findings.

Alopecia areata (AA) is an autoimmune form of nonscarring hair loss. The aim of the study was to assess the serum concentration of interferon gamma (IFN-γ) and CD8 cell expression in lesional skin biopsies in correlation with the disease severity, activity, duration, and trichoscopic findings in patients with AA. The study included 30 patients with AA and 15 age- and sex-matched healthy controls. Trichoscopy was performed and photographs were captured for the alopecic areas, and the enzyme-linked immunosorbent assay technique was used for serum level of IFN-γ assessment and immunohistochemistry for CD8 cells. The results obtained indicate that IFN-γ serum level in patients was significantly higher than that of control subjects, and significantly correlated with the activity status and the duration of the disease. CD8+ T cells infiltrate intensity significantly correlated with severity. Yellow dots (YDs), vellus hair, black dot, and exclamation marks were the most common trichoscopic findings. The presence of black dots significantly correlated to the disease activity, duration, serum IFN-γ, and CD8+ infiltrate intensity. The presence of YDs significantly correlated with the mean serum IFN-γ level. Exclamation marks significantly correlated with the disease activity and the degree of CD8+ infiltrate. In conclusion, trichoscopy could be a reliable indicator of the IFN-γ serum level and CD8+ T cell infiltrate intensity in AA patient.