RETRACTED: Hosni Mahmoud, H.A.; Alabdulkreem, E. Bidirectional Neural Network Model for Glaucoma Progression Prediction. J. Pers. Med. 2023, 13, 390.

The Journal of Personalized Medicine retracts the article Bidirectional Neural Network Model for Glaucoma Progression Prediction [...].

Bee venom ameliorates oxidative stress and histopathological changes of hippocampus, liver and testis during status epileptics.

The unrelenting progression of neurodegenerative diseases has a negative impact on affected individuals, their families, and society. Recurrent epileptic seizures are the hallmark of epilepsy, and treating it effectively remains difficult. Clarify and understanding effects of the antiepileptic drugs (AEDs) in epilepsy by comparing the therapeutic effects between rats receiving valproic acid (VPA) and Bee venom (BV) was aimed throughout the present study. Four male Wistar rat groups were included: control, epileptic group receiving pilocarpine (PILO), epileptic group treated with VPA and BV respectively. Cognitive functions were assessed by evaluating latency time in hot plate, despair swim test, grooming, rearing and ambulation frequency in the open field. BV has ameliorative effect on electrolytes balancing, assured by decreasing lipid peroxidation, nitric oxide and increasing catalase, superoxide dismutase and glutathione peroxidase activities. BV enhanced restoration of liver functions indicated by alanine transaminase (ALT) and aspartate transaminase (AST), total proteins, and albumin; hormonal parameters total and free testosterone, follicle stimulating hormone (FSH) and Luteinizing hormone (LH) were preserved by BV with great recovery of hippocampus, liver and testicular histopathology and ultrastructure comparing with the epileptic rats. The present findings suggested that BV and its active components offer fresh options for controlling epilepsy and prospective methods via minimize or manage the severe consequences.

Bidirectional Neural Network Model for Glaucoma Progression Prediction.

Deep learning models are usually utilized to learn from spatial data, only a few studies are proposed to predict glaucoma time progression utilizing deep learning models. In this article, we present a bidirectional recurrent deep learning model (Bi-RM) to detect prospective progressive visual field diagnoses. A dataset of 5413 different eyes from 3321 samples is utilized as the learning phase dataset and 1272 eyes are used for testing. Five consecutive diagnoses are recorded from the dataset as input and the sixth progressive visual field diagnosis is matched with the prediction of the Bi-RM. The precision metrics of the Bi-RM are validated in association with the linear regression algorithm (LR) and term memory (TM) technique. The total prediction error of the Bi-RM is significantly less than those of LR and TM. In the class prediction, Bi-RM depicts the least prediction error in all three methods in most of the testing cases. In addition, Bi-RM is not impacted by the reliability keys and the glaucoma degree.

Intelligent Monitoring Model for Fall Risks of Hospitalized Elderly Patients.

Early detection of high fall risk is an important process of fall prevention in hospitalized elderly patients. Hospitalized elderly patients can face several falling risks. Monitoring systems can be utilized to protect health and lives, and monitoring models can be less effective if the alarm is not invoked in real time. Therefore, in this paper we propose a monitoring prediction system that incorporates artificial intelligence. The proposed system utilizes a scalable clustering technique, namely the Catboost method, for binary classification. These techniques are executed on the Snowflake platform to rapidly predict safe and risky incidence for hospitalized elderly patients. A later stage employs a deep learning model (DNN) that is based on a convolutional neural network (CNN). Risky incidences are further classified into various monitoring alert types (falls, falls with broken bones, falls that lead to death). At this phase, the model employs adaptive sampling techniques to elucidate the unbalanced overfitting in the datasets. A performance study utilizes the benchmarks datasets, namely SERV-112 and SV-S2017 of the image sequences for assessing accuracy. The simulation depicts that the system has higher true positive counts in case of all health-related risk incidences. The proposed system depicts real-time classification speed with lower training time. The performance of the proposed multi-risk prediction is high at 87.4% in the SERV-112 dataset and 98.71% in the SV-S2017 dataset.

A Deep Learning Model for Stroke Patients' Motor Function Prediction.

Deep learning models are effectively employed to transfer learning to adopt learning from other areas. This research utilizes several neural structures to interpret the electroencephalogram images (EEG) of brain-injured cases to plan operative imagery-computerized interface models for controlling left and right hand movements. This research proposed a model parameter tuning with less training time using transfer learning techniques. The precision of the proposed model is assessed by the aptitudes of motor imagery detection. The experiments depict that the best performance is attained with the incorporation of the proposed EEG-DenseNet and the transfer model. The prediction accuracy of the model reached 96.5% with reduced time computational cost. These high performance proves that the EEG-DenseNet model has high prospective for motor imagery brain-injured therapy systems. It also productively exhibited the effectiveness of transfer learning techniques for enhancing the accuracy of electroencephalogram brain-injured therapy models.

The Prediction of Consumer Behavior from Social Media Activities.

Consumer behavior variants are evolving by utilizing advanced packing models. These models can make consumer behavior detection considerably problematic. New techniques that are superior to customary models to be utilized to efficiently observe consumer behaviors. Machine learning models are no longer efficient in identifying complex consumer behavior variants. Deep learning models can be a capable solution for detecting all consumer behavior variants. In this paper, we are proposing a new deep learning model to classify consumer behavior variants using an ensemble architecture. The new model incorporates two pretrained learning algorithms in an optimized fashion. This model has four main phases, namely, data gathering, deep neural modeling, model training, and deep learning model evaluation. The ensemble model is tested on Facemg BIG-D15 and TwitD databases. The experiment results depict that the ensemble model can efficiently classify consumer behavior with high precision that outperforms recent models in the literature. The ensemble model achieved 98.78% accuracy on the Facemg database, which is higher than most machine learning consumer behavior detection models by more than 8%.

A Decoding Prediction Model of Flexion and Extension of Left and Right Feet from Electroencephalogram.

Detection of limb motor functions utilizing brain signals is a significant technique in the brain signal gain model (BSM) that can be effectively employed in various biomedical applications. Our research presents a novel technique for prediction of feet motor functions by applying a deep learning model with cascading transfer learning technique to use the electroencephalogram (EEG) in the training stage. Our research deduces the electroencephalogram data (EEG) of stroke incidence to propose functioning high-tech interfaces for predicting left and right foot motor functions. This paper presents a transfer learning with several source input domains to serve a target domain with small input size. Transfer learning can reduce the learning curve effectively. The correctness of the presented model is evaluated by the abilities of motor functions in the detection of left and right feet. Extensive experiments were performed and proved that a higher accuracy was reached by the introduced BSM-EEG neural network with transfer learning. The prediction of the model accomplished 97.5% with less CPU time. These accurate results confirm that the BSM-EEG neural model has the ability to predict motor functions for brain-injured stroke therapy.

Pneumonia Transfer Learning Deep Learning Model from Segmented X-rays.

Pneumonia is a common disease that occurs in many countries, more specifically, in poor countries. This disease is an obstructive pneumonia which has the same impression on pulmonary radiographs as other pulmonary diseases, which makes it hard to distinguish even for medical radiologists. Lately, image processing and deep learning models are established to rapidly and precisely diagnose pneumonia disease. In this research, we have predicted pneumonia diseases dependably from the X-ray images, employing image segmentation and machine learning models. A public labelled database is utilized with 4000 pneumonia disease X-rays and 4000 healthy X-rays. ImgNet and SqueezeNet are utilized for transfer learning from their previous computed weights. The proposed deep learning models are trained for classifying pneumonia and non-pneumonia cases. The following processes are presented in this paper: X-ray segmentation utilizing BoxENet architecture, X-ray classification utilizing the segmented chest images. We propose the improved BoxENet model by incorporating transfer learning from both ImgNet and SqueezeNet using a majority fusion model. Performance metrics such as accuracy, specificity, sensitivity and Dice are evaluated. The proposed Improved BoxENet model outperforms the other models in binary and multi-classification models. Additionally, the Improved BoxENet has higher speed compared to other models in both training and classification.

Isatin Counteracts Diethylnitrosamine/2-Acetylaminofluorene-Induced Hepatocarcinogenesis in Male Wistar Rats by Upregulating Anti-Inflammatory, Antioxidant, and Detoxification Pathways.

Hepatocellular carcinoma (HCC) represents around 85% of all known types of liver cancers and is estimated to be the fifth most common cause of cancer-related death worldwide. The current study assessed the preventive efficacy of isatin on diethylnitrosamine (DENA)/2-acetylaminofluorene (2-AAF)-induced hepatocarcinogenesis in male Wistar rats and investigated the underlying cellular and molecular mechanisms. HCC was initiated by intraperitoneal injection of DENA (150 mg/kg/week) for two weeks, followed by oral 2-AAF (20 mg/kg) every other day for three successive weeks. Oral isatin or vehicle (control) was administered at 25 mg/kg for 20 weeks during and following HCC induction. Isatin ameliorated the deleterious effects of DENA/2-AAF on liver function as evidenced by reduced serum levels of AST, ALT, total bilirubin, albumin, and liver tumor biomarkers (CA19.9 and AFP) compared to control DENA/2-AAF-treated rats. Histopathological evaluations demonstrated that isatin-mediated protection against hepatocarcinogenesis was accompanied by a decline in hepatic lipid peroxidation, a marker of oxidative stress, and enhanced antioxidant capacity, as evidenced by increased glutathione and superoxide dismutase expression. Isatin treatment also upregulated expression of the major stress-response transcription factor Nrf2 and the detoxifying enzymes NAD(P)H quinine oxidoreductase and glutathione-S-transferase alpha 2 and downregulated expression of the proliferation marker Ki67. Moreover, isatin significantly reduced the DENA/2-AAF-induced decrease in hepatic expression of anti-apoptotic Bcl2 and the DENA/2-AAF-induced increases in pro-inflammatory and pro-apoptotic factors (TNF-α, NF-κB p50, NF-κB p65, p53, and caspase 3). Thus, it can be concluded that isatin may protect against chemically induced hepatocarcinogenesis by enhancing cellular antioxidant, anti-inflammatory, and detoxification mechanisms, in part through upregulation of the Nrf2 signaling pathway.

A Fast Hybrid Classification Algorithm with Feature Reduction for Medical Images.

In this paper, we are introducing a fast hybrid fuzzy classification algorithm with feature reduction for medical images. We incorporated the quantum-based grasshopper computing algorithm (QGH) with feature extraction using fuzzy clustering technique (C-means). QGH integrates quantum computing into machine learning and intelligence applications. The objective of our technique is to the integrate QGH method, specifically into cervical cancer detection that is based on image processing. Many features such as color, geometry, and texture found in the cells imaged in Pap smear lab test are very crucial in cancer diagnosis. Our proposed technique is based on the extraction of the best features using a more than 2600 public Pap smear images and further applies feature reduction technique to reduce the feature space. Performance evaluation of our approach evaluates the influence of the extracted feature on the classification precision by performing two experimental setups. First setup is using all the extracted features which leads to classification without feature bias. The second setup is a fusion technique which utilized QGH with the fuzzy C-means algorithm to choose the best features. In the setups, we allocate the assessment to accuracy based on the selection of best features and of different categories of the cancer. In the last setup, we utilized a fusion technique engaged with statistical techniques to launch a qualitative agreement with the feature selection in several experimental setups.

Methodology for Exploring Patterns of Epigenetic Information in Cancer Cells Using Data Mining Technique.

Epigenetic changes are a necessary characteristic of all cancer types. Tumor cells usually target genetic changes and epigenetic alterations as well. It is most beneficial to identify epigenetic similar features among cancer various types to be able to discover the appropriate treatments. The existence of epigenetic alteration profiles can aid in targeting this goal. In this paper, we propose a new technique applying data mining and clustering methodologies for cancer epigenetic changes analysis. The proposed technique aims to detect common patterns of epigenetic changes in various cancer types. We demonstrated the validation of the new technique by detecting epigenetic patterns across seven cancer types and by determining epigenetic similarities among various cancer types. The experimental results demonstrate that common epigenetic patterns do exist across these cancer types. Additionally, epigenetic gene analysis performed on the associated genes found a strong relationship with the development of various types of cancer and proved high risk across the studied cancer types. We utilized the frequent pattern data mining approach to represent cancer types compactly in the promoters for some epigenetic marks. Utilizing the built frequent pattern item set, the most frequent items are identified and yield the group of the bi-clusters of these patterns. Experimental results of the proposed method are shown to have a success rate of 88% in detecting cancer types according to specific epigenetic pattern.

Automated Keratoconus Detection by 3D Corneal Images Reconstruction.

This paper presents a technique for the detection of keratoconus via the construction of a 3D eye images from 2D frontal and lateral eye images. Keratoconus is a disease that affects the cornea. Normal case eyes have a round-shaped cornea, while patients who suffer from keratoconus have a cone-shaped cornea. Early diagnosis can decrease the risk of eyesight loss. Our aim is to create a method of fully automated keratoconus detection using digital-camera frontal and lateral eye images. The presented technique accurately determines case severity. Geometric features are extracted from 2D images to estimate depth information used to build 3D images of the cornea. The proposed methodology is easy to implement and time-efficient. 2D images of the eyes (frontal and lateral) are used as input, and 3D images from which the curvature of the cornea can be detected are produced as output. Our method involves two main steps: feature extraction and depth calculation. Machine learning from the 3D images dataset Dataverse, specifically taken by the Cornea/Anterior Segment OCT SS-1000 (CASIA), was performed. Results show that the method diagnosed the four stages of keratoconus (severe, moderate, mild, and normal) with an accuracy of 97.8%, as compared to manual diagnosis done by medical experts.

Machine Translation Utilizing the Frequent-Item Set Concept.

In this paper, we introduce new concepts in the machine translation paradigm. We treat the corpus as a database of frequent word sets. A translation request triggers association rules joining phrases present in the source language, and phrases present in the target language. It has to be noted that a sequential scan of the corpus for such phrases will increase the response time in an unexpected manner. We introduce the pre-processing of the bilingual corpus through proposing a data structure called Corpus-Trie (CT) that renders a bilingual parallel corpus in a compact data structure representing frequent data items sets. We also present algorithms which utilize the CT to respond to translation requests and explore novel techniques in exhaustive experiments. Experiments were performed on specific language pairs, although the proposed method is not restricted to any specific language. Moreover, the proposed Corpus-Trie can be extended from bilingual corpora to accommodate multi-language corpora. Experiments indicated that the response time of a translation request is logarithmic to the count of unrepeated phrases in the original bilingual corpus (and thus, the Corpus-Trie size). In practical situations, 5-20% of the log of the number of the nodes have to be visited. The experimental results indicate that the BLEU score for the proposed CT system increases with the size of the number of phrases in the CT, for both English-Arabic and English-French translations. The proposed CT system was demonstrated to be better than both Omega-T and Apertium in quality of translation from a corpus size exceeding 1,600,000 phrases for English-Arabic translation, and 300,000 phrases for English-French translation.

A novel technique for automated concealed face detection in surveillance videos.

Face detection perceives great importance in surveillance paradigm and security paradigm areas. Face recognition is the technique to identify a person identity after face detection. Extensive research has been done on these topics. Another important research problem is to detect concealed faces, especially in high-security places like airports or crowded places like concerts and shopping centres, for they may prevail security threat. Also, in order to help effectively in preventing the spread of Coronavirus, people should wear masks during the pandemic especially in the entrance to hospitals and medical facilities. Surveillance systems in medical facilities should issue warnings against unmasked people. This paper presents a novel technique for concealed face detection based on complexion detection to challenge a concealed face assumption. The proposed algorithm first determine of the existence of a human being in the surveillance scene. Head and shoulder contour will be detected. The face will be clustered to cluster patches. Then determination of presence or absent of human skin will be determined. We proposed a hybrid approach that combines normalized RGB (rgb) and the YCbCr space color. This technique is tested on two datasets; the first one contains 650 images of skin patches. The second dataset contains 800 face images. The algorithm achieves an average detection rate of 97.51% for concealed faces. Also, it achieved a run time comparable with existing state-of-the-art concealed face detection systems that run in real time.

Study of the difficult glycemic control in relation to the presence of diabetes-autoantibodies in a sample of Egyptians with type 1 diabetes.

BACKGROUND: T1DM is divided into 1A (immune-mediated), 1B (virus-triggered, genetic and idiopathic). Presence of auto-antibodies may be correlated to glycemic control. AIM: Assessment relation between the autoantibodies and the poor glycemic control in T1DM. METHODS: 60 patients T1DM 30 males, 30 females, subjected to full history, clinical, anthropometric assessment and laboratory assessment of fasting C-peptide, FBS, 2 h PP glucose, HbA1c, GADA, ICA and IAA level. Classified into two groups; Group I: negative auto-antibodies, Group II: positive auto-antibodies, Group II was further classified into 3 sub-groups, Group II a:1 positive autoantibody, Group II b: 2 positive autoantibodies and Group II c: 3 positive autoantibodies. RESULTS: HbA1c was significantly higher in group II than group I (11.85 ±â€¯1.61% vs. 8.52 ±â€¯0.41%, p = 0.000). HbA1c was highest in group IIc followed by IIb then IIa (12.25 ±â€¯1.48% vs. 11.57 ±â€¯1.59% vs. 10.78 ±â€¯1.73%, p = 0.038). Total insulin units per day was significantly higher in group II than group I (109.83 ±â€¯7.77 U/day vs. 100.83 ±â€¯1.83 U/day, p = 0.007). Duration of diabetes was significantly higher in group I than group II (10.17 ±â€¯1.94 years vs. 8.11 ±â€¯2.20 years, p = 0.033). HbA1c, total insulin units per day and duration of diabetes were independent predictive factors for presence of autoantibodies (p = 0.007, p = 0.033 and p = 0.043 respectively). CONCLUSION: Autoantibodies affect the glycemic control presented by high HbA1c; also it causes increase in total insulin units needed by patients; the more autoantibodies, the higher HbA1c, the more insulin units required to control glycemic state.

Significance of p27 Immunostaining in B-Cell Neoplasm.

P27 is an important cell cycle regulatory protein. Many reports have validated the utility of p27 as a prognostic marker in different human cancers and to prove its prognostic role in B-cell neoplasm; 80 newly diagnosed B-cell neoplasm patients with mean age of 46.6 years recruited from Hematology/Oncology Unit of Ain Shams University Hospitals during the period from January 2008 till June 2010 were studied for their p27 immunostaining results which showed that all cases of chronic lymphocytic leukemia (CLL) were positive for p27, whereas all mantly cell lymphoma cases were negative for it. There was significantly higher p27 positivity in CLL cases compared with non-Hodgkin lymphoma and that indolent cases showed significantly higher rate of positivity when compared with aggressive and highly aggressive cases. So, we can use this marker to differentiate CLL and mantly cell lymphoma in cases of confusion.

Study of alpha hemoglobin stabilizing protein expression in patients with ß thalassemia and sickle cell anemia and its impact on clinical severity.

UNLABELLED: The α hemoglobin stabilizing protein (AHSP) binds α-Hb and prevents its precipitation limiting free α-Hb toxicities. Our aim was to study AHSP expression in ß thalassemia syndromes in relation to their clinical severity and to compare it with its level in sickle cell anemia. We compared patients with ß-thalassemia (n=37) (ß-thalassemia major (BTM) (n=19) and ß-thalassemia intermedia (BTI) (n=18)) with 12 patients with sickle cell anemia as regards clinical severity, age at presentation, transfusion dependency, mean pre-transfusion hemoglobin level, use of hydroxyurea and AHSP expression by real time quantitative PCR. Median (and IQR) AHSP expression was significantly higher in patients with sickle cell anemia 2275 (3898) compared to thalassemia 283 (718), P=0.001, with no significant difference between BTM and BTI (P=0.346). It was also significantly higher in non-transfusion dependent patients with ß thalassemia (NTDT) compared to transfusion dependent ones (P=0.019), and in patients on hydroxyurea therapy (P<0.001). However, there was no significant difference in its level according to clinical severity score (P=0.946) or splenectomy status (P=0.145). CONCLUSION: AHSP expression was higher in patients with sickle cell anemia versus thalassemia, with no significant difference between BTM and BTI. Expression was higher in patients with NTDT and on hydroxyurea therapy.

Clinical to Molecular Screening Paradigm for ß-Thalassemia Carriers.

ß-Thalassemia (ß-thal) represents a major health problem worldwide and particularly in Egypt. Its prevention, compared to treatment, is cost-effective, possible and practical. In this study we evaluate a proposed paradigm for detection of the ß-thal carrier state. The present study included 1627 children and adolescents of both sexes, presenting as outpatients to clinics of Ain-Shams University Hospitals, Cairo, Egypt, from November 1 2009 to June 30 2010. In the first phase, after performing a complete blood count (CBC), 280 microcytic hypochromic patients were selected. These cases were further analyzed by iron profile and high performance liquid chromatography (HPLC); in the second phase, hybridization detected 22 common ß-globin mutations in 74.0% of the suspected cases. Thus, by HPLC, the Hb A2 level of >3.5% provided 100.0% sensitivity, 70.0% specificity, 75.0% positive predictive value (PPV), 100.0% negative predictive value (NPV) and accuracy of 70.0% to identify ß-thal trait and at a cut-off of 4.0%, it provided 97.4% sensitivity, 72.7% specificity, 92.6% PPV, 88.8% NPV and a diagnostic accuracy of 92%. High performance liquid chromatography is a reliable and cost effective primary screening tool for ß-thal trait at a Hb A2 level of ≥4.0%, while molecular testing is mandatory only for selected cases with borderline Hb A2 values between 3.5 and 4.0%.

Comprehensive study on laboratory biomarkers for prediction and diagnosis of deep venous thrombosis.

Deep venous thrombosis (DVT) is based upon clinical suspicion in patients at risk and confirmatory duplex imaging of the deep venous system of the affected extremity. The aim of the present study was to determine different cutoff points of D-dimer, P-selectin and microparticles that could be used in early diagnosis and prediction of impending DVT in symptomatic patients with normal duplex ultrasound. Three groups of individuals were examined: 50 healthy volunteers (Group I); 75 patients with positive duplex ultrasound for DVT (Group II) and 75 symptomatic patients, but with negative duplex ultrasound for DVT (Group III). D-dimer was measured by immunoturbidimetric assay, P-selectin by flow cytometry and microparticles by ELISA. D-dimer, P-selectin and microparticles levels were significantly higher in Group II and III patients when compared with Group I individuals. Using receiver-operating characteristic curves, we determined that cutoff levels of 0.92 mg/l for D-dimer, 17.8% for P-selectin and 16.5 nmol/l for microparticles can accurately rule out DVT. New cutoff levels were estimated for the three studied biomarkers that differentiated the group of DVT-negative duplex patients without thrombosis from those patients of the same group who developed thrombosis being 2.81 mg/l for D-dimer, 30.2% for P-selectin and 26 nmol/l for microparticles. D-dimer, P-selectin and microparticles can be used to diagnose and detect impending DVT, thus identifying patients at high risk that could benefit from early anticoagulant therapy without the need for imaging studies.

Significance of neuropilin-1 expression in acute myeloid leukemia.

OBJECTIVE: Neuropilin-1 is a vascular endothelial growth factor receptor that acts as a mediator of angiogenesis. Its importance in hematological malignancies such as acute myeloid leukemia (AML) remains to be elucidated. The aim of this study was to evaluate the significance of neuropilin-1 expression in AML patients by both flow cytometry and real-time polymerase chain reaction (PCR) in regard to its diagnostic and prognostic values. MATERIALS AND METHODS: Bone marrow aspirates of 44 patients with de novo AML and 12 relapsed AML patients were examined in this study. Ten subjects with nonhematological malignancy serving as the control group were also included. RESULTS: Neuropilin-1 expression by flow cytometry showed a highly significant increase in de novo and relapsed AML patients with a mean of 27.1±17.5% and 21.5±16.6%, respectively, compared to control group's mean of 3.4±1.9%. A cut-off value of 6% was established as differentiating patients from the control group. By real-time PCR, no statistical significance was found in de novo and relapsed AML patients with a mean of 1.9±3.6 IU/L and 0.3±0.2 IU/L, respectively, compared to the control group's mean of 0.3±0.1 IU/L. Neuropilin-1 surface expression by flow cytometry showed a significant correlation with total leukocyte count and a negative correlation with hemoglobin level in de novo AML patients. In relapsed AML patients, positive significant correlations were found with age, bone marrow blast percentage, and CD14. Neuropilin-1 mRNA level by real-time PCR showed a positive significant correlation with peripheral blood blast percentage and CD117 and a negative correlation with hemoglobin level in de novo AML patients. In relapsed patients, a positive correlation was found with lactate dehydrogenase. CONCLUSION: Neuropilin-1 can be used as a tool for diagnosis and prognosis in AML patients. CONFLICT OF INTEREST: None declared.