Cross vision transformer with enhanced Growth Optimizer for breast cancer detection in IoMT environment.

The recent advances in artificial intelligence modern approaches can play vital roles in the Internet of Medical Things (IoMT). Automatic diagnosis is one of the most important topics in the IoMT, including cancer diagnosis. Breast cancer is one of the top causes of death among women. Accurate diagnosis and early detection of breast cancer can improve the survival rate of patients. Deep learning models have demonstrated outstanding potential in accurately detecting and diagnosing breast cancer. This paper proposes a novel technology for breast cancer detection using CrossViT as the deep learning model and an enhanced version of the Growth Optimizer algorithm (MGO) as the feature selection method. CrossVit is a hybrid deep learning model that combines the strengths of both convolutional neural networks (CNNs) and transformers. The MGO is a meta-heuristic algorithm that selects the most relevant features from a large pool of features to enhance the performance of the model. The developed approach was evaluated on three publicly available breast cancer datasets and achieved competitive performance compared to other state-of-the-art methods. The results show that the combination of CrossViT and the MGO can effectively identify the most informative features for breast cancer detection, potentially assisting clinicians in making accurate diagnoses and improving patient outcomes. The MGO algorithm improves accuracy by approximately 1.59% on INbreast, 5.00% on MIAS, and 0.79% on MiniDDSM compared to other methods on each respective dataset. The developed approach can also be utilized to improve the Quality of Service (QoS) in the healthcare system as a deployable IoT-based intelligent solution or a decision-making assistance service, enhancing the efficiency and precision of the diagnosis.

Boosting Sinh Cosh Optimizer and arithmetic optimization algorithm for improved prediction of biological activities for indoloquinoline derivatives.

Quantitative Structure Activity Relation (QSAR) models are mathematical techniques used to link structural characteristics with biological activities, thus considered a useful tool in drug discovery, hazard evaluation, and identifying potentially lethal molecules. The QSAR regulations are determined by the Organization for Economic Cooperation and Development (OECD). QSAR models are helpful in discovering new drugs and chemicals to treat severe diseases. In order to improve the QSAR model's predictive power for biological activities of naturally occurring indoloquinoline derivatives against different cancer cell lines, a modified machine learning (ML) technique is presented in this paper. The Arithmetic Optimization Algorithm (AOA) operators are used in the suggested model to enhance the performance of the Sinh Cosh Optimizer (SCHO). Moreover, this improvement functions as a feature selection method that eliminates superfluous descriptors. An actual dataset gathered from previously published research is utilized to evaluate the performance of the suggested model. Moreover, a comparison is made between the outcomes of the suggested model and other established methodologies. In terms of pIC50 values for different indoloquinoline derivatives against human MV4-11 (leukemia), human HCT116 (colon cancer), and human A549 (lung cancer) cell lines, the suggested model achieves root mean square error (RMSE) of 0.6822, 0.6787, 0.4411, and 0.4477, respectively. The biological application of indoloquinoline derivatives as possible anticancer medicines is predicted with a high degree of accuracy by the suggested model, as evidenced by these findings.

Ionic Liquids Immobilized Synthesis of New Xanthenes Derivatives and their Antiproliferative, Molecular Docking, and Morphological Studies.

BACKGROUND: Xanthenes and benzoxanthenesare are highly valuable compounds in organic chemistry and medicinal chemistry. Xanthene derivatives were found to have many applications in medicinal chemistry. OBJECTIVE: This work aims to explore the synthesis of xanthene derivatives with various substituents and find the possibility of their uses as anticancer agents. METHOD: The basic starting compound through this work was the 2,3-dihydro-1H-xanthen-1-one (3), which was synthesized from the reaction of cyclohexan-1,3-dione and 2-hydroxybenzaldehyde. Compound 3 synthesized new thiophene, pyrimidine, isoxazole, and thiazole derivatives based on the xanthenes nucleus. Fused xanthene derivatives were obtained through further heterocyclization reactions. Multicomponent reactions expressed in this work were carried out in the presence of solvent catalyzed by Et3N and in solvent-free ionic liquid immobilized catalyst. RESULTS: Cytotoxicity for the newly synthesized compounds toward cancer cell lines was measured, and the results revealed that many compounds exhibited high inhibitions. CONCLUSION: The antiproliferative activity of the synthesized compounds was studied on six selected cancer cell lines. The nature of the heterocyclic ring and the variations of substituted groups showed a high effect through the inhibitions of the tested compound.

Optimal Skin Cancer Detection Model Using Transfer Learning and Dynamic-Opposite Hunger Games Search.

Recently, pre-trained deep learning (DL) models have been employed to tackle and enhance the performance on many tasks such as skin cancer detection instead of training models from scratch. However, the existing systems are unable to attain substantial levels of accuracy. Therefore, we propose, in this paper, a robust skin cancer detection framework for to improve the accuracy by extracting and learning relevant image representations using a MobileNetV3 architecture. Thereafter, the extracted features are used as input to a modified Hunger Games Search (HGS) based on Particle Swarm Optimization (PSO) and Dynamic-Opposite Learning (DOLHGS). This modification is used as a novel feature selection to alloacte the most relevant feature to maximize the model's performance. For evaluation of the efficiency of the developed DOLHGS, the ISIC-2016 dataset and the PH2 dataset were employed, including two and three categories, respectively. The proposed model has accuracy 88.19% on the ISIC-2016 dataset and 96.43% on PH2. Based on the experimental results, the proposed approach showed more accurate and efficient performance in skin cancer detection than other well-known and popular algorithms in terms of classification accuracy and optimized features.

Antibiotic Resistance, Virulence Gene Detection, and Biofilm Formation in Aeromonas spp. Isolated from Fish and Humans in Egypt.

The genus Aeromonas is widely distributed in aquatic environments and is recognized as a potential human pathogen. Some Aeromonas species are able to cause a wide spectrum of diseases, mainly gastroenteritis, skin and soft-tissue infections, bacteremia, and sepsis. The aim of the current study was to determine the prevalence of Aeromonas spp. in raw fish markets and humans in Zagazig, Egypt; identify the factors that contribute to virulence; determine the isolates' profile of antibiotic resistance; and to elucidate the ability of Aeromonas spp. to form biofilms. The examined samples included fish tissues and organs from tilapia (Oreochromis niloticus, n = 160) and mugil (Mugil cephalus, n = 105), and human skin swabs (n = 51) and fecal samples (n = 27). Based on biochemical and PCR assays, 11 isolates (3.2%) were confirmed as Aeromonas spp. and four isolates (1.2%) were confirmed as A. hydrophila. The virulence genes including haemolysin (hyl A) and aerolysin (aer) were detected using PCR in A. hydrophila in percentages of 25% and 50%, respectively. The antimicrobial resistance of Aeromonas spp. was assessed against 14 antibiotics comprising six classes. The resistance to cefixime (81.8%) and tobramycin (45.4%) was observed. The multiple antibiotic resistance (MAR) index ranged between 0.142-0.642 with 64.2% of the isolates having MAR values equal to 0.642. Biofilm formation capacity was assessed using a microtiter plate assay, and two isolates (18.1%) were classified as biofilm producers. This study establishes a baseline for monitoring and controlling the multidrug-resistant Aeromonas spp. and especially A. hydrophila in marine foods consumed in our country to protect humans and animals.

Medical Image Classifications for 6G IoT-Enabled Smart Health Systems.

As day-to-day-generated data become massive in the 6G-enabled Internet of medical things (IoMT), the process of medical diagnosis becomes critical in the healthcare system. This paper presents a framework incorporated into the 6G-enabled IoMT to improve prediction accuracy and provide a real-time medical diagnosis. The proposed framework integrates deep learning and optimization techniques to render accurate and precise results. The medical computed tomography images are preprocessed and fed into an efficient neural network designed for learning image representations and converting each image to a feature vector. The extracted features from each image are then learned using a MobileNetV3 architecture. Furthermore, we enhanced the performance of the arithmetic optimization algorithm (AOA) based on the hunger games search (HGS). In the developed method, named AOAHG, the operators of the HGS are applied to enhance the AOA's exploitation ability while allocating the feasible region. The developed AOAG selects the most relevant features and ensures the overall model classification improvement. To assess the validity of our framework, we conducted evaluation experiments on four datasets, including ISIC-2016 and PH2 for skin cancer detection, white blood cell (WBC) detection, and optical coherence tomography (OCT) classification, using different evaluation metrics. The framework showed remarkable performance compared to currently existing methods in the literature. In addition, the developed AOAHG provided results better than other FS approaches according to the obtained accuracy, precision, recall, and F1-score as performance measures. For example, AOAHG had 87.30%, 96.40%, 88.60%, and 99.69% for the ISIC, PH2, WBC, and OCT datasets, respectively.

Consensus Evidence-Based Clinical Practice Recommendations for the Diagnosis and Treat-To-Target Management of Osteoporosis in Chronic Kidney Disease Stages G4-G5D and Post-transplantation: An Initiative of Egyptian Academy of Bone Health.

Objective: The aim of this study was to reach a consensus on an updated version of the recommendations for the diagnosis and Treat-to-Target management of osteoporosis that is effective and safe for individuals with chronic kidney disease (CKD) G4-G5D/kidney transplant. Methods: Delphi process was implemented (3 rounds) to establish a consensus on 10 clinical domains: (1) study targets, (2) risk factors, (3) diagnosis, (4) case stratification, (5) treatment targets, (6) investigations, (7) medical management, (8) monitoring, (9) management of special groups, (10) fracture liaison service. After each round, statements were retired, modified, or added in view of the experts' suggestions, and the percent agreement was calculated. Statements receiving rates of 7-9 by more than 75% of experts' votes were considered as achieving consensus. Results: The surveys were sent to an expert panel (n = 26), of whom 23 participated in the three rounds (2 were international experts and 21 were national). Most of the participants were rheumatologists (87%), followed by nephrologists (8.7%), and geriatric physicians (4.3%). Eighteen recommendations, categorized into 10 domains, were obtained. Agreement with the recommendations (rank 7-9) ranged from 80 to 100%. Consensus was reached on the wording of all 10 clinical domains identified by the scientific committee. An algorithm for the management of osteoporosis in CKD has been suggested. Conclusion: A panel of international and national experts established a consensus regarding the management of osteoporosis in CKD patients. The developed recommendations provide a comprehensive approach to assessing and managing osteoporosis for all healthcare professionals involved in its management.

The Role of Ultrasound in Evaluating the Effect of Urate-lowering Drugs in Gout Patients.

BACKGROUND: Gout is one of the most common inflammatory arthritis, where identification of MSU crystals in synovial fluid is a widely used diagnostic measure. Ultrasonography has a great sensitivity in detecting signs of MSU deposits, such as tophi and double contour (DC), as mentioned in the latest gout criteria, allowing early clinical diagnosis and therapy. OBJECTIVE: The objective of this study was to evaluate the changes in ultrasound of gout patients' knee and 1st metatarsophalangeal joint (MTP1) after initiation of urate-lowering therapy (ULT) drugs in the six-month period. METHODS: Forty-three patients, fulfilling the ACR/EULAR 2015 criteria of gout with a score of >8, were enrolled; they were in between attacks and not on ULT for the last 6 months, or SUA concentration (SUA) of >6.0 mg/dL. Full examination, evaluation of joints pain by visual analog scale (VAS), ultrasonography (US) for tophus and DC at the knee, and MTP1 were performed at baseline and at 3 and 6 months (M3, M6) after starting ULT. RESULT: After 6 months of treatment, patients reached the target SUA level showed higher disappearance of DC sign (p<0.05) and a decrease in tophus size (p<0.05). The percentage of tophus size at 6th month was 26.4% and 3% for DC sign disappearance, which was more at MTP1. CONCLUSION: Ultrasound examination in screening for gout tophi or DC sign before starting ULT and during follow-up is important and complements clinical examination.

Nutritional and Quality Characteristics of Some Foods Fortified with Dried Mushroom Powder as a Source of Vitamin D.

Vitamin D plays a vital role in synthesizing calcium-carrying proteins in the small intestine and helps the absorption of calcium in the body, thus reducing the risk of rickets in children and osteoporosis in adults, especially in women. So, the objective of this study was to evaluate the nutritional value and quality characteristics of some food products such as waffles, breadsticks and salad cream fortified with dried mushroom powder (DMP) after exposure to sunlight for 60 min as a source of vitamin D. The exposure of mushroom to sunlight for 60 min before drying increased its content of vitamin D by 158% more than fresh mushroom (not exposed to sunlight). The DMP was added to the product's formula by a ratio of 1, 2, and 3%. The addition of DMP increased protein, ash, fat, and vitamin D2 and D3 contents in all products, while carbohydrates and moisture contents were decreased in both waffles, and breadsticks. The hardness of both waffles and breadsticks was decreased with increasing the levels of DMP added, while the addition of DMP led to enhance bioactive compounds and antioxidant activity in all products. The sensory evaluation of waffles, breadsticks, and salad cream containing DMP was not changed than control sample. The results found that the intake of 100 g of salad cream, waffles, and breadstick (containing 3% DMP) could by providing more than the recommended daily allowances (RDA) of vitamin D. Therefore, this study recommended the use of DMP (by a ratio of 3%) in fortifying food products in order to meet the RDA of vitamin D.

Can Complete Blood Count Picture Tell Us More About the Activity of Rheumatological Diseases?

Background: In clinical practice, distinguishing disease activity in patients with rheumatological illnesses is challenging. Objectives: We aimed to investigate clinical associations of hemogram-derived indices, namely: red cell distribution width (RDW), mean platelet volume (MPV), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and systemic immune-inflammation index (SII) with disease activity in patients with rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and ankylosing spondylitis (AS). Methods: In 250 patients with rheumatological disease and 100 healthy age-matched controls, we investigated disease activity scores and indicators and evaluated their association with hemogram-derived indices values. Results: Compared with the control group, RDW, MPV, and PLR significantly increased (P < .001) in the three studied disorders (RA, SLE, and AS), but LMR dramatically decreased. SII was considerably higher in RA and AS patients compared with controls but not in SLE patients. On the other hand, NLR rose dramatically in SLE patients compared with controls (P = .043), but did not change much in RA and AS patients (P > .05). RDW and MPV showed significant changes (P < .001) in the three studied diseases (RA, SLE, and AS) according to disease activity. They significantly increased across worsening activity scores. Only in the SLE group, PLR was significantly increased with disease activity (P < .001), while LMR showed a significant decrease (P = .016). Conclusions: Clinicians must pay close attention to complete blood count (CBC) analysis and its various derived ratios to better characterize the activity of rheumatological disorders and anticipate the disease course and prognosis.

Production of Chicken Patties Supplemented with Cantaloupe By-Products: Impact on the Quality, Storage Stability, and Antioxidant Activity.

This study investigated the effect of supplementation with cantaloupe peel (CP) and seeds (CS) (3, 6, 9, and 12%) powder on the quality and antioxidant activity of raw and cooked chicken patties during storage (-20°C/3 months). The addition of CP and CS powder increased protein, fat, ash, and fiber values of chicken patties compared with control, while carbohydrate, pH, and TBA were decreased at zero time and after 3 months of storage. The WHC, cooking yield, fat retention, and moisture retention were increased by increasing CP and CS powder addition ratios, while cooking loss and shrinkage were decreased. Also, CP and CS powder improved antioxidant activity, microbiological quality, and overall acceptability of chicken patties. The hardness of raw and cooked chicken patties was decreased with increasing CP and CS addition ratios. It is recommended to use CP and CS powder as functional ingredients in the preparation of functional foods.

Synthesis of Fused Quinoline Derivatives With Antiproliferative Activities and Tyrosine Kinases, Pim-1 Kinase Inhibitions.

Cyclohexan-1,3-dione (1) reacted with either 2-aminoprop-1-ene-1,1,3-tricarbonitrile (2a) or diethyl 3-amino-2-cyanopent-2-enedioate (2b) to give the 5,6,7,8-tetrahydronaphthalene derivatives 3a and 3b, respectively. The latter compounds underwent further heterocyclization reactions to give the thieno[2',3':5,6]benzo[1,2-e][1,3]oxazine derivatives. On the other hand, the reaction of compound 1 with trichloroacetonitrile afforded the (2,2,2-trichloroethylidene)cyclohexane derivative 14. The latter underwent a series of reactions to produce 2,3,6,7-tetrahydroquinazoline, dihydrothieno[2,3-h]isoquinoline, octahydrobenzo[h]quinazoline and dihydrothieno[2,3-h]isoquinoline derivatives. The synthesized compounds were tested toward six cancer cell lines where most of them gave high inhibitions with c-Met enzymatic activity, with tyrosine kinases and Pim-1 inhibitions. The results obtained will encourage further work through such compounds to produce optimized anticancer agents.

Enhanced Feature Selection Based on Integration Containment Neighborhoods Rough Set Approximations and Binary Honey Badger Optimization.

This article appoints a novel model of rough set approximations (RSA), namely, rough set approximation models build on containment neighborhoods RSA (CRSA), that generalize the traditional notions of RSA and obtain valuable consequences by minifying the boundary areas. To justify this extension, it is integrated with the binary version of the honey badger optimization (HBO) algorithm as a feature selection (FS) approach. The main target of using this extension is to assess the quality of selected features. To evaluate the performance of BHBO based on CRSA, a set of ten datasets is used. In addition, the results of BHOB are compared with other well-known FS approaches. The results show the superiority of CRSA over the traditional RS approximations. In addition, they illustrate the high ability of BHBO to improve the classification accuracy overall the compared methods in terms of performance metrics.

Clinical characteristics and outcomes of post-COVID-19 pulmonary fibrosis: A case-control study.

ABSTRACT: The development of pulmonary fibrosis is a rare complication of the novel coronavirus disease 2019 (COVID-19). Limited information is available in the literature about that, and the present study aimed to address this gap.This case-control study included 64 patients with post-COVID-19 pulmonary fibrosis who were hospitalized for COVID-19.The percentage of patients aged ≥65 years (44%) who demised was higher than those who survived (25%). Male patients (62%) had higher mortality than female patients (37%). The most frequently reported clinical symptoms were shortness of breath (98%), cough (91%), and fever (70%). Most COVID-19 patients with pulmonary fibrosis (81%) were admitted to an intensive care unit (ICU), and 63% required mechanical ventilation. Bilateral lung infiltrates (94%), "ground glass" opacity (91%), "honeycomb" lung (25%), and pulmonary consolidation (9%) were commonly identified in COVID-19 patients with pulmonary fibrosis who survived. The findings for computed tomography and dyspnea scale were significantly higher in severe cases admitted to the ICU who required mechanical ventilation. A higher computerized tomography score also correlated significantly with a longer duration of stay in hospital and a higher degree of dyspnea. Half of the COVID-19 patients with pulmonary fibrosis (50%) who survived required oxygen therapy, and those with "honeycomb" lung required long-term oxygen therapy to a far greater extent than others. Cox regression revealed that smoking and asthma were significantly associated with ICU admission and the risk of mortality.Post-COVID-19 pulmonary fibrosis is a severe complication that leads to permanent lung damage or death.

Anti-proliferative, c-Met Inhibitions, and PAINS Evaluations of New Thiophene, Thiazole, Coumarin, Pyran, and Pyridine Derivatives.

BACKGROUND: 1,3-Diones are versatile reagents used for many heterocyclic transformations. Among such groups of compounds, cyclohexane-1,3-dione is widely used in organic synthesis to produce biologically active compounds. OBJECTIVE: In this work, target molecules were synthesized from tetrahydrobenzo[b]thiophen-3- carboxamide derivative with different substituents, and their structure-activity relationships were discussed in detail. METHODS: Cyclohexane-1,3-dione underwent different multi-component reactions to produce fused thiophene, thiazole, coumarin, pyran, and pyridine derivatives. The anti-proliferative activity of the newly synthesized compounds toward the six cancer cell lines, namely A549, H460, HT-29, MKN-45, U87MG, and SMMC-7721 was studied. In addition, inhibitions of the most active compounds toward cancer cell lines classified according to the disease were also studied. Furthermore, Pan Assay Interference compounds (PAINS) of the selected compounds were analyzed, along with the c- Met inhibitions. RESULTS: Anti-proliferative evaluations were performed for all of the synthesized compounds, in which the varieties of substituents through the aryl ring and the heterocyclic ring afforded compounds with high activities. Inhibition activity against the cancer cell lines classified according to the disease, c-Met, and PAINS of the synthesized compounds were measured. CONCLUSION: Compounds 3, 13a, 13b, 14a, 16f, 17a, 28, 30a, and 31were the most cytotoxic compounds toward the six cancer cell lines. Inhibition toward cancer cell lines classified according to the disease showed that, in most cases, the presence of the electronegative CN and or Cl groups within the molecule was responsible for its high activity.

An Electric Fish-Based Arithmetic Optimization Algorithm for Feature Selection.

With the widespread use of intelligent information systems, a massive amount of data with lots of irrelevant, noisy, and redundant features are collected; moreover, many features should be handled. Therefore, introducing an efficient feature selection (FS) approach becomes a challenging aim. In the recent decade, various artificial methods and swarm models inspired by biological and social systems have been proposed to solve different problems, including FS. Thus, in this paper, an innovative approach is proposed based on a hybrid integration between two intelligent algorithms, Electric fish optimization (EFO) and the arithmetic optimization algorithm (AOA), to boost the exploration stage of EFO to process the high dimensional FS problems with a remarkable convergence speed. The proposed EFOAOA is examined with eighteen datasets for different real-life applications. The EFOAOA results are compared with a set of recent state-of-the-art optimizers using a set of statistical metrics and the Friedman test. The comparisons show the positive impact of integrating the AOA operator in the EFO, as the proposed EFOAOA can identify the most important features with high accuracy and efficiency. Compared to the other FS methods whereas, it got the lowest features number and the highest accuracy in 50% and 67% of the datasets, respectively.

Chemical Constitution and Antimicrobial Activity of Kombucha Fermented Beverage.

Kombucha is a traditional beverage of sweetened black tea fermented with a symbiotic association of acetic acid bacteria and yeasts. In this study, kombucha fermented beverage (KFB) appeared to include nine chemical groups (alcohols, acids, lactones, condensed heterocyclic compounds, antibiotics, esters, aldehydes, fatty acids, and alkaloids) of many bioactive metabolites, as elucidated by gas chromatography-mass spectrometry (GC-MS) and IR spectra. The fermented metabolic components of KFB seem collectively to act in a synergistic action giving rise to the antimicrobial activity. Four types of kombucha preparations (fermented, neutralized, heat-treated and unfermented) were demonstrated with respect to their antimicrobial activity against some pathogenic bacterial and fungal strains using agar well diffusion assay. KFB exerted the strongest antimicrobial activities when compared with neutralized and heat-treated kombucha beverages (NKB and HKB). Staphylococcus aureus ATCC6538 (S. aureus) and Escherichia coli ATCC11229 (E. coli) were the organisms most susceptible to the antimicrobial activity of kombucha beverage preparations. Finally, the KFB preparation showed remarkable inhibitory activity against S. aureus and E. coli bacteria in a brain heart infusion broth and in some Egyptian fruit juices (apple, guava, strawberry, and tomato). These data reveal that kombucha is not only a prophylactic agent, but also appears to be promising as a safe alternative biopreservative, offering protection against pathogenic bacteria and fungi.

Multi-component Reactions of Cyclohexan-1,3-diketones to Produce Fused Pyran Derivatives with Antiproliferative Activities and Tyrosine Kinases and Pim-1 Kinase Inhibitions.

In this work the multi-component reactions of either of the arylhydrazocyclohexan-1,3-dione derivatives 3a-c with either of benzaldehyde (4a), 4-chlorobenzaldehyde (4b) or 4-methoxybenzaldehyde (4c) and either malononitrile (5a) or ethyl cyanoacetate (5b) giving the 5,6,7,8-tetrahydro-4H-chromene derivatives 6a-r, respectively, are presented. The reaction of two equivalents of cyclohexan-1,3-dione with benzaldehyde gave the hexahydro-1H-xanthene-1,8(2H)-dione derivative 7. On the other hand, the multi-component reactions of compound 1 with dimedone and benzaldehyde gave 13. Both of 7 and 13 underwent heterocyclization reactions to produce fused thiophene, pyran and thiazole derivatives. Selected compounds among the synthesized compounds were tested against six cancer cell lines where most of them gave high inhibitions; especially compounds 3b, 3c, 6b, 6c, 6d, 6f, 6i, 6m, 6n, 8b, 14a, 15 and 16 being the most cytotoxic compounds. Further tests against the five tyrosine kinases c-Kit, Flt-3, VEGFR-2, EGFR, and PDGFR and Pim-1 kinase showed that compounds 3c, 6c, 6d, 6f, 6n, 14a and 15 were the most potent of the tested compounds toward the five tyrosine kinases and compounds 3c, 6c, 6d, 6n and 15 displayed the highest inhibitions toward Pim-1 kinase.

Chemical Constitution and Antimicrobial Activity of Kefir Fermented Beverage.

Kefir beverage (KB) is a fermented milk initiated by kefir grains rich with starter probiotics. The KB produced in this study seemed to contain many chemical compounds elucidated by gas chromatography-mass spectrometry (GC-MS) and IR spectra. These compounds could be classified into different chemical groups such as alcohols, phenols, esters, fatty esters, unsaturated fatty esters, steroids, polyalkenes, heterocyclic compounds and aromatic aldehydes. Both KB and neutralized kefir beverage (NKB) inhibited some pathogenic bacteria including Escherichia coli ATCC11229 (E. coli), Listeria monocytogenes ATCC 4957 (L. monocytogenes), Bacillus cereus ATCC 14579 (B. cereus), Salmonella typhimurium ATCC 14028 (Sal. typhimurium) as well as some tested fungal strains such as Aspergillus flavus ATCC 16872 (A. flavus) and Aspergillus niger ATCC 20611 (A. niger), but the inhibitory activity of KB was more powerful than that obtained by NKB. It also appeared to contain four lactic acid bacteria species, one acetic acid bacterium and two yeast species. Finally, the KB inhibited distinctively both S. aureus and Sal. typhimurium bacteria in a brain heart infusion broth and in some Egyptian fruit juices, including those made with apples, guava, strawberries and tomatoes.

Automatic clustering method to segment COVID-19 CT images.

Coronavirus pandemic (COVID-19) has infected more than ten million persons worldwide. Therefore, researchers are trying to address various aspects that may help in diagnosis this pneumonia. Image segmentation is a necessary pr-processing step that implemented in image analysis and classification applications. Therefore, in this study, our goal is to present an efficient image segmentation method for COVID-19 Computed Tomography (CT) images. The proposed image segmentation method depends on improving the density peaks clustering (DPC) using generalized extreme value (GEV) distribution. The DPC is faster than other clustering methods, and it provides more stable results. However, it is difficult to determine the optimal number of clustering centers automatically without visualization. So, GEV is used to determine the suitable threshold value to find the optimal number of clustering centers that lead to improving the segmentation process. The proposed model is applied for a set of twelve COVID-19 CT images. Also, it was compared with traditional k-means and DPC algorithms, and it has better performance using several measures, such as PSNR, SSIM, and Entropy.