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.

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.

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.

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.

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.

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.

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.

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.

IoT Workflow Scheduling Using Intelligent Arithmetic Optimization Algorithm in Fog Computing.

Instead of the cloud, the Internet of things (IoT) activities are offloaded into fog computing to boost the quality of services (QoSs) needed by many applications. However, the availability of continuous computing resources on fog computing servers is one of the restrictions for IoT applications since transmitting the large amount of data generated using IoT devices would create network traffic and cause an increase in computational overhead. Therefore, task scheduling is the main problem that needs to be solved efficiently. This study proposes an energy-aware model using an enhanced arithmetic optimization algorithm (AOA) method called AOAM, which addresses fog computing's job scheduling problem to maximize users' QoSs by maximizing the makespan measure. In the proposed AOAM, we enhanced the conventional AOA searchability using the marine predators algorithm (MPA) search operators to address the diversity of the used solutions and local optimum problems. The proposed AOAM is validated using several parameters, including various clients, data centers, hosts, virtual machines, tasks, and standard evaluation measures, including the energy and makespan. The obtained results are compared with other state-of-the-art methods; it showed that AOAM is promising and solved task scheduling effectively compared with the other comparative methods.

Post-COVID-19 arthritis: is it hyperinflammation or autoimmunity?

BACKGROUND:  Various musculoskeletal and autoimmune manifestations have been described in patients with coronavirus disease 2019 (COVID-19). Objectives: This study aims to investigate the prevalence and etiology of arthritis in post-COVID Egyptian patients. Methods: We included 100 post-COVID Egyptian patients who recovered 6 months ago and assessed several inflammatory and autoimmune markers. Results: The prevalence of post-COVID arthritis was 37%. Ankle, knee, and wrist were the most commonly affected joints. Old age (P = 0.010), smoking (P = 0.001), and arthralgia (P = 0.049) were all linked with post-COVID arthritis. Levels of pretreatment (baseline) interleukin (IL)-6 (46.41 ± 3.67 vs. 24.03 ± 2.46; P = 0.001), as well as 6-month post-COVID C-reactive protein (CRP; 98.49 ± 67.55 vs. 54.32 ± 65.73; P = 0.002), and erythrocyte sedimentation rate (ESR; 109.08 ± 174.91 vs. 58.35 ± 37.87; P = 0.029) were significantly higher in patients with arthritis compared to those without. On the other hand, complement C3 (P = 0.558) and C4 (P = 0.192), anti-nuclear antibodies (P = 0.709), and anti-cyclic citrullinated peptides (anti-CCP; P = 0.855) did not show significant differences. Only pretreatment IL-6 level was the significant single predictor of post-COVID arthritis with an odds ratio (95% confidence interval) of 3.988 (1.460-10.892) and a P-value of 0.007. CONCLUSION:  The strong association observed with inflammatory markers (ESR and CRP) and the insignificant association with serologic markers of autoimmunity (ANA and anti-CCP) in our study support the notion that the underlying mechanism of post-COVID-19 arthritis is primarily due to the hyperinflammatory process associated with COVID-19 infection, and not the result of an autoimmune reaction. IL-6 levels before therapy can predict post-COVID arthritis allowing for early management.

Efficient artificial intelligence forecasting models for COVID-19 outbreak in Russia and Brazil.

COVID-19 is a new member of the Coronaviridae family that has serious effects on respiratory, gastrointestinal, and neurological systems. COVID-19 spreads quickly worldwide and affects more than 41.5 million persons (till 23 October 2020). It has a high hazard to the safety and health of people all over the world. COVID-19 has been declared as a global pandemic by the World Health Organization (WHO). Therefore, strict special policies and plans should be made to face this pandemic. Forecasting COVID-19 cases in hotspot regions is a critical issue, as it helps the policymakers to develop their future plans. In this paper, we propose a new short term forecasting model using an enhanced version of the adaptive neuro-fuzzy inference system (ANFIS). An improved marine predators algorithm (MPA), called chaotic MPA (CMPA), is applied to enhance the ANFIS and to avoid its shortcomings. More so, we compared the proposed CMPA with three artificial intelligence-based models include the original ANFIS, and two modified versions of ANFIS model using both of the original marine predators algorithm (MPA) and particle swarm optimization (PSO). The forecasting accuracy of the models was compared using different statistical assessment criteria. CMPA significantly outperformed all other investigated models.

An Unusual Presentation of a Rare Scleroderma Mimic: What is Behind the Scenes?

BACKGROUND: Scleroderma or systemic sclerosis (SSc) is a rare systemic autoimmune disease. Many conditions mimic the presentation of SSc, especially skin thickening and fibrosis. One of these conditions is scleredema, a rare collagen and mucin deposition disorder which was found to be associated with diabetes mellitus, streptococcal infection or monoclonal gammopathy. CASE PRESENTATION: A 55 years old female presented with insidious onset and progressive course of diffuse skin thickening of face, neck, arms, forearms, thighs, chest, back, and excluding both hands and feet of 6 years' duration associated with arthralgia, dysphagia and dyspnea on exertion of 1- year duration. There was no history of Raynaud's phenomenon. Erythrocyte sedimentation rate was 100 mm/1st h, autoantibodies for SSc were negative, nail fold capillaroscopy normal, pulmonary function tests showed restrictive pattern and high-resolution computed tomography showed interstitial lung fibrosis. Patient was not fulfilling the American collage of rheumatology/European League Against Rheumatism classification criteria for SSc. Skin biopsy was done and revealed histological appearance of scleredema. Investigations were done for disease association with scleredema. The patient was not diabetic, antistreptolysin O titer was normal, serum protein electrophoresis, immunofixation and bone marrow biopsy were done, and the patient was diagnosed as scleredema associated with immunoglobulin A kappa multiple myeloma. Treatment by combination of bortezomib, cyclophosphamide, and dexamethasone was started with marked clinical and hematological improvement. CONCLUSION: Many conditions mimic SSc including scleredema, which may be the initial presentation of multiple myeloma. Rheumatologists and dermatologists should be able to recognize these conditions to provide the suitable management and follow-up for these patients.

Uses of Cyclohexan-1,4-dione for the Synthesis of 2-Amino-4,5-dihydrobenzo[b]thiophen-6(7H)-one Derivatives with Anti-proliferative and Pim-1 Kinase Activities.

The reaction of cyclohexan-1,4-dione with elemental sulfur and any of the 2-cyano-N-arylacetamide derivatives 2a-c gave the 2-amino-4,5-dihydrobenzo[b]thiophen-6(7H)-one derivatives 3a-c to be used in some heterocyclization reactions. The multicomponent reactions of any of compounds 3a-c with aromatic aldehydes 6a-c and either of malononitrile or ethylcyanoacetate gave the 5,9-dihydro-4H-thieno[2,3-f]chromene derivatives 9a-r, respectively. The anti-proliferative evaluation of the newly synthesized compounds against the six cancer cell lines A549, HT-29, MKN-45, U87MG, SMMC-7721 and H460 showed that the nine compounds 3c, 5c, 9e, 9h, 9i, 9j, 9l, 9q, 11e and 13e with highest cytotoxcity. Toxicity of these compounds against shrimp larvae revealed that compounds 3c, 9j, 9q, and 13e showed no toxicity against the tested organisms. The c-Met kinase inhibition of the most potent compounds showed that compounds 9j, 9q, 10e, 12e and 13e have the highest activities. Compounds 9j, 9l, 9q and 11e showed high activity towards tyrosine kinases. Moreover, compounds 9j, 9q and 13e showed the highest inhibitor activity towards Pim-1 kinase.