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Abstract Female obesity is a worldwide health issue linked to chronic metabolic low-grade inflammation (metaflammation) causing multiple obesity-related co-morbid conditions. We aimed to assess the serum levels of wingless integration site family member 5 A (Wnt5a), leptin, and tumor necrosis factor-alpha (TNF-α) as markers of obesity-associated metaflammation and investigate the association with toll-like receptors2 (TLR2) gene (Arg753Gln) single nucleotide polymorphism (SNP) among Egyptian females. The study included 60 females with obesity and 30 matched controls. Serum levels of Wnt5a, leptin, and TNF-α were assessed by ELISA, while TLR2 (Arg753Gln) genotyping was done by PCR-RFLP. Serum Wnt5a, leptin, and TNF-α showed significantly higher levels in females with obesity than controls and a significant increase with higher classes of obesity. They showed significant positive correlations with each other. Only TNF-α and leptin were associated with metabolic syndrome (MetS) among the obesity group. According to TLR2 (Arg753Gln) SNP, the homozygous GG genotype was associated with elevated levels of Wnt5a, leptin, and TNF-α compared to the AA + GA model carriers. No significant differences were found in the distribution of TLR2 Arg753Gln (rs5743708) genotypes and alleles according to obesity or MetS, and the regression analysis showed no significant risk association. Serum Wnt5a, leptin, and TNF-α levels increase in women with obesity and the A allele of TLR2 (Arg753Gln) SNP could be protective against obesity-associated metaflammation.

Abstract Background Rheumatoid arthritis (RA) is a chronic autoimmune condition that causes synovitis and functional impairment. Currently, the most often utilized biomarkers for monitoring disease activity and severity are acute phase proteins, erythrocyte sedimentation rate (ESR), and C-reactive protein (CRP). However, discrepancies have been found between the clinical inflammation and their levels. Therefore, there is a strong need for a novel biomarker to forecast how RA will proceed clinically and monitor the response to therapy. This study assessed the diagnostic value of serum cystatin D in RA patients and its potential as a biomarker for disease activity monitoring. Results RA patients had considerably greater serum levels of cystatin D than the control group. These values showed a positive correlation with Disease Activity Score-28 (DAS28), ESR, CRP, grayscale synovitis, power Doppler synovitis, grayscale tenosynovitis (p

The mandibular condyle is a rounded bony projection with an upper biconvex, oval surface in the axial plane. Its form differs significantly among different individuals and age groups. This study aims to address the variability in mandibular condyle morphology, which can be indicative of Temporomandibular Joint disorders (TMD). Given the clinical importance of accurate condyle characterization, we developed a novel detection method leveraging deep learning and feature selection technologies. This method explicitly employs the YOLOv8 network to initially identify the region of interest (ROI) in digital panoramic images. Subsequently, the MobileViT system extracts detailed features from these regions. We introduced a modified Mountaineering Team-Based Optimization Algorithm to refine the feature selection process, which efficiently isolates the most relevant features from the extracted set. Our experimental design involved a robust dataset of 3000 digital panoramic images, classified into four distinct morphological types: round, pointed, angled, and flat. We assessed the performance of our developed method through various metrics, focusing on its ability to detect and describe the morphology of the condyle. The results demonstrate a high capability of the model, achieving an accuracy of 81.5% in binary classification and 83.5% in multi-classification scenarios.

Objectives. To detect subclinical carotid atherosclerosis in gout patients and its relation to musculoskeletal ultrasound (MSK US) manifestations and serum sclerostin (SCL) levels. Patients and methods. Thirty Egyptian gouty patients were classified into two groups: Group A included 15 patients with diagnostic MSK US manifestations of gout (double contour sign), and Group B included 15 patients without MSK US findings. MSK US was done for both knees and the first MTP joints. Carotid duplex ultrasonography was done to evaluate carotid intima-media-thickness (CIMT) and serum SCL level was measured in all patients by ELISA technique. Results. Gouty patients with MSK US findings had a significantly higher increase in serum SCL levels than those without MSK US findings (p 25.05 ng/ml. At this level, there was a significant difference between the patients with a CIMT >0.9 mm and the patients with a CIMT ≤0.9 mm (p 25.05 ng/ml in gout patients might predispose patients to radiographic findings of MSK US of gout and subclinical atherosclerosis.

The integration of metaheuristics with machine learning methodologies presents significant advantages, particularly in optimization and computational intelligence. This amalgamation leverages the global search capabilities of metaheuristics alongside the pattern recognition and predictive prowess of machine learning, facilitating enhanced convergence rates and solution quality in complex problem spaces. The Quantum Long Short-Term Memory (QLSTM) emerges as a highly efficient deep learning model tailored to tackle such intricate engineering problems. The QLSTM's architecture, comprising data encoding, variational, and quantum measurement layers, facilitates the effective encoding and processing of civil engineering data, leading to heightened prediction accuracy. However, the task of determining optimal values for QLSTM parameters presents challenges due to its NP-problem nature and time-consuming characteristics. To address this, we propose an alternative technique to optimize the QLSTM based on a modified Electric Eel Foraging Optimization (MEEFO). The MEEFO is a modified version of the original EEFO that applies triangular mutation operators to boost the search capability of the traditional EEFO. Thus, the MEEFO optimizes the QLSTM and boosts its prediction performance. To validate the efficacy of our proposed method, we conduct comprehensive experiments utilizing five real-world engineering datasets related to construction and structure engineering. The evaluation outcomes unequivocally demonstrate that the MMEFO significantly enhances the performance of the QLSTM.

Aerial photography is a remote sensing technique used for target detection, enabling both qualitative and quantitative analysis. The segmentation process is considered one of the most important processes to improve the analysis of Aerial images. In this study, we introduce an alternative multilevel threshold image segmentation based on a modified Snow ablation optimizer (SAO) algorithm. This modification is conducted using the strengths of Comprehensive learning and Double attractors which aims to enhance the exploration and exploitation abilities of the SAO during the process of discovering the optimal threshold levels that are used to segment the Aerial photography image. To validate the quality of the modified version of SAO, named DCSAO, a set of experimental series is conducted using the CEC2022 benchmark function and sixteen Aerial images at different threshold levels. In addition, we compared the results of DCSAO with different well-known Metaheuristic techniques. The results show the superior performance of DCSAO in comparison to other algorithms according to the performance metrics.

Post-acute myocardial infarction (AMI) fibrosis is a pathophysiologic process characterised by activation of the profibrotic mediator, transforming growth factor-β (TGF-β). AMI is associated with a substantial increase in the levels of extracellular adenosine triphosphate (eATP), which acts on the purinergic P2X7-receptor (P2X7-R) and triggers an inflammatory response that contributes to myocardial fibrotic remodelling. P2X7-R has been implicated in several cardiovascular diseases; however, its role in the regulation of cardiac fibrosis remains unclear. Therefore, the current study aimed to determine the effect of the P2X7-R antagonist, A740003, on post-AMI fibrosis, via the profibrotic TGF-β1/Smad signalling pathway, and elucidate whether its effect is mediated via the modulation of GSK-3β. AMI was induced by surgical ligation of the left anterior descending coronary artery, Thereafter, animals were divided into groups: sham control, MI-untreated, MI-vehicle, and MI-A740003 (50 mg/kg/day) and treated for seven days accordingly. The heart weight/body weight ratio of untreated-ligated rats significantly increased by 15.1 %, creatine kinase‐MB (CK‐MB) significantly increased by 40 %, troponin‐I levels significantly increased by 25.4 %, and lactate dehydrogenase significantly increased by 47.2 %, indicating myocardial damage confirmed by morphological changes and massive cardiac fibrosis. The protein expression of cardiac fibronectin, TGF-β1, and p-Smad2 were also upregulated by 143 %, 40 %, and 8 %, respectively, indicating cardiac fibrosis. The treatment of ligated rats with A740003 led to improvement in all the above-mentioned parameters. Overall, A740003 exhibits potential cardio-protective effects on post-AMI fibrotic remodelling in the animal model of AMI through P2X7-R blockade, possibly by downregulating the profibrotic TGF-β1/Smad signalling pathway and restoring GSK-3β phosphorylation. Altogether, treatment with A740003 could serve as a new cardioprotective strategy to attenuate post-AMI fibrotic remodelling.

This paper presents an alternative breast cancer classification method based on enhancing the efficiency of the Nutcracker optimizer (NO) algorithm using Chaos Game Optimization (CGO). In addition, we use the Cross Vision Transformer to extract features from breast images. After that, the relevant features are allocated using the modified version of NO based on CGO. This modification aims to enhance the exploration ability of the NO algorithm to discover the region of a feasible solution (an optimal subset of features). The performance of the developed model is validated by using twelve functions from the CEC2022 benchmark and comparing the results with traditional CGO and NO algorithms. In addition, to assess the applicability of the developed technique, a set of three datasets, and the results were compared with other techniques. The results illustrate the high ability of the developed method to enhance the detection of breast cancer and find the optimal solution of CEC2022 functions according to different performance measures.

Abstract Background Despite the recent advances in the management of inflammatory arthritis, a considerable proportion of arthritis patients remain symptomatic. This cohort has recently been identified as ‘difficult to treat’ (D2T). In view of the limited evidence base, management of these patients has been a challenge particularly in view of its associated significant economic health burden. A better understanding of the D2T may help recognise or develop new therapeutic targets and facilitate earlier intervention in the disease course to prevent the progression of such condition. The aim of this work is to address the unmet needs in the management of D2T arthritis and develop a comprehensive approach towards the identification and proper assessment of those patients. Results At the completion of round 3 Delphi process, a total of 20 items were obtained and divided into 5 domains. From 88.9 to 100% of respondents agreed with the recommendations (ranks 7–9). All 20 of the clinical standards that the scientific committee identified were agreed upon in terms of wording, recommendation grade, and level of evidence (i.e. 75% of respondents strongly agreed or agreed). Conclusion D2T inflammatory arthritis remains a relevant clinical challenge, despite the endorsement of the treat-to-target approach and the availability of a broad range of targeted arthritis medications. This study provided a comprehensive definition of the condition to facilitate the identification of this patients’ group. It also highlighted the goals and principles aiming at providing an effective framework for D2T assessment, closely monitor and set up a strategy to intervene in standard clinical practice.

Abstract Background Systemic lupus erythematosus (SLE) is a chronic systemic autoimmune disease characterized by having varying clinical presentation, severity, unpredictable course as well as outcomes. Recent disease-modifying conventional and biologic agents have enhanced rates of attaining both short- and long-term management goals, including minimization of glucocorticoid dose and use. This study was carried out to develop an up-to-date evidence-based, consensus on clinical practice guidelines for treat-to-target management of systemic lupus erythematosus in adults. Results The response rate to the online questionnaires, sent to the expert panel who participated in the three rounds, was 95.5%. At the end of round 3, a total of 14 recommendation sections were proposed for the T2T management of patients with SLE. Agreement with the recommendations (rank 7–9) ranged from 90.9–100%. Consensus was reached (i.e., ≥ 80% of respondents strongly agreed or agreed) on the proposed statements. Conclusion These recommendations provide a consensus on the treat-to-target management of patients with SLE. They provide strategies to reach optimal outcomes in common clinical scenarios, based on a combination of evidence and expert opinion.

Abstract Background Despite the associated high prevalence of morbidity and mortality, osteoporotic fragility fractures remain underdiagnosed and undertreated. Furthermore, those who sustain a fragility fracture are at imminent risk of sustaining subsequent fractures. Post-fracture care (PFC) programs are systematic, coordinated care programs that recognize, evaluate, and manage older adults who sustained a fragility fracture with the goal of managing all the risk factors and preventing succeeding fractures. Main text This work was carried out to outline the PFC program adopted in Egypt and its applicability in standard clinical practice. A review of literature was conducted to identify an evidence-informed PFC strategies and protocols, which outlines the optimal manner to manage older adults living with fragility fractures. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) checklist was used to guide the reporting of this review. Based on this, a PFC integrated model of care based on a patient-centered approach has been developed aiming to optimize the outcomes. Conclusion This manuscript described the integrated model of care adopted in Egypt to provide care for older adults presenting with fragility fractures. This will pave the way to standardize patient identification and management. Additionally, to prevent occurrence of subsequent fractures and to enhance equity of care for patients with fragility fracture and osteoporosis, expansion of such service to rural and remote areas is highly recommended.

Abstract Background Intervertebral disc degeneration (IVD) is a main underlying cause for low back pain (LBP). Oswestry Disability Index (ODI) is a valuable tool used to measure patient’s low back functional outcome. Our aim is to evaluate the relation between clinical assessment of LBP and health related quality of life with the use of ODI and magnetic resonance image (MRI) findings in patients with degenerative prolapsed IVD disease. Result There was a significant correlation between Pfirrmann grades and ODI (p < 0.0001) and with type of disc morphology mostly protrusion (p < 0.0001) but insignificant correlation with visual analogue scale (VAS) (p = 0.198) and most of the clinical parameters (p > 0.05). There was a significant correlation between ODI and VAS (p = 0.003). Higher Pfirrmann grades were found at lower lumbosacral levels L4-5 and L5-S1. Conclusion Higher Pfirrmann grades correlated with the increased ODI. No association was found between MRI grading in lumbosacral spine with pain intensity and most of the clinical parameters among persons with discogenic LBP. Chronic LBP is widely accepted to be a multifactorial disorder that could not be contained within a single lumbar compartment. Clinician should not depend on MRI findings only in the diagnosis. History from the patient and clinical examination is particularly important to reach the final diagnosis. ODI might be convenient in achieving patient’s confidence and decreasing unnecessary investigations.

The Social Internet of Things (SIoT) has revolutionized user experience through various applications and networking services like Social Health Monitoring, Social Assistance, Emergency Alert Systems, and Collaborative Learning Platforms. However, transferring different types of data between the interconnected objects in the SIoT environment, including sensor data, user-generated data, and social interaction data, poses challenges due to their high dimensionality. This paper presents an alternative SIoT method that improves resource efficiency, system performance, and decision-making using the Barnacles Mating Optimizer (BMO). The BMO incorporates Triangular mutation and dynamic Opposition-based learning to enhance search space exploration and prevent getting stuck in local optima. Two experiments were conducted using UCI datasets from different applications and SIoT-related datasets. The results demonstrate that the developed method, DBMT, outperforms other algorithms in predicting social-related datasets in the IoT environment.

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.

Abstract Background Busy rheumatologists, and busy patients as well as policy makers, require accurate, succinct, transparent, easily digested summaries of evidence and recommendations for management. Our objective was to develop an up-to-date evidence-based, consensus, clinical practice guidelines for treat-to-target management of rheumatoid arthritis in adults. Results Ninety-four (94.7%) of the expert committee completed the 2-round e-Delphi surveys. A total of 33 recommendation items, addressing the main rheumatoid arthritis (RA) domains, were identified. The level of agreement (rate 7–9), for the statements which reached consensus, ranged from 85 to 100%. Consensus was achieved on the wording of all the clinical practice guidelines identified by the scientific committee. A management algorithm for the management of rheumatoid arthritis have been developed. Conclusion These updated recommendations reflect the most recent evidence for the management of RA. It also outlines the multidisciplinary team role in enhancing the RA patients’ care. The recommendations offer strategies to achieve optimum treat-to-target outcomes. However, standards of care are defined based on the clinical data obtained for individual patients and are prone to modification. High-quality, broad scope evidence-based clinical practice guidelines offer a path for bridging the gap between best practice, policy, local settings and patients’ choice.

Abstract Background Electrodiagnostic tests (EDXTs) have been considered the gold standard method for the diagnosis of tarsal tunnel syndrome (TTS); however, definitive tests has not yet been discovered. Our aim was to develop new nerve conduction provocative techniques in the double and single leg stance as well as combined ankle dorsiflexion with foot eversion accompanied by compression for the diagnosis of suspected TTS. Results Routine combined nerve conduction studies (NCSs) using medial plantar (MP) and lateral plantar (LP) nerves had a 60.9% sensitivity for the diagnosis of TTS. The diagnostic sensitivity increased when combining the three novel tests reaching 82.6% and 78.3% using either MP or LP NCSs respectively. The diagnostic sensitivity further increased to reach 100% when combining the MP and LP novel NCSs considering either the latency or amplitude. Conclusion The full diagnostic sensitivity for TTS reached 100% by using a battery of novel tests. The proposed diagnostic workup by this study recommends using these biomechanically challenging tests to complete the diagnostic battery of tests especially in symptomatic patients with negative routine tests.

Abstract Background Lupus nephritis (LN) affects a substantial number of the patients living with Systemic lupus erythematosus (SLE), representing a major cause of morbidity and mortality. Patients with lupus nephritis should be referred to a lupus nephritis expert who can confirm the diagnosis, assess the level of disease activity, and offer guidance on treatment and monitoring of the disease, as well as its consequences and side effects. The aim of this guideline was to develop recommendations for the management of adult lupus patients, including lupus nephritis diagnosis, assessment, and monitoring. Nineteen key clinical questions were identified by scientific committee according to the Patient/Population, Intervention, Comparison, Outcomes and Timing (PICOT) approach. Literature review team performed a systematic review to summarize evidence advocating the benefits and harms of available pharmacologic and nonpharmacologic therapies for LN. Subsequently, recommendations were formulated. The level of evidence was determined for each section using the Oxford Centre for Evidence-based Medicine (CEBM) system. A 2-round Delphi process was conducted with 24 experts. All rounds were conducted online. A consensus was achieved on the direction and the strength of the recommendations. Results An online questionnaire was sent to an expert panel who participated in the two rounds (response rate 100%). At the end of round 2, a total of 19 recommendation items, categorized into 11 sections to address the main LN categories, were obtained. The percentage of those who agreed with the recommendations (ranks 7–9) ranged from 90.5 to 100%. The phrasing of all 19 clinical standards identified by the scientific committee was agreed upon (i.e., 75% of respondents strongly agreed or agreed). Conclusion These recommendations provide an updated consensus on the pharmacological treatment of lupus nephritis and strategies to reach optimal treat to target outcomes in common clinical scenarios, based on a combination of evidence and expert opinion. Best treatment decisions should be tailored to each individual patient situation.

Abstract Background Nephritis is known to be one of the most serious complications of lupus and a strong predictor of poor outcome. This study was carried out aiming at setting up an up-to-date recommendation for the management of women living with lupus nephritis and planning for a family throughout conception, pregnancy, and the postpartum period. Ten key clinical questions were identified by the scientific committee according to the Patient/Population, Intervention, Comparison, Outcomes and Timing (PICOT) approach. The literature review team performed a systematic review to summarise evidence advocating the benefits and harms of available pharmacologic and nonpharmacologic therapies for women living with lupus nephritis (LN) and planning for a family. Subsequently, recommendations were formulated. The level of evidence was determined for each section using the Oxford Centre for Evidence-Based Medicine (CEBM) system. A 2-round Delphi process was conducted with 24 experts. All rounds were conducted online. A consensus was achieved on the direction and the strength of the recommendations. Results An online questionnaire was sent to an expert panel who participated in the two rounds (response rate 100%). At the end of round 2, a total of 20 recommendation items, categorised into 10 domains to address the main LN with pregnancy categories, were obtained. The percentage of those who agreed with the recommendations (rank 7–9) ranged from 88.5 to 100%. On the phrasing of all the clinical standards defined by the scientific committee, a consensus was reached (i.e., 75% of respondents strongly agreed or agreed). An algorithm for the management of LN with pregnancy has been suggested. Conclusion These recommendations provide an updated consensus on the pharmacological treatment of LN with pregnancy and strategies to reach optimal outcomes for both the mother and newborn in common clinical scenarios, based on a combination of evidence and expert opinion. Best treatment decisions should be tailored to each individual patient’s situation.

Abstract Background We aimed to provide up-to-date, evidence-based and consensus-based recommendations for Treat-to-Target management of psoriatic arthritis (PsA) and associated clinical manifestations. In this recommendations, 14 key clinical questions were identified by scientific committee according to the Patient/Population, Intervention, Comparison, Outcomes and Timing (PICOT) approach. Literature Review team performed a systematic review to summarize evidence advocating the benefits and harms of available pharmacologic and non-pharmacologic therapies for psoriatic arthritis. Subsequently, recommendations were formulated. The level of evidence was determined for each section using the Oxford Centre for Evidence-based Medicine (CEBM) system. A 3-round Delphi process was conducted with 19 experts whom were drawn from different governorates and health centers across Egypt with diverse in their experiences, including private, governmental workplace, tertiary university hospitals, and insurance hospitals. All rounds were conducted online. A consensus was achieved on the direction and the strength of the recommendations. Results An online questionnaire was sent to an expert panel who participated in the three rounds (response rate 100%). At the end of round 3, a total of 51 recommendation items, categorized into 6 sections to address the main 6 psoriatic arthritis categories, were obtained. Agreement with the recommendations (rank 7–9) ranged from 89.5 to 100%. Consensus was reached (i.e., ≥ 75%of respondents strongly agreed or agreed) on the wording of all the 51 clinical standards identified by the scientific committee. Algorithms for the management of psoriatic arthritis have been suggested. Conclusion These recommendations provide an updated consensus on the pharmacological treatment of psoriatic arthritis and strategies to reach optimal treat-to-target outcomes in in common clinical scenarios, based on a combination of evidence and expert opinion. Best treatment decisions should be tailored to each individual patient situation.

Abstract Introduction New therapies, management approaches, and evidence regarding the management of gout have become available over the past years. This triggered the need for an updated recommendation for gout management. Through an up-to-date consensus evidence-based clinical practice guideline for the management of gout including recommendations for management of gout flares, optimum usage of urate lowering therapy for chronic gout, as well as patient education and lifestyle guidance. A wide systematic literature review was performed, and evidence-based recommendations were extrapolated, based on 16-key questions identified according to population, intervention, comparator, and outcomes (PICO) approach. These were evaluated by a panel consisted of 17 rheumatology experts via online surveys over a 2-round Delphi process. The purpose of this study is to offer an updated, consensus-evidence-based, and in the meantime patient-focused, expert recommendations for the treat-to-target approach of gout management. Results Results revealed that after round 2 ended, a total of 30-recommendation items, categorized into 10 domains, were obtained. Agreement with the recommendations (rank 7–9) ranged from 90 to 100%. Consensus was reached (i.e., ≥ 75% of respondents strongly agreed or agreed) on the wording, the grade of recommendation, and level of evidence of all the 30 clinical standards identified by the scientific committee. Conclusions This guideline provides updated evidence-based recommendations for the prevention and treatment of acute as well as chronic gout. This guideline provides an approach for physicians and patients making decisions on the management of gout. It will also facilitate improvement and uniformity of care.

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.

Abstract Background Post-coronavirus disease (COVID-19) syndrome is defined as the persistence of symptoms for more than 3 to 12 weeks after infection with the COVID-19 virus that cannot be attributed to another etiology. This study was conducted in our university hospital aiming to analyze the medium-term persistent symptoms in post-COVID-19 patients through a comprehensive and structured clinical assessment and evaluating the incidence, association, and risk factors of the post COVID-19 symptoms and their effect on the functional status of the survivors. Results Of 170 recruited individuals, about 66 (38.82%) reported post-COVID-19 symptoms. Post-viral fatigue was the most common symptom (23.5%), followed by arthralgia and myalgia in 32 patients (18.8%). Lower functional status was reported among some of the survivors which can be attributed to the severity of the disease and the presence of post-COVID symptoms among these patients. The post-COVID-19 syndrome showed an association with patient age, severity of the disease, and the presence of preexisting comorbidities. Conclusion A significant functional impact was found in some COVID-19 survivors after COVID-19 infection. Age, severity of the disease, and presence of preexisting comorbidities are critical risk factors for the development of post-COVID-19 syndrome.

Abstract Background Plantar fasciitis due to calcaneal spur is a common cause of heel pain and functional disability, and its management presents a huge challenge for clinicians which results sometimes in unpleasant clinical outcomes. The efficacy of extracorporeal shock wave therapy (ESWT) as an alternative therapeutic option to surgical management after failure of conservative treatment has been addressed. Our aim was to evaluate the efficacy of ESWT in the treatment of plantar fasciitis in calcaneal spur patients using ultrasonography. Results The mean plantar fascia (PF) thickness was statistically significantly higher in the calcaneal spur patient group (5.66 ± 1.14 mm) than in the healthy control group (2.40 ± 0.35 mm), (P = 0.001). Significant PF thickness reduction, visual analog scale (VAS), and Roles and Maudsley score (RMS) improvement were observed (P < 0.001) after 4 sessions of ESWT. Conclusion PF thickness increases significantly in calcaneal spur patients and responds to treatment. ESWT decreases the thickness of the PF and improves pain and function significantly.

Adansonia digitata L. is an African tree commonly called baobab. This tree is effectively used in traditional medicine to treat cardiovascular disorders. Hyperlipidemia is a well-known cardiovascular risk factor associated with the increased incidence of mortality worldwide. This study aimed to demonstrate the mechanism of baobab polyphenols in the activities of hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase and pancreatic lipase as lipid metabolic enzymes. Molecular docking and an incentive for drug design showed that all the polyphenols in baobab bound to the proteins with higher affinity and a lower binding energy compared with simvastatin as the positive control (ΔG: from −5.5 kcal/mol to −6.5 kcal/mol). The same polyphenols exhibited a considerable binding affinity to pancreatic lipase (ΔG: from −7.5 kcal/mol to −9.8 kcal/mol) in comparison with the control and HMG-CoA reductase. Quercetin showed the best docking score from the selected Baobab polyphenols (ΔG = −9.8 kcal/mol). The root mean square deviation (RMSD) results indicated that stable epicatechin and quercetin complexes were demonstrated with HMG-CoA reductase, and other less stable complexes were developed using rutin and chlorogenic acid. Moreover, the analysis of the root mean square fluctuation (RMSF) simulation results was consistent with that of the RMSD. The RMSF value for all the baobab polyphenols, including the crystal control ligand, was kept between 0.80 and 8.00 Å, similarly to simvastatin, and less than 4.8 Å for pancreatic lipase. Chlorogenic acid, quercetin, epicatechin, and rutin had negative ΔG binding scores from highest to lowest. The same ligands displayed more negative ΔG binding scores than those observed in HMG-CoA reductase and crystal control ligand (methoxyundecyl phosphinic acid) in their simulation with pancreatic lipase. In conclusion, baobab polyphenols interact with HMG-CoA reductase and pancreatic lipase to inhibit their substrate binding and block their activity.

Introduction: Garlic has many pharmacological properties such as antibacterial, anti-inflammatory, and antioxidant activities. This study aimed to investigate the possible mechanisms of the hepatoprotective effect of white garlic extract (WGE) and black garlic extract (BGE) against preneoplastic lesions induced by N-nitrosodiethylamine (NDEA) in rats. Methods: Forty-two rats were randomly distributed into six equal groups. Oral administration of WGE and BGE began 3 weeks before injection of NDEA. Group 1 was kept as the negative control, while the other groups were injected by a single intraperitoneal dose of NDEA in the 3rd week, followed by 2 subcutaneous injections/week of CCl4 till six weeks to induce preneoplastic lesions. Group 2 kept positive control and groups 3, 4, 5, and 6 were given WGE and BGE each of them at 250 and 500 mg/kg, respectively, for six weeks from the beginning. Serum liver enzymes, total protein, albumin (Alb), total bilirubin (TBil), and antioxidant enzymes were measured. Malondialdehyde (MDA), glutathione (GSH), tumor biomarkers, along with the content of 8-hydroxy-2-deoxyguanosine (8-OHdG) in liver DNA were estimated and liver histopathology was performed. Results: WGE and BGE significantly decreased the serum liver enzymes, TBil, tumor biomarkers, lipid peroxidation but increased total protein levels. The extracts significantly increased antioxidant enzymes, decreased 8-OHdG content in liver DNA, and alleviated histopathological lesions in the liver. Conclusion: The results affirm the hepatoprotective effect of WGE and BGE against NDEA-induced preneoplastic lesions in rats. This effect may be due to inhibition of lipid peroxidation, reduction of tumor biomarkers, enhancement of antioxidant enzymes, or reduction of 8-OHdG content in liver DNA.

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.

Features Selection (FS) techniques have been applied to several real-world applications which contain high dimension data. These FS techniques have main objectives that aim to achieve them, such as removing irrelevant features and increasing classification accuracy. This is considered a bi-objectives optimization problem that requires a suitable technique that can balance between the objectives. So, different sets of FS techniques have been developed, and those techniques that depend on meta-heuristic (MH) established their performance overall traditional FS techniques. However, these MH approaches still require more enhancement to neutralize their exploration and exploitation abilities during the searching process. Enhancing the meta-heuristic optimization algorithm using the perspective of fractional calculus (FC) is an attractive and novel approach. In this paper, the slime mould algorithm (SMA) is modified using the FC for handling the optimizer drawback of the inefficient diversification phase. As a result, a fractional-order SMA is proposed to avoid the local solutions and discover the search landscape efficiently via considering a historic memorize of agents’ positions. The proposed FOSMA is applied to extract features from a set of real-world data and increase classification accuracy. For boosting the optimizer performance while processing with these datasets, the rough set (RS) is used as the fitness function to handle the uncertainty inside the real-world data. Finally, the proposed FOSMA’s results are compared with a set of well-known FS techniques to investigate its performance. The comparison illustrates the superiority of FOSMA in providing high accuracy.

Abstract Background The association between carpal tunnel syndrome (CTS) and ulnar nerve entrapment is unclear, and the extra-median paresthesia in the ulnar nerve territory innervation in CTS is unexplained. Our purpose is to evaluate the neurophysiologic changes in ulnar nerve sensory fibers secondary to idiopathic CTS in comparison to normal subjects, and their relation to the severity of CTS. Results The difference between CTS and control hands regarding all parameters of ulnar motor nerve conduction studies (MNCS) was not statistically significant (p > 0.05). There was a statistically significant difference in all parameters of median sensory conduction study (SCS) and MNCS between CTS and control hands (p < 0.0001) except for conduction velocity and in all parameters of median and ulnar SCS recording digit 4 (D4) (p < 0.05). The mean value of abnormalities of ulnar SCS recording D4 and digit 5 (D5) was significantly higher in moderate and severe CTS hands (p < 0.05). There were significant negative correlations between median motor and sensory latency and ulnar sensory amplitude recording D4 and D5. Conclusion Ulnar sensory nerve abnormalities exist among CTS patients, which were more in moderate and severe grades. The drop in amplitude of ulnar nerve sensory response argues in favor of the possible impact of CTS on the ulnar nerve and did not indicate axonal deterioration of the ulnar nerve.

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.

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.

Objectives: To design and implement a career guidance program for medical students and evaluate its effectiveness. Methodology: A quasi-experimental single group design was applied in this ethically approved study at Jouf University, Sakaka, Saudi Arabia. A modified Medical Career Development Inventory (MCDI) covering 5 areas was utilized aimed at assessing how much students’ thinking or planning had improved after implementing the designed career guidance program. The study followed the widely used approach for curriculum development: Kern's 6 steps for course design. A comprehensive sample (n=82) taken from the Phase II (4th and 5th year) male (n=47) and female (n=35) students in the college of medicine during the academic year of 2018-2019. Results: Using focus group interview techniques on 50 students, we found that 27% requested lectures about career decision-making, 53% highlights about the Saudi Medical License Exam, 20% information on different specialties, 65% internal/external postgraduate programs, and 35% pros and cons of each specialty. The post-intervention results showed that the total MCDI scores increased (pre-test mean±SD of 2.60 ±0.29 versus post-test mean±SD of 3.16± 0.20, p: 0.018), suggesting an improved degree of vocational development. Conclusion: This study significantly improved career development levels among our students by assisting their readiness for career decision-making. Integrating career guidance in the formal curriculum is recommended. Continuous....

Feature selection (FS) methods play essential roles in different machine learning applications. Several FS methods have been developed; however, those FS methods that depend on metaheuristic (MH) algorithms showed impressive performance in various domains. Thus, in this paper, based on the recent advances in MH algorithms, we introduce a new FS technique to modify the performance of the Dwarf Mongoose Optimization (DMO) Algorithm using quantum-based optimization (QBO). The main idea is to utilize QBO as a local search of the traditional DMO to avoid its search limitations. So, the developed method, named DMOAQ, benefits from the advantages of the DMO and QBO. It is tested with well-known benchmark and high-dimensional datasets, with comprehensive comparisons to several optimization methods, including the original DMO. The evaluation outcomes verify that the DMOAQ has significantly enhanced the search capability of the traditional DMO and outperformed other compared methods in the evaluation experiments.

Free Space Optical (FSO) communication systems have extensively invaded the speed of smart city evolution due to the current surge in demand for wireless communication spots that can match recent challenges due to high technical leaps in smart city evolution. As the number of users is vastly increasing throughout all networks in the form of machines, devices, and variously distinct objects, FSO is a hugely recommended robust communication system that mitigates a lot of RF disadvantages on the field with no need for licensing, fast rollout time, and low cost. This paper shows an exploit of a Low Power Field Programmable Gate Array (FPGA) based FSO communication system designed for Line of Sight (LOS) Building to Building Communication over a distance of 12 m using a 650 nm Visible Light (VL) red laser source via On-Off Keying (OOK) and higher-level Intensity Modulation (IM)/Pulse Width Modulation (PWM) schemes. The implemented system reached a doubled data rate than OOK of 230 kbps using the IM technique. Traffic monitoring and building security status can be frequently updated between adherent buildings, each scanning its zone real-time conditions and sharing them with the neighboring links.

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.

The presence of cationic dyes, even in a tiny amount, is harmful to aquatic life and pollutes the environment. Therefore, it is essential to remove these hazardous dyes to protect the life of marine creatures from these pollutants. In this research, crystal violet (CV) dye elimination was performed using a lignin copper ferrite (LCF) adsorbent. The adsorbent was synthesized and characterized using FTIR, Raman, SEM, EDX with mapping, and VSM, which proved the successful formation of magnetic LCF. Adsorption experiments were performed using different effective parameters. The highest adsorption potential (97%) was executed at mild operating conditions, with a 5 min contact time at room temperature and pH 8. The adsorption kinetic study utilized four kinetic models: first-order, second-order, intraparticle diffusion, and Elovich. The results revealed that the adsorption process complies with the pseudo-first-order with a maximum adsorption capacity of 34.129 mg/g, proving that the adsorption process mechanism is a physical adsorption process. Three isotherm models, Langmuir, Freundlich, and Temkin, were examined. The adsorption mechanism of CV onto LCF was also followed by the Langmuir and Freundlich models. The thermodynamic parameters were examined and revealed that the adsorption onto LCF was an exothermic process. It was proposed that the adsorption process is a spontaneous exothermic process. LCF appears to forcefully remove toxic CV dye from textile wastewater.

Purpose: Aging is associated with hepatic morphological and physiological deterioration due to the accumulation of endogenous and exogenous free radicals and the resultant oxidative stress. The present study aims to investigate the effect of Haematococcus pluvialis microalgae on hepatic changes associated with D-galactose (D-Gal)-induced aging in rats. Methods: Aging was induced in rats by daily intraperitoneal injection of D-Gal (200 mg/kg/day) for eight consecutive weeks. D-Gal-injected rats were treated by astaxanthin (ATX)-rich H. pluvialis biomass, its carotenoid and polar fractions for two weeks. Twenty four hours after the last dose, blood samples were collected and the liver tissues were isolated for further biochemical and histopathological examinations. Results: D-Gal induced aging was associated with an elevation in serum liver function parameters, hepatic oxidative stress biomarkers viz., catalase (CAT), glutathione transferase (GST) and myeloperoxidase (MPO), as well as decreased expression of nuclear factor like-2 (Nrf2). Moreover, induction of aging exhibited an elevation of hepatic inflammatory cytokine; interleukin-6 (IL-6) and its modulator; nuclear factor Kappa B (NF-KB). However, treatment of D-Gal injected rats with ATX-rich H. pluvialis restored the serum liver function parameters as well as hepatic CAT, GST and MPO levels with an elevated expression of Nrf2. Treatment with ATX-rich H. pluvialis was also accompanied with a decrease in hepatic levels of NF-KB and IL-6. Histopathological examination emphasized all the previous results. Similarly, all trans-astaxanthin showed high affinity towards Nrf2 with -7.93 kcal/mol estimated free energy of binding as well as moderate affinities towards IL-6 and NF-KB through a docking study. Conclusion: ATX-rich H. pluvialis showed beneficial effects by ameliorating the hepatic changes associated with D-Gal induced aging in rats due to its modulatory role of the Nrf2/Keap pathway.

The swelling potentiality is a vital property of fine-grained soils strictly related to the index properties and chemical composition. The integration of machine learning techniques and geotechnical parameters provided a new integrative approach for predicting the free swelling index (FSI) and the swelling pressure (SP). In this paper, an adaptive neuro-fuzzy inference system (ANFIS) using named Reptile Search Algorithm (RSA) is presented to predict the swelling potentiality for fine-grained soils in the foundation bed at El Sherouk city, Egypt. The developed predictive model, named RSA-ANFIS, used as input measured 108 natural fine-grained soil samples of index geotechnical parameters and chemical composition as input data and the measured data of the free swelling index and the swelling pressure as output data. To justify the performance of the developed model, a comparative study was carried out, and the results show that the developed RSA-ANFIS has a high performance over the competitive methods in terms of coefficient of determination, root mean square error (RMSE), and mean absolute error (MAE). This new integrative approach is considered at the highly developed stage to predict and improve the analysis of multi-parameter soil behavior and could be applied in other objective variable datasets.

Feature selection (FS) is a well-known preprocess step in soft computing and machine learning algorithms. It plays a critical role in different real-world applications since it aims to determine the relevant features and remove other ones. This process (i.e., FS) reduces the time and space complexity of the learning technique used to handle the collected data. The feature selection methods based on metaheuristic (MH) techniques established their performance over all the conventional FS methods. So, in this paper, we presented a modified version of new MH techniques named Atomic Orbital Search (AOS) as FS technique. This is performed using the advances of dynamic opposite-based learning (DOL) strategy that is used to enhance the ability of AOS to explore the search domain. This is performed by increasing the diversity of the solutions during the searching process and updating the search domain. A set of eighteen datasets has been used to evaluate the efficiency of the developed FS approach, named AOSD, and the results of AOSD are compared with other MH methods. From the results, AOSD can reduce the number of features by preserving or increasing the classification accuracy better than other MH techniques.

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.

Feature selection is a well-known prepossessing procedure, and it is considered a challenging problem in many domains, such as data mining, text mining, medicine, biology, public health, image processing, data clustering, and others. This paper proposes a novel feature selection method, called AOAGA, using an improved metaheuristic optimization method that combines the conventional Arithmetic Optimization Algorithm (AOA) with the Genetic Algorithm (GA) operators. The AOA is a recently proposed optimizer; it has been employed to solve several benchmark and engineering problems and has shown a promising performance. The main aim behind the modification of the AOA is to enhance its search strategies. The conventional version suffers from weaknesses, the local search strategy, and the trade-off between the search strategies. Therefore, the operators of the GA can overcome the shortcomings of the conventional AOA. The proposed AOAGA was evaluated with several well-known benchmark datasets, using several standard evaluation criteria, namely accuracy, number of selected features, and fitness function. Finally, the results were compared with the state-of-the-art techniques to prove the performance of the proposed AOAGA method. Moreover, to further assess the performance of the proposed AOAGA method, two real-world problems containing gene datasets were used. The findings of this paper illustrated that the proposed AOAGA method finds new best solutions for several test cases, and it got promising results compared to other comparative methods published in the literature.

Superixel is one of the most efficient of the image segmentation approaches that are widely used for different applications. In this paper, we developed an image segmentation based on superpixel and an automatic clustering using q-Generalized Pareto distribution under linear normalization (q-GPDL), called ASCQPHGS. The proposed method uses the superpixel algorithm to segment the given image, then the Density Peaks clustering (DPC) is employed to the results obtained from the superpixel algorithm to produce a decision graph. The Hunger games search (HGS) algorithm is employed as a clustering method to segment the image. The proposed method is evaluated using two different datasets, collected form Berkeley segmentation dataset and benchmark (BSDS500) and standford background dataset (SBD). More so, the proposed method is compared to several methods to verify its performance and efficiency. Overall, the proposed method showed significant performance and it outperformed all compared methods using well-known performance metrics.

Retinoids are essential during early cardiovascular morphogenesis. However, recent studies showed their important role in cardiac remodeling in rats with hypertension and following myocardial infarction. The present study aimed to investigate the effect of zeaxanthin heneicosylate (ZH); a carotenoid ester isolated from Dunaliella salina microalgae, on cardiac dysfunction ensuing d-galactose injection in rats. Rats injected with d-GAL (200 mg/kg; I.P) for 8 weeks were orally treated with ZH (250 μg/kg) for 28 consecutive days. Results showed that d-GAL injection caused dramatic electrocardiographic changes as well as marked elevation in serum levels of homocysteine, creatinine kinase isoenzyme and lactate dehydrogenase. A reduction in the cardiac contents of glucose transporter-4 and superoxide dismutase along with the elevation of inducible nitric oxide synthetase and interleukin-6 was also noticed. Oral administration of ZH significantly improved the above mentioned cardiac aging manifestations; this was further emphasized through histopathological examinations. The effect of ZH is mediated through the interaction with retinoid receptor alpha (RAR-α) as evidenced through a significant elevation of RAR-α expression in cardiac tissue following the lead of an in silico molecular docking study. In conclusion, zeaxanthin heneicosylate isolated from D. salina ameliorated age-associated cardiac dysfunction in rats through the activation of retinoid receptors.