Integrating freelance models with fractional derivatives, and artificial neural networks: A comprehensive approach to advanced computation

In order to improve results, this work investigates how the Freelance Model (FM), Fractional Derivative (FD), and Artificial Neural Network (ANN) may all function together. We suggest a new method that combines the varied skills of freelancers with the precision of fractional derivatives and the adaptability of neural networks to maximize the benefits of each. This proposed strategy provides a new perspective to the computational methodologies and holds a promising impact on diverse industries. Future developments and applications can be made possible by this promising path toward enhanced performance in complex systems and data-driven areas. Twenty neurons have been selected and data has been trained and validated in the, following manner 70 %, 15 % and 15 %. The consistency of method has been shown using the correlation/regression and histograms in order to solve the model. The results presented here not only validate the efficacy of our approach but also open avenues for further exploration and advancements in the dynamic field of advanced computation.

DEEP-EP: Identification of epigenetic protein by ensemble residual convolutional neural network for drug discovery

Epigenetic proteins (EP) play a role in the progression of a wide range of diseases, including autoimmune disorders, neurological disorders, and cancer. Recognizing their different functions has prompted researchers to investigate them as potential therapeutic targets and pharmacological targets. This paper proposes a novel deep learning-based model that accurately predicts EP. This study introduces a novel deep learning-based model that accurately predicts EP. Our approach entails generating two distinct datasets for training and evaluating the model. We then use three distinct strategies to transform protein sequences to numerical representations: Dipeptide Deviation from Expected Mean (DDE), Dipeptide Composition (DPC), and Group Amino Acid (GAAC). Following that, we train and compare the performance of four advanced deep learning models algorithms: Ensemble Residual Convolutional Neural Network (ERCNN), Generative Adversarial Network (GAN), Convolutional Neural Network (CNN), and Gated Recurrent Unit (GRU). The DDE encoding combined with the ERCNN model demonstrates the best performance on both datasets. This study demonstrates deep learning's potential for precisely predicting EP, which can considerably accelerate research and streamline drug discovery efforts. This analytical method has the potential to find new therapeutic targets and advance our understanding of EP activities in disease.

An ensemble computational model for prediction of clathrin protein by coupling machine learning with discrete cosine transform

Clathrin protein (CP) plays a pivotal role in numerous cellular processes, including endocytosis, signal transduction, and neuronal function. Dysregulation of CP has been associated with a spectrum of diseases. Given its involvement in various cellular functions, CP has garnered significant attention for its potential applications in drug design and medicine, ranging from targeted drug delivery to addressing viral infections, neurological disorders, and cancer. The accurate identification of CP is crucial for unraveling its function and devising novel therapeutic strategies. Computational methods offer a rapid, cost-effective, and less labor-intensive alternative to traditional identification methods, making them especially appealing for high-throughput screening. This paper introduces CL-Pred, a novel computational method for CP identification. CL-Pred leverages three feature descriptors: Dipeptide Deviation from Expected Mean (DDE), Bigram Position Specific Scoring Matrix (BiPSSM), and Position Specific Scoring Matrix-Tetra Slice-Discrete Cosine Transform (PSSM-TS-DCT). The model is trained using three classifiers: Support Vector Machine (SVM), Extremely Randomized Tree (ERT), and Light eXtreme Gradient Boosting (LiXGB). Notably, the LiXGB-based model achieves outstanding performance, demonstrating accuracies of 94.63% and 93.65% on the training and testing datasets, respectively. The proposed CL-Pred method is poised to significantly advance our comprehension of clathrin-mediated endocytosis, cellular physiology, and disease pathogenesis. Furthermore, it holds promise for identifying potential drug targets across a spectrum of diseases.Communicated by Ramaswamy H. Sarma.

Deep-VEGF: deep stacked ensemble model for prediction of vascular endothelial growth factor by concatenating gated recurrent unit with two-dimensional convolutional neural network

Vascular endothelial growth factor (VEGF) is involved in the development and progression of various diseases, including cancer, diabetic retinopathy, macular degeneration and arthritis. Understanding the role of VEGF in various disorders has led to the development of effective treatments, including anti-VEGF drugs, which have significantly improved therapeutic methods. Accurate VEGF identification is critical, yet experimental identification is expensive and time-consuming. This study presents Deep-VEGF, a novel computational model for VEGF prediction based on deep-stacked ensemble learning. We formulated two datasets using primary sequences. A novel feature descriptor named K-Space Tri Slicing-Bigram position-specific scoring metrix (KSTS-BPSSM) is constructed to extract numerical features from primary sequences. The model training is performed by deep learning techniques, including gated recurrent unit (GRU), generative adversarial network (GAN) and convolutional neural network (CNN). The GRU and CNN are ensembled using stacking learning approach. KSTS-BPSSM-based ensemble model secured the most accurate predictive outcomes, surpassing other competitive predictors across both training and testing datasets. This demonstrates the potential of leveraging deep learning for accurate VEGF prediction as a powerful tool to accelerate research, streamline drug discovery and uncover novel therapeutic targets. This insightful approach holds promise for expanding our knowledge of VEGF's role in health and disease.Communicated by Ramaswamy H. Sarma.

Elemental composition of Talang Queenfish (Scomberoides commersonnianus Lacépède, 1801) in relation to body size and condition factor from Pakistan

Abstract Fish is thought to be the most appropriate for indication of toxicity in water. So, purpose of present study was to determine the concentrations of few metals (Na, K, Ca, Mg, Fe, Cd, Pb and Zn) in relation to the body size and condition factor in Scomberoides commersonnianus. A total of 73 samples of marine fish, S. commersonnianus with various sizes that ranged from 93.4-1180 g of body weight and total length 20.5-56.9cm were randomly collected from the Arabian Sea of Karachi Pakistan, for examination of elemental composition. The concentration of Calcium found maximum while Cadmium found minimum in S. commersonnianus. Concentrations of Zn showed highly significant (P<0.001) negative, while Na and Ca exhibited strong positive correlation with the fish size. Correlations of Pb, K and Mg were found least significant (P<0.05) with the wet weight and total length of S. commersonnianus. Whereas, Fe and Cd were remained insignificantly correlated (P>0.05) with the fish size. Condition factor showed highly significant correlation (P<0.001) with all studied metals except cadmium and lead which were found insignificantly correlated (P>0.05) with condition factor of S. commersonnianus.

Enhancing Skin Lesion Detection: A Multistage Multiclass Convolutional Neural Network-Based Framework

The early identification and treatment of various dermatological conditions depend on the detection of skin lesions. Due to advancements in computer-aided diagnosis and machine learning approaches, learning-based skin lesion analysis methods have attracted much interest recently. Employing the concept of transfer learning, this research proposes a deep convolutional neural network (CNN)-based multistage and multiclass framework to categorize seven types of skin lesions. In the first stage, a CNN model was developed to classify skin lesion images into two classes, namely benign and malignant. In the second stage, the model was then used with the transfer learning concept to further categorize benign lesions into five subcategories (melanocytic nevus, actinic keratosis, benign keratosis, dermatofibroma, and vascular) and malignant lesions into two subcategories (melanoma and basal cell carcinoma). The frozen weights of the CNN developed-trained with correlated images benefited the transfer learning using the same type of images for the subclassification of benign and malignant classes. The proposed multistage and multiclass technique improved the classification accuracy of the online ISIC2018 skin lesion dataset by up to 93.4% for benign and malignant class identification. Furthermore, a high accuracy of 96.2% was achieved for subclassification of both classes. Sensitivity, specificity, precision, and F1-score metrics further validated the effectiveness of the proposed multistage and multiclass framework. Compared to existing CNN models described in the literature, the proposed approach took less time to train and had a higher classification rate.

Machine learning-based model for accurate identification of druggable proteins using light extreme gradient boosting

The identification of druggable proteins (DPs) is significant for the development of new drugs, personalized medicine, understanding of disease mechanisms, drug repurposing, and economic benefits. By identifying new druggable targets, researchers can develop new therapies for a range of diseases, leading to better patient outcomes. Identification of DPs by machine learning strategies is more efficient and cost-effective than conventional methods. In this study, a computational predictor, namely Drug-LXGB, is introduced to enhance the identification of DPs. Features are discovered by composition, transition, and distribution (CTD), composition of K-spaced amino acid pair (CKSAAP), pseudo-position-specific scoring matrix (PsePSSM), and a novel descriptor, called multi-block pseudo amino acid composition (MB-PseAAC). The dimensions of CTD, CKSAAP, PsePSSM, and MB-PseAAC are integrated and utilized the sequential forward selection as feature selection algorithm. The best characteristics are provided by random forest, extreme gradient boosting, and light eXtreme gradient boosting (LXGB). The predictive analysis of these learning methods is measured via 10-fold cross-validation. The LXGB-based model secures the highest results than other existing predictors. Our novel protocol will perform an active role in designing novel drugs and would be fruitful to explore the potential target. This study will help better to capture a more universal view of a potential target.Communicated by Ramaswamy H. Sarma.

A bi-layer model for identification of piwiRNA using deep neural learning

piwiRNA is a kind of non-coding RNA (ncRNA) that cannot be translated into proteins. It helps in understanding the study of gametes generation and regulation of gene expression over both transcriptional and post-transcriptional levels. piwiRNA has the function of instructing deadenylation, animal fertility, silencing transposons, fighting viruses, and regulating endogenous genes. Due to the great significance of piwiRNA, prediction of piwiRNA is essential for crucial cellular functions. Several predictors were established for prediction of piwiRNA. However, improving the prediction of piwiRNA is highly desirable. In the current study, we developed a more promising predictor named, BLP-piwiRNA. The features are explored by reverse complement k-mer, gapped-k-mer composition, and k-mer composition. The feature set of all descriptors is fused and the best features are selected by cascade and relief feature selection strategies. The best feature sets are provided to random forest (RF), deep neural network (DNN), and support vector machine (SVM). The models validation are examined by 10-fold test. DNN with optimal features of Cascade feature selection approach secured the highest prediction results. The results illustrate that BLP-piwiRNA effectively outperforms the existing studies. The proposed approach would be beneficial for both research community and drug development industry. BLP-piwiRNA would serve as novel biomarkers and therapeutic targets for tumor diagnostics and treatment.Communicated by Ramaswamy H. Sarma.

Deep-AGP: Prediction of angiogenic protein by integrating two-dimensional convolutional neural network with discrete cosine transform

Angiogenic proteins (AGPs) play a primary role in the formation of new blood vessels from pre-existing ones. AGPs have diverse applications in cancer, including serving as biomarkers, guiding anti-angiogenic therapies, and aiding in tumor imaging. Understanding the role of AGPs in cardiovascular and neurodegenerative diseases is vital for developing new diagnostic tools and therapeutic approaches. Considering the significance of AGPs, in this research, we first time established a computational model using deep learning for identifying AGPs. First, we constructed a sequence-based dataset. Second, we explored features by designing a novel feature encoder, called position-specific scoring matrix-decomposition-discrete cosine transform (PSSM-DC-DCT) and existing descriptors including Dipeptide Deviation from Expected Mean (DDE) and bigram-position-specific scoring matrix (Bi-PSSM). Third, each feature set is fed into two-dimensional convolutional neural network (2D-CNN) and machine learning classifiers. Finally, the performance of each learning model is validated by 10-fold cross-validation (CV). The experimental results demonstrate that 2D-CNN with proposed novel feature descriptor achieved the highest success rate on both training and testing datasets. In addition to being an accurate predictor for identification of angiogenic proteins, our proposed method (Deep-AGP) might be fruitful in understanding cancer, cardiovascular, and neurodegenerative diseases, development of their novel therapeutic methods and drug designing.

Comparative analysis between mono approach and multi approaches in managing T2DM: a survey-based study

OBJECTIVE

The aim of the study was to analytically compare the therapeutic effect of the addition of diet modification and regular exercise to oral agents.

MATERIALS AND METHODS

This cross-sectional study surveyed 1248 participants via online and offline modes by employing a questionnaire about an individual's management of diabetes. Group 1 patients follow Single Approach Management for Type 2 Diabetes Mellitus (T2DM), while Group 2 and Group 3 patients follow Multi-Approach Management for T2DM. Based on the answers, the participants were classified into three groups. The diagnostic criteria of Diabetes Mellitus Type-2 in this study were done by determining morning Fasting Blood Sugar (FBS) and glycated Hemoglobin (HbA1c).

RESULTS

The current study found 656 single oral agent users and 592 combination regimens users among all the study participants. The study also found that, among all participants, 511 patients were on mild to moderate diet modification while 325 patients were on moderate to severe diet modification. The study also noted that 232 males and 215 females took Complementary and Alternative Medicines (CAM). Of 447 patients, 12 showed menstrual abnormalities (2.6%), and 18 had mild diarrhoea (4%). The study also found that there is a vital significance of lowering FBS and HbA1c with the management strategies (p=0.000). The study showed a strong association between group 3 and improved FBS and HbA1c (p=0.000).

CONCLUSIONS

This study reveals that diet modification and regular exercise improve FBS and HbA1c levels significantly. Hence, diet modification and exercise can be added as adjuvants and should be incorporated into the management guidelines of T2DM.

Does freelancing have a future? Mathematical analysis and modeling

During the past few years, freelancing has grown exponentially due to the pandemic and subsequent economical changes in the world. In fact, in the last ten years, a drastic increase in freelancing has been observed; people quit their jobs to be their own boss. There are various reasons for this: downsizing of employees, not having fun in their jobs, unemployment, part time work to earn more, etc. Observing this vast change, many individuals on Facebook/YouTube, NGOs, and government departments started teaching freelancing as a course; to date, thousands of youngsters have been trained to start their careers as freelancers. It has been observed that the ratio of informed freelancers is more successful than those who start their careers independently. We construct a compartmental model to explore the influence of information on the expansion of freelancing in this article, which was motivated by this surge in freelancing. Following that, the model is subjected to dynamical analysis utilizing dynamical systems and differential equation theory. To validate our analytical conclusions, we used numerical simulation.

Impact of atrial fibrillation in patients with colorectal cancer: a national inpatient sample database analysis

Background

Atrial fibrillation (AF) is the most common cardiac arrhythmia affecting approximately 1–2% overall population (1). Its causal relationship with colorectal cancer (CRC) is much for debate. According to one hypothesis, the presence of autoantibodies directed against ionic channels or acetylcholine receptors can predispose to the development of atrial fibrillation (2–3). Thus, AF may be regarded as an inflammatory complication in patients with colon cancer. Our study objective was to determine if AF impacts the outcome of patients with CRC.

Method

We analyzed the National Inpatient Sample (NIS) database from Oct-2015 to Dec 2018 using Stata 16.0. The NIS databases are released under the Healthcare Cost and Utilization Project, which includes inpatient admissions from the United States' participating hospitals. Total population with CRC were identified using their respective ICD-10 diagnostic codes then divided based on AF. To determine atrial fibrillation association with mortality and complications, we used multivariate logistic regression analysis using weights to generate nationally representative results. Later, a propensity-matched population analysis was done for the accuracy of the results.

Result

We found 245,305 patients admitted with CRC between Oct 2015 to Dec-2018 in the USA, out of which 28,170 (11.5%) were having AF. The mean age for the patients with AF was 77±10 compare to 65±14 years in those without AF. Patients with AF were associated with higher comorbidities and had a high population percentage with Carlson category three or above. There were 1,456 (5.2%) mortalities in the AF group compared to 5,689 (2.6%) in the other. The higher odds of mortality in patients with AF was present in multivariate logistic regression analysis in both non-propensity matched [1.71 (1.45–2.02), P-value: &lt;0.000] and propensity-matched [1.44 (1.18–1.75), P-value: &lt;0.001] cohorts. Patients with AF were hospitalized longer (9.20±7.8 vs. 6.85±7.0 days), leading to a high admission costs (US$ 25,875±22,875 vs. 20,087±19,314). Odds of complications such as need for blood transfusions [1.61 (1.05–1.29), P-value: 0.005], hemorrhage requiring blood transfusion [1.17 (1.05–1.29), P-value: 0.003], lower-GI bleed [1.31 (1.21–1.43), P-value: &lt;0.001], sepsis [1.45 (1.30–1.62), P-value: &lt;0.001], respiratory failure [1.39 (1.15–1.67), P-value: 0.001] etc. were also higher in group of patients with CRC and AF.

Conclusion

In our retrospective, propensity-matched national inpatient sample analyses of patients admitted with colorectal cancer, atrial fibrillation is associated with higher morbidity and mortality. AF was associated with a high burden of complications with prolonged hospital stay leading to increased health care expenditures.

Funding Acknowledgement

Type of funding sources: None.

Gender based outcome of IABP implantation in patients with acute coronary syndrome and cardiogenic shock: a national inpatient sample database analysis

Background

Intra-Aortic Balloon counter-pulsation is frequently used as a circulatory support device in patients requiring hemodynamic support - in cardiogenic shock and in patients at risk of hemodynamic decompensation during a high-risk coronary intervention. Impact of IABP in this patient population has been variable. Certain studies have shown a beneficial effect of IABP on selected populations having acute coronary syndrome with cardiogenic shock (1–3). Our objective was to compare the outcomes based on gender in the ACS population with cardiogenic shock and IABP placement.

Methods

We analyzed the National Inpatient Sample database from Oct-2015 to Dec-2017 released under Healthcare Cost utilization Project in the USA using Stata 16.0. The population was identified using respective ICD-10 codes. We excluded the population with sudden cardiac arrest, pulmonary embolism, and patients with anatomical post-MI complications. Multivariate logistic regression analysis was done to determine the difference in outcomes based on gender using clinically relevant variables. Later, propensity-matched cohort analysis was performed based on the regression variables.

Results

Of 36, 990 patients who met our inclusion criteria 25,670 (69%) were male and 11,320 (31%) were female. The average age for male and female populations was 66±11 and 69±12 years. Femnales were more likely to have higher Charlson co-morbidity index three or above. We found higher mortality in the female population [3,146 (27.79%)] compared to male [5,884 (22.92%)] in univariate analyses. Propensity-matched multivariate regression analysis showed no difference [OR: 1.06 (0.91–1.22) with P-value: 0.482] in mortality after adjusting for clinically relevant variables. Subgroup analysis in STEMI and NSTEMI populations did not show a difference. The average hospital stay was similar in both cohorts, with the male having a higher cost per stay. We found no difference in most of the complications included in our study except for higher coronary artery dissection [OR: 2.98 (1.73–5.13), P-value: &lt;0.001] and lower rates of AKI [OR: 0.72 (0.63–0.83), P-value: &lt;0.001], AKI requiring hemodialysis [OR: 0.74 (0.56–0.97), P-value:0.031] and ventricular tachycardia [OR: 0.73 (0.64–0.84), P-value: &lt;0.001] in the female population.

Conclusion

The inpatient population of ACS with Cardiogenic shock and IABP insertion showed no significant difference in mortality between males and females which was valid for subgroup analysis of NSTEMI and STEMI groups. Complications such as coronary artery dissection were higher, whereas AKI, AKI requiring hemodialysis, and ventricular tachycardias, were lower in females than males.

Funding Acknowledgement

Type of funding sources: None.

Isolation and characterization of bacteria residing in the oral, gut, and fecal samples of different pheasant species

There is a paucity of research conducted on microbial prevalence in pheasants. The microbiota of captive birds has zoonotic significance and must be characterize. Present study is therefore planned to assess the microbiota from oral, fecal and gut content of captive avian species. It will be helpful in characterization of harmful microbes. Different samples taken from oral, gut and feces of ring-necked pheasants (Phasianus colchicus), green pheasants (Phasianus versicolor), golden pheasant (Chrysolophus pictus) and silver pheasant (Lophura nycthemera). Samples were collected, diluted, and inoculated onto different agar plates (MacConkey, SS agar, MSA and nutrient agar) for cultivation of bacterial species. Colonies of E.coli, Staphylococcus spp. Brachyspira spp. and Campylobacter spp were observed based on colony morphology. Colony forming unit showed E. coli as frequently found bacteria in fecal, oral and gut contents of all the above pheasants. The overall significance difference was found among bacterial species of golden pheasants, green pheasant, ring-necked pheasant, and silver pheasants. It was concluded that E.coli is predominant isolated from heathy pheasants followed by Campylobacter, Staphylococcus and Brachyspira.

The impact of cow dung augmentation on soil restoration and bio-accumulation of metals (Lead and Cadmium) in Pheretima posthuma (Annelida: Clitellata)

To investigate the role of cow dung in soil reclamation and bio assimilation along with bio accumulation of heavy metals in earthworm (P. posthuma) (N=900) earthworms were used and treatment groups of CD-soil mixture of different proportion of cow dung were designed. Nonlethal doses of lead acetate and cadmium chloride were added in treatment groups. Mature P. posthuma were released in each experimental pot maintaining the favorable conditions. The pH, carbon, nitrogen, phosphorus, exchangeable cations, and heavy metal level of each mixture was evaluated. The results indicated that bio-assimilation of Pb and Cd by P. posthuma were significantly (P ˂ 0.01) higher in different soil-CD treatments compared to control. Highest bio-assimilation of both metals was observed in T1 of both groups (Pb = 563.8 mg/kg and Cd = 42.95 mg/kg). The contents of both metals were significantly (P ˂ 0.05) lowered in casting. The nutrient concentration in the final castings of all soil-CD treatments were also equally transformed from less or insoluble to more soluble and available for plants, except for carbon level which increased with CD proportion. It is concluded that cow dung as organic matter has a positive effect on soil reclamation and bio-assimilation of metals by P. posthuma.

Thalassemia, a human blood disorder

A group of inherited blood defects is known as Thalassemia is among the world's most prevalent hemoglobinopathies. Thalassemias are of two types such as Alpha and Beta Thalassemia. The cause of these defects is gene mutations leading to low levels and/or malfunctioning α and β globin proteins, respectively. In some cases, one of these proteins may be completely absent. α and β globin chains form a globin fold or pocket for heme (Fe++) attachment to carry oxygen. Genes for alpha and beta-globin proteins are present in the form of a cluster on chromosome 16 and 11, respectively. Different globin genes are used at different stages in the life course. During embryonic and fetal developmental stages, γ globin proteins partner with α globin and are later replaced by β globin protein. Globin chain imbalances result in hemolysis and impede erythropoiesis. Individuals showing mild symptoms include carriers of alpha thalassemia or the people bearing alpha or beta-thalassemia trait. Alpha thalassemia causes conditions like hemolytic anemia or fatal hydrops fetalis depending upon the severity of the disease. Beta thalassemia major results in hemolytic anemia, growth retardation, and skeletal aberrations in early childhood. Children affected by this disorder need regular blood transfusions throughout their lives. Patients that depend on blood transfusion usually develop iron overload that causes other complications in the body systems like renal or hepatic impairment therefore, thalassemias are now categorized as a syndrome. The only cure for Thalassemias would be a bone marrow transplant, or gene therapy with currently no significant success rate. A thorough understanding of the molecular basis of this syndrome may provide novel insights and ideas for its treatment, as scientists have still been unable to find a permanent cure for this deadly disease after more than 87 years since it is first described in 1925.

Translation, validation and cross-cultural adaptation of the Malay emotion regulation checklist (ERC-M): A preliminary study

INTRODUCTION

Emotion Regulation Checklist (ERC) has been used globally and translated to several languages, including Brazilian Portuguese, Italian and Persian. The aim of this study is to translate and validate ERC to the Malay language and to measure the reliability and validity of the translated version of this scale among Malaysian parents.

METHODS

This study involved forward and back translation method. The translated questionnaire was then pretested and piloted among 10 parents and 50 participants, respectively. The procedure was repeated using the same questionnaire to evaluate the test-retest reliability.

RESULTS

The ERC-Malay (ERC-M) has excellent qualitative and quantitative measurements in both item-level content validation index (I-CVI) and scale-level content validation index (S-CVI). In addition, the ERC-M demonstrated good internal consistency from Cronbach's alpha and test-retest reliability based on the Intraclass Correlation Coefficient (ICC) in all domains.

CONCLUSION

ERC-M can potentially be used as a tool to evaluate emotion for the population with emotional dysregulation issue, such as autism spectrum disorder.

Ecological impacts of climate change on the snow leopard (Panthera unica) in South Asia

Snow leopard (Panthera unica) is a felid which lives in the highly rugged areas of alpine regions in different mountain ranges of South and Central Asia. This solitary animal needs large spaces for its ranges but due to climate change and relatively faster rate of global warming in South Asian mountain ranges, its habitat is going to shrink and fragment by tree-line shifts and change in hydrology of the area. Vegetative modification of montane flora and competition with domestic goats will create its prey's population to decline along with a chance of a direct conflict and competition with the common leopard. Common leopard being more adaptable, grouped, and larger in size can be a significant stressor for a smaller and solitary snow leopard. Habitat would shrink, and snow leopard can possibly move upslope or northward to central Asian ranges and their predicted migratory patterns are unknown.

POS0305 PHYSICIAN AND PATIENT ATTITUDES TOWARDS TREAT-TO-TARGET, ITS IMPLEMENTATION AND STATED TREATMENT GOALS IN PATIENTS WITH RHEUMATOID ARTHRITIS IN A REAL-WORLD SETTING ACROSS EUROPE

Background:

The principles of treat to target (T2T) include defining an appropriate treatment target, assessed at pre-defined intervals, with a commitment to changing therapeutic approach if the target is not met (1). T2T is recommended as a key strategy for the treatment of rheumatoid arthritis (RA).

Objectives:

To explore attitudes towards T2T, its implementation and stated treatment goals among physicians and their patients with RA.

Methods:

The Adelphi RA Disease Specific Programme™ was a large, quantitative, point-in-time survey conducted amongst rheumatologists (n=296) and their consulting patients with RA (n=3042) in Europe (France, Germany, Italy, Spain, UK) between Q4 2019–Q3 2020. Physicians were recruited via publicly available lists, completing an online survey and medical record extraction for their next 10–12 consecutive patients. The same patients were invited to voluntarily complete a self-report questionnaire (n=1098, 36% response), collecting data on attitudes towards T2T and treatment goals.

Results:

Physicians reported that 76% of patients were in remission (DAS28: <2.6) or had low disease activity (DAS28: 2.6 – 3.2), and 24% had moderate-high disease activity (DAS28: >3.2). Patient mean age was 53.0 years (SD 14.0), mean time since diagnosis was 7.2 years (SD 7.2). The proportion of patients currently receiving an advanced therapy (AT; defined as biologic or targeted synthetic DMARD) was 68%, of whom 70% were on a first line AT. No difference was observed between disease activity groups.

In the physician survey, 86% of physicians stated they followed T2T principals in at least some of their RA patients, and would utilize a T2T approach in RA patients with moderate-high disease activity (61%), the most uncontrolled patients (37%) and those who do not respond well to initial therapy (34%). In this sample of real-world RA patients, 66% were reported by physicians to be on a T2T plan at the time of data collection. The most common physician-reported targets were remission (DAS28: <2.6) (75%), improvement of quality of life (QoL) (41%) and reduction of pain (31%), with 85% of physicians perceiving these treatment goals were fully or partially met. The most stated reasons for not implementing T2T was physician preference not to adjust current treatment (34%), patient preference not to adjust current treatment (23%), and there are no achievable goals for this patient (16%).

Overall, 29% of patients reported they were involved in setting their T2T goals, while 34% stated their T2T goals were set by their physicians only, and 29% perceived no T2T goal had been set (n=620). The most common overall T2T goals from the patient perspective were remission (61%), controlling symptoms (41%), and reducing impact on QoL (34%). Of those patients who acknowledged a T2T goal had been set (n=407), 77% reported their T2T goal was fully or partially achieved.

Of 719 patients who had moderate-high disease activity, 57% were on a T2T plan, with 46% of physicians perceiving these treatment goals were fully or partially met. The most common physician-stated reason for not implementing T2T was a lack of achievable targets (29%).

Conclusion:

Rheumatologists in this study reported a strong belief in T2T. The most common physician-set T2T goals were remission, improvement of QoL and reduction of pain, corresponding with T2T goals as reported by patients. However, a third of patients in this cohort were not aware of a defined T2T objective in their management, which may be a result of a perceived lack of achievable goals by physicians. It may be desirable to promote more patient involvement in defining achievable targets amongst those with moderate-high disease activity who despite best efforts may not reach a clinical state of remission. Further research is needed to identify and understand goals important to RA patients.

References:

[1]van Vollenhoven R. Treat-to-target in rheumatoid arthritis - are we there yet? Nat Rev Rheumatol. 2019;15(3):180-6.

Acknowledgements:

This study was funded by Galapagos NV, Belgium.

Medical writing support was provided by Gary Sidgwick, PhD (Adelphi Real World, Bollington, UK) and editorial support was provided by Debbie Sherwood, BSc, CMPP (Aspire Scientific, Bollington, UK), both funded by Galapagos NV.

Disclosure of Interests:

Bruno Fautrel Consultant of: AbbVie, Amgen, Biogen, BMS, Celgene, Celltrion, Fresenius Kabi, Gilead, Janssen, Lilly, Medac, MSD, Mylan, NORDIC Pharma, Novartis, Pfizer, Roche, Sandoz, Sanofi-Genzyme, SOBI, UCB, Grant/research support from: AbbVie, Lilly, MSD, Pfizer, Roberto Caporali Speakers bureau: AbbVie, Amgen, Bristol Myers Squibb, Celltrion, Galapagos, Gilead, Lilly, Pfizer, Roche, UCB, Sanofi, Fresenius Kabi, Samsung Bioepis, MSD, Consultant of: Galapagos, Gilead, Lilly, Janssen, MSD, Elizabeth Holdsworth Employee of: Adelphi Real World, Bethany Donaghy Employee of: Adelphi Real World, Mona Khalid Shareholder of: Galapagos, Employee of: Galapagos, Mark Moore Shareholder of: Gilead Sciences, Speakers bureau: Gilead Sciences (only as employee), Paid instructor for: Gilead Sciences (only as employee), Consultant of: Gilead Sciences (only as employee), Grant/research support from: Gilead Sciences (only as employee), Employee of: Gilead Sciences, and previously Sanofi and AstraZeneca, Katrien Van Beneden Shareholder of: Galapagos, Employee of: Galapagos, Yves Piette Consultant of: AbbVie, Amgen, Galapagos, Grünenthal and Sandoz, Grant/research support from: Amgen, Mylan and UCB, Susana Romero-Yuste Speakers bureau: AbbVie, Amgen, Bristol Myers Squibb, Grunenthal, Kern Pharma, Lilly, Roche, Sandoz, Sanofi, UCB, Janssen, Consultant of: AbbVie, Biogen, Fresenius, Galapagos, Gebro, Janssen, Lilly, Grant/research support from: Bristol Myers Squibb, MSD, Novartis, Pfizer, Jasper Broen Shareholder of: Pharming Group, Consultant of: Galapagos, Gilead, Novartis, Peter C. Taylor Consultant of: AbbVie, Biogen, Galapagos, Gilead, GlaxoSmithKline, Janssen, Lilly, Pfizer, Roche, Sanofi, Nordic Pharma, Fresenius, UCB, Grant/research support from: Celgene, Galapagos, Gilead, Lilly

Should COVID-19 patients >75 years be Ventilated? An Outcome Study

BACKGROUND

Elderly patients with COVID-19 disease are at increased risk for adverse outcomes. Current data regarding disease characteristics and outcomes in this population are limited.

AIM

To delineate the adverse factors associated with outcomes of COVID-19 patients ≥75 years of age.

DESIGN

Retrospective cohort study.

METHODS

Patients were classified into mild/moderate, severe/very severe and critical disease (intubated) based on oxygen requirements. The primary outcome was in-hospital mortality.

RESULTS

A total of 355 patients aged ≥75 years hospitalized with COVID-19 between 19 March and 25 April 2020 were included.Mean age was 84.3 years. One-third of the patients developed critical disease. Mean length of stay was 7.10 days. Vasopressors were required in 27%, with the highest frequency in the critical disease group (74.1%). Overall mortality was 57.2%, with a significant difference between severity groups (mild/moderate disease: 17.4%, severe/very severe disease: 71.3%, critical disease: 94.9%, P < 0.001).Increased age, dementia, and severe/very severe and critical disease groups were independently associated with increased odds for mortality while diarrhea was associated with decreased odds for mortality (OR: 0.12, 95% CI: 0.02-0.60, P < 0.05). None of the cardiovascular comorbidities were significantly associated with mortality.

CONCLUSION

Age and dementia are associated with increased odds for mortality in patients ≥75 years of age hospitalized with COVID-19. Those who require intubation have the greatest odds for mortality. Diarrhea as a presenting symptom was associated with lower odds for mortality.

AB0641 EFFECT OF AGE ON PARATHYROID HORMONE RESPONSE TO VITAMIN D INSUFFICIENCY IN PRIMARY HYPERPARATHYROIDISM

Background:

Primary hyperparathyroidism (PHPT) is a common endocrine condition, commonly seen with increasing age. In vast majority, it is diagnosed incidentally and causes no particular symptoms. Symptoms are usually related to acute hypercalcaemia or the complications of chronically elevated serum calcium level. Vitamin D deficiency is common among general population and in patients with PHPT. Studies in secondary hyperparathyroidism (SHPT) have shown that parathyroid hormone (PTH) response is affected by age, with those over 80 showing greater rise in PTH levels. We wanted to see if age has a similar impact on PTH response to vitamin D in those with PHPT.

Objectives:

To evaluate the impact of age on PTH response to vitamin D insufficiency in those with PHPT.

Methods:

Patients with primary hyperparathyroidism (PHPT), attending general endocrine clinic of a district general hospital, were divided into two groups based on age; less than 70 (n=73) and 70 and above (n=61).Each group was subdivided into vitamin D insufficient (VDI) and vitamin D sufficient (VDS) subgroups. We compared calcium and parathyroid hormone levels and forearm BMD (presented as T score) in VDI and VDS subgroups in the two age groups, at the time of diagnosis. Data were analyzed using unpaired t-test and presented as mean ± SEM, using Graphpad Prism 9.0.1.

Results:

There was significant difference in Vitamin D levels in VDI and VDS subgroups, in both age groups (<70; mean vitamin D 27.98 vs. 68.44, p<0.0001; ≥70; mean vitamin D 34.44 vs. 75.74, p<0.0001). The two groups were significantly different in terms of age (mean age 58 vs. 76, p<0.0001). Although there was no difference in calcium and forearm BMD in VDI and VDS, in both age groups, those under 70 showed a greater PTH response to vitamin D insufficiency (mean PTH 19.29 vs. 12.91 respectively, p<0.001).

Conclusion:

While in SHPT, those with increasing age show greater rise in PTH levels, our data show that in PHPT, younger patients show a greater PTH rise in response to vitamin D insufficiency. Further work is needed to elucidate the underlying mechanisms.

References:

[1]Wyskida et al., Parathyroid hormone response to different vitamin D levels in population-based old and very-old Polish cohorts, Experimental Gerontology, Volume 127, 2019, 110735, ISSN 0531-5565, https://doi.org/10.1016/j.exger.2019.110735.

[2]Malik M Z, Latiwesh O B, Nouh F, et al. (August 15, 2020) Response of Parathyroid Hormone to Vitamin D Deficiency in Otherwise Healthy Individuals. Cureus 12(8): e9764. doi:10.7759/cureus.9764

Disclosure of Interests:

None declared.

Synthesis, structural, spectral and biological evaluation of metals endowed 1,2,4-triazole

Biologically active triazole Schiff base ligand 2,4-dichloro-6-[(1H-1,2,4-triazol-3-ylimino)methyl]phenol (A) has been synthesized by the condensation reaction of an equimolar amount of 1H-1,2,4-triazole-3-amine and 3,5-dichlorosalicylaldehyde and then it coordinated with salts of metals [VO(IV), Fe(II), Co(II), Ni(II), Cu(II) and Zn(II)] in 2:1 molar ratio to derive a series of transition metal chelates (1)-(6). All the compounds were characterized by various physical, spectral, analytical techniques and elemental analysis. Spectral characterization and magnetic moment data of complexes revealed square pyramidal geometry for vanadium complex and octahedral for remaining (2)-(6) complexes. Quantum chemical calculation has been carried out to explore optimized geometry and electronic structure of the ligand. Density functional theory (DFT) with B3LYP/6-311+g(d,p) method was performed to determine vibrational bands, frontier molecular orbitals (FMOs) and natural bond analysis (NBO) of the ligand. NBO analysis showed that the ligand bears higher molecular stability because of hyper conjugate interactions. Computational study results revealed that there was a close interaction of theoretical and experimental spectroscopic data. Global reactivity descriptors calculated by the energies of FMOs, indicated ligand to be bioactive. The synthesized compounds were studied for antibacterial, antifungal, antioxidant and antiglycation activity and the results revealed that ligand has remarkable activity which considerably increased upon chelation.                     KEY WORDS: Triazole ligand, Transition metal chelates, Antibacterial, Antifungal, Antioxidant, Computational study   Bull. Chem. Soc. Ethiop. 2020, 34(2), 335-351 DOI: https://dx.doi.org/10.4314/bcse.v34i2.11

Outcomes in patients with severe COVID-19 disease treated with tocilizumab: a case-controlled study

BACKGROUND

COVID-19 is an ongoing threat to society. Patients who develop the most severe forms of the disease have high mortality. The interleukin-6 inhibitor tocilizumab has the potential to improve outcomes in these patients by preventing the development of cytokine release storm.

AIMS

To evaluate the outcomes of patients with severe COVID-19 disease treated with the interleukin-6 inhibitor tocilizumab.

METHODS

We conducted a retrospective, case-control, single-center study in patients with severe to critical COVID-19 disease treated with tocilizumab. Disease severity was defined based on the amount of oxygen supplementation required. The primary endpoint was the overall mortality. Secondary endpoints were mortality in non-intubated patients and mortality in intubated patients.

RESULTS

A total of 193 patients were included in the study. Ninety-six patients received tocilizumab, while 97 served as the control group. The mean age was 60 years. Patients over 65 years represented 43% of the population. More patients in the tocilizumab group reported fever, cough and shortness of breath (83%, 80% and 96% vs. 73%, 69% and 71%, respectively). There was a non-statistically significant lower mortality in the treatment group (52% vs. 62.1%, P = 0.09). When excluding intubated patients, there was statistically significant lower mortality in patients treated with tocilizumab (6% vs. 27%, P = 0.024). Bacteremia was more common in the control group (24% vs. 13%, P = 0.43), while fungemia was similar for both (3% vs. 4%, P = 0.72).

CONCLUSION

Our study showed a non-statistically significant lower mortality in patients with severe to critical COVID-19 disease who received tocilizumab. When intubated patients were excluded, the use of tocilizumab was associated with lower mortality.

P4727Comparing in-hospital outcomes of transcatheter mitral valve repair vs surgical mitral valve repair in patients with pulmonary hypertension

Background

The comparative outcomes of transcatheter mitral valve repair with Mitraclip (TMVR) versus surgical mitral valve repair (SMVR) in pulmonary hypertension (PH) patients are underexplored.

Methods

The Nationwide Inpatient Sample (NIS) database was queried from 2009 to 2015 and those who underwent TMVR or SMVR with PH were identified. International Classification of Diseases (ICD) codes were used to identify the target population, primary and secondary outcomes. Propensity scores (PS) were calculated for each patient. A standardized morbidity ratio (SMR) weight was then calculated for each patient in both groups. Absolute standardized differences (ASD) were used to compare the baseline characteristics.

Results

We identified a total of 341 and 4,665 cases from NIS who underwent TMVR and SMVR, respectively, over the study period. The mean age of patients was 67 yrs. Patients who underwent TMVR were older (75 vs. 66, ASD =0.32). Women comprised 47.8% of TMVR admission while 42.16% of SMVR admissions were females. Patients who underwent TMVR had a significantly higher Elixhauser comorbidity score >4 (43.4% vs 34.2%, ASD =0.3). TMVR group had higher rates of previous myocardial infarctions (16.7% vs. 7.1%, ASD = 0.30), congestive heart failure (80% vs. 6.2%, ASD = 0.44), percutaneous intervention (PCI) (15.6% vs. 5.9%, ASD = 0.32), coronary artery bypass grafting (CABG) (22.8% vs. 2.7%, ASD = 0.63), cerebrovascular disease (13.5% vs. 5.5%, ASD = 0.27), chronic obstructive pulmonary disease (30.8% vs. 21.1%, ASD = 0.22)and chronic renal failure (43.1% vs. 18.9%, ASD = 0.54). Inpatient mortality was similar between the 2 groups (3.6% vs 6.2%, p=0.573). TMVR patients had less hemorrhage requiring transfusion (8.1% vs 21.1, p<0.001), acute kidney injury (22.2% vs 33.6%, p<0.001), pacemaker placement (1.20% vs 6.89%, p<0.001), cardiogenic shock (4.5% vs 11.5%, p=0.0012) and mechanical circulatory device need (3.9% vs 9.7%, p=0.003). The TMVR group had lower median hospital cost ($53,824 vs $68,311, p<0.001) and LOS (7.5 days vs 13.1 days, p<0.001).

Conclusions

While in-hospital mortality in PH patients after TMVR compared to SMVR, TMVR was associated with significantly lower periprocedural complications.

Evaluation of pigeon egg yolk for post thaw quality, enzyme leakage and fertility of buffalo (Bubalus bubalis) bull spermatozoa

Egg yolk is used as a cryoprotectant in semen preservation. However, its composition varies according to the species which may influence its effectiveness during the freeze-thaw process. Therefore, study was conducted to identify the optimum level of pigeon egg yolk (PEY) in Tris citric acid (TCA) extender for freezability and in vivo fertility of buffalo semen. Semen was collected at weekly intervals for a period of three weeks (replicates) from 6 Nili Ravi buffalo bulls (2 ejaculates/bull/replicate) and diluted with TCA extender (50 × 10⁶ motile spermatozoa ml^-1) containing 5%, 10%, 15% and 20% PEY or 20% CEY (control) and cryopreserved. Post-thaw sperm quality and extracellular enzymes leakage was assessed after thawing. Sperm motility, plasma membrane integrity, livability and viability was significantly higher in extenders containing 10% and 15% PEY compared to 5% PEY, 20% PEY or 20% CEY (controls). A dose-dependent decrease was recorded in the chromatin damage for the PEY, being lowest for the 15% and 20% PEY which was significantly less compared to controls (20% CEY). The extracellular GOT and LDH leakage was significantly lower (P < 0.05) in extender containing 10% and 15% PEY compared to the controls. Semen collected from 2 bulls, cryopreserved in extenders containing 15% PEY or 20% chicken egg yolk was assessed for fertility after artificial inseminations. A total of 400 buffaloes were inseminated (100 inseminations/extender/bull). The overall fertility rate was significantly higher (P < 0.05) with semen cryopreserved in extender containing 15% PEY (56%) compared to 20% CEY (42%; controls). In conclusion, pigeon egg yolk at 15% offers advantages over 20% chicken egg yolk in terms of in vitro post-thaw semen quality and in vivo fertility of buffalo.

Sperm preparation through Sephadex™ filtration improves in vitro fertilization rate of buffalo oocytes

Routinely, swim-up method is used to separate high-quality sperm; however, long processing time and close cell-to-cell contact during the centrifugation step are inevitable elements of oxidative stress to sperm. The objective was to evaluate Sephadex^™ and glass wool filtration to separate motile, intact and viable sperm for in vitro fertilization in buffalo. The cumulus-oocyte complexes (COCs) were collected from ovaries of slaughtered buffaloes by aspiration and matured for 24 hr in CO₂ incubator at 38.5°C and 5% CO₂ . Matured COCs were rinsed twice in fertilization TALP and placed in the pre-warmed fertilization medium without sperm. Cryopreserved buffalo semen was thawed at 37°C for 30 s and processed through Sephadex^™ , glass wool filtration and swim-up (control). Total and motile sperm recovery rates were assessed, resuspended in fertilization TALP and incubated for 15-20 min in CO₂ incubator. Samples prepared by each method were divided into two aliquots: one aliquot was studied for sperm quality (progressive motility, membrane integrity, viability, liveability), while the other was subjected to co-incubation with sets of 10-15 in vitro matured oocytes. Data on sperm quality were analysed by ANOVA, while in vitro fertilizing rates were compared by chi-squared test using SPSS-20. Least significant difference (LSD) test was used to compare treatment means. Glass wool filtration yielded higher total and motile sperm recovery rate, while Sephadex^™ filtration improved (p < .05) sperm quality (progressive motility, membrane integrity, viability, liveability). Sperm preparation through Sephadex filtration yielded higher in vitro fertilization rate in terms of cleavage rate compared to glass wool filtration and swim-up (control). In conclusion, cryopreserved Nili-Ravi buffalo sperm selected through Sephadex filtration showed improved quality and yielded better fertilization rates (cleavage rate) of in vitro matured/fertilized oocytes. Sephadex filtration could be a promising technique for use in in vitro fertilization in buffalo.

Antimicrobial, antioxidant, cytotoxicity and LC-MS analyses of Aerva javanica: an ethnomedicinally important plant

In this study, Aerva javanica was used to extract the essential oil with notable medicinal activities. The chemical composition was investigated by high-performance liquid chromatography (HPLC) and liquid chromatography/mass spectrometry (LC-MS). Ten major chemical compounds were identified as flavonoids derivatives, dihydroxylated and glycosylated metabolites. The antimicrobial, antioxidant, and cytotoxicity activities were tested using agar well-diffusion assay, 1, 1-diphenyl-2-picrylhydrazyl (DPPH) free-radical scavenging and linoleic acid oxidation assays and hemolytic assay against human erythrocytes (RBCs), respectively. Plant extracts exhibited different extents of antimicrobial activities against selected bacterial and fungal strains; however, the essential oil displayed potent antimicrobial activity against all the tested strains. The percentage inhibition of linoleic acid oxidation and inhibitory concentration (IC50) were recorded to be in the range of 42.45-96.21% and 14.21-38.18 μg/mL, respectively. Cytotoxicity profile of A. javanica extracts and essential oil was found in the range of 5.82 to 14.47%. In conclusion, A. javanica essential oil could be a potential alternative to chemical additives in the food and pharmaceutical industries.

Molecular characterization of ascaris from Tibetan pigs by three mitochondrial markers of nad1, cox1 and cox2

Ascaris is a helminthic parasite, which infects a wide range of host species causing ascariasis, a predominant disease worldwide. This parasite causes significant economic losses to the pig industry. The current study was designed to determine the Ascaris nematode by the genetic characterization of three mitochondrial (mt) genes namely NADH dehydrogenase subunit 1 (nad1), cytochrome oxidase subunit 1 (cox1) and cytochrome oxidase subunit 2 (cox2). A high infection rate of Ascaris nematode has been found in Tibetan pigs at the slaughter houses in Tibet Autonomous Region of China. The nad1, cox1 and cox2 genes sequences collected from adult Ascaris individuals were amplified by polymerase chain reaction. The cloned-amplicons and the positive products were sequenced and phylogenetic analysis was performed. The results indicated that the Ascaris infecting the Tibetan pigs were Ascaris suum (A. suum). This is the first report on the isolation, identification and genetic characterization of three mitochondrial genomes (nad1, cox1, and cox2) of A. suum originated from Tibetan pigs at high altitudes in Tibet.

GnRH-agonist implants suppress reproductive function and affects ovarian LHR and FSHR expression in prepubertal female cats

Effect of a GnRH-agonist (deslorelin) was studied on reproductive function and ovarian luteinizing hormone receptor (LHR) and follicle stimulating hormone receptor (FSHR) expression in prepubertal female cats that were either implanted with 4.7-mg deslorelin (implanted: n = 6) or not (controls: n = 18) or ovariohysterectomized at prepubertal age (prepubertal OVH: n = 6). Body weights, fecal estradiol, and sexual behavior of implanted and control cats were monitored for 48 weeks followed by collection of ovaries and uteri. Ovaries and uteri were collected from control cats at follicular, luteal, and inactive stage (n = 6/group) and from prepubertal OVH cats at prepubertal age. Ovaries and uteri were analyzed for anatomical/histological characteristics. Ovaries were also analyzed for LHR and FSHR expression. Statistical analysis showed higher (P ≤ 0.05) body weight in control than implanted cats only during 22nd to 26th weeks of the study. Estrus was observed in control cats only. Deslorelin reduced (P ≤ 0.05) ovarian weight and number of antral follicles but did not affect endometrial thickness and gland diameter. However, myometrial thickness of implanted cats was significantly lower than control cats at follicular and luteal stage. Ovarian LHR mRNA expression was lower (P ≤ 0.05) in implanted cats than control cats at follicular stage. FSHR mRNA and LHR protein expression did not differ among the three groups. FSHR protein expression was lower (P ≤ 0.05) in prepubertal OVH cats and was not affected by deslorelin. In conclusion, deslorelin suppresses reproductive function in prepubertal female cats for at least 48 weeks possibly through a change in the ovarian mRNA expression of LHR.

Synthesis and thermo-physical properties of deep eutectic solvent-based graphene nanofluids

This study introduces a new class of heat transfer fluids by dispersing functionalised graphene oxide nanoparticles (GNPs) in ammonium and phosphonium-based deep eutectic solvents (DESs) without the aid of a surfactant. Different molar ratios of salts and hydrogen bond donors (HBD) were used to synthesise DESs for the preparation of different concentrations of graphene nanofluids (GNFs). The concentrations of GNPs were 0.01 wt%, 0.02 wt% and 0.05 wt %. Homogeneous and stable suspensions of nanofluids were obtained by high speed homogenisation and an ultrasonication process. The stability of the GNFs was determined through visual observation for 4 weeks followed by a centrifugal process (5000-20,000 rpm) for 30 min in addition to zeta potential studies. Dispersion of the GNPs in DES was observed using an optical microscope. The synthesised DES-based GNFs showed no particle agglomeration and formation of sediments in the nanofluids. Thermo-physical properties such as thermal conductivity and specific heat of the nanofluids were also investigated in this research. The highest thermal conductivity enhancement of 177% was observed. The findings of this research provide a new class of engineered fluid for heat transfer applications as a function of temperature, type and composition DESs as well as the GNPs concentration.

A Study on the Approximation of Clustered Data to Parameterized Family of Fuzzy Membership Functions for the Induction of Fuzzy Decision Trees

This paper investigates the Triangular, Trapezoidal and Gaussian approximation methods for the purpose of induction of Fuzzy Decision Trees (FDT). The generation of FDT is done using a Fuzzy ID3 induction algorithm. In this work three fuzzy partitioning techniques which form the basis for our investigation are given attention, namely Fuzzy C Means clustering (FCM), Grid partitioning and Subtractive clustering (Subclust). Our contribution lies in studying the effect of various approximations on the generation of FDT giving specific attention to the classification accuracy of FDT. In this study we show that the accuracy levels of FDT generated using FCM clustered raw data, bypassing the approximation step, is acceptable and this method has several advantages too. Several computational experiments are conducted and non parametric statistical tests are performed to find if any significant differences exist between the method of bypassing the approximation step and the other methods which include approximation. Ten FDTs are developed and used in this study. These FDT’s differ in their fuzzy partitioning techniques and the approximation methods used.