Search results for: predictive modlleing

Authors: Hwang Ahreum, Kang Taejoon, Kim Bongsoo

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Nanosensors for high sensitive detection of diseases have been widely studied to improve the quality of life. Here, we suggest robust nano-plasmonic diagnostic sensor using cysteine tagged protein G (Cys3-protein G) and ultraflat, ultraclean and single-crystalline Au nanoplates. Protein G formed on an ultraflat Au surface provides ideal background for dense and uniform immobilization of antibodies. The Au is highly stable in diverse biochemical environment and can immobilize antibodies easily through Au-S bonding, having been widely used for various biosensing applications. Especially, atomically smooth single-crystalline Au nanomaterials synthesized using chemical vapor transport (CVT) method are very suitable to fabricate reproducible sensitive sensors. As the C-reactive protein (CRP) is a nonspecific biomarker of inflammation and infection, it can be used as a predictive or prognostic marker for various cardiovascular diseases. Cys3-protein G immobilized uniformly on the Au nanoplate enable CRP antibody (anti-CRP) to be ordered in a correct orientation, making their binding capacity be maximized for CRP detection. Immobilization condition for the Cys3-protein G and anti-CRP on the Au nanoplate is optimized visually by AFM analysis. Au nanoparticle - Au nanoplate (NPs-on-Au nanoplate) assembly fabricated from sandwich immunoassay for CRP can reduce zero-signal extremely caused by nonspecific bindings, providing a distinct surface-enhanced Raman scattering (SERS) enhancement still in 10-18 M of CRP concentration. Moreover, the NP-on-Au nanoplate sensor shows an excellent selectivity against non-target proteins with high concentration. In addition, comparing with control experiments employing a Au film fabricated by e-beam assisted deposition and linker molecule, we validate clearly contribution of the Au nanoplate for the attomolar sensitive detection of CRP. We expect that the devised platform employing the complex of single-crystalline Au nanoplates and Cys3-protein G can be applied for detection of many other cancer biomarkers.

Keywords: Au nanoplate, biomarker, diagnostic sensor, protein G, SERS

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Authors: Francesca Nicolosi, Teresa Cosola

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Climate changes in Mediterranean areas, such as the increase of average seasonal temperatures, the urban heat island phenomenon, the intensification of solar radiation and the extreme weather threats, cause disruption events, so that climate adaptation has become a pressing issue. Due to the strategic role that the built heritage holds in terms of environmental impact and energy waste and its potentiality, it is necessary to assess the vulnerability and the adaptive capacity of the existing building to climate change, in order to define different mitigation scenarios. The aim of this research work is to define an optimized and integrated methodology for the assessment of resilience levels and adaptation scenarios for existing buildings in Mediterranean urban areas. Moreover, the study of resilience indicators allows us to define building environmental and energy performance in order to identify the design and technological solutions for the improvement of the building and its urban area potentialities. The methodology identifies step-by-step different phases, starting from the detailed study of characteristic elements of urban system: climatic, natural, human, typological and functional components are analyzed in their critical factors and their potential. Through the individuation of the main perturbing factors and the vulnerability degree of the system to the risks linked to climate change, it is possible to define mitigation and adaptation scenarios. They can be different, according to the typological, functional and constructive features of the analyzed system, divided into categories of intervention, and characterized by different analysis levels (from the single building to the urban area). The use of software simulations allows obtaining information on the overall behavior of the building and the urban system, to generate predictive models in the medium and long-term environmental and energy retrofit and to make a comparative study of the mitigation scenarios identified. The studied methodology is validated on a case study.

Keywords: climate impact mitigation, energy efficiency, existing building heritage, resilience

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Authors: Calogero Lo Destro

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Numerous nations around the world are facing exceptional challenges in employing measures to stop the spread of COVID-19. Following the recommendations of the World Health Organization, a series of preventive measures have been adopted. However, individuals must comply with these rules and recommendations in order to make these measures effective. While COVID-19 was climaxing, it seemed of crucial importance to analyze which psychosocial factors contribute to the acceptance of such preventive behavior, thus favoring the management of COVID-19 worldwide health crisis. In particular, the identification of aspects related to obstacles and facilitation of adherence to social distancing has been considered crucial in the containment of the virus spread. Since the virus was firstly detected in China, Asian people could be considered a relevant outgroup targeted for exclusion. We also hypothesized social distance could be influenced by characteristics of the target, such as smiling or coughing. 260 participants participated in this research on a voluntary basis. They filled a survey designed to explore a series of COVID-19 measures (such as exposure to virus and fear of infection). We also assessed participants state and trait anxiety. The dependent variable was social distance, based on a measure of seating distance designed ad hoc for the present work. Our hypothesis that participants could report greater distance in response to Asian people was not confirmed. On the other hand, significantly lower distance in response to smiling compared to coughing targets was reported. Adopting a regression analysis model, we found that participants' social distance, in response to both coughing and smiling targets, was predicted by fear of infection and by the perception COVID-19 could become a pandemic. Social distance in response to the coughing target was also significantly and positively predicted by age and state anxiety. In summary, the present work has sought to identify a set of psychological variables, which may still be predictive of social distancing.

Keywords: COVID-19, social distancing, health, preventive behaviors, risk of infection

Procedia PDF Downloads 123

Authors: Pranay Mathur, Carlo Michelassi, Simi Karatha, Gilda Pedoto

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Industrial plant uptime is top most importance for reliable, profitable & sustainable operation. Trip and failed start has major impact on plant reliability and all plant operators focussed on efforts required to minimise the trips & failed starts. The performance of these CTQs are measured with 2 metrics, MTBT(Mean time between trips) and SR (Starting reliability). These metrics helps to identify top failure modes and identify units need more effort to improve plant reliability. Baker Hughes Trip reduction program structured to reduce these unwanted trip 1. Real time machine operational parameters remotely available and capturing the signature of malfunction including related boundary condition. 2. Real time alerting system based on analytics available remotely. 3. Remote access to trip logs and alarms from control system to identify the cause of events. 4. Continuous support to field engineers by remotely connecting with subject matter expert. 5. Live tracking of key CTQs 6. Benchmark against fleet 7. Break down to the cause of failure to component level 8. Investigate top contributor, identify design and operational root cause 9. Implement corrective and preventive action 10. Assessing effectiveness of implemented solution using reliability growth models. 11. Develop analytics for predictive maintenance With this approach , Baker Hughes team is able to support customer in achieving their Reliability Key performance Indicators for monitored units, huge cost savings for plant operators. This Presentation explains these approach while providing successful case studies, in particular where 12nos. of LNG and Pipeline operators with about 140 gas compressing line-ups has adopted these techniques and significantly reduce the number of trips and improved MTBT

Keywords: reliability, availability, sustainability, digital infrastructure, weibull, effectiveness, automation, trips, fail start

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Authors: Fidelia A. Orji, Julita Vassileva

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This research aims to develop machine learning models for students' academic performance and study strategy prediction, which could be generalized to all courses in higher education. Key learning attributes (intrinsic, extrinsic, autonomy, relatedness, competence, and self-esteem) used in building the models are chosen based on prior studies, which revealed that the attributes are essential in students’ learning process. Previous studies revealed the individual effects of each of these attributes on students’ learning progress. However, few studies have investigated the combined effect of the attributes in predicting student study strategy and academic performance to reduce the dropout rate. To bridge this gap, we used Scikit-learn in python to build five machine learning models (Decision Tree, K-Nearest Neighbour, Random Forest, Linear/Logistic Regression, and Support Vector Machine) for both regression and classification tasks to perform our analysis. The models were trained, evaluated, and tested for accuracy using 924 university dentistry students' data collected by Chilean authors through quantitative research design. A comparative analysis of the models revealed that the tree-based models such as the random forest (with prediction accuracy of 94.9%) and decision tree show the best results compared to the linear, support vector, and k-nearest neighbours. The models built in this research can be used in predicting student performance and study strategy so that appropriate interventions could be implemented to improve student learning progress. Thus, incorporating strategies that could improve diverse student learning attributes in the design of online educational systems may increase the likelihood of students continuing with their learning tasks as required. Moreover, the results show that the attributes could be modelled together and used to adapt/personalize the learning process.

Keywords: classification models, learning strategy, predictive modeling, regression models, student academic performance, student motivation, supervised machine learning

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Authors: Eric Dede, Marcus Tindall, John W. Cherrie, Steve Hankin, Christopher Collins

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Current exposure models used in contaminated land risk assessment are highly conservative. Use of these models may lead to over-estimation of actual exposures, possibly resulting in negative financial implications due to un-necessary remediation. Thus, we are carrying out a study seeking to improve our understanding of human exposure to selected toxic elements in soil: arsenic (As), cadmium (Cd), chromium (Cr), nickel (Ni), and lead (Pb) resulting from allotment land-use. The study employs biomonitoring and physiologically-based pharmacokinetic (PBPK) modelling to quantify human exposure to these elements. We recruited 37 allotment users (adults > 18 years old) in Scotland, UK, to participate in the study. Concentrations of the elements (and their bioaccessibility) were measured in allotment samples (soil and allotment produce). Amount of produce consumed by the participants and participants’ biological samples (urine and blood) were collected for up to 12 consecutive months. Ethical approval was granted by the University of Reading Research Ethics Committee. PBPK models (coded in MATLAB) were used to estimate the distribution and accumulation of the elements in key body compartments, thus indicating the internal body burden. Simulating low element intake (based on estimated ‘doses’ from produce consumption records), predictive models suggested that detection of these elements in urine and blood was possible within a given period of time following exposure. This information was used in planning biomonitoring, and is currently being used in the interpretation of test results from biological samples. Evaluation of the models is being carried out using biomonitoring data, by comparing model predicted concentrations and measured biomarker concentrations. The PBPK models will be used to generate bioavailability values, which could be incorporated in contaminated land exposure models. Thus, the findings from this study will promote a more sustainable approach to contaminated land management.

Keywords: biomonitoring, exposure, PBPK modelling, toxic elements

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Authors: Ansoné Human, Nantes Kirsten, Tanja Verster, Willem D. Schutte

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Every year the telecommunications industry suffers huge losses due to fraud. Mobile fraud, or generally, telecommunications fraud is the utilisation of telecommunication products or services to acquire money illegally from or failing to pay a telecommunication company. A South African telecommunication operator developed two internal fraud scorecards to mitigate future risks of application fraud events. The scorecards aim to predict the likelihood of an application being fraudulent and surprise fraudsters before they surprise the telecommunication operator by identifying fraud at the time of application. The scorecards are utilised in the vetting process to evaluate the applicant in terms of the fraud risk the applicant would present to the telecommunication operator. Telecommunication providers can utilise these scorecards to profile customers, as well as isolate fraudulent and/or high-risk applicants. We provide the complete methodology utilised in the development of the scorecards. Furthermore, a Determination and Discrimination (DD) ratio is provided in the methodology to select the most influential variables from a group of related variables. Throughout the development of these scorecards, the following was revealed regarding fraudulent cases and fraudster behaviour within the telecommunications industry: Fraudsters typically target high-value handsets. Furthermore, debit order dates scheduled for the end of the month have the highest fraud probability. The fraudsters target specific stores. Applicants who acquire an expensive package and receive a medium-income, as well as applicants who obtain an expensive package and receive a high income, have higher fraud percentages. If one month prior to application, the status of an account is already in arrears (two months or more), the applicant has a high probability of fraud. The applicants with the highest average spend on calls have a higher probability of fraud. If the amount collected changes from month to month, the likelihood of fraud is higher. Lastly, young and middle-aged applicants have an increased probability of being targeted by fraudsters than other ages.

Keywords: application fraud scorecard, predictive modeling, regression, telecommunications

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Authors: Zahid Ullah, Atlas Khan

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In today's data-driven world, the role of mathematics in bridging the gap between theory and applications is becoming increasingly vital. This abstract highlights the significance of mathematics as a powerful tool for analyzing, interpreting, and extracting meaningful insights from vast amounts of data. By integrating mathematical principles with real-world applications, researchers can unlock the full potential of data-driven decision-making processes. This abstract delves into the various ways mathematics acts as a bridge connecting theoretical frameworks to practical applications. It explores the utilization of mathematical models, algorithms, and statistical techniques to uncover hidden patterns, trends, and correlations within complex datasets. Furthermore, it investigates the role of mathematics in enhancing predictive modeling, optimization, and risk assessment methodologies for improved decision-making in diverse fields such as finance, healthcare, engineering, and social sciences. The abstract also emphasizes the need for interdisciplinary collaboration between mathematicians, statisticians, computer scientists, and domain experts to tackle the challenges posed by the data-driven landscape. By fostering synergies between these disciplines, novel approaches can be developed to address complex problems and make data-driven insights accessible and actionable. Moreover, this abstract underscores the importance of robust mathematical foundations for ensuring the reliability and validity of data analysis. Rigorous mathematical frameworks not only provide a solid basis for understanding and interpreting results but also contribute to the development of innovative methodologies and techniques. In summary, this abstract advocates for the pivotal role of mathematics in bridging theory and applications in a data-driven world. By harnessing mathematical principles, researchers can unlock the transformative potential of data analysis, paving the way for evidence-based decision-making, optimized processes, and innovative solutions to the challenges of our rapidly evolving society.

Keywords: mathematics, bridging theory and applications, data-driven world, mathematical models

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Authors: Snigdha Sahai, Devaki Chikkavenkatappa Yellappa

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This study outlines approaches for testing and validating stochastic models used in epidemiology, focusing on the integration and functional testing of simulation code. It details methods for combining simple functions into comprehensive simulations, distinguishing between deterministic and stochastic components, and applying tests to ensure robustness. Techniques include isolating stochastic elements, utilizing large sample sizes for validation, and handling special cases. Practical examples are provided using R code to demonstrate integration testing, handling of incorrect inputs, and special cases. The study emphasizes the importance of both functional and defensive programming to enhance code reliability and user-friendliness.

Keywords: computational epidemiology, epidemiology, public health, infectious disease modeling, statistical analysis, health data analysis, disease transmission dynamics, predictive modeling in health, population health modeling, quantitative public health, random sampling simulations, randomized numerical analysis, simulation-based analysis, variance-based simulations, algorithmic disease simulation, computational public health strategies, epidemiological surveillance, disease pattern analysis, epidemic risk assessment, population-based health strategies, preventive healthcare models, infection dynamics in populations, contagion spread prediction models, survival analysis techniques, epidemiological data mining, host-pathogen interaction models, risk assessment algorithms for disease spread, decision-support systems in epidemiology, macro-level health impact simulations, socioeconomic determinants in disease spread, data-driven decision making in public health, quantitative impact assessment of health policies, biostatistical methods in population health, probability-driven health outcome predictions

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Authors: Basit Aftab, Zhichao Wang, Feng Zhongke

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Land Use and Land Cover Change (LULCC) is a critical environmental issue that has significant effects on biodiversity, ecosystem services, and climate change. This study examines the spatiotemporal dynamics of land use and land cover (LULC) across a three-decade period (1992–2022) in a district area. The goal is to support sustainable land management and urban planning by utilizing the combination of remote sensing, GIS data, and observations from Landsat satellites 5 and 8 to provide precise predictions of the trajectory of urban sprawl. In order to forecast the LULCC patterns, this study suggests a hybrid strategy that combines the Random Forest method with Multilayer Perceptron (MLP) and Markov Chain analysis. To predict the dynamics of LULC change for the year 2035, a hybrid technique based on multilayer Perceptron and Markov Chain Model Analysis (MLP-MCA) was employed. The area of developed land has increased significantly, while the amount of bare land, vegetation, and forest cover have all decreased. This is because the principal land types have changed due to population growth and economic expansion. The study also discovered that between 1998 and 2023, the built-up area increased by 468 km² as a result of the replacement of natural resources. It is estimated that 25.04% of the study area's urbanization will be increased by 2035. The performance of the model was confirmed with an overall accuracy of 90% and a kappa coefficient of around 0.89. It is important to use advanced predictive models to guide sustainable urban development strategies. It provides valuable insights for policymakers, land managers, and researchers to support sustainable land use planning, conservation efforts, and climate change mitigation strategies.

Keywords: land use land cover, Markov chain model, multi-layer perceptron, random forest, sustainable land, remote sensing.

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Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan

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Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should properly evaluate their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, Neural Networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable to offer an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.

Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills

Procedia PDF Downloads 80

Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan

Abstract:

Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should evaluate properly their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, neural networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable of offering an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.

Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills

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Authors: Gehan Hussein, Hala Agha, Rasha Abdelraof, Marina George, Antoine Fakhri

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Background: β-Thalassemia major (TM) and sickle cell disease (SCD) are inherited hemoglobin disorders resulting in chronic hemolytic anemia. Cardiovascular involvement is an important cause of morbidity and mortality in these groups of patients. The narrow border is between overt myocardial dysfunction and clinically silent left ventricular (LV) and / or right ventricular (RV) dysfunction in those patients. 3 D Speckle tracking echocardiography (3D STE) is a novel method for the detection of subclinical myocardial involvement. We aimed to study myocardial affection in SCD and TM using 3D STE, comparing it with conventional echocardiography, correlate it with serum ferritin level and lactate dehydrogenase (LDH). Methodology: Thirty SCD and thirty β TM patients, age range 4-18 years, were compared to 30 healthy age and sex matched control group. Cases were subjected to clinical examination, laboratory measurement of hemoglobin level, serum ferritin, and LDH. Transthoracic color Doppler echocardiography, 3D STE, tissue Doppler echocardiography, and aortic speckle tracking were performed. Results: significant reduction in global longitudinal strain (GLS), global circumferential strain (GCS), and global area strain (GAS) in SCD and TM than control (P value <0.001) there was significantly lower aortic speckle tracking in patients with TM and SCD than control (P value< 0.001). LDH was significantly higher in SCD than both TM and control and it correlated significantly positive mitral inflow E, (p value:0.022 and 0.072. r: 0.416 and -0.333 respectively) lateral E/E’ (p value.<0.001and 0.818. r. 0.618 and -0. 044.respectively) and septal E/E’ (p value 0.007 and 0.753& r value 0.485 and -0.060 respectively) in SCD but not TM and significant negative correlation between LDH and aortic root speckle tracking (value 0.681& r. -0.078.). The potential diagnostic accuracy of LDH in predicting vascular dysfunction as represented by aortic root GCS with a sensitivity 74% and aortic root GCS was predictive of LV dysfunction in SCD patients with sensitivity 100% Conclusion: 3D STE LV and RV systolic dysfunction in spite of their normal values by conventional echocardiography. SCD showed significantly lower right ventricular dysfunction and aortic root GCS than TM and control. LDH can be used to screen patients for cardiac dysfunction in SCD, not in TM

Keywords: thalassemia major, sickle cell disease, 3d speckle tracking echocardiography, LDH

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Authors: Tigabu Dagne Akal

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While advances in computer and communications technology have made the network ubiquitous, they have also rendered networked systems vulnerable to malicious attacks devised from a distance. These attacks or intrusions start with attackers infiltrating a network through a vulnerable host and then launching further attacks on the local network or Intranet. Nowadays, system administrators and network professionals can attempt to prevent such attacks by developing intrusion detection tools and systems using data mining technology. In this study, the experiments were conducted following the Knowledge Discovery in Database Process Model. The Knowledge Discovery in Database Process Model starts from selection of the datasets. The dataset used in this study has been taken from Massachusetts Institute of Technology Lincoln Laboratory. After taking the data, it has been pre-processed. The major pre-processing activities include fill in missed values, remove outliers; resolve inconsistencies, integration of data that contains both labelled and unlabelled datasets, dimensionality reduction, size reduction and data transformation activity like discretization tasks were done for this study. A total of 21,533 intrusion records are used for training the models. For validating the performance of the selected model a separate 3,397 records are used as a testing set. For building a predictive model for intrusion detection J48 decision tree and the Naïve Bayes algorithms have been tested as a classification approach for both with and without feature selection approaches. The model that was created using 10-fold cross validation using the J48 decision tree algorithm with the default parameter values showed the best classification accuracy. The model has a prediction accuracy of 96.11% on the training datasets and 93.2% on the test dataset to classify the new instances as normal, DOS, U2R, R2L and probe classes. The findings of this study have shown that the data mining methods generates interesting rules that are crucial for intrusion detection and prevention in the networking industry. Future research directions are forwarded to come up an applicable system in the area of the study.

Keywords: intrusion detection, data mining, computer science, data mining

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Authors: Stefano Ruggieri, Rubinia C. Bonfanti

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Research on leadership in sports organizations has proved extremely fruitful in recent decades, favoring the growing and diffusion of figures such as mental coaches, trainers, etc. Recent scholarly attention on organizations has been directed towards the phenomenon of leader self-sacrifice, wherein leaders who display such behavior are perceived by their followers as more effective, charismatic, and legitimate compared to those who prioritize self-interest. This growing interest reflects the importance of leaders who prioritize the collective welfare over personal gain, as they inspire greater loyalty, trust, and dedication among their followers, ultimately fostering a more cohesive and high-performing team environment. However, there is limited literature on the mechanisms through which self-sacrifice influences both group dynamics (such as cohesion and team identification) and individual factors (such as self-competence). The aim of the study is to analyze the impact of the leader self-sacrifice on cohesion, team identification and self-competence. Team identification is a crucial determinant of individual identity, delineated by the extent to which a team member aligns with a specific organizational team rather than broader social collectives. This association motivates members to synchronize their actions with the collective interests of the group, thereby fostering cohesion among its constituents, and cultivating a shared sense of purpose and unity within the team. In the domain of team sports, particularly soccer and water polo, two studies involving 447 participants (men = 238, women = 209) between 22 and 35 years old (M = 26.36, SD = 5.51) were conducted. The first study employed a correlational methodology to investigate the predictive capacity of self-sacrifice on cohesion, team identification, self-efficacy, and self-competence. The second study utilized an experimental design to explore the relationship between team identification and self-sacrifice. Together, these studies provided comprehensive insights into the multifaceted nature of leader self-sacrifice and its profound implications for group cohesion and individual well-being within organizational settings. The findings underscored the pivotal role of leader self-sacrifice in not only fostering stronger bonds among team members but also in enhancing critical facets of group dynamics, ultimately contributing to the overall effectiveness and success of the team.

Keywords: cohesion, leadership, self-sacrifice, sports organizations, team-identification

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Authors: Garreth Kestell, Lukah Dykes, Danielle Zerk, Kyla Trewartha, Rhianon Marshall, Elena Rudnik

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Adolescent mental health is an international priority and the impact of innovative service models must be evaluated. Secondary school-based mental health services (SBMHS) involving private general practitioners and psychologists are a model of care being trialed in South Australia. Measures of depression, anxiety, and stress are routinely collected throughout psychotherapy sessions. This research set out to quantify the impact of psychotherapy for rural adolescents in a school setting and explore the importance of session frequency. Methods: Demographics, session date and DASS21 scores from students (n=65) seen in 2016 by three psychologists working at the SBMHS were recorded. Students were aged 13-18 years (M=15.43, SD= 1.24), mostly female (F=51, M=14), attended between 1 and 23 sessions with a median of 6 sessions (MAD 5.93) in one-year. The treating psychologist collected self-administered DASS21 scores. A mixed model analysis was used with age, sex, treating psychologist, months from first session, and session number as fixed effects, with response variables of DASS depression, anxiety, and stress scores. Results: 71.5% were classified as having extreme or severe anxiety and half had extreme or severe depression and/or stress scores. On average males had a greater increase in DASS scores over time but males attending more sessions benefited most from therapy. Discussion: Psychologists are treating rural adolescents in schools for severe anxiety, depression, and stress. This pilot study indicates that a predictive model combining demographics, session frequency, and DASS scores may help identify who is most likely to benefit from individual psychotherapy. Variations in DAS scores of individuals over time indicate the need for the collection of information such as living situation and exposure to alcohol. A larger sample size and additional data are currently being collected to allow for a more robust analysis.

Keywords: adolescent health, psychotherapy, school based mental health services, DAS21

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Authors: Douglas MacCutcheon, Florian Pausch, Robert Ljung, Lorna Halliday, Stuart Rosen

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Contemporary classrooms are increasingly inclusive of children with mild to moderate disabilities and children from different language backgrounds (bilinguals, multilinguals), but classroom environments and standards have not yet been adapted adequately to meet these challenges brought about by this inclusivity. Additionally, classrooms are becoming noisier as a learner-centered as opposed to teacher-centered teaching paradigm is adopted, which prioritizes group work and peer-to-peer learning. Challenging listening conditions with distracting sound sources and background noise are known to have potentially negative effects on children, particularly those that are prone to struggle with speech perception in noise. Therefore, this research investigates two groups vulnerable to these environmental effects, namely children with a mild to moderate hearing loss (MMHLs) and sequential bilinguals learning in their second language. In the MMHL study, this group was assessed on speech-in-noise perception, and a number of receptive language and cognitive measures (auditory working memory, auditory attention) and correlations were evaluated. Speech reception thresholds were found to be predictive of language and cognitive ability, and the nature of correlations is discussed. In the bilinguals study, sequential bilingual children’s listening comprehension, speech-in-noise perception, listening effort and release from masking was evaluated under a number of different ecologically valid acoustic scenarios in order to pinpoint the extent of the ‘native language benefit’ for Swedish children learning in English, their second language. Scene manipulations included target-to-distractor ratios and introducing spatially separated noise. This research will contribute to the body of findings from which educational institutions can draw when designing or adapting educational environments in inclusive schools.

Keywords: sequential bilinguals, classroom acoustics, mild to moderate hearing loss, speech-in-noise, release from masking

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Authors: Grace McCambridge, Alanna Keady, Madhur Agrawal, Dequina Nicholas Alvarado, Barbara Nikolajczyk, Leena Panneerseelan-Bharath

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Age is a non-modifiable risk factor for the inflammation that underlies pathologies such as type 2 diabetes mellitus (T2DM). Inflammation, as indicated by circulating cytokines, rises in aging, but mechanisms that promote this ‘inflammaging’ remain poorly defined. Furthermore, downstream consequences of inflammaging, including the development of an inflammatory profile that predicts comorbidities like T2DM, remain speculative. We tested the possibility that natural aging-associated changes in autophagy, a process that is compromised in both aging and T2DM, regulates inflammatory profiles in older subjects. Our data showed that circulating CD4⁺ T cells from older compared to younger subjects have (i) defects in autophagy; (ii) higher mitochondria accumulation; (iii) a failure to metabolically shift from oxidative phosphorylation to anaerobic glycolysis upon αCD3/CD28 activation; (iv) more reactive oxygen species (ROS) accumulation; and (v) a cytokine profile that recapitulates the Th17 profile that predicts T2DM. ROS scavenging in cells from older subjects restored mitochondrial mass and membrane potential (indicators of improved autophagy) and reduced Th17 cytokines to amounts made by T cells from younger subjects. Knock-down of the autophagy protein Atg3 in T cells from younger subjects increased mitochondrial accumulation and Th17 cytokines. To begin translating these findings to clinical practice, we showed that physiological concentrations of the diabetes drug metformin (100 µM) added in vitro enhanced autophagy, prevented mitochondria and ROS accumulation, increased anaerobic glycolysis, and decreased Th17 cytokines in activated CD4⁺ T cells from older subjects. Metformin therefore improves autophagy and multiple downstream pro-inflammatory mechanisms CD4⁺ T cells from older subjects. We conclude that autophagy improvement ameliorates the development of a T2DM-predictive Th17 profile in aging, and thus holds promise for delay or prevention of aging-associated metabolic decline.

Keywords: autophagy, mitochondrial turnover, ROS, glycolysis

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Authors: Pelin Balcik Ercin

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Introduction: ZEB transcription factor family member ZEB2, has a role in epithelial to mesenchymal transition during development and metastasis. The altered circulating extracellular miRNAs expression is observed in diseases, and extracellular miRNAs have an important role in cancer cell microenvironment. In ChIP-Seq study, the expression of miR-2110 was found to be regulated by ZEB2. In this study, the effects of miR2110 on cell proliferation and migration of hepatocellular carcinoma (HCC) cells were examined. Material and Methods: SNU398 cells transfected with mimic miR2110 (20nM) (HMI0375, Sigma-Aldrich) and negative control miR (HMC0002, Sigma-Aldrich). MicroRNA isolation was accomplished with miRVANA isolation kit according to manufacturer instructions. cDNA synthesis was performed expression, respectively, and calibrated with Ct of controls. The real-time quantitative PCR (RT-qPCR) reaction was performed using the TaqMan Fast Advanced Master Mix (Thermo Sci.). Ct values of miR2110 were normalized to miR-186-5p and miR16-5p for the intracellular gene. Cell proliferation analysis was analyzed with the xCELLigence RTCA System. Wound healing assay was analyzed with the ImageJ program and relative fold change calculated. Results: The mimic-miR-2110 transfected SNU398 cells nearly nine-fold (log2) more miR-2110 expressed compared to negative control transfected cells. The mimic-miR-2110 transfected HCC cell proliferation significantly inhibited compared to the negative control cells. Furthermore, miR-2110-SNU398 cell migration capacity was relatively four-fold decreased compared to negative control-miR-SNU398 cells. Conclusion: Our results suggest the miR-2110 inhibited cell proliferation and also miR-2110 negatively affect cell migration compared to control groups in HCC cells. These data suggest the complexity of microRNA EMT transcription factors regulation. These initial results are pointed out the predictive biomarker capacity of miR-2110 in HCC.

Keywords: epithelial to mesenchymal transition, EMT, hepatocellular carcinoma cells, micro-RNA-2110, ZEB2

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Authors: Asnakew Mulualem Tegegne, Tarun Kumar Lohani, Abunu Atlabachew Eshete

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Groundwater potential delineation is essential for efficient water resource utilization and long-term development. The scarcity of potable and irrigation water has become a critical issue due to natural and anthropogenic activities in meeting the demands of human survival and productivity. With these constraints, groundwater resources are now being used extensively in Ethiopia. Therefore, an innovative convolutional neural network (CNN) is successfully applied in the Gunabay watershed to delineate groundwater potential based on the selected major influencing factors. Groundwater recharge, lithology, drainage density, lineament density, transmissivity, and geomorphology were selected as major influencing factors during the groundwater potential of the study area. For dataset training, 70% of samples were selected and 30% were used for serving out of the total 128 samples. The spatial distribution of groundwater potential has been classified into five groups: very low (10.72%), low (25.67%), moderate (31.62%), high (19.93%), and very high (12.06%). The area obtains high rainfall but has a very low amount of recharge due to a lack of proper soil and water conservation structures. The major outcome of the study showed that moderate and low potential is dominant. Geodetoctor results revealed that the magnitude influences on groundwater potential have been ranked as transmissivity (0.48), recharge (0.26), lineament density (0.26), lithology (0.13), drainage density (0.12), and geomorphology (0.06). The model results showed that using a convolutional neural network (CNN), groundwater potentiality can be delineated with higher predictive capability and accuracy. CNN-based AUC validation platform showed that 81.58% and 86.84% were accrued from the accuracy of training and testing values, respectively. Based on the findings, the local government can receive technical assistance for groundwater exploration and sustainable water resource development in the Gunabay watershed. Finally, the use of a detector-based deep learning algorithm can provide a new platform for industrial sectors, groundwater experts, scholars, and decision-makers.

Keywords: CNN, geodetector, groundwater influencing factors, Groundwater potential, Gunabay watershed

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Authors: Aziza S. Al Adhoobi, Amna Al Ruheili, Saud M. Al Jufaili

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This study investigates the potential effects of climate change on the habitat suitability of two Aphaniops species (Teleostei: Aphaniidae) found in the Oman Mountains and the Southwestern Arabian Coast. Aphaniops kruppi, an endemic species, is found in various water bodies such as wadis, springs, aflaj, spring-fed streams, and some coastal backwaters. Aphaniops stoliczkanus, on the other hand, inhabits brackish and freshwater habitats, particularly in the lower parts of wadies and aflaj, and exhibits euryhaline characteristics. Using Maximum Entropy Modeling (MaxEnt) in conjunction with ArcGIS (10.8.2) and CHELSA bioclimatic variables, topographic indices, and other pertinent environmental factors, the study modeled the potential impacts of climate change based on three Representative Concentration Pathways (RCPs 2.6, 7.0, 8.5) for the periods 2011-2040, 2041-2070, and 2071-2100. The model demonstrated exceptional predictive accuracy, achieving AUC values of 0.992 for A. kruppi and 0.983 for A. stoliczkanus. For A. kruppi, the most influential variables were the mean monthly climate moisture index (Cmi_m), the mean diurnal range (Bio2), and the sediment transport index (STI), accounting for 39.9%, 18.3%, and 8.4%, respectively. As for A. stoliczkanus, the key variables were the sediment transport index (STI), stream power index (SPI), and precipitation of the coldest quarter (Bio19), contributing 31%, 20.2%, and 13.3%, respectively. A. kruppi showed an increase in habitat suitability, especially in low and medium suitability areas. By 2071-2100, high suitability areas increased slightly by 0.05% under RCP 2.6, but declined by -0.02% and -0.04% under RCP 7.0 and 8.5, respectively. A. stoliczkanus exhibited a broader range of responses. Under RCP 2.6, all suitability categories increased by 2071-2100, with high suitability areas increasing by 0.01%. However, low and medium suitability areas showed mixed trends under RCP 7.0 and 8.5, with declines of -0.17% and -0.16%, respectively. The study highlights that climatic and topographical factors significantly influence the habitat suitability of Aphaniops species in Oman. Therefore, species-specific conservation strategies are crucial to address the impacts of climate change.

Keywords: Aphaniops kruppi, Aphaniops stoliczkanus, Climate change, Habitat suitability, MaxEnt

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Authors: Stephen Anim, Emmanuel O. Sarpong, Daniel Apaak

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This study investigates the relationship between morphological predictors and running ability, emphasizing gender-specific variations among male and female athletes in Cape Coast Metropolis (CCM), Ghana. The dynamic interplay between an athlete's physique and their performance capabilities holds particular relevance in the realm of sports science, influencing training methodologies and talent identification processes. The research aims to contribute comprehensive insights into the morphological determinants of running proficiency, with a specific focus on the local athletic community in Cape Coast Metropolis. Utilizing a correlational research design, a thorough analysis of morphological features, encompassing 22 morphological features including body weight, 6 measurements related to body length, 7 body girth, and knee diameter, and 7 skinfold measurements against 50m dash, among male and female athletes, was conducted. The study involved 420 athletes both male (N=210) and female (N=210) aged 16-22 from 10 Senior High Schools (SHS) in the Cape Coast Metropolis, providing a representative sample of the local athletic community. The collected data were statistically analysed using means and standard deviation, and stepwise multiple regression to determine how morphological variables contribute to and predict running proficiency outcomes. The investigation revealed that athletes from Senior High Schools (SHS) in Cape Coast Metropolis (CCM) exhibit well-developed physiques and sufficient fitness levels suitable for overall athletic performance, taking into account gender differences. Moreover, the findings suggested that approximately 77% of running ability could be attributed to morphological factors, leading to diverse predictive models for male and female athletes within SHS in CCM, Ghana. Consequently, these formulated equations hold promise for predicting running ability among young athletes, particularly in the context of SHS environments.

Keywords: body fat, body girth, body length, morphological features, running ability, senior high school

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Authors: Retang A. Mokua, Julia Glenday, Jacobus M. Nel

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Understanding hydrological processes in mountain headwater catchments, such as the Jonkershoek Valley, is crucial for improving the predictive capability of hydrologic modeling in the Cape Fold Mountain region of South Africa, incorporating the influence of the Table Mountain Group fractured rock aquifers. Determining the contributions of various possible surface and subsurface flow pathways in such catchments has been a challenge due to the complex nature of the fractured rock geology, low ionic concentrations, high rainfall, and streamflow variability. The study aimed to describe the mechanisms of streamflow generation during two seasons (dry and wet). In this study, stable isotopes of water (18O and 2H), hydrochemical tracer electrical conductivity (EC), hydrometric data were used to assess the spatial and temporal variation in flow pathways and geographic sources of stream water. Stream water, groundwater, two shallow piezometers, and spring samples were routinely sampled at two adjacent headwater sub-catchments and analyzed for isotopic ratios during baseflow conditions between January 2018 and January 2019. From these results, no significance (p > 0.05) in seasonal variations in isotopic ratios were observed, the stream isotope signatures were consistent throughout the study period. However, significant seasonal and spatial variations in the EC were evident (p < 0.05). The findings suggest that, in the dry season, baseflow generation mechanisms driven by groundwater and interflow as discharge from perennial springs in these catchments are the primary contributors. The wet season flows were attributed to interflow and perennial and ephemeral springs. Furthermore, the observed seasonal variations in EC were indicative of a greater proportion of sub-surface water inputs. With these results, a conceptual model of streamflow generation processes for the two seasons was constructed.

Keywords: electrical conductivity, Jonkershoek valley, stable isotopes, table mountain group

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Authors: Pajaree Naramitpanich, Natchanok Jansawang, Panwilai Chomchid

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The aims of this are compared between the students’ logical thinking abilities of their learning for designing the 5-lesson instructional plans of the 2-instructional methods, namely; the STEM Education and the Creative Learning Process (CLP) for developing students’ logical thinking abilities that a sample consisted of 90 students from two chemistry classes of different learning outcomes in Wapi Phathum School with the cluster random sampling technique was used at the 11th grade level. To administer of their learning environments with the 45-experimenl student group by the STEM Education method and the 45-controlling student group by the Creative Learning Process. These learning different groups were obtained using the 5 instruments; the 5-lesson instructional plans of the STEM Education and the Creative Learning Process to enhance the logical thinking tests on Mineral issue were used. The efficiency of the Creative Learning Processes (CLP) Model and the STEM Education’s innovations of these each five instructional lesson plans based on criteria are higher than of 80/80 standard level with the IOC index from the expert educators. The averages mean scores of students’ learning achievement motives were assessed with the Pre and Post Techniques and Logical Thinking Ability Test (LTAT) and dependent t-test analysis were differentiated between the CLP and the STEM, significantly. Students’ perceptions of their chemistry classroom environment inventories with the MCI with the CLP and the STEM methods also were found, differently. Associations between students’ perceptions of their chemistry classroom learning environment inventories on the CLP Model and the STEM Education learning designs toward their logical thinking abilities toward chemistry, the predictive efficiency of R2 values indicate that 68% and 76% of the variances in students’ logical thinking abilities toward chemistry to their controlling and experimental chemistry classroom learning environmental groups with the MCI were correlated at .05 levels, significantly. Implementations of this result are showed the students’ learning by the CLP of the potential thinking life-changing roles in most their logical thinking abilities that it is revealed that the students perceive their abilities to be highly learning achievement in chemistry group are differentiated with the STEM education of students’ outcomes.

Keywords: design, the lesson instructional plans, the stem education, the creative learning process, logical thinking ability, different, learning outcome, student, chemistry class

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Authors: Faiz N. K. Yusufi, Aquil Ahmed, Jamal Ahmad

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Diabetes in India is growing at an alarming rate and the complications caused by it need to be controlled. Coronary heart disease (CHD) is one of the complications that will be discussed for prediction in this study. India has the second most number of diabetes patients in the world. To the best of our knowledge, there is no CHD risk score for Indian type 2 diabetes patients. Any form of CHD has been taken as the event of interest. A sample of 750 was determined and randomly collected from the Rajiv Gandhi Centre for Diabetes and Endocrinology, J.N.M.C., A.M.U., Aligarh, India. Collected variables include patients data such as sex, age, height, weight, body mass index (BMI), blood sugar fasting (BSF), post prandial sugar (PP), glycosylated haemoglobin (HbA1c), diastolic blood pressure (DBP), systolic blood pressure (SBP), smoking, alcohol habits, total cholesterol (TC), triglycerides (TG), high density lipoprotein (HDL), low density lipoprotein (LDL), very low density lipoprotein (VLDL), physical activity, duration of diabetes, diet control, history of antihypertensive drug treatment, family history of diabetes, waist circumference, hip circumference, medications, central obesity and history of CHD. Predictive risk scores of CHD events are designed by cox proportional hazard regression. Model calibration and discrimination is assessed from Hosmer Lemeshow and area under receiver operating characteristic (ROC) curve. Overfitting and underfitting of the model is checked by applying regularization techniques and best method is selected between ridge, lasso and elastic net regression. Youden’s index is used to choose the optimal cut off point from the scores. Five year probability of CHD is predicted by both survival function and Markov chain two state model and the better technique is concluded. The risk scores for CHD developed can be calculated by doctors and patients for self-control of diabetes. Furthermore, the five-year probabilities can be implemented as well to forecast and maintain the condition of patients.

Keywords: coronary heart disease, cox proportional hazard regression, ROC curve, type 2 diabetes Mellitus

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Authors: Periklis Brakatsoulas

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Recent empirical research shows a growing interest in investment decision-making under market anomalies that contradict the rational paradigm. Momentum is undoubtedly one of the most robust anomalies in the empirical asset pricing research and remains surprisingly lucrative ever since first documented. Although predominantly phenomena identified across equities, momentum premia are now evident across various asset classes. Yet few many attempts are made so far to provide traders a diversified portfolio of strategies across different assets and markets. Moreover, literature focuses on patterns from past returns rather than mechanisms to signal future price directions prior to momentum runs. The aim of this paper is to develop a diversified portfolio approach to price distortion signals using daily position data on stocks, credit derivatives, and bonds. An algorithm allocates assets periodically, and new investment tactics take over upon price momentum signals and across different ranking groups. We focus on momentum life cycles, trend reversals, and price acceleration signals. The main effort here concentrates on the density, time span and maturity of momentum phenomena to identify consistent patterns over time and measure the predictive power of buy-sell signals generated by these anomalies. To tackle this, we propose a two-stage modelling process. First, we generate forecasts on core macroeconomic drivers. Secondly, satellite models generate market risk forecasts using the core driver projections generated at the first stage as input. Moreover, using a combination of the ARFIMA and FIGARCH models, we examine the dependence of consecutive observations across time and portfolio assets since long memory behavior in volatilities of one market appears to trigger persistent volatility patterns across other markets. We believe that this is the first work that employs evidence of volatility transmissions among derivatives, equities, and bonds to identify momentum life cycle patterns.

Keywords: forecasting, long memory, momentum, returns

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Authors: Joshua Adan Valdez, Shawn Gallaher

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Global climate change has impacted atmospheric temperatures contributing to rising sea levels, decreasing sea ice, and increased freshening of high latitude oceans. This freshening has contributed to increased stratification inhibiting local mixing and nutrient transport, modifying regional circulations in polar oceans. In recent years, the Western Arctic has seen an increase in freshwater volume at an average rate of 397+-116km3/year across the Beaufort Gyre. The majority of the freshwater volume resides in the Beaufort Gyre surface lens driven by anticyclonic wind forcing, sea ice melt, and Arctic river runoff, and is typically defined as water fresher than 34.8. The near-isothermal nature of Arctic seawater and non-linearities in the equation of state for near-freezing waters result in a salinity-driven pycnocline as opposed to the temperature-driven density structure seen in the lower latitudes. In this study, we investigate the relationship between freshwater content and dynamic ocean topography (DOT). In situ measurements of freshwater content are useful in providing information on the freshening rate of the Beaufort Gyre; however, their collection is costly and time-consuming. Utilizing NASA’s ICESat-2’s DOT remote sensing capabilities and Air Expendable CTD (AXCTD) data from the Seasonal Ice Zone Reconnaissance Surveys (SIZRS), a linear regression model between DOT and freshwater content is determined along the 150° west meridian. Freshwater content is calculated by integrating the volume of water between the surface and a depth with a reference salinity of ~34.8. Using this model, we compare interannual variability in freshwater content within the gyre, which could provide a future predictive capability of freshwater volume changes in the Beaufort-Chukchi Sea using non-in situ methods. Successful employment of the ICESat-2’s DOT approximation of freshwater content could potentially demonstrate the value of remote sensing tools to reduce reliance on field deployment platforms to characterize physical ocean properties.

Keywords: Cryosphere, remote sensing, Arctic oceanography, climate modeling, Ekman transport

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Authors: Mustafa M. Donma, Orkide Donma

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Obesity is defined as a severe chronic disease characterized by a low-grade inflammatory state. Therefore, inflammatory markers gained utmost importance during the evaluation of obesity and metabolic syndrome (MetS), a disease characterized by central obesity, elevated blood pressure, increased fasting blood glucose and elevated triglycerides or reduced high density lipoprotein cholesterol (HDL-C) values. Some inflammatory markers based upon complete blood cell count (CBC) are available. In this study, it was questioned which inflammatory marker was the best to evaluate the differences between various obesity groups. 514 pediatric individuals were recruited. 132 children with MetS, 155 morbid obese (MO), 90 obese (OB), 38 overweight (OW) and 99 children with normal BMI (N-BMI) were included into the scope of this study. Obesity groups were constituted using age- and sex-dependent body mass index (BMI) percentiles tabulated by World Health Organization. MetS components were determined to be able to specify children with MetS. CBC were determined using automated hematology analyzer. HDL-C analysis was performed. Using CBC parameters and HDL-C values, ratio markers of inflammation, which cover neutrophil-to-lymphocyte ratio (NLR), derived neutrophil-to-lymphocyte ratio (dNLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), monocyte-to-HDL-C ratio (MHR) were calculated. Statistical analyses were performed. The statistical significance degree was considered as p < 0.05. There was no statistically significant difference among the groups in terms of platelet count, neutrophil count, lymphocyte count, monocyte count, and NLR. PLR differed significantly between OW and N-BMI as well as MetS. Monocyte-to HDL-C value exhibited statistical significance between MetS and N-BMI, OB, and MO groups. HDL-C value differed between MetS and N-BMI, OW, OB, MO groups. MHR was the ratio, which exhibits the best performance among the other CBC-based inflammatory markers. On the other hand, when MHR was compared to HDL-C only, it was suggested that HDL-C has given much more valuable information. Therefore, this parameter still keeps its value from the diagnostic point of view. Our results suggest that MHR can be an inflammatory marker during the evaluation of pediatric MetS, but the predictive value of this parameter was not superior to HDL-C during the evaluation of obesity.

Keywords: children, complete blood cell count, high density lipoprotein cholesterol, metabolic syndrome, obesity

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Authors: Jitendra Pratap, Daphne Prybyszcuk, Luke Elliott, Arnold Ng

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Introduction: Coronary CT angiography is a non-invasive imaging technique for the assessment of coronary artery disease and has high sensitivity and negative predictive value. However, the correlation between the degree of CT coronary stenosis and the significance of hemodynamic obstruction is poor. The assessment of myocardial perfusion has mostly been undertaken by Nuclear Medicine (SPECT), but it is now possible to perform stress myocardial CT perfusion (CTP) scans quickly and effectively using CT scanners with high temporal resolution. Myocardial CTP is in many ways similar to neuro perfusion imaging technique, where radiopaque iodinated contrast is injected intravenously, transits the pulmonary and cardiac structures, and then perfuses through the coronary arteries into the myocardium. On the Siemens Force CT scanner, a myocardial perfusion scan is performed using a dynamic axial acquisition, where the scanner shuffles in and out every 1-3 seconds (heart rate dependent) to be able to cover the heart in the z plane. This is usually performed over 38 seconds. Report: A CT myocardial perfusion scan can be utilised to complement the findings of a CT Coronary Angiogram. Implementing a CT Myocardial Perfusion study as part of a routine CT Coronary Angiogram procedure provides a ‘One Stop Shop’ for diagnosis of coronary artery disease. This case study demonstrates that although the CT Coronary Angiogram was within normal limits, the perfusion scan provided additional, clinically significant information in regards to the haemodynamics within the myocardium of a patient with Hypertrophic Obstructive Cardio Myopathy (HOCM). This negated the need for further diagnostics studies such as cardiac ECHO or Nuclear Medicine Stress tests. Conclusion: CT coronary angiography with adenosine stress myocardial CTP was utilised in this case to specifically exclude coronary artery disease in conjunction with accessing perfusion within the hypertrophic myocardium. Adenosine stress myocardial CTP demonstrated the reduced myocardial blood flow within the hypertrophic myocardium, but the coronary arteries did not show any obstructive disease. A CT coronary angiogram scan protocol that incorporates myocardial perfusion can provide diagnostic information on the haemodynamic significance of any coronary artery stenosis and has the potential to be a “One Stop Shop” for cardiac imaging.

Keywords: CT, cardiac, myocardium, perfusion

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Authors: Reena K., Mamatha H. G., Somshekarayya, P. Kumar

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Objectives: The annual proficiency testing of intermediate reference laboratories is conducted by the National Reference Laboratory (NRL) to assess the efficiency of the laboratories to correctly identify Mycobacterium tuberculosis and to determine its drug susceptibility pattern. The proficiency testing results from 2013 to 2017 were analyzed to determine laboratories that were consistent in reporting quality results and those that had difficulty in doing so. Methods: A panel of twenty cultures were sent out to each of these laboratories. The laboratories were expected to grow the cultures in their own laboratories, set up drug susceptibly testing by all the methods they were certified for and report the results within the stipulated time period. The turnaround time for reporting results, specificity, sensitivity positive and negative predictive values and efficiency of the laboratory in identifying the cultures were analyzed. Results: Most of the laboratories had reported their results within the stipulated time period. However, there was enormous delay in reporting results from few of the laboratories. This was mainly due to improper functioning of the biosafety level III laboratory. Only 40% of the laboratories had 100% efficiency in solid culture using Lowenstein Jensen medium. This was expected as a solid culture, and drug susceptibility testing is not used for diagnosing drug resistance. Rapid molecular methods such as Line probe assay and Genexpert are used to determine drug resistance. Automated liquid culture system such as the Mycobacterial growth indicator tube is used to determine prognosis of the patient while on treatment. It was observed that 90% of the laboratories had achieved 100% in the liquid culture method. Almost all laboratories had achieved 100% efficiency in the line probe assay method which is the method of choice for determining drug-resistant tuberculosis. Conclusion: Since the liquid culture and line probe assay technologies are routinely used for the detection of drug-resistant tuberculosis the laboratories exhibited higher level of efficiency as compared to solid culture and drug susceptibility testing which are rarely used. The infrastructure of the laboratory should be maintained properly so that samples can be processed safely and results could be declared on time.

Keywords: annual proficiency testing, drug susceptibility testing, intermediate reference laboratory, national reference laboratory

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