Pemilihan kerjaya di kalangan pelajar aliran perdagangan sekolah menengah teknik : satu kajian kes

This research is a survey to determine the career chosen of form four student in commerce streams. The important aspect of the career chosen has been divided into three, first is information about career, type of career and factor that most influence students in choosing a career. The study was conducted at Sekolah Menengah Teknik Kajang, Selangor Darul Ehsan. Thirty six form four students was chosen by using non-random sampling purpose method as respondent. All information was gather by using questionnaire. Data collected has been analyzed in form of frequency, percentage and mean. Results are performed in table and graph. The finding show that information about career have been improved in students career chosen and mass media is the main factor influencing students in choosing their career

HIGH PERFORMANCE MODELLING AND COMPUTING IN COMPLEX MEDICAL CONDITIONS: REALISTIC CEREBELLUM SIMULATION AND REAL-TIME BRAIN CANCER DETECTION

The personalized medicine is the medicine of the future. This innovation is supported by the ongoing technological development that will be crucial in this field. Several areas in the healthcare research require performant technological systems, which elaborate huge amount of data in real-time. By exploiting the High Performance Computing technologies, scientists want to reach the goal of developing accurate diagnosis and personalized therapies. To reach these goals three main activities have to be investigated: managing a great amount of data acquisition and analysis, designing computational models to simulate the patient clinical status, and developing medical support systems to provide fast decisions during diagnosis or therapies. These three aspects are strongly supported by technological systems that could appear disconnected. However, in this new medicine, they will be in some way connected. As far as the data are concerned, today people are immersed in technology, producing a huge amount of heterogeneous data. Part of these is characterized by a great medical potential that could facilitate the delineation of the patient health condition and could be integrated in our medical record facilitating clinical decisions. To ensure this process technological systems able to organize, analyse and share these information are needed. Furthermore, they should guarantee a fast data usability. In this contest HPC and, in particular, the multicore and manycore processors, will surely have a high importance since they are capable to spread the computational workload on different cores to reduce the elaboration times. These solutions are crucial also in the computational modelling, field where several research groups aim to implement models able to realistically reproduce the human organs behaviour to develop their simulators. They are called digital twins and allow to reproduce the organ activity of a specific patient to study the disease progression or a new therapy. Patient data will be the inputs of these models which will predict her/his condition, avoiding invasive and expensive exams. The technological support that a realistic organ simulator requires is significant from the computational point of view. For this reason, devices as GPUs, FPGAs, multicore devices or even supercomputers are needed. As an example in this field, the development of a cerebellar simulator exploiting HPC will be described in the second chapter of this work. The complexity of the realistic mathematical models used will justify such a technological choice to reach reduced elaboration times. This work is within the Human Brain Project that aims to run a complete realistic simulation of the human brain. Finally, these technologies have a crucial role in the medical support system development. Most of the times during surgeries, it is very important that a support system provides a real-time answer. Moreover, the fact that this answer is the result of the elaboration of a complex mathematical problem, makes HPC system essential also in this field. If environments such as surgeries are considered, it is more plausible that the computation is performed by local desktop systems able to elaborate the data directly acquired during the surgery. The third chapter of this thesis describes the development of a brain cancer detection system, exploiting GPUs. This support system, developed as part of the HELICoiD project, performs a real-time elaboration of the brain hyperspectral images, acquired during surgery, to provide a classification map which highlights the tumor. The neurosurgeon is facilitated in the tissue resection. In this field, the GPU has been crucial to provide a real-time elaboration. Finally, it is possible to assert that in most of the fields of the personalized medicine, HPC will have a crucial role since they consist in the elaboration of a great amount of data in reduced times, aiming to provide specific diagnosis and therapies for the patient

Clinical proteomics for precision medicine: the bladder cancer case

Precision medicine can improve patient management by guiding therapeutic decision based on molecular characteristics. The concept has been extensively addressed through the application of –omics based approaches. Proteomics attract high interest, as proteins reflect a “real-time” dynamic molecular phenotype. Focusing on proteomics applications for personalized medicine, a literature search was conducted to cover: a) disease prevention, b) monitoring/ prediction of treatment response, c) stratification to guide intervention and d) identification of drug targets. The review indicates the potential of proteomics for personalized medicine by also highlighting multiple challenges to be addressed prior to actual implementation. In oncology, particularly bladder cancer, application of precision medicine appears especially promising. The high heterogeneity and recurrence rates together with the limited treatment options, suggests that earlier and more efficient intervention, continuous monitoring and the development of alternative therapies could be accomplished by applying proteomics-guided personalized approaches. This notion is backed by studies presenting biomarkers that are of value in patient stratification and prognosis, and by recent studies demonstrating the identification of promising therapeutic targets. Herein, we aim to present an approach whereby combining the knowledge on biomarkers and therapeutic targets in bladder cancer could serve as basis towards proteomics- guided personalized patient management

Advances in computational modelling for personalised medicine after myocardial infarction

Myocardial infarction (MI) is a leading cause of premature morbidity and mortality worldwide. Determining which patients will experience heart failure and sudden cardiac death after an acute MI is notoriously difficult for clinicians. The extent of heart damage after an acute MI is informed by cardiac imaging, typically using echocardiography or sometimes, cardiac magnetic resonance (CMR). These scans provide complex data sets that are only partially exploited by clinicians in daily practice, implying potential for improved risk assessment. Computational modelling of left ventricular (LV) function can bridge the gap towards personalised medicine using cardiac imaging in patients with post-MI. Several novel biomechanical parameters have theoretical prognostic value and may be useful to reflect the biomechanical effects of novel preventive therapy for adverse remodelling post-MI. These parameters include myocardial contractility (regional and global), stiffness and stress. Further, the parameters can be delineated spatially to correspond with infarct pathology and the remote zone. While these parameters hold promise, there are challenges for translating MI modelling into clinical practice, including model uncertainty, validation and verification, as well as time-efficient processing. More research is needed to (1) simplify imaging with CMR in patients with post-MI, while preserving diagnostic accuracy and patient tolerance (2) to assess and validate novel biomechanical parameters against established prognostic biomarkers, such as LV ejection fraction and infarct size. Accessible software packages with minimal user interaction are also needed. Translating benefits to patients will be achieved through a multidisciplinary approach including clinicians, mathematicians, statisticians and industry partners

Nanoinformatics: developing new computing applications for nanomedicine

Nanoinformatics has recently emerged to address the need of computing applications at the nano level. In this regard, the authors have participated in various initiatives to identify its concepts, foundations and challenges. While nanomaterials open up the possibility for developing new devices in many industrial and scientific areas, they also offer breakthrough perspectives for the prevention, diagnosis and treatment of diseases. In this paper, we analyze the different aspects of nanoinformatics and suggest five research topics to help catalyze new research and development in the area, particularly focused on nanomedicine. We also encompass the use of informatics to further the biological and clinical applications of basic research in nanoscience and nanotechnology, and the related concept of an extended ?nanotype? to coalesce information related to nanoparticles. We suggest how nanoinformatics could accelerate developments in nanomedicine, similarly to what happened with the Human Genome and other -omics projects, on issues like exchanging modeling and simulation methods and tools, linking toxicity information to clinical and personal databases or developing new approaches for scientific ontologies, among many others

Privacy and Accountability in Black-Box Medicine

Black-box medicine—the use of big data and sophisticated machine learning techniques for health-care applications—could be the future of personalized medicine. Black-box medicine promises to make it easier to diagnose rare diseases and conditions, identify the most promising treatments, and allocate scarce resources among different patients. But to succeed, it must overcome two separate, but related, problems: patient privacy and algorithmic accountability. Privacy is a problem because researchers need access to huge amounts of patient health information to generate useful medical predictions. And accountability is a problem because black-box algorithms must be verified by outsiders to ensure they are accurate and unbiased, but this means giving outsiders access to this health information. This article examines the tension between the twin goals of privacy and accountability and develops a framework for balancing that tension. It proposes three pillars for an effective system of privacy-preserving accountability: substantive limitations on the collection, use, and disclosure of patient information; independent gatekeepers regulating information sharing between those developing and verifying black-box algorithms; and information-security requirements to prevent unintentional disclosures of patient information. The article examines and draws on a similar debate in the field of clinical trials, where disclosing information from past trials can lead to new treatments but also threatens patient privacy