Malaysian Journal of Medical and Biological Research
A peer-reviewed journal that welcomes quality articles in all aspects of Medical and Biological Research. The point of the Malaysian Journal of Medical and Biological Research (MJMBR) is to make public the outcome of creative experimental research that contributes notably to facts in medical and biological sciences.
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Service Quality of Selected Private Hospitals in Bangladesh
Service quality is directly connected with the development of the organization in terms of earnings and market share. In the case of the healthcare sector, quality improvement is mandatory to maximize patient satisfaction and maintain competitiveness in the industry. In recent years, Bangladesh has developed the health industry in both public and private sectors, providing quality services that enhance patient satisfaction. This study is focused on the perceptions of service quality in selected private hospitals – Bangladesh Specialized Hospital and Ibn Sina Hospital. The SERVQUAL model was applied to study the gap between patients' perceptions and expectations. The five dimensions for this study – tangibility, reliability, responsiveness, assurance, and empathy depict their relative importance in the empirical research. Factor analysis was performed on a sample size of 155 patients. Findings indicate that tangibility is the uppermost trait, whereas reliability is the least trait in these selected private hospitals. Private medical hospitals' management needs to be more cautious to enhance service quality, patient satisfaction, and patient loyalty in the overall context of the healthcare delivery in Bangladesh.
Copyright (c) 2024 Malaysian Journal of Medical and Biological Research https://creativecommons.org/licenses/by-nc/4.0
The Role of Community Clinics in Shaping Health Outcomes in Rural Bangladesh: A Qualitative Study
Rural Bangladesh faces significant challenges in accessing quality healthcare due to limited medical facilities and scarce resources. To address these issues, the Government of Bangladesh has established Community Clinics to provide comprehensive primary healthcare services to underserved rural populations. This study qualitatively explores the role of Community Clinics in influencing health outcomes in rural Bangladesh. It examines key aspects, including community participation, service delivery mechanisms, and the impact of these clinics on the health and well-being of local communities. The findings highlight the potential of Community Clinics to play a vital role in improving health outcomes and strengthening the rural healthcare system.
Enhancing Agricultural Efficiency with Robotics and AI-Powered Autonomous Farming Systems
This research investigates the influence of robotics and AI-driven autonomous farming systems on improving agricultural efficiency. The primary objectives are to assess how these technologies enhance agricultural efficiency, accuracy, and resource use and to identify their adoption barriers and trends. The study evaluated agricultural robots and AI case studies using secondary data. According to research, automatic labor-intensive jobs and data-driven choices by robots and AI boost precision agricultural productivity, output, and resource consumption. However, upfront costs, complex technology needs, and data management concerns remain. Tackling these problems is essential for achieving fair and equal adoption. The policy implications indicate that integrating these technologies requires financial incentives, training programs, and robust data protection regulations. The research emphasizes the potential of robots and AI to revolutionize agriculture by overcoming these restrictions and capitalizing on new trends. This transformation aims to enhance efficiency and sustainability to meet the increasing global needs.
Copyright (c) 2025 Malaysian Journal of Medical and Biological Research https://creativecommons.org/licenses/by-nc/4.0
Harnessing Biomedical Signals: A Modern Fusion of Hadoop Infrastructure, AI, and Fuzzy Logic in Healthcare
This research investigates the combination of Hadoop infrastructure, artificial intelligence (AI), and fuzzy logic in analyzing biological signals. The goal is to improve the efficiency of data processing, accuracy of diagnosis, and management of uncertainty in healthcare. Secondary data, performance measurements, and case studies are analyzed to evaluate the technology. The significant results indicate that Hadoop's scalable architecture significantly decreases the time required for preprocessing, while AI approaches dramatically enhance the accuracy of diagnosis for different biological inputs. Fuzzy logic aids in managing ambiguity and produces interpretable outcomes, improving diagnostic accuracy. However, creating fuzzy logic rules, getting high-quality data, and using computer resources remain issues. The policy implications include a need for better sharing of data, more excellent professional training, and the creation of uniform integration procedures. These steps will enhance the widespread use of these sophisticated technologies, resulting in more precise and efficient interpretation of biological signals and eventually enhancing patient care and results.
Copyright (c) 2021 Malaysian Journal of Medical and Biological Research https://creativecommons.org/licenses/by-nc/4.0
Species-Level Differentiation of Microbes within Multispecies Microscopic Polyculture via Complex Deep Learning Strategies
The study aims to investigate the differentiation of microbes at the species level within multispecies microscopic polycultures using advanced deep-learning techniques. Utilizing advanced deep learning models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), this approach focuses on training with extensive datasets of microscopic images to improve the accuracy of microbial identification, spatial mapping, and analysis of temporal dynamics. Fundamental discoveries encompass enhanced precision in microbial identification, unraveling microbial communities' spatial and temporal dynamics, and creating models to forecast microbial responses. Emphasizing the importance of ongoing investment in computational infrastructure, data-sharing initiatives, and interdisciplinary collaboration is crucial for responsibly advancing microbial community analysis. This study emphasizes the significant impact that deep learning technologies can have on understanding microbial ecosystems, solving environmental issues, and driving innovation in biotechnology and medical research.
Deep Learning-Enhanced Image Segmentation for Medical Diagnostics
Deep learning-enhanced picture segmentation has transformed medical diagnostics by accurately and efficiently delineating anatomical features and clinical anomalies. This article examines how deep learning affects medical image segmentation, identifies the main methods, and evaluates the results and obstacles. This study covers recent field research and innovations using secondary data. CNNs, attention mechanisms, and generative models like GANs have increased segmentation performance in neuroimaging, oncology, cardiology, pathology, and radiology. However, issues must still be solved with model interpretability, dependency on massive annotated datasets, and imaging technique variability. Policy implications emphasize the need for consistent imaging methods, data-sharing agreements, and explainable AI to build clinical trust and acceptance. Federated learning requires reformed data privacy laws to protect patient privacy and enable collaborative model development. Innovative research and deliberate policy actions can improve deep learning in medical diagnostics, increasing patient care and clinical outcomes.
Copyright (c) 2020 Malaysian Journal of Medical and Biological Research https://creativecommons.org/licenses/by-nc/4.0
Application of High-Pressure Processing in Food Preservation: Impact on Microbial Safety and Nutritional Quality
High-pressure processing (HPP) has attracted much interest in food preservation because it can improve microbiological safety and maintain nutritional value. This study aims to investigate how HPP affects consumer acceptability, nutritional quality, microbial safety, and market implications. A thorough analysis of the research and literature was done to clarify HPP technology's advantages, opportunities, difficulties, and effectiveness. The results show that HPP successfully regulates bacteria populations while maintaining the food items' sensory qualities and nutritional integrity. On the other hand, issues, including the expense of equipment, adherence to regulations, customer knowledge, and environmental concerns, need to be addressed. To encourage the ethical and efficient use of HPP technology, policy implications include the need for consumer education, sustainable practices, regulatory harmonization, and research funding. HPP has a great deal of promise to improve food sustainability, quality, and safety. Reducing adoption hurdles and encouraging ethical use of HPP technology are crucial to achieving its goals and guaranteeing a safer, healthier food supply for people worldwide.
Copyright (c) 2022 Malaysian Journal of Medical and Biological Research https://creativecommons.org/licenses/by-nc/4.0
Robust Signal Processing Techniques for Biomedical Applications: Enhancing Diagnosis and Treatment
In biomedical applications, reliable signal processing methods are essential for improving diagnosis and treatment plans. To enhance patient outcomes, this study intends to investigate machine learning, independent component analysis (ICA), wavelet transform, adaptive filtering, and other techniques in analyzing physiological data and medical imaging. The methodology entails a thorough analysis of the body of literature already in existence, with an emphasis on the concepts, practices, and uses of robust signal-processing methods in biological contexts. Important discoveries demonstrate how robust signal processing techniques can reduce artifacts and noise, extract valuable features, allow real-time feedback and monitoring, and aid in customized treatment planning. To fully realize the potential of robust signal processing techniques in healthcare, policy implications emphasize the significance of funding research and development, creating standards and guidelines, encouraging education and training, and cultivating cooperation and data sharing. Robust signal processing methods have the potential to completely transform the medical field by giving doctors insightful knowledge about physiological signals and imaging data, which will eventually enhance patient outcomes, diagnosis, and therapy.
Utilization of Agricultural Drones in Farming by Harnessing the Power of Aerial Intelligence
Agricultural drones, sometimes called uncrewed aerial vehicles (UAVs) or unmanned aerial systems (UAS), have become a game-changing innovation in today's farming practices. These airborne gadgets equipped with sophisticated sensors and imaging capabilities have made agricultural techniques of the past obsolete. This article explores the concept of airborne intelligence and serves as a lens through the many facets of farm drones and their impact on farming. We delve into how drones transform the farm scene, from crop monitoring and precision agriculture to data-driven decision-making, and we look at how drones enable these approaches.
Decoding the Future: Artificial Intelligence in Healthcare
AI holds the prospect of bringing about revolutionary advances in a variety of fields, including imaging (where it will improve diagnostic accuracy), drug discovery (where it will revolutionize the process), and customized care (where it will enable such advances). The paper digs into the ethical considerations that surround the incorporation of AI and places a strong emphasis on the complex balance that must be maintained between innovation and patient privacy. It highlights AI's role in streamlining healthcare workflows, predictive analytics for proactive management, and the changing environment of surgical robotics and virtual health assistants. This article serves as a roadmap to the future. As such, it emphasizes the importance of responsible AI adoption to ensure beneficial developments in healthcare delivery.
Copyright (c) 2017 Malaysian Journal of Medical and Biological Research https://creativecommons.org/licenses/by-nc/4.0
Navigating Global Pandemic Impact: Insights from Bangladesh's Health Sector Challenges and Mitigation Strategies
The global impact of COVID-19 on the world economy, societal systems, and healthcare has been profound. Developing countries, particularly Bangladesh, faced severe consequences, exacerbating humanitarian challenges. This article examines the disparities in national responses during the early stages of the pandemic, emphasizing the diverse influence on local and regional levels. Marginalized groups, especially those in poverty, bore the brunt of the crisis. Governmental levels grappled with extreme uncertainty. Employing secondary data analysis and a descriptive research design, the study delves into the regional effects of the COVID-19 crisis with a specific focus on Bangladesh's health sector. The primary goal is highlighting pandemic difficulties and proposing mitigation strategies, offering valuable insights for crisis management and public policy.
Revolutionizing Healthcare: The Impact of Robotics on Health Services
This in-depth essay investigates the revolutionary effects of robotics on the medical field, focusing on areas such as robotic surgery, telemedicine, rehabilitation robotics, and robotic prosthetics. Specifically, the article examines the uses of robotics in these areas. The study tackles better accuracy, enhanced patient experiences, skill augmentation, and ethical considerations in examining its substantial influence on patient outcomes and the professionals who provide healthcare. It explores problems, such as technological and legislative considerations, while imagining a future integrating artificial intelligence and human-robot collaboration. To successfully reach the goal of a seamless confluence of robotics and healthcare, multidimensional teamwork is required to optimize benefits and successfully negotiate ethical challenges.
Android & iOS Health Apps for Track Physical Activity and Healthcare
In recent years, there has been a surge in the number of applications for mental health (also known as MHapps) that have been developed and are now available to users of smartphones. MHapps and other technology-based solutions have to play an essential part in the future of mental health care; nevertheless, there is no one guide for developing evidence-based MHapps. MHapps have the potential to play a vital part in the future of mental health care. Many of the MHapps that are now available either need to contain features that would significantly enhance their functionality or have poorly optimize could be better. In addition, the creators of MHapps rarely undertake or publish experimental validation of their apps based on trial and error. A prior systematic review found that many of the hundreds of MHapps on the market had any evidence at all that was based on clinical trials. Using data collected from the Android app market as a source of observation, this study aimed to evaluate the impact of information cues on users' uptake of anxiety-related apps.
Copyright (c) 2019 Malaysian Journal of Medical and Biological Research https://creativecommons.org/licenses/by-nc/4.0
Utilization of Machine Learning in a Responsible Manner in the Healthcare Sector
Artificial Intelligence and Machine Learning technologies have improved the ability to forecast and recognize health emergencies, disease populations, and disease status and immune response. Many people still need to be convinced about using ML-based approaches in healthcare, yet their incorporation is increasing regardless of such reservations. Here are brief descriptions and examples of supervised, unsupervised, and reinforcement learning algorithms and machine learning-based methodologies. Second, we discuss how ML is used in healthcare, like X-rays, DNA analysis, EHRs, and MRIs. We also offer solutions to the problems that arise when applying ML to healthcare, such as system privacy and ethical considerations, and point the way toward potential future applications.
Copyright (c) 2016 Malaysian Journal of Medical and Biological Research https://creativecommons.org/licenses/by-nc/4.0
Evaluation of Hospital Information Systems (HIS) in terms of their Suitability for Tasks
In the context of hospital information systems (HIS), "suitability for the task" refers to several factors, including not imposing additional tasks on users, executing everyday activities in their entirety, and displaying all relevant information on the screen. In this descriptive cross-sectional study, a multi-stage sampling strategy was utilized to select hospitals to participate in the study. In other words, the first thing done was to locate hospitals with companies that supplied them with health information systems. Then, one hospital was selected among those that were provided funding by each of the companies. The ISO Metric Standard Questionnaire 9241 was utilized for this investigation. The evaluation of HIS aptitude for the job will be based on his responses to these 15 questions in the questionnaire. The data were examined by employing descriptive tests and the software SPSS. According to the investigation results, the average number of HIS characteristics suitable for the undertaking was 3.04 out of 5. This feature has an average rating of 2.96 out of 5 stars from paraclinical users. The scores of 3.03 and 3.13 given by secretaries and nurses, respectively, are presented here. Items that received bad ratings, such as the imposition of tasks that were not within the user's obligations, highlight the fact that the majority of users consider that such systems are not following their responsibilities, despite all of the claims that have been made regarding the benefits of computer information systems in the field of medicine. In other words, these kinds of systems have not only failed to simplify the regular duties of their users, but they have also resulted in the users having to execute those actions multiple times. The users initially write the data down on paper before entering it into the digital information systems. As a result, those responsible for developing computerized information systems used in healthcare should emphasize the appropriateness of these systems for the job.
Copyright (c) 2019 Sai Srujan Gutlapalli, Mounika Mandapuram, Manjunath Reddy, Anusha Bodepudi https://creativecommons.org/licenses/by-nc/4.0
AI Fitness Checks, Maintenance and Monitoring on Systems Managing Content & Data: A Study on CMS World
Artificial intelligence health checks, monitoring, and maintenance on system managing content in the CMS world are essential to prevent damage and avoidable expenses. To also avoid loss of time from massive downtime, AI has been employed for maintenance functions. Servers which are the houses and homes for data and content from computers, have to be maintained to allow the system to function at an optimal level. The use of AI for this maintenance involves a lot of factors. This work discusses these factors and how the various aspects work to monitor and maintain systems managing content and data. AI is programmed using health monitoring scripts which are commands executed by a particular programming language. This scripts program the AI to check and monitor servers for maintenance function. This maintenance aims to track CPU optimization and allow for the best functioning of the CPU using preventive and predictive maintenance techniques.
Asthma Patients' Cloud-Based Health Tracking and Monitoring System in Designed Flashpoint
Asthma is a chronic illness that causes improper respiratory organ function and breathing problems. Three hundred fifty million people worldwide have bronchial asthma, or one in 12 adults. Self-monitoring is the first step in managing chronic illness. This lets doctors and people monitor and address health conditions in real-time. Telemonitoring is a phrase used in IT to remotely monitor the health of patients who are not in hospitals or medical centers. Wearable medical sensors, such as IoT-based remote asthma and blood pressure sensors, capture real-time information from remotely located patients. The medical information is then transmitted through the Internet for medical diagnosis and therapy. Classical Spirometry measures how effectively a patient's lungs function and requires supervision. We want to support impacted patients; thus, we built a monitoring system. With sensors including heartbeat, dust, temperature, and humidity, the device will collect health-related data and upload it to the cloud, helping doctors diagnose patients. This study uses private cloud computing to track and monitor real-time medical information in approved areas. In addition, the private cloud-based environment called a bounded telemonitoring system is meant to capture real-time medical details of patients in the medical centers inside and outside medical wards. In addition, a new wireless sensor network scenario is intended to monitor patients' health information 24/7. This research secures medical information access and guides future medical system development.
Impact of Health Financing on Healthcare Quality and Affordability in Malaysia: A Conceptual Review
In comparison to the majority of essential health indices, the health care system in Malaysia has performed exceptionally well. Despite this, it is still behind the techniques used in many other Asian countries, despite its great potential. The primary reason for this is that patients in Malaysia are subjected to a significant amount of financial risk while seeking medical treatment. The implications of health care finance in Malaysia are investigated in this study, with a particular focus on access and equality issues. This exemplifies the urgent requirement for novel ways to the financing of medical care that are compliant with the norms that have been established. According to the findings of this study, it is recommended that efforts should be increased by employing policy changes to include money pooling and risk sharing, subsidization for the poor and the vulnerable, and mandatory enrollment. In addition, it is recommended that the efforts be increased by employing policy changes to include money pooling and risk sharing. Finally, the implication for public policy indicates that the government ought to commit to the tremendously feasible improvement of the nation's healthcare system through spending.
Homeopathy in Epididymal Cyst Management: A Case Report
An epididymal cyst is a scrotal complaint that is usually resolved by surgery. However, multiple post-surgical complications are common, including recurrence, hematoma, abscess, and chronic scrotal swelling. This case study presents homeopathic medicine managing a fairly large-sized epididymal cyst which might encourage physicians to re-think surgical options. A 24 years male presented with pain in the lower pelvic region and swelling in the scrotum; an ultra-sonogram of the scrotum diagnosed a 2.8 cm × 1.9 cm sized epididymal cyst, and Fine Needle Aspiration Cytology (FNAC) confirmed that it was a spermatocele. He was prescribed homeopathic medicine, i.e., Arnica montana, Calcarea fluorica, and Calcarea phosphorica, for his spermatocele. After seven weeks of homeopathic treatment, the cyst size was reduced to 4.8mm×2.2mm. This case study concludes that homeopathic medication resulted in a significant outcome. It also encourages the consideration of homeopathic remedies as an alternative to surgery in treating epididymal cysts.
Copyright (c) 2022 Nazmul Hasan, Md. Abdul Matin
Developmental Therapy – A Generic Professional Expertise within Child Development Centers in Bangladesh
The Developmental Therapist (DT) is a unique professional training in occupational therapy, physiotherapy, and speech and language therapy. There is unmet demand for developmental therapists in Bangladesh to manage the increasing number of childhood disabilities. However, no academic institute provides training for Developmental therapists in Bangladesh. This paper explains a three-month structured training program for the exercise of the developmental therapist. After completing this training, the DTs gained some essential skills which help them to assess the functional development of children with neurodevelopmental disabilities, identify their problems and provide appropriate management.
Copyright (c) 2022 Nasrin Sultana, Asma Begum Shilpi, Dilara Begum, Naila Zaman Khan
Biomarkers of Lung Cancer: Meta-analysis of Biomarkers Used to Identify Types of Lung Cancers Based on the Morphology and Histology
Cancer of the lung is a malignant lung tumor with a wide range of histological variants. The tumor arises from different types of cells, such as bronchioles, epithelium, bronchial mucous glands, or the alveoli. How effective the treatment depends on the histological variant of the lung tumor. It is therefore necessary that the histology of cancer and the respective biomarkers be accurately identified. Detection of malignant cells is possible only when the clinician has an accurate knowledge of the origin and nature of these cells. It is pertinent to state that these malignant cells release certain biomarkers into the general circulation. Currently, screening for malignant tumors is done with various panels of biomarkers. To date, there is no one serum biomarker capable of identifying the various lung cancer types. As such, accurate diagnosis is done only with immunohistochemistry and histological analysis of tumor biopsies. This article discusses the different protein biomarkers employed in the diagnosis of lung cancer and recent advances in uniform biomarker discovery.
Copyright (c) 2021 Srikrishna Chanakya Karnatapu, Saloni Doke
Outcomes of Intervention in Children with Language Difficulties in Bangladesh
Background: Both clinical audits within hospitals, and population-based surveys of childhood disability in Bangladesh, have shown that large numbers of children have speech and language difficulties. This study determined the improvement of language skills of children presenting with difficulties to the Speech, Language and Communication (SLC) clinic of the Child Development Center (CDC) in Dhaka Shishu (Children) Hospital (DSH). Methodology: This is a retrospective study where records of children enrolled from April 2009 to March 2014, who had visited the SLC Clinic at least 3 times over a span of 6 months were analyzed. Preverbal language skills, comprehension, and expressive language levels were measured informally based upon play and interactive sessions and observation of function. Interventions involved training parents on informal intervention techniques following some international standard guidelines. Pre and post-intervention observations on preverbal, comprehensive, and expressive language skills were recorded to determine outcomes. Results: Of the 706 enrolled children 11.0%, 79.2%, 9.5%, .3% were 0-<2 years, 2-<5 years, >5-9 and 10-16 years old, respectively. 69.5% of children were males. Preverbal skills (attention span, awaiting, eye contact, attention sharing, turn-taking, copying), comprehension, and expressive language status showed significant improvement between the first and last visit (p= 0.000) Conclusions: Interactive play, music, books, etc. are important means of improving communication between parents and children. Professionals working with developmentally delayed children need to be trained to utilize these strategies, with the provision of appropriate facilities within clinical settings. A large majority of children can be assisted to overcome delays and optimize their potential.
Copyright (c) 2021 Asma Begum Shilpi, Nasrin Sultana, Shelina Akhter, Humaira Muslima, Naila Zaman Khan
Automatic Diagnosis of Diabetes Using Machine Learning: A Review
The health sector, like the other sectors, contains a large amount of data that should be used to better understand and treat the various ailments that are prevalent. For example, diabetes is a condition that is becoming more prevalent but that may be managed if discovered at an early stage. The algorithms of machine learning (ML) can be utilized for this purpose. We have examined the various machine learning methods and the attributes that can be utilized to train these algorithms for the purpose of detecting diabetic complications.
Copyright (c) 2020 Takudzwa Fadziso, Harshini Priya Adusumalli
The Use of Big Data Analytics in Medical Applications
The field of Big Data Analytics does not have a linear capacity for growth. It is based on a specified structure. Big data is now most useful for data backup purposes, rather than for anything else. Big Data is a collection of data sets that are both numerous and complicated in nature, and it is becoming increasingly popular. They consist of both organized and unstructured data that is constantly changing at a rate that is inconvenient for traditional relational database systems and existing analytical tools to keep pace with. There is constantly some new information being introduced. It also contributes to the resolution of India's major concerns. It also contributes to closing the data gap. Healthcare is the preservation or advancement of health by the prevention, interpretation, and medical treatment of the disorder, ill health, abuse, and other significant physical, mental, and spiritual degeneration in the mortal body. Health care is conveyed by health professionals in the form of unified health experts, specialists, physician associates, midwives, nurses, antibiotics, pharmacy, psychology, and other health-related fields of expertise. Additionally, it has an introduction, challenging elements and concerns, Big Data Analytics in use, technical specifications, research applications, industrial applications, and future applications. This article aims to provide knowledge in the field of big data analytics and its use in the medical arena.
Copyright (c) 2016 Mahesh Babu Pasupuleti
Clinical Study To Assess The Efficacy of A Novel Hair Loss Treatment
Background: Hair loss affects men and women around the world. Objective: This study analyzed the efficacy of a hair formula named Lotus 39 by Valentino De Salva (Lotus 39) on hair loss. Methods: Men and women between ages 42 and 72 applied Lotus 39 to their scalp every day for 8 weeks. For ethical reasons, a nonrandomized controlled study protocol was selected Results: According to the data, Lotus 39 stops hair loss, causes an increase in vellus hair, and an increase total hair count. Limitations: No control was assigned due to funding, baseline pictures were taken at the onset of the study, during, and at the end of the study to draw a depiction of hair improvement over time. Conclusion: This study suggests that Lotus 39 improves the health of the scalp and hair. IRB Approval Status: Reviewed and approved by IntegReview IRB; approval #CRLNJ2019-0198
Copyright (c) 2022 Jamie Thomas Parker, PhD
Efficacy of Augmented Reality in Medical Education
Augmented reality has had a great development in various fields such as education, architecture, marketing, and tourism. However, there is another field in which it can be really useful, so much so that it can save lives: medicine. In this article, there is a detailed explanation of how augmented reality proved itself effective in medical education. The hospitals and health centers increasingly have more modern equipment that allows accurate diagnoses and effective treatments put up. This is the case of advances such as X-rays, magnetic resonances, ultrasound treatments, or computed tomography, among many others. The uses of augmented reality in medicine are very varied, although it is still a technology in an early stage of development. It is very likely that, in a short time, the applications of AR in the health sector will multiply. Moreover, it might be used in severe rather than typical or random criteria.
Copyright (c) 2019 Venkata Naga Satya Surendra Chimakurthi
Biomarkers and Bioactivity in Drug Discovery using a Joint Modelling Approach
Biomarkers that are validated and robust are required for the enhancement of diagnosis, the observation of drug-related activity, therapeutic reactions, and as the blueprint for developing safer and more direct therapeutic efforts for a variety of chronic ailments. Various kinds of biomarkers have proven impactful when it comes to the discovery and development of drugs, but the procedure that involves identifying and verifying ailment-specific biomarkers has proven to be hassling. In recent times, there have been some advancements in multiple omics (also known as multi-omics) methods like transcriptomic, cytometry, genomics, proteomics, metabolomics and imaging. These advancements have made it possible for the discovery and development of distinct biomarkers for complicated chronic ailments to be accelerated expeditiously. In spite of the fact that numerous drawbacks still need to be looked into, ongoing efforts for the discovery and improvement of illness-associated biomarkers will go a long way in optimizing decision-making across the entire process of drug development and expand our comprehension of the infection processes. In addition, when the preclinical biomarkers are effectively translated into the clinic, the way will pave well to an equally effective implementation of personalized therapies throughout complicated illness environments to become beneficial to patients, healthcare service providers and the industry of bio-pharma.
Molecular Generators and Optimizers Failure Modes
In recent years, there has been an uptick in interest in generative models for molecules in drug development. In the field of de novo molecular design, these models are used to make molecules with desired properties from scratch. This is occasionally used instead of virtual screening, which is limited by the size of the libraries that can be searched in practice. Rather than screening existing libraries, generative models can be used to build custom libraries from scratch. Using generative models, which may optimize molecules straight towards the desired profile, this time-consuming approach can be sped up. The purpose of this work is to show how current shortcomings in evaluating generative models for molecules can be avoided. We cover both distribution-learning and goal-directed generation with a focus on the latter. Three well-known targets were downloaded from ChEMBL: Janus kinase 2 (JAK2), epidermal growth factor receptor (EGFR), and dopamine receptor D2 (DRD2) (Bento et al. 2014). We preprocessed the data to get binary classification jobs. Before calculating a scoring function, the data is split into two halves, which we shall refer to as split 1/2. The ratio of active to inactive users. Our goal is to train three bioactivity models with equal prediction performance, one to be used as a scoring function for chemical optimization and the other two to be used as performance evaluation models. Our findings suggest that distribution-learning can attain near-perfect scores on many existing criteria even with the most basic and completely useless models. According to benchmark studies, likelihood-based models account for many of the best technologies, and we propose that test set likelihoods be included in future comparisons.
Risk of Neurodegeneration in Patients with Morgellons Disease: A Focus on the Potential Risk of Developing Alzheimer’s
Morgellons disease is a distressing dermatologic condition that typically presents with multiple, non-healing skin lesions with filamentous protrusions. Initial presentation of the condition can often include various neurological symptoms with dermatologic symptoms. Common neurological symptoms include neuropathy, cognitive impairment, and dementia. Recent studies reveal that spirochetes are a causative agent of Morgellons disease. Chronic spirochete infection is strongly associated with neurodegeneration seen in dementia and Alzheimer’s disease. This paper will provide a brief overview of Morgellons and Alzheimer’s disease and will explore the possible risk of developing Alzheimer’s in Morgellons patients.
Implementation of Artificial Intelligence in Agriculture: A Review for CMS Optimization
Agriculture has a critical role to play in the financial domain. Likewise, automation of multiple processes in agriculture has been a great concern as well as an alarming subject across the world. The population all over the world is growing at a high rate and with this increment, demand for agriculture and its jobs is also growing exponentially. The usual techniques that were used by the farmers are not efficient enough to meet these requirements. Along these lines, new digital techniques are presented. These new strategies satisfy the proper management of agricultural products as well as services so that farmers can make the most of technology to increase their profit rates. AI in the agricultural landscape has initiated a revolutionary change. It has guarded the harvest yield from different declining factors such as environmental changes, over population, dynamic business demands, and food safety issues. By using artificial intelligence we can foster smart farming practices to limit the loss of farmers and give them high returns. Using artificial intelligence platforms, one can collect an enormous amount of information from government and public sites or real-time monitoring and collection of different information is likewise possible by utilizing IoT (Internet of Things) and afterward can be explored with precision to empower the farmers for resolving every one of the issues faced by farmers in the agriculture area. This research is conducted in order to help local farmers everywhere in the world to manage their agriculture practices all the more effectively. The strategy discussed in this paper is leveraging the model of waterfall methodology for planning and creating a system smart enough by performing a sequential cycle that starts with data collection, requirement analysis, plan, coding, and testing and finally implements that system as a whole. This system can also be used to foster ideas to manage normal issues in agriculture information systems, to improve the policy programs, the augmentation, and analysis practices, and to manage data on agriculture. Finally, conclusion about agricultural information systems are discussed and suggestions for additional development of agriculture data systems is presented.