Revolution In Medicine Research Articles

Exploring the Current Trends of Artificial Intelligence in Stem Cell Therapy: A Systematic Review.

The concept of healing in medicine has been taking a new form where scientists and researchers are in pursuance of regenerative medicine. Until now, doctors have "reacted" to disease by treating the symptoms; however, modern medicine is transforming toward regeneration rather than reactive treatment, which is where stem cell therapy comes into the play-the concept of replacing damaged cells with brand new cells that perform the same function better. Stem cell treatment is currently being used to treat autoimmune, inflammatory, neurological, orthopedic, and traumatic disorders, with various research being undertaken for a wide range of diseases. It could also be the answer to anti-aging and a disease-free state. Despite the benefits, numerous errors could prevail in treating patients with stem cells. With the advancement of technology and research in the modern period, medicine is beginning to turn to artificial intelligence (AI) to address the complicated errors that could occur in regenerative medicine. For successful treatment, one must achieve precision and accuracy when analyzing healthy and productive stem cells that possess all the properties of a native cell. This review intends to discuss and study the application of AI in stem cell therapy and how it influences how medicine is practiced, thus creating a path to a regenerative future with negligible adverse effects.The following databases were used for a literature search: PubMed, Google Scholar, PubMed Central, and Institute of Electrical and Electronics Engineers (IEEE) Xplore. After a thorough analysis, studies were chosen, keeping in mind the inclusion and exclusion criteria set by the authors of this review, which comprised reports published within the last six years in the English language. The authors also made sure to include studies that sufficed the quality of each report assessed using appropriate quality appraisal tools, after which eight reports were found to be eligible and were included in this review. This research mainly revolves around machine learning, deep neural networks (DNN), and other subclasses of AI encompassed in these categories.While there are concerns and limitations in implementing various mediums of AI in stem cell therapy, the analysis of the eligible studies concluded that artificial intelligence provides significant benefits to the global healthcare ecosystem in numerous ways, such as determining the viability, functionality, biosafety, and bioefficacy of stem cells, as well as appropriate patient selection. Applying AI to this novelty brings out the precision, accuracy, and a revolution in regenerative medicine. In addition, stem cell therapy is not currently FDA approved (except for the blood-forming stem cells) and, to date, is considered experimental with no clear outline of risks and benefits. Given this limitation, studies are conducted regularly around the world in hopes for a concrete conclusion where technological advances such as AI could help in shaping the future of regenerative medicine.

AlphaFold heralds a data-driven revolution in biology and medicine.

Protein structures predicted using artificial intelligence will aid medical research, but the greatest benefit will come if clinical data can be similarly used to better understand human disease.

Pravne, socijalne i etičke implikacije editovanja humanog genoma primenom tehnologije CRISPR/Cas9

Discovery of CRISPR/Cas9 technology produced a revolution in human medicine, because of the availability, efficiency and low cost, which has raised a number of questions. Given that by applying CRISPR/Cas9 technology we can program our future children and extend their life expectancy, question is whether we should allow it. The point of the paper is to determine the limits of legal admissibility and ethical justification of this procedure, considering contemporary legal theoretical views, ethical values and social significance. Using normative, comparative and sociological method the authors analyze the impact of biotechnology development, in the context of genetic interventions, on redefining the regulatory framework. Critical consideration in the context of legal standardization of human genetic interventions and meeting the interests of all participants, has been identified as a core subject of research, which will be considered in accordance with a holistic approach to the realization of human rights.

Ethics in Public and Community Psychiatry

Public and community psychiatrists who care for patients with serious mental illnesses regularly face ethical challenges. This article summarizes key ethical considerations in the care of a population that can be difficult to engage and may reject treatment, sometimes necessitating coercive care. The digital revolution in medicine, with its promises and perils and accelerated by coronavirus disease 2019 (COVID-19), has added ethical complexity regarding privacy and access to care. Shared decision-making, a process to resolve ethical dilemmas, and commitments to professionalism and reflective practice are emphasized as three ways to practice ethically in public and community psychiatrists' daily work. Additionally, COVID-19 has highlighted that engagement in social justice is a critical element of ethical practice for physicians today. [ Psychiatr Ann . 2021;51(6):272–277.]

Basic and Preclinical Research for Personalized Medicine

Basic and preclinical research founded the progress of personalized medicine by providing a prodigious amount of integrated profiling data and by enabling the development of biomedical applications to be implemented in patient-centered care and cures. If the rapid development of genomics research boosted the birth of personalized medicine, further development in omics technologies has more recently improved our understanding of the functional genome and its relevance in profiling patients’ phenotypes and disorders. Concurrently, the rapid biotechnological advancement in diverse research areas enabled uncovering disease mechanisms and prompted the design of innovative biological treatments tailored to individual patient genotypes and phenotypes. Research in stem cells enabled clarifying their role in tissue degeneration and disease pathogenesis while providing novel tools toward the development of personalized regenerative medicine strategies. Meanwhile, the evolving field of integrated omics technologies ensured translating structural genomics information into actionable knowledge to trace detailed patients’ molecular signatures. Finally, neuroscience research provided invaluable models to identify preclinical stages of brain diseases. This review aims at discussing relevant milestones in the scientific progress of basic and preclinical research areas that have considerably contributed to the personalized medicine revolution by bridging the bench-to-bed gap, focusing on stem cells, omics technologies, and neuroscience fields as paradigms.

Artificial Intelligence and Precision Medicine: A Perspective.

This article aims to present how the advanced solutions of artificial intelligence and precision medicine work together to refine medical management. Multi-omics seems the most suitable approach for biological analysis of data on precision medicine and artificial intelligence. We searched PubMed and Google Scholar databases to collect pertinent articles appearing up to 5 March 2021. Genetics, oncology, radiology, and the recent coronavirus disease (COVID-19) pandemic were chosen as representative fields addressing the cross-compliance of artificial intelligence (AI) and precision medicine based on the highest number of articles, topicality, and interconnectedness of the issue. Overall, we identified and perused 1572 articles. AI is a breakthrough that takes part in shaping the Fourth Industrial Revolution in medicine and health care, changing the long-time accepted diagnostic and treatment regimens and approaches. AI-based link prediction models may be outstandingly helpful in the literature search for drug repurposing or finding new therapeutical modalities in rapidly erupting wide-scale diseases such as the recent COVID-19.

Insurance coverage of the electromyography during sleep for sleep bruxism and revolution of dental medicine

Insurance coverage of the electromyography during sleep for sleep bruxism and revolution of dental medicine

QSAR, molecular docking, design, and pharmacokinetic analysis of 2-(4-fluorophenyl) imidazol-5-ones as anti-breast cancer drug compounds against MCF-7 cell line

The anti-proliferative activities of Novel series of 2-(4-fluorophenyl) imidazol-5-ones against MCF-7 breast cancer cell line were explored via in-slico studies which includes Quantitative structure–activity relationship QSAR, molecular docking studies, designing new compounds, and analyzing the pharmacokinetics properties of the designed compounds. From the QSAR analysis, model number one emerged the best as seen from the arithmetic assessments of (R2) = 0.6981, (R2adj) = 0.6433, (Q2) = 0.5460 and (R2pred) of 0.5357. Model number one was used in designing new derivative compounds, with higher effectiveness against estrogen positive breast cancer (MCF-7 cell line). The Molecular docking studies between the derivatives and Polo-like kinases (Plk1) receptor proved that the derivatives of 2-(4-fluorophenyl) imidazol-5-ones bind tightly to the receptor, thou ligand 24 and 27 had the highest binding affinities of −8.8 and − 9.1 kcal/mol, which was found to be higher than Doxorubicin with a docking score of −8.0 kcal/mol. These new derivatives of 2-(4-fluorophenyl) imidazol-5-ones shall be excellent inhibitors against (plk1). The pharmacokinetics analysis performed on the new structures revealed that all the structures passed the test and also the Lipinski rule of five, and they could further proceed to pre-clinical tests. They both revealed a revolution in medicine for developing novel anti-breast cancer drugs against MCF-7 cell line.

The Association for Human Pharmacology in the Pharmaceutical Industry London Meeting October 2019: Impending Change, Innovation, and Future Challenges.

The Association for Human Pharmacology in the Pharmaceutical Industry (AHPPI) annual meeting focused on impending change, innovation, and future challenges facing early phase drug development as we move into the second decade of the 21th century. The meeting opened with discussion around the technical revolution in pharmaceutical medicine over the 4 decades since the AHPPI was founded and how transformative technologies have accompanied the introduction of processes such as physiologically based pharmacokinetic modeling. During the meeting examples were presented of how in terms of the development of new therapies, the classic phases of clinical drug development are becoming a thing of the past and the lines between the phases have begun to blur, particularly in the field of oncology. The contribution that monoclonal antibodies have made to medicine and the next chapter in their design and use was also discussed. A representative of the UK’s Medicine and Healthcare Products Regulatory Agency discussed the increasing numbers of requests to approve complex innovative design trials, how novel trial designs are impacting on the traditional linear “phase” approach to drug development and the common pitfalls associated with them. Guidance was provided from a regulator’s viewpoint on what was meant by the term “novel design” and how to submit successful trial applications for such complex trials. In an Oxford-style debate, the audience discussed the motion that “there is no longer a need to include placebo subjects in early clinical trials.” The keynote speaker focused on delivering change in complex environments such as the field of drug development. The afternoon session included presentations on the challenges associated with drug product design, the complexities within non-oral dosage forms and proposed new methods of formulations for drug delivery. Presentations were also given on advances in mechanistic and computational pharmacokinetic modeling and how they have proved to be valuable tools to rationalize and facilitate the process of drug development.

Individualizing pharmacogenomic test results in the context of the microbiome.

The field of pharmacogenetic testing was hailed as one of the early successful clinical applications arising from the personalized (or precision) medicine revolution. Substantial progress has been made to identify genes and genetic variants involved in drug response and establish clinical implementation programs. Yet, drug response is a complex trait and recent work has highlighted the key role played by the gut microbiome. As the study of the gut microbiome and pharmacogenetics converge, it may be possible to generate more precise predictions of drug response and improve health outcomes to treatments. Substantial effort will be needed to understand the dynamic impact of the microbiome and the interplay with host genetics and how to implement expanded pharmacogenetic testing.

Artificial intelligence in pulmonary medicine: computer vision, predictive model and COVID-19.

Artificial intelligence (AI) is transforming healthcare delivery. The digital revolution in medicine and healthcare information is prompting a staggering growth of data intertwined with elements from many digital sources such as genomics, medical imaging and electronic health records. Such massive growth has sparked the development of an increasing number of AI-based applications that can be deployed in clinical practice. Pulmonary specialists who are familiar with the principles of AI and its applications will be empowered and prepared to seize future practice and research opportunities. The goal of this review is to provide pulmonary specialists and other readers with information pertinent to the use of AI in pulmonary medicine. First, we describe the concept of AI and some of the requisites of machine learning and deep learning. Next, we review some of the literature relevant to the use of computer vision in medical imaging, predictive modelling with machine learning, and the use of AI for battling the novel severe acute respiratory syndrome-coronavirus-2 pandemic. We close our review with a discussion of limitations and challenges pertaining to the further incorporation of AI into clinical pulmonary practice.

On stethoscopes, patient records, artificial intelligence, and zettabytes: A glimpse into the future of digital medicine in Mexico

Science and technology are modifying medicine at a dizzying pace. Although access in our country to the benefits of innovations in the area of devices, data storage and artificial intelligence are still very restricted, the advance of digital medicine offers the opportunity to solve some of the biggest problems faced by medical practice and public health in Mexico. The potential areas where digital medicine can be disruptive are accessibility to quality medical care, centralization of specialties in large cities, dehumanization of medical treatment, lack of resources to access evidence-supported treatments, and among others. This review presents some of the advances that are guiding the new revolution in medicine, discusses the potential barriers to implementation, and suggest crucial elements for the path of incorporation of digital medicine in Mexico.

How Can Microfluidic and Microfabrication Approaches Make Experiments More Physiologically Relevant?

Microfabricated and microfluidic devices enable standardized handling, precise spatiotemporal manipulation of cells and liquids, and recapitulation of cellular environments, tissues, and organ-level biology. We asked researchers how these devices can make invitro experiments more physiologically relevant.

Principles of N-Linked Glycosylation Variations of IgG-Based Therapeutics: Pharmacokinetic and Functional Considerations.

The development of recombinant therapeutic proteins has been a major revolution in modern medicine. Therapeutic-based monoclonal antibodies (mAbs) are growing rapidly, providing a potential class of human pharmaceuticals that can improve the management of cancer, autoimmune diseases, and other conditions. Most mAbs are typically of the immunoglobulin G (IgG) subclass, and they are glycosylated at the conserved asparagine position 297 (Asn-297) in the CH2 domain of the Fc region. Post-translational modifications here account for the observed high heterogeneity of glycoforms that may or not impact the stability, pharmacokinetics (PK), efficacy, and immunogenicity of mAbs. These modifications are also critical for the Fc receptor binding, and consequently, key antibody effector functions including antibody-dependent cell-mediated cytotoxicity (ADCC) and complement-dependent cytotoxicity (CDC). Moreover, mAbs produced in non-human cells express oligosaccharides that are not normally found in serum IgGs might lead to immunogenicity issues when administered to patients. This review summarizes our understanding of the terminal sugar residues, such as mannose, sialic acids, fucose, or galactose, which influence therapeutic mAbs either positively or negatively in this regard. This review also discusses mannosylation, which has significant undesirable effects on the PK of glycoproteins, causing a decreased mAbs’ half-life. Moreover, terminal galactose residues can enhance CDC activities and Fc–C1q interactions, and core fucose can decrease ADCC and Fc–FcγRs binding. To optimize the therapeutic use of mAbs, glycoengineering strategies are used to reduce glyco-heterogeneity of mAbs, increase their safety profile, and improve the therapeutic efficacy of these important reagents.

HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach.

Image classification is central to the big data revolution in medicine. Improved information processing methods for diagnosis and classification of digital medical images have shown to be successful via deep learning approaches. As this field is explored, there are limitations to the performance of traditional supervised classifiers. This paper outlines an approach that is different from the current medical image classification tasks that view the issue as multi-class classification. We performed a hierarchical classification using our Hierarchical Medical Image classification (HMIC) approach. HMIC uses stacks of deep learning models to give particular comprehension at each level of the clinical picture hierarchy. For testing our performance, we use biopsy of the small bowel images that contain three categories in the parent level (Celiac Disease, Environmental Enteropathy, and histologically normal controls). For the child level, Celiac Disease Severity is classified into 4 classes (I, IIIa, IIIb, and IIIC).

The 4emedicine: Emergency, Environmental, Electronics and Extreme medicine. Overview of the available and on‐going related controlled trials with a survey on the overlap areas and on the use of bioinformatics

4eMedicine refers to the current digital revolution in medicine facing harmful scenarios for individuals’ and community health. Priorities of general concern are Emergency, Extreme and Environmental medicine issues, needing careful digital risk analysis and management. Limited information deriving by controlled clinical investigations and by bioinformatics approaches to big‐data management are available.4e Electronic, Emergency, Extreme and Environmental medicine (Figure ) shares the objective of a more effective, sustainable and comprehensive approach to practice, innovation and research in many health domains. Actually, extreme medicine shares with emergency and environment medicine challenges on Earth and beyond Earth, to health and well‐being (hot, cool, sun and ice exposure, starvation, sea, deserts and mountains, different gravity, endurance and extreme sports, war and catastrophic scenarios). In many subsets, with advanced or limited resources and facilities, the study of extreme physiology and psychology, also by models, is still a neglected or concealed component of medical research and best practice evidences. Challenges to human body and mind in extreme situations were, are and will be sources of information on unsuspected coping, reactions and solutions.The aim is to display a research profile of this multidisciplinary approach using the 321,599 research studies recorded at October 2019 in the ClinicalTrials.gov, detailing the areas of overlap by five main keywords: Electronics, Environment, Extreme, Emergency, Bioinformatics (Table 1). Also Pubmed was searched concurrently (Table 2).Results256 Pubmed articles claim the use of bioinformatics approaches in areas of binary overlap of the 4eMedicine components. Nonetheless, completed or ongoing investigations in ClinicalTrials.gov are fewer, only 91.ConclusionEmergency Medicine and complementary, but equally relevant Extreme Medicine, are the fields of experience and of the challenges to understanding mechanisms of disease and survival in different environments. New answers may still derive from ongoing or future comprehensive clinical practice and investigations, better if conducted by clinical trials and bioinformatics tools: this is needed for managing numerous and different data. Such awareness warrants greater cross‐disciplinary working and a quest for more supportive management of big data‐bases. Enhancing research, increasing literacy, competences and skills in research methods and bioinformatics, promoting international harmonization of emergency service facilities, also by reliable and sustainable e‐learning courses, are realistic goals and ongoing actions.Support or Funding InformationThis abstract is also an information tool of the EU iProcure Security Project, https://project.iprocuresecurity.eu/, Strategic Partnership of Emergency Medical Service, and of the BioS Project https://bios‐project.eu/site/. The European Commission support for the production of this publication does not constitute an endorsement of the contents which reflect the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein.The Four Pillars of 21st Century 4eMedicine, within the current digital revolution, are Electronic, Emergency, Extreme and Enviromental Medicine. All should be managed by appropriate scientific approaches which include accurate and reliable project planning and bioinformatics.Figure 1 Absolute number of completed and on‐going investigations in ClinicalTiials.gov Overlapping Electronics Environment Emergency Extreme Bioinformatics Electronics – 30 67 24 13 Environment 30 – 26 29 15 Emergency 67 26 ‐‐ 82 50 Extreme 24 29 82 ‐‐ 13 Bioinformatics 13 15 50 13 ‐‐ Absolute number of published investigations in PUBMED Overlapping Research Trials Electronics Environment Emergency Extreme Bioinformatics & computational Electronics – 18 4 17 Environment 108 ‐‐ 1806 159 263 Emergency 18 1806 ‐‐ 58 50 Extreme 4 159 58 ‐‐ 26 Bioinformatics & computational 17 263 50 26 ‐‐ ClinicalTrials.gov is a database of privately and publicly funded clinical studies conducted around the world, here detailing the areas of overlap of five general fields of medical and scientific work: Electronics, Environment, Extreme, Emergency, Bioinformatics (Table 1). Pubmed database was searched concurrently for finding by the same criteria areas of overlap of the same issues (Table 2).

Autologous Platelet Rich Plasma (A-PRP) in Unexplained Infertility: A Revolution in Reproductive Medicine

Background: Any treatment for unknown infertility is empiric by default, and the broad range of treatment, including expectant management, superovulation, IUI, IVF and IVF–intracytoplasmic sperm injection, reflects the uncertainty with this diagnosis. There are limited data to support the efficacy of many of these treatments, and no uniform protocol exists in clinical practice. Autologous platelet rich plasma (A-PRP) can be a novel technique which has never been explored before much in this field. Objective: To compare autologous platelet rich plasma with expectant management in cases of unexplained infertility. Materials and methods: All cases (50) of primary unexplained infertility were treated with either A-PRP or by expectant management on the basis of their endometrial thickness (7 mm). All cases of thin endometrium (<7 mm) were subjected to A-PRP (25 cases). Whereas rest of the cases (25 in number) was monitored as per expectant management protocol for a maximum of three cycles after which they were switched over to being treated with PRP. The main outcomes measured were number of follicles, endometrial thickness, pregnancy rate and miscarriage rates. The statistical analysis was evaluated with IBM SPSS Statistics for Windows, Version 24.0, IBM Corp, and Chicago, IL. Results: Out of 46 patients, 25 patients (54.35%) got conceived with a single dose of intra-uterine autologous platelet rich plasma injection, which was found to be statistically highly significant. The correlation analysis with Spearman’s Rank correlation coefficient (rho ρ) was 0.891 which show highly positive correlation between intrauterine autologous platelet rich plasma injection and pregnancy rate and 0.247 which show weakly positive correlation between expectant management and pregnancy rate. Conclusion: Autologous platelet rich plasma (A-PRP) is a novel technique than can treat unexplained infertility with favorable outcomes.

Sphere-induced reprogramming of RPE cells into dual-potential RPE stem-like cells.

BackgroundThe retinal pigment epithelium (RPE) has the potential to regenerate the entire neuroretina upon retinal injury in amphibians. In contrast, this regenerative capacity has been lost in mammals. The reprogramming of differentiated somatic cells into induced pluripotent stem cells (iPSCs) by viral transduction of exogenous stem cell factors has triggered a revolution in regenerative medicine. However, the risks of potential mutation(s) caused by random viral vector insertion in host genomes and tumor formation in recipients hamper its clinical application. One alternative is to immortalize adult stem cells with limited potential or to partially reprogram differentiated somatic cells into progenitor-like cells through non-integration protocols.MethodsSphere-induced RPE stem cells (iRPESCs) were generated from adult mouse RPE cells. Their stem cell functionality was studied in a mouse model of retinal degeneration. The molecular mechanism underlying the sphere-induced reprogramming was investigated using microarray and loss-of-function approaches.FindingsWe provide evidence that our sphere-induced reprogramming protocol can immortalize and transform mouse RPE cells into iRPESCs with dual potential to differentiate into cells that express either RPE or photoreceptor markers both in vitro and in vivo. When subretinally transplanted into mice with retinal degeneration, iRPESCs can integrate to the RPE and neuroretina, thereby delaying retinal degeneration in the model animals. Our molecular analyses indicate that the Hippo signaling pathway is important in iRPESC reprogramming.InterpretationThe Hippo factor Yap1 is activated in the nuclei of cells at the borders of spheres. The factors Zeb1 and P300 downstream of the Hippo pathway are shown to bind to the promoters of the stemness genes Oct4, Klf4 and Sox2, thereby likely transactivate them to reprogram RPE cells into iRPESCs.FundNational Natural Science Foundation of China and the National Institute of Health USA.

Detection of Cystic Fibrosis Symptoms Based on X-Ray Images Using Machine Learning- Pilot Study

Discovery of X-rays by W.K. Roentgen on November 8, 1895, and the possibility of using them for imaging of the human body was a real revolution in medicine at that time...

On stethoscopes, patient records, artificial intelligence and zettabytes: a glimpse into the future of digital medicine in Mexico.

Science and technology are modifying medicine at a dizzying pace. Although access in our country to the benefits of innovations in the area of devices, data storage and artificial intelligence is still very restricted, the advance of digital medicine offers the opportunity to solve some of the biggest problems faced by medical practice and public health in Mexico. The potential areas where digital medicine can be disruptive are: accessibility to quality medical care, centralization of specialties in large cities, dehumanization of medical treatment, lack of resources to access evidence-supported treatments, among others. This review presents some of the advances that are guiding the new revolution in medicine, discusses the potential and potential barriers to implementation, and suggests crucial elements for the path of incorporation of digital medicine in Mexico.