Clinical Medical Research Research Articles

Trends in the Development of Digital Technologies in Medicine.

The introduction of digital technologies has led to transformation of the healthcare domain and alterations in the nature of medical services. This article reviews Russian and non-Russian practices in the use of digital technologies to solve clinical medical tasks and scientific research in seeking new methods and means for the diagnosis and treatment of diseases and the management of healthcare organizations.

Contributions to computed tomography

I would like to add a historical footnote to the excellent article by John Boone and Cynthia McCollough in the September 2021 issue of Physics Today (page 34). The origins of computed tomography (CT) can be traced to William Oldendorf’s pioneering work in the late 1950s and 1960s. Oldendorf was a professor of neurology at the UCLA School of Medicine when he developed a prototype of an automated tomographic device in which he used his son’s electric train set, a phonograph turntable, an alarm clock motor, and other household items. It was the first demonstration of “a radiographic method of producing cross-sectional images of soft tissue by back-projection and reconstruction.”11. L. J. West, J. C. Mazziotta, A. Yuwiler, in University of California: In Memoriam, 1993, University of California Academic Senate (1993), p. 133.In his 1961 breakthrough paper, Oldendorf laid out CT’s basic concept,22. W. H. Oldendorf, IRE Trans. Biomed. Electron. 8, 68 (1961). https://doi.org/10.1109/TBMEL.1961.4322854 which Allan Cormack later used to develop its underlying mathematics. In October 1963 Oldendorf received a US patent for a “radiant energy apparatus for investigating selected areas of the interior of objects obscured by dense material.”33. W. H. Oldendorf, “Radiant energy apparatus for investigating selected areas of the interior of objects obscured by dense material,” US Patent 3,106,640 (8 October 1963).The 1975 Albert Lasker Clinical Medical Research Award recognized the importance of Oldendorf’s contributions to discoveries that enabled CT. He shared the prize with Godfrey Hounsfield, who with Cormack would receive the Nobel Prize in Physiology or Medicine four years later for “the development of computer assisted tomography.”Some have speculated that Oldendorf was on the original Nobel announcement but was removed at the last minute at the behest of certain members of the Nobel Assembly at the Karolinska Institute, which votes on the nominating committee’s recommendations. It is possible some assembly members felt that the inclusion of a clinician would cheapen the award, making it appear overly pragmatic and thereby reducing its prestige.44. W. J. Broad, Science 207, 37 (1980). https://doi.org/10.1126/science.6985744Oldendorf gave a lecture at UCLA shortly after the Nobel announcement was made. In it, he reviewed the work that earned him a Lasker and should have made him a Nobel laureate. Everyone who heard Oldendorf’s presentation that day (myself included) came away convinced he was unjustly deprived of the pinnacle of scientific recognition. Readers wanting to learn more about Oldendorf’s contributions to tomography and their historical context should consult his book on the topic.55. W. H. Oldendorf, The Quest for an Image of Brain: Computerized Tomography in the Perspective of Past and Future Imaging Methods, Raven Press (1980).ReferencesSection:ChooseTop of pageReferences <<CITING ARTICLES1. L. J. West, J. C. Mazziotta, A. Yuwiler, in University of California: In Memoriam, 1993, University of California Academic Senate (1993), p. 133. Google Scholar2. W. H. Oldendorf, IRE Trans. Biomed. Electron. 8, 68 (1961). https://doi.org/10.1109/TBMEL.1961.4322854, Google ScholarCrossref3. W. H. Oldendorf, “Radiant energy apparatus for investigating selected areas of the interior of objects obscured by dense material,” US Patent 3,106,640 (8 October 1963). Google Scholar4. W. J. Broad, Science 207, 37 (1980). https://doi.org/10.1126/science.6985744, Google ScholarCrossref5. W. H. Oldendorf, The Quest for an Image of Brain: Computerized Tomography in the Perspective of Past and Future Imaging Methods, Raven Press (1980). Google Scholar© 2022 American Institute of Physics.

Perception about the Role of Biostatistics in Medical Curriculum: A Cross-Sectional Study among Medical Students

Introduction: A good understanding of biostatistics can improve clinical thinking, decision-making, evaluations, and medical research. Undoubtedly, medical professionals are becoming aware of the importance of learning and applying biostatistical methods in their research. This study was done to assess the knowledge and attitude among medical graduates regarding the role and application of biostatistics in medical science. Method: The study was a cross-sectional study conducted in a tertiary care teaching hospital among 120 medical students pursuing MBBS third and final year. Systematic random sampling was used to select the study participants. A pretested validated semi-structured questionnaire was used for data collection. Results: Majority of the study participants, strongly agree that the subject helps in interpretation (75.8%), and is important in medical practice (61.7%). However, 85% of the study participants feel that software is difficult to use. 90% of the study participants find the subject hard to understand. The mean score of subject content, understanding, the current impact was significantly higher among the fourth-year students than the third-year students. There was no significant difference between the genders. Conclusion: Introduction of data collection sessions and appraisal of excerpts in published articles can provide practical knowledge and accentuate the role of bio statistics in health care.

Applications of Body Imaging Techniques in Clinical Practice and Biomedical Research: An Overview

Background: Applications of imaging technology modalities have accumulated evidence that individual components of body composition (BC) have significant influences on chronic disease onset, disease progression, treatment responses and health outcomes. Objective: To analyse the currently available body imaging techniques and their applications in clinical practice and medical research. Methods: To review the various body imaging techniques and their applications in clinical practice and medical research, Medline, PubMed, Google scholar, ResearchGate and other databases were searched. Furthermore, references of selected studies and documents available in different libraries were also searched. Findings: Imaging modalities have provided a systematic method for differentiating phenotypes of BC that diverge from normal, i.e. having low bone mass (osteopenia/osteoporosis), low muscle mass (sarcopenia), high fat mass (obesity), or high fat with low muscle mass (sarcopenic obesity). Tremendous advances were made over the past decades in the sensitivity and quality of imaging techniques such as Duel-Energy X-Ray Absorptiometry (DXA), Computed Axial Tomography (CT), Ultrasound (US), Magnetic Resonance Imaging (MRI), Magnetic Resonance Spectroscopy (MRS), Positron Emission Tomography (PET), Bioelectrical Impedance Analysis (BIA) etc. These imaging techniques have been useful to differentiate layers or depots within tissues and cells enhancing our understanding of distinct mechanistic, metabolic and functional roles of BC within human phenotypes. Conclusion: In the present overview, we focused on DXA, CT and US for the use in clinical practice and biomedical research relevant to future investigation of human BC and how they may be applied to remedy the pandemic of obesity, diabetes and metabolic syndrome. Bangladesh Med Res Counc Bull 2021; 47(2): 103-109

Stirring by anisotropic squirming

We consider a fluid stirred by the locomotions of squirmers through it and generalize the stochastic hydrodynamic model proposed by Thiffeault and Childress, Phys. Lett. A (2010) and Lin et al., J. Fluid Mech. (2011) to the case in which the swimmers move in anisotropically random directions. A non-diagonal effective diffusivity tensor is derived with which the diffusive preference of a passive particle along any given direction can be computed to provide more details of the phenomena beyond scalar statistics. We further identify a fraction from the orthogonal decomposition of the drift-induced particle displacement to distinguish the underlying nonlinear mixing mechanism for different types of swimmers. Numerical simulations verify the analytical results with explicit examples of prescribed, anisotropic stirring motions. We also connect our formulation to several measures used in clinical medical research such as diffusion tensor imaging where anisotropic diffusion has a significant consequence.

Classification of EEG Signal-Based Encephalon Magnetic Signs for Identification of Epilepsy-Based Neurological Disorder.

Magnetoencephalography (MEG) is now widely used in clinical examinations and medical research in many fields. Resting-state magnetoencephalography-based brain network analysis can be used to study the physiological or pathological mechanisms of the brain. Furthermore, magnetoencephalography analysis has a significant reference value for the diagnosis of epilepsy. The scope of the proposed research is that this research demonstrates how to locate spikes in the phase locking functional brain connectivity network of the Desikan-Killiany brain region division using a neural network approach. It also improves detection accuracy and reduces missed and false detection rates. The automatic classification of epilepsy encephalomagnetic signals can make timely judgments on the patient's condition, which is of tremendous clinical significance. The existing literature's research on the automatic type of epilepsy EEG signals is relatively sufficient, but the research on epilepsy EEG signals is relatively weak. A full-band machine learning automatic discrimination method of epilepsy brain magnetic spikes based on the brain functional connection network is proposed. The four classifiers are comprehensively compared. The classifier with the best effect is selected, and the discrimination accuracy can reach 93.8%. Therefore, this method has a good application prospect in automatically identifying and labeling epileptic spikes in magnetoencephalography.

Hollow covalent organic framework-sheltering CRISPR/Cas12a as an in-vivo nanosensor for ATP imaging

In addition to applications in genome editing, clustered regularly interspaced short palindromic repeats (CRISPR) have recently been engineered for medical diagnostics based on their trans-cleavage activity owing to their high base resolution and isothermal signal amplification. However, trans-cleavage activity is too fragile to be applied in vivo. Herein, we introduce a hollow covalent organic framework (COF)-sheltering CRISPR/aptamer-based sensor (h-CCS) for ATP imaging in living animals. The CRISPR/aptamer-based complex is comprised of the CRISPR-Cas12a system, fluorophore quencher-labeled single-stranded DNA substrate (ssDNA-FQ), and a DNA activator that pre-hybridizes with ATP aptamer to prevent the trans-cleavage activity of the Cas12a system in the absence of ATP. After being encapsulated in a hollow COF, the constructed nanoreactor is highly robust and can be lit up by ATP for in vivo imaging. Considering the unique properties of h-CCS, this strategy offers great potential to broaden applications of not only CRISPR-Cas systems but also other proteins in porous matrixes for clinical diagnostics, medical research, and biomimetic nanodevices.

A knowledge-driven network for fine-grained relationship detection between miRNA and disease.

Increasing biological evidence indicated that microRNAs (miRNAs) play a vital role in exploring the pathogenesis of various human diseases (especially in tumors). Mining disease-related miRNAs is of great significance for the clinical diagnosis and treatment of diseases. Compared with the traditional experimental methods with the significant limitations of high cost, long cycle and small scale, the methods based on computing have the advantages of being cost-effective. However, although the current methods based on computational biology can accurately predict the correlation between miRNAs and disease, they can not predict the detailed association information at a fine level. We propose a knowledge-driven approach to the fine-grained prediction of disease-related miRNAs (KDFGMDA). Different from the previous methods, this method can finely predict the clear associations between miRNA and disease, such as upregulation, downregulation or dysregulation. Specifically, KDFGMDA extracts triple information from massive experimental data and existing datasets to construct a knowledge graph and then trains a depth graph representation learning model based on knowledge graph to complete fine-grained prediction tasks. Experimental results show that KDFGMDA can predict the relationship between miRNA and disease accurately, which is of far-reaching significance for medical clinical research and early diagnosis, prevention and treatment of diseases. Additionally, the results of case studies on three types of cancers, Kaplan-Meier survival analysis and expression difference analysis further provide the effectiveness and feasibility of KDFGMDA to detect potential candidate miRNAs. Availability: Our work can be downloaded from https://github.com/ShengPengYu/KDFGMDA.

From Clinical Trial to Education: Methodologies, Assumptions, and Directions

In the field of education, emphasis on evidence-based practice, randomized controlled trials (RCTs), causal inference, and process evaluation can all find their roots back in clinical trials and medical research. This response paper surveys contemporary literature in psychometrics, process evaluation, and RCTs aiming to evaluate the feasibility and limitations of RCTs as a methodology in education and provide future directions. Based on the systematic literature review, the author argues: (1) A lack of significant positive treatment effect does not indicate that RCTs are not worth the investment. (2) A careful evaluation of the intervention itself, implementation process, and measurement instrument is recommended for RCTs. (3) There is the need to reframe some causal inference assumptions in an education setting. The paper also provides several examples of reframing assumptions and comments on the caveats. In conclusion, the author foresees a promising future for RCTs in education with the appropriate reframing of assumptions, process evaluation and replication, and recognition of the validity of parallel methodologies.

The second strategic plan of medical ethics: a national report.

Medical ethics faces several challenges in different aspects of education, research, and treatment in medicine and healthcare practice. Design and implementation of a national strategic plan can pave the way for the development of a roadmap in various countries to strengthen ethics and address these challenges. To create a comprehensive plan compatible with the Iranian healthcare system, a multidisciplinary team of main stakeholders compiled a national strategic plan of medical ethics following several focus group discussion sessions and two workshops (2014-2017). Ultimately, the plan was confirmed by the Supreme Council for the Medical Ethics of the Ministry of Health and Medical Education. The current paper is a national report of the process and the medical ethics strategic plan in Iran. We have also tracked signs of progress and achievements in the country.In conclusion, this valuable effort has led to significant success in the implementation of medical ethics in clinical medicine, medical research, and education by using all the resources in our country. The participation of all the stakeholders, especially healthcare professionals in this way is required.

Decentralized Federated Learning for Healthcare Networks: A Case Study on Tumor Segmentation

Smart healthcare relies on artificial intelligence (AI) functions for learning and analysis of patient data. Since large and diverse datasets for training of Machine Learning (ML) models can rarely be found in individual medical centers, classical centralized AI requires moving privacy-sensitive data from medical institutions to data centers that process the fused information. Training on data centers thus requires higher communication resource/energy demands while violating privacy. This is considered today as a significant bottleneck in pursuing scientific collaboration across trans-national clinical medical research centers. Recently, federated learning (FL) has emerged as a distributed AI approach that enables the cooperative training of ML models, without the need of sharing patient data. This paper dives into the analysis of different FL methods and proposes a real-time distributed networking framework based on the Message Queuing Telemetry Transport (MQTT) protocol. In particular, we design a number of solutions for ML over networks, based on FL tools relying on a parameter server (PS) and fully decentralized paradigms driven by consensus methods. The proposed approach is validated in the context of brain tumor segmentation, using a modified version of the popular U-NET model with representative clinical datasets obtained from the daily clinical workflow. The FL process is implemented on multiple physically separated machines located in different countries and communicating over the Internet. The real-time test-bed is used to obtain measurements of training accuracy vs. latency trade-offs, and to highlight key operational conditions that affect the performance in real deployments.

Clinical Study of Deep Learning-Based Giant Needling Combined with Conventional Acupuncture for Treatment of Limb Dysfunction Due to Stroke

With the development of computer technology and the deepening of machine learning research, the application prospects of artificial intelligence technology in the medical field are becoming more and more broad. Deep learning algorithm is a method tool that realizes machine learning so as to be as close as possible to artificial intelligence. Its application in clinical medicine is the current hot direction of artificial intelligence in the medical field. Therefore, clinical medical research combining deep learning algorithms with traditional therapies is of great significance to the application and development of deep learning in the medical field. This article uses deep learning algorithms to study a clinical treatment plan that combines the giant needling method in traditional medicine with conventional acupuncture therapy for stroke patients with limb dysfunction. This article mainly uses the convolutional neural network in the deep learning algorithm to establish a tracking prediction model. Through the time-series signal of the neural response of stroke patients, acupoint detection and medical image processing, the study of the giant needling method combined with conventional acupuncture therapy on the limb dysfunction of stroke the clinical treatment effect. This article analyzes the limb function and therapeutic effect of the giant needling method combined with conventional acupuncture therapy before and after the clinical treatment of stroke-related limb dysfunction. This paper studies the advantages and disadvantages of deep learning algorithms and other machine learning algorithms in the clinical data processing of stroke-related limb dysfunction through comparative analysis. Studies have shown that during the six months of stroke recovery, which is mainly based on the treatment of limb dysfunction, deep learning has a prediction accuracy of 91.44% for stroke patients limb dysfunction. Data collection, medical imaging and processing for clinical studies on stroke limb dysfunction the prediction accuracy is far superior to other machine learning algorithms.

Ultrasensitive Electrochemical Detection of cancer-Related Point Mutations Based on Surface-Initiated Three-Dimensionally Self-Assembled DNA Nanostructures from Only Two Palindromic Probes.

Sensitive and selective detection of proto-oncogenes, especially recognition of point mutation, is of great importance in cancer diagnosis. Here, a ligation-mediated technique is demonstrated for the construction of an intertwined three-dimensional DNA nanosheet (3D SDN) on an electrode surface from only two palindromic hairpin probes (HP1 and HP2), creating a powerful electrochemical biosensor (E-biosensor) for the detection of the p53 gene. First, a capturing probe (CP) is immobilized on an electrode surface via Au-S chemistry, forming an electrochemical sensing interface. In the presence of the target p53 (T), the triggering probe is covalently linked to CP by a ligase. Moreover, target hybridization/ligation/dehybridization process is repeated, amplifying the target hybridization event and increasing the content of surface-confined triggering fragments. As a result, HP1 is opened and in turn interacts with HP2, forming intertwined 3D SDN where HP1 and HP2 are alternately arranged in parallel. Common hybridization and interaction between palindromic fragments are responsible for the assembly in the horizontal and vertical directions, respectively. An electrochemical indicator, methylene blue (MB), can be inserted into 3D SDN, generating a strong electrochemical signal. Utilizing the 3D SDN-based E-biosensor, the target DNA is detected down to 3 fM with a linear response range from 10 fM to 10 nM. Single point mutations are reliably identified even in fetal bovine serum and cellular homogenate. Because of the several advantages of simple design, good universality, inexpensive instrumentation, high assay specificity, and sensitivity, the 3D SDN-based E-biosensor is expected to provide a potential platform for screening point mutation required by early clinical diagnostics and medical research.

Associations of Monocytes and the Monocyte/High-Density Lipoprotein Ratio With Extracranial and Intracranial Atherosclerotic Stenosis.

Background: Although the monocyte/high-density lipoprotein ratio (MHR) has been shown to be a potential marker of inflammatory of cardiovascular and cerebrovascular diseases, there are few studies on its relationships with the degree of intracranial and extracranial atherosclerotic stenosis and the stenosis distribution.Methods: In total, 271 patients were admitted for digital subtraction angiography (DSA) examination and were classified into a non-stenosis group and a stenosis group. (1) The two groups were compared and the arteries were categorized according to the degree of intracranial or extracranial atherosclerotic stenosis (if ≥two branches were stenotic, the artery with the most severe stenosis was used). (2) Clinical baseline data and laboratory indexes of patients grouped according to stenosis location (intracranial vs. extracranial) were collected.Results: (1) MHR × 102 [odds ratio (OR) = 1.119, p < 0.001], age (OR = 1.057, p = 0.007), and lymphocyte count (OR = 0.273, p = 0.002) significantly affected the presence of cerebral atherosclerotic stenosis, with an MHR area of 0.82 under the receiver operating characteristic (ROC) curve (AUC) and an optimal diagnostic value of 0.486. Analyses of the moderate, mild, and severe stenosis groups showed that MHR × 102 (OR = 1.07, p < 0.001) significantly affected the severity of stenosis in patients. (2) In the analysis of stenosis at different sites, the rate of extracranial artery stenosis in patients who smoked (OR = 3.86, p = 0.023) and had a reduced lymphocyte level (OR = 0.202, p = 0.001) was remarkably greater than that in patients who smoked (OR = 3.86, p = 0.023). With increasing age, the rate of extracranial artery stenosis raised sharply. With the increase in the MHR level, the stenosis rate of each group was highly greater than that of the non-stenosis group.Conclusion: The MHR has a predictive value for the diagnosis of extracranial and intracranial atherosclerotic stenosis and is correlated with the degree and distribution of stenosis.Trial Registration: Clinical Medical Research Center Project of Qinghai Province (2017-SF-L1). Qinghai Provincial Health Commission Project (Grant #2020-wjzdx-29).

A systematic approach to the disclosure of genomic findings in clinical practice and research: a proposed framework with colored matrix and decision-making pathways

BackgroundWhether and how to disclose genomic findings obtained in the course of genomic clinical practice and medical research has been a controversial global bioethical issue over the past two decades. Although several recommendations and judgment tools for the disclosure of genomic findings have been proposed, none are sufficiently systematic or inclusive or even consistent with each other. In order to approach the disclosure/non-disclosure practice in an ethical manner, optimal and easy-to-use tools for supporting the judgment of physicians/researchers in genomic medicine are necessary.MethodsThe bioethics literature on this topic was analyzed to parse and deconstruct the somewhat overlapping and therefore ill-defined key concepts of genomic findings, such as incidental, primary, secondary, and other findings. Based on the deconstruction and conceptual analyses of these findings, we then defined key parameters from which to identify the strength of duty to disclose (SDD) for a genomic finding. These analyses were then applied to develop a framework with the SDD matrix and systematic decision-making pathways for the disclosure of genomic findings.ResultsThe following six major parameters (axes), along with sub-axes, were identified: Axis 1 (settings and institutions where findings emerge); Axis 2 (presence or absence of intention and anticipatability in discovery); Axis 3 (maximal actionability at the time of discovery); Axis 4 (net medical importance); Axis 5 (expertise of treating physician/researcher); and Axis 6 (preferences of individual patients/research subjects for disclosure). For Axes 1 to 4, a colored SDD matrix for genomic findings was developed in which levels of obligation for disclosing a finding can be categorized. For Axes 5 and 6, systematic decision-making pathways were developed via the SDD matrix.ConclusionWe analyzed the SDD of genomic findings and developed subsequent systematic decision-making pathways of whether and how to disclose genomic findings to patients/research subjects and their relatives in an ethical manner. Our comprehensive framework may help physicians and researchers in genomic medicine make consistent ethical judgments regarding the disclosure of genomic findings.

How do healthcare providers construe patient complexity? A qualitative study of multimorbidity in HIV outpatient clinical practice

IntroductionPatient complexity is an increasingly used concept in clinical practice, policy debates and medical research. Yet the literature lacks a clear definition of its meaning and drivers from the health...

Primer-template conversion-based cascade signal amplification strategy for sensitive and accurate detection of polynucleotide kinase activity

Here, a primer-template conversion-based cascade signal amplification strategy is described for the sensitive detection of polynucleotide kinase (PNK) activity. This strategy integrated rolling circle amplification (RCA) and multiple-repeated-strand displacement amplification (MRSDA) with G-quadruplex based fluorescence lighting-up assay. A delicate dumbbell-shaped DNA probe with 5′-hydroxyl terminus was designed, in which G-quadruplex and half recognition site of nicking enzyme Nb.BbvCI were encoded in two loops respectively. Under the action of PNK, the 5′ terminus on dumbbell probe was firstly phosphorylated, and then the dumbbell was cyclized with the catalyzation of T4 ligase to become the RCA template. The RCA process produced multiple copies of the prolonged primer. After that, under the assistance of nicking enzyme Nb.BbvCI, a primer-template conversion occurred, which converted the primer and template of RCA into the template and primer of the subsequent MRSDA, respectively. The MRSDA generated multiple repeated ssDNA sequences which possessed G-quadruplexes for outputting signal by lighting-up fluorescence of thioflavin T (ThT). The cascade signal amplification of RCA and MRSDA provided high detection sensitivity, and the target-dependence of template in cascade signal amplification led to a low background. The method showed excellent detection limit of 0.2 × 10−6 U μL−1 in buffer and 5 cells in cell lysate sample. Moreover, this method displayed favorable selectivity when interfering proteins were present. The developed strategy has good practical potential for PNK activity detection in clinical diagnosis and medical research.

Artificial Intelligence in Healthcare

Artificial intelligence (AI) is the ability of a computer program or machine to think or learn that possess human-like intelligence. These computing devices use this intelligence to provide services such as speech recognition, natural language processing and identifying disease in healthcare. To work efficiently, AI requires adequate data that is used to train systems. The efficiency of any AI system depends on the availability of this data. This article is mainly focused on recent advents in the technology of Artificial Intelligence. The importance of AI in healthcare is identified and described in this report. The applications of Artificial Intelligence in healthcare such as clinical care, medical research, drug research and public healthcare are briefly discussed here. The purpose of this article is to demonstrate that artificial intelligence is being used in all domains of life and particularly in the field of healthcare. This report presents the role of Artificial Intelligence in healthcare.

Persistent progress

The highly effective and safe mRNA-based severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines draw on decades of painstaking research to overcome the many hurdles for delivering, expressing, and avoiding toxicity of therapeutic mRNA. Cell editor Nicole Neuman talked with Dr. Katalin Karikó and Dr. Drew Weissman, recipients of the 2021 Lasker∼DeBakey Clinical Medical Research Award, to learn more about their quest to develop mRNA-based therapeutics, which led them to the crucial discovery that modification of mRNA could prevent toxicity and increase expression. This conversation has been adapted for print below, with editing for clarity, accuracy, and length.

What Is the Most Important and Impactful Paper Related to Movement Disorder Therapy Published in the 20th Century?

What Is the Most Important and Impactful Paper Related to Movement Disorder Therapy Published in the 20th Century?