Special Issue "Advanced Analytical Methods in Clinical Diagnosis and Therapy 2.0"

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Epidemiology & Public Health".

Deadline for manuscript submissions: closed (31 January 2021).

Special Issue Editors

Prof. Dr. Liliana Rogozea
E-Mail Website
Guest Editor
Transilvania University of Brasov, Faculty of Medicine, Brasov, Romania
Interests: medicine; medical and health profession education; healthcare management; bioethics; history of science; telemedicine; rehabilitation
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Special Issue Information

Dear Colleagues,

To identify a suitably adapted therapy, physicians and researchers need to collect and analyze information about the molecular biomarkers specific to each patient (for example, nucleic acids, proteins, and/or metabolites). Biomarkers can serve as molecular markers and emphasize genetic variations that affect drug response; for the development of new drugs, highlighting adverse effects and/or disease progression; and as new tools and technologies developed for diagnosis and therapy, and ethical and regulatory issues related to personalized medicine.

The simultaneous use by the physician of molecular diagnostics with imaging methods and analytical tools can lead to more accurate diagnoses, as well as to the identification of effective medical treatments or prevention strategies for each patient.

In this Special Issue, we invite manuscripts dealing with advances in healthcare/clinical practices, the study of direct observation of patients and general medical research, and ethical and public health issues, without being limited to them. Both original research and review articles are welcomed.

Original research papers should describe the development, characterization/evaluation, simulations, and utilization of different advanced analytical methods in clinical diagnosis and therapy, and reviews should provide an up-to-date and critical overview of state-of-the-art of those methods. The subject areas are shown at https://www.mdpi.com/journal/jcm/about.

Please feel free to contact us and send us any suggestions that you would like to discuss beforehand. We look forward to and welcome your participation in this Special Issue.

Prof. Dr. Monica Florescu
Prof. Dr. Liliana Rogozea
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Analytical methods
  • Diagnosis
  • Therapy
  • Clinical laboratory
  • Medical imaging
  • Precision medicine
  • Telemedicine
  • Rehabilitation medicine

Published Papers (5 papers)

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Research

Article
Clinical Implications of the General Movement Optimality Score: Beyond the Classes of Rasch Analysis
J. Clin. Med. 2021, 10(5), 1069; https://doi.org/10.3390/jcm10051069 - 04 Mar 2021
Viewed by 372
Abstract
This article explores the clinical implications of the three different classes drawn from a Rasch analysis of the general movements optimality scores (GMOS) of 383 infants. Parametric analysis of the class membership examines four variables: age of assessment, brain injury presence, general movement [...] Read more.
This article explores the clinical implications of the three different classes drawn from a Rasch analysis of the general movements optimality scores (GMOS) of 383 infants. Parametric analysis of the class membership examines four variables: age of assessment, brain injury presence, general movement patterns, and 2-year-old outcomes. GMOS separated infants with typical (class 3) from atypical development, and further separated cerebral palsy (class 2) from other neurodevelopmental disorders (class 1). Each class is unique regarding its quantitative and qualitative representations on the four variables. The GMOS has strong psychometric properties and provides a quantitative measure of early motor functions. The GMOS can be confidently used to assist with early diagnosis and predict distinct classes of developmental outcomes, grade motor behaviors, and provide a solid base to study individual general movement developmental trajectories. Full article
(This article belongs to the Special Issue Advanced Analytical Methods in Clinical Diagnosis and Therapy 2.0)
Article
Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (FTIR) and Artificial Neural Networks Applied to Investigate Quantitative Changes of Selected Soluble Biomarkers, Correlated with H. pylori Infection in Children and Presumable Consequent Delayed Growth
J. Clin. Med. 2020, 9(12), 3852; https://doi.org/10.3390/jcm9123852 - 27 Nov 2020
Cited by 3 | Viewed by 489
Abstract
Helicobacter pylori infections causing gastroduodenal disorders are a common medical problem. The aim of this study was to determine the specific motives of infrared spectroscopy (IR) spectra of sera from H. pylori-infected and uninfected children applied to investigate quantitatively-selected soluble biomarkers correlated [...] Read more.
Helicobacter pylori infections causing gastroduodenal disorders are a common medical problem. The aim of this study was to determine the specific motives of infrared spectroscopy (IR) spectra of sera from H. pylori-infected and uninfected children applied to investigate quantitatively-selected soluble biomarkers correlated with H. pylori infection in children and presumable consequent delayed growth. Sera from 41 children infected with H. pylori (Hp(+)) and 43 uninfected (Hp(−)) under the care of the Polish Mother’s Hospital in Lodz, Poland, were analyzed. The H. pylori status was confirmed by gastroscopy, 13C urea breath testing, and anti-H. pylori IgG antibodies. Infrared spectra were measured using an FTIR/FT-NIR Spectrum 400 spectrometer (PerkinElmer). The IR spectrum was measured in the wavenumber range 3000–750 cm−1 and subjected to mathematical calculation of the first derivative. Based on the chi-square test, 10 wavenumbers of spectra correlating with H. pylori infection were selected for use in designing an artificial neural network. Ten parts of the IR spectra correlating with H. pylori infection were identified in the W2 and W3 windows associated mainly with proteins and the W4 window related to nucleic acids and hydrocarbons. Artificial neural networks for H. pylori infection were developed based on chemometric data. By mathematical modeling, children were classified towards H. pylori infection in conjunction with elevated levels of selected biomarkers in serum potentially related to growth retardation. The study concludes that IR spectroscopy and artificial neural networks may help to confirm H. pylori-driven growth disorders in children. Full article
(This article belongs to the Special Issue Advanced Analytical Methods in Clinical Diagnosis and Therapy 2.0)
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Article
Rapid Phenotype-Driven Gene Sequencing with the NeoSeq Panel: A Diagnostic Tool for Critically Ill Newborns with Suspected Genetic Disease
J. Clin. Med. 2020, 9(8), 2362; https://doi.org/10.3390/jcm9082362 - 23 Jul 2020
Cited by 1 | Viewed by 709
Abstract
New genomic sequencing techniques have shown considerable promise in the field of neonatology, increasing the diagnostic rate and reducing time to diagnosis. However, several obstacles have hindered the incorporation of this technology into routine clinical practice. We prospectively evaluated the diagnostic rate and [...] Read more.
New genomic sequencing techniques have shown considerable promise in the field of neonatology, increasing the diagnostic rate and reducing time to diagnosis. However, several obstacles have hindered the incorporation of this technology into routine clinical practice. We prospectively evaluated the diagnostic rate and diagnostic turnaround time achieved in newborns with suspected genetic diseases using a rapid phenotype-driven gene panel (NeoSeq) containing 1870 genes implicated in congenital malformations and neurological and metabolic disorders of early onset (<2 months of age). Of the 33 newborns recruited, a genomic diagnosis was established for 13 (39.4%) patients (median diagnostic turnaround time, 7.5 days), resulting in clinical management changes in 10 (76.9%) patients. An analysis of 12 previous prospective massive sequencing studies (whole genome (WGS), whole exome (WES), and clinical exome (CES) sequencing) in newborns admitted to neonatal intensive care units (NICUs) with suspected genetic disorders revealed a comparable median diagnostic rate (37.2%), but a higher median diagnostic turnaround time (22.3 days) than that obtained with NeoSeq. Our phenotype-driven gene panel, which is specific for genetic diseases in critically ill newborns is an affordable alternative to WGS and WES that offers comparable diagnostic efficacy, supporting its implementation as a first-tier genetic test in NICUs. Full article
(This article belongs to the Special Issue Advanced Analytical Methods in Clinical Diagnosis and Therapy 2.0)
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Article
A Proteomics-Based Analysis Reveals Predictive Biological Patterns in Fabry Disease
J. Clin. Med. 2020, 9(5), 1325; https://doi.org/10.3390/jcm9051325 - 02 May 2020
Cited by 2 | Viewed by 1138
Abstract
Background: Fabry disease (FD) is an X-linked progressive lysosomal disease (LD) due to glycosphingolipid metabolism impairment. Currently, plasmatic globotriaosylsphingosine (LysoGb3) is used for disease diagnosis and monitoring. However, this biomarker is inconstantly increased in mild forms and in some female patients. Materials [...] Read more.
Background: Fabry disease (FD) is an X-linked progressive lysosomal disease (LD) due to glycosphingolipid metabolism impairment. Currently, plasmatic globotriaosylsphingosine (LysoGb3) is used for disease diagnosis and monitoring. However, this biomarker is inconstantly increased in mild forms and in some female patients. Materials and Methods: We applied a targeted proteomic approach to explore disease-related biological patterns that might explain the disease pathophysiology. Forty proteins, involved mainly in inflammatory and angiogenesis processes, were assessed in 69 plasma samples retrieved from the French Fabry cohort (FFABRY) and from 83 healthy subjects. For predictive performance assessment, we also included other LD samples (Gaucher, Pompe and Niemann Pick C). Results: The study yielded four discriminant proteins that include three angiogenesis proteins (fibroblast growth factor 2 (FGF2), vascular endothelial growth factor A (VEGFA), vascular endothelial growth factor C (VEGFC)) and one cytokine interleukin 7 (IL-7). A clear elevation of FGF2 and IL-7 concentrations was observed in FD compared to other LD samples. No correlation was observed between these proteins and globotriaosylsphingosine (LysoGb3). A significant correlation exists between IL-7 and residual enzyme activity in a non-classical phenotype. This highlights the orthogonal biological information yielded by these proteins that might help in stratifying Fabry patients. Conclusion: This work highlights the potential of using proteomics approaches in exploring FD and enhancing FD diagnosis and therapeutic monitoring performances. Full article
(This article belongs to the Special Issue Advanced Analytical Methods in Clinical Diagnosis and Therapy 2.0)
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Article
A New Method of Measuring the Volumetric Change of Alveolar Bone Around Dental Implants Using Computed Tomography
J. Clin. Med. 2020, 9(4), 1238; https://doi.org/10.3390/jcm9041238 - 24 Apr 2020
Cited by 1 | Viewed by 797
Abstract
This study proposes a method for measuring the volumetric change of alveolar bone after dental implant surgery using computed tomography (CT). A total of 40 implants in 20 patients (15 males and 5 females) were selected. The types of implants used were group [...] Read more.
This study proposes a method for measuring the volumetric change of alveolar bone after dental implant surgery using computed tomography (CT). A total of 40 implants in 20 patients (15 males and 5 females) were selected. The types of implants used were group 1: 24 CMI IS-II Active implants (Neobiotech Co., Seoul, Republic of Korea) and group 2: 16 SLActive Bone Level implants (Institut Straumann AG, Basel, Switzerland). The OnDemand3D software (CyberMed, Seoul, Korea) was used for analysis. The volumetric change of the alveolar bone around an implant fixture is measured as follows: (1) Establish two cylinders: the main cylinder with the implant axis as the central axis (radius of implant + 3 mm) and the error correction cylinder (radius of implant + 1 mm). (2) The height of the cylinder extended from the top of the fixture to a 3 mm coronal portion. (3) Calculate the volumetric change of the alveolar bone (Vd) by subtracting the volume of the error correction cylinder from the main cylinder between CT images taken immediately after the implant placement and 12 months later. After a one-year installation, the volumetric change of alveolar bone, ΔV (cc) had increased in both groups (group 1: −0.011 ± 0.015 cc, group 2: −0.012 ± 0.017 cc) with statistical significance (p < 0.05), and the difference between the groups was not statistically significant (p > 0.05). This three-dimensional assessment method would be a useful clinical reference for the assessment of marginal bone change after implant surgery. Full article
(This article belongs to the Special Issue Advanced Analytical Methods in Clinical Diagnosis and Therapy 2.0)
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