Zinc status in public health: exploring emerging research trends through bibliometric analysis of the historical context from 1978 to 2022.

The current study aims to provide a roadmap for future research by analyzing the research structures and trends in scholarly publications related to the status of zinc in public health. Only journal articles published between 1978 and 2022 are included in the refined bibliographical outputs retrieved from the Web of Science (WoS) database. The first section announces findings based on WoS categories, such as discipline heterogeneity, times cited and publications over time, and citation reports. The second section then employs VoSViewer software for bibliometric analysis, which includes a thorough examination of co-authorship among researchers, organizations, and countries and a count of all bibliographic databases among documents. The final section discusses the research's weaknesses and strengths in zinc status, public health, and potential future directions; 7158 authors contributed to 1730 papers (including 339 with publications, more than three times). "Keen, C.L." is a researcher with the most publications and a better understanding of zinc status in public health. Meanwhile, the USA has been the epicenter of research on the status of zinc in public health due to the highest percentage of publications with the most citations and collaboration with the rest of the world, with the top institution being the University of California, Davis. Future research can be organized collaboratively based on hot topics from co-occurrence network mapping and bibliographic couplings to improve zinc status and protect public health.

COVID-19 Prediction With Machine Learning Technique From Extracted Features of Photoplethysmogram Morphology.

At present, COVID-19 is spreading widely around the world. It causes many health problems, namely, respiratory failure and acute respiratory distress syndrome. Wearable devices have gained popularity by allowing remote COVID-19 detection, contact tracing, and monitoring. In this study, the correlation of photoplethysmogram (PPG) morphology between patients with COVID-19 infection and healthy subjects was investigated. Then, machine learning was used to classify the extracted features between 43 cases and 43 control subjects. The PPG data were collected from 86 subjects based on inclusion and exclusion criteria. The systolic-onset amplitude was 3.72% higher for the case group. However, the time interval of systolic-systolic was 7.69% shorter in the case than in control subjects. In addition, 12 out of 20 features exhibited a significant difference. The top three features included dicrotic-systolic time interval, onset-dicrotic amplitude, and systolic-onset time interval. Nine features extracted by heatmap based on the correlation matrix were fed to discriminant analysis, k-nearest neighbor, decision tree, support vector machine, and artificial neural network (ANN). The ANN showed the best performance with 95.45% accuracy, 100% sensitivity, and 90.91% specificity by using six input features. In this study, a COVID-19 prediction model was developed using multiple PPG features extracted using a low-cost pulse oximeter.

G-quadruplex microspheres-based optical RNA biosensor for arthropod-borne virus pathogen detection: A proof-of-concept with dengue serotype 2.

Dengue virus (DENV) is a positive-sense single-stranded RNA virus and that the detection of viral RNA itself is highly desirable, which can be achieved by using RNA biosensor diagnostic method. Herein, acrylic micropolymer-based optical RNA biosensor was developed by binding anionic copper(II) phthalocyanine (CPC) planar aromatic ligand to the G-quadruplex DNA probe via end-stacking with π-system of the guanine (G) quartet, and a blue coloration was developed on the G-quadruplex microspheres. Hybridization of G-quadruplex DNA probe with target DENV serotype 2 (DENV2) RNA unfolded the G-quadruplex, and rendering release of the CPC planar optical label, causing discoloration of the G-quadruplex microbiosensor. Optical characterization of the RNA biosensor was performed by means of fiber optic reflectance spectrophotometer at maximum reflectance wavelength of 774 nm. The reflectance response enhancement of the RNA-responsive G-quadruplex-based reflectometric biosensor was linearly proportional to the target oligo DENV2 RNA concentration in the range of 2 zM-2 µM, with a 0.447 zM limit of detection and a rapid response time of 30 min. Heightening in the reflectance signal based on structural transition of G-quadruplex in response to target RNA was successfully implemented in real-time DENV2 detection in non-invasive human fluid samples (i.e. saliva and urine) under informed consent.

Sandwich-Type DNA Micro-Optode Based on Gold-Latex Spheres Label for Reflectance Dengue Virus Detection.

A DNA micro-optode for dengue virus detection was developed based on the sandwich hybridization strategy of DNAs on succinimide-functionalized poly(n-butyl acrylate) (poly(nBA-NAS)) microspheres. Gold nanoparticles (AuNPs) with an average diameter of ~20 nm were synthesized using a centrifugation-based method and adsorbed on the submicrometer-sized polyelectrolyte-coated poly(styrene-co-acrylic acid) (PSA) latex particles via an electrostatic method. The AuNP-latex spheres were attached to the thiolated reporter probe (rDNA) by Au-thiol binding to functionalize as an optical gold-latex-rDNA label. The one-step sandwich hybridization recognition involved a pair of a DNA probe, i.e., capture probe (pDNA), and AuNP-PSA reporter label that flanked the target DNA (complementary DNA (cDNA)). The concentration of dengue virus cDNA was optically transduced by immobilized AuNP-PSA-rDNA conjugates as the DNA micro-optode exhibited a violet hue upon the DNA sandwich hybridization reaction, which could be monitored by a fiber-optic reflectance spectrophotometer at 637 nm. The optical genosensor showed a linear reflectance response over a wide cDNA concentration range from 1.0 × 10-21 M to 1.0 × 10-12 M cDNA (R2 = 0.9807) with a limit of detection (LOD) of 1 × 10-29 M. The DNA biosensor was reusable for three consecutive applications after regeneration with mild sodium hydroxide. The sandwich-type optical biosensor was well validated with a molecular reverse transcription polymerase chain reaction (RT-PCR) technique for screening of dengue virus in clinical samples, e.g., serum, urine, and saliva from dengue virus-infected patients under informed consent.

Acrylic-based genosensor utilizing metal salphen labeling approach for reflectometric dengue virus detection.

An optical genosensor based on Schiff base complex (Zn2+ salphen) DNA label and acrylic microspheres (AMs) as polymer support of the capturing DNA probe (cpDNA) was developed for dengue virus serotype 2 (DEN-2) detection via reflectance spectrophotometric method. The solid-state optical DNA biosensor showed high selectivity and specificity up to one-base mismatch in the target DNA sequence owing to the salphen chemical structure that is rich in localized electrons, and allowed π-π stacking interaction between stacked base pairs of double-stranded DNA (dsDNA). The reflectometric DNA microsensor demonstrated a broad linear detection range towards DEN-2 DNA from 1 × 10-15 M to 1 × 10-3 M with a low limit of detection (LOD) obtained at 1.21 × 10-16 M. The DNA biosensor gave reproducible optical response with a satisfactory relative standard deviation (RSD) at 3.1%, (n = 3), and the reflectance response was stable even after four regeneration cycles of the DNA biosensor. The optical genosensor was proven comparable with standard reverse transcription polymerase chain reaction (RT-PCR) in detecting DEN-2 genome acquired from clinical samples of serum, urine and saliva of dengue virus infected patients under informed consent. The developed reflectometric DNA biosensor is advantageous in offering an early DEN-2 diagnosis, when fever symptom started to manifest in patient.