The Incidence of Sensorineural Hearing Loss Diagnoses in People with and without Parkinson's Disease.

Diabetes has emerged as a major global
public health challenge
in the 21st century. Against the backdrop of the rapid socioeconomic
development and changing in lifestyle, its prevention and control
have become increasingly urgent. According
to the latest epidemiological data from the International Diabetes
Federation, the global incidence of diabetes has been rising at a
compound annual growth rate of 1.1% since 1990, with the total number
of patients reaching 425 million. If current trends remain unaddressed,
this number is projected to exceed 783 million by 2045. The continued
expansion of the disease spectrum not only substantially increases
direct medical costs but also leads to a significant rise in secondary
conditions such as diabetic nephropathy, retinopathy, diabetic foot,
and cardiovascular complications. Against this
backdrop, the development of efficient and accurate early detection
technologies has become a critical breakthrough in curbing the diabetes
epidemic. Current clinical glucose monitoring techniques primarily
rely on venipuncture and electrochemical glucose meters. While the former can measure multiple biomarkers, it is limited
by the need for professional operation and delayed results. The latter
offers portability but is susceptible to environmental factors such
as temperature, humidity, and pH. In recent years, continuous glucose
monitoring systems such as Freestyle Libre and Dexcom G6 have significantly
improved the patient experience through dynamic monitoring. However, their high cost limits widespread adoption. These technical
challenges have prompted researchers to turn to emerging detection
technologies, particularly the integration of microwave sensing and
nanomaterials, opening new pathways for the development of next generation
glucose monitoring systems. Microwave biosensing technology
has demonstrated unique and irreplaceable
advantages in biomedical detection due to its noninvasiveness, high
sensitivity, and rapid response capabilities. For instance, Sandra Costanzo systematically optimized three core
structures-patch antennas, patch resonators, and split-ring resonators-evaluating
key metrics such as resolution, -factor, and robustness.
They innovatively proposed the incorporation of a solid matching layer
between the resonant sensor and the measured body, significantly enhancing
sensor stability and laying a solid foundation for improved accuracy
in glucose monitoring. Furthermore, the
rapid development and broad application of nanomaterials have opened
new avenues for innovation in glucose sensing. Chung developed an enzymatic glucose sensor based
on carbon-coated tin sulfide (C–SnS) nanomaterials. Using a
two-step process involving hydrothermal synthesis and chemical vapor
deposition, they prepared C–SnS nanosheets, which were combined
with glucose oxidase (GO) and modified
onto a glassy carbon electrode (GCE) to construct a GO/C–SnS/GCE sensor. This configuration effectively
facilitates electron transfer between the enzyme and the electrode,
substantially improving detection sensitivity. In addition to traditional invasive methods, minimally
invasive and noninvasive glucose monitoring technologies have gradually
entered the research spotlight and public awareness. For example, Jeyakumar et al. designed an RF resonant sensor combined
with principal component analysis (PCA) to enhance the accuracy of
noninvasive glucose monitoring, offering a solution that balances
high sensitivity with high accuracy for diabetes management. Although existing studies confirm the potential
of microwave biosensors in glucose monitoring, several challenges
remain. These results include susceptibility to interference
from various blood components and suboptimal sensor stability. Moreover,
due to significant physiological variations among individuals, the
accuracy and generalizability of noninvasive glucose detection require
further improvement. Based on the research landscape, this study develops
a novel microwave sensor applicable to both invasive and noninvasive
glucose monitoring. For invasive detection, novel nanomaterials are
incorporated to enhance sensitivity and anti-interference performance.
In the noninvasive direction, simulation modeling and machine learning
algorithms are employed to optimize the monitoring process and improve
measurement accuracy, while model training and prediction enhance
the generalizability across individuals. This research provides new conceptual and practical pathways for
advancing noninvasive glucose monitoring technology. In this
work, we developed a compact microwave biosensor measured
20 mm × 30 mm, fabricated on a substrate with a dielectric constant
of 2.55 and a loss tangent of 0.0019. The design was co-optimized
using Ansys high frequency structure simulator (HFSS) electromagnetic
simulations and keysight advanced design system (ADS) circuit modeling.
To further improve detection performance, a zinc oxide/carbon nanotube
(ZnO/CNT) composite coa