Use of 3D Printing Techniques to Fabricate Implantable Microelectrodes for Electrochemical Detection of Biomarkers in the Early Diagnosis of Cardiovascular and Neurodegenerative Diseases

This Review provides a comprehensive overview of 3D printing techniques to fabricate implantable microelectrodes for the electrochemical detection of biomarkers in the early diagnosis of cardiovascular and neurodegenerative diseases. Early diagnosis of these diseases is crucial to improving patient outcomes and reducing healthcare systems' burden. Biomarkers serve as measurable indicators of these diseases, and implantable microelectrodes offer a promising tool for their electrochemical detection. Here, we discuss various 3D printing techniques, including stereolithography (SLA), digital light processing (DLP), fused deposition modeling (FDM), selective laser sintering (SLS), and two-photon polymerization (2PP), highlighting their advantages and limitations in microelectrode fabrication. We also explore the materials used in constructing implantable microelectrodes, emphasizing their biocompatibility and biodegradation properties. The principles of electrochemical detection and the types of sensors utilized are examined, with a focus on their applications in detecting biomarkers for cardiovascular and neurodegenerative diseases. Finally, we address the current challenges and future perspectives in the field of 3D-printed implantable microelectrodes, emphasizing their potential for improving early diagnosis and personalized treatment strategies.</p

Metal-insulator transitions in tetrahedral semiconductors under lattice change

Although most insulators are expected to undergo insulator to metal transition on lattice compression, tetrahedral semiconductors Si, GaAs and InSb can become metallic on compression as well as by expansion. We focus on the transition by expansion which is rather peculiar; in all cases the direct gap at $\Gamma$ point closes on expansion and thereafter a zero-gap state persists over a wide range of lattice constant. The solids become metallic at an expansion of 13 % to 15 % when an electron fermi surface around L-point and a hole fermi surface at $\Gamma$-point develop. We provide an understanding of this behavior in terms of arguments based on symmetry and simple tight-binding considerations. We also report results on the critical behavior of conductivity in the metal phase and the static dielectric constant in the insulating phase and find common behaviour. We consider the possibility of excitonic phases and distortions which might intervene between insulating and metallic phases.Comment: 12 pages, 8 figure

Percutaneous Wearable Biosensors: A Brief History and Systems Perspective

Wearable biosensors are envisioned to disrupt both delivery and accessibility of healthcare by providing real‐time, continuous monitoring of informative and predictive physiological markers in convenient, user‐friendly, and portable designs. In recent years, there has been myriad demonstrations of biosensor‐integrated clothing and skin‐borne biosensor patches, enabled by device miniaturization, reduced power consumption, and new biosensing chemistries. Despite these impressive demonstrations, most consumer‐grade wearables have been limited to biophotonic and biopotential sensing methods to extrapolate information such as pulse, blood oxygenation, and electrocardiograms. The only commercial example of wearable electrochemical sensing methods is for glucose monitoring. However, there is a growing interest in developing percutaneous biosensors for monitoring in interstitial fluid (ISF), which offers direct access to popular analytes such as glucose, lactate, and urea, as well as new targets like hormones, antibodies, and even medications. Herein, a brief context for the current status of wearable biosensors is provided and assess the major engineering successes and pitfalls of percutaneous biosensors over the past five years, with a view to identifying areas for further developments that will enable deployable, clinical‐ or consumer‐grade systems

Metal Oxide-based Gas Sensor Array for the VOCs Analysis in Complex Mixtures using Machine Learning

Detection of Volatile Organic Compounds (VOCs) from the breath is becoming a viable route for the early detection of diseases non-invasively. This paper presents a sensor array with three metal oxide electrodes that can use machine learning methods to identify four distinct VOCs in a mixture. The metal oxide sensor array was subjected to various VOC concentrations, including ethanol, acetone, toluene and chloroform. The dataset obtained from individual gases and their mixtures were analyzed using multiple machine learning algorithms, such as Random Forest (RF), K-Nearest Neighbor (KNN), Decision Tree, Linear Regression, Logistic Regression, Naive Bayes, Linear Discriminant Analysis, Artificial Neural Network, and Support Vector Machine. KNN and RF have shown more than 99% accuracy in classifying different varying chemicals in the gas mixtures. In regression analysis, KNN has delivered the best results with R2 value of more than 0.99 and LOD of 0.012, 0.015, 0.014 and 0.025 PPM for predicting the concentrations of varying chemicals Acetone, Toluene, Ethanol, and Chloroform, respectively in complex mixtures. Therefore, it is demonstrated that the array utilizing the provided algorithms can classify and predict the concentrations of the four gases simultaneously for disease diagnosis and treatment monitoring

Charge Delocalization in Self-Assembled Mixed-Valence Aromatic Cation Radicals

The spontaneous assembly of aromatic cation radicals (D+•) with their neutral counterpart (D) affords dimer cation radicals (D2+•). The intermolecular dimeric cation radicals are readily characterized by the appearance of an intervalence charge-resonance transition in the NIR region of their electronic spectra and by ESR spectroscopy. The X-ray crystal structure analysis and DFT calculations of a representative dimer cation radical (i.e., the octamethylbiphenylene dimer cation radical) have established that a hole (or single positive charge) is completely delocalized over both aromatic moieties. The energetics and the geometrical considerations for the formation of dimer cation radicals is deliberated with the aid of a series of cyclophane-like bichromophoric donors with drastically varied interplanar angles between the cofacially arranged aryl moieties. X-ray crystallography of a number of mixed-valence cation radicals derived from monochromophoric benzenoid donors established that they generally assemble in 1D stacks in the solid state. However, the use of polychromophoric intervalence cation radicals, where a single charge is effectively delocalized among all of the chromophores, can lead to higher-order assemblies with potential applications in long-range charge transport. As a proof of concept, we show that a single charge in the cation radical of a triptycene derivative is evenly distributed on all three benzenoid rings and this triptycene cation radical forms a 2D electronically coupled assembly, as established by X-ray crystallography

Rational design of a planar junctionless field-effect transistor for sensing biomolecular interactions

In the ElectroMed project, we are interested in screening certain peptide sequences for their ability to selectively interact with antibodies or MHC proteins. This poses a combinatorial challenge that requires a highly multiplexed setup of label-free immunosensors. Label-free FET-based immunosensors are good candidates due to their high multiplexing capability and fast response time. Nanowire-based FET sensors have shown high sensitivity but are unreliable for clinical applications due to drift and gate stability issues. To address this, a label-free immuno-FET architecture based on planar junctionless FET devices is proposed. This geometry can improve the signal-to-noise ratio due to its larger planar structure, which is less prone to defects that cause noise and is better suited to the functionalization of different receptor molecules

Rational Mutational Analysis of a Multidrug MFS Transporter CaMdr1p of Candida albicans by Employing a Membrane Environment Based Computational Approach

CaMdr1p is a multidrug MFS transporter of pathogenic Candida albicans. An over-expression of the gene encoding this protein is linked to clinically encountered azole resistance. In-depth knowledge of the structure and function of CaMdr1p is necessary for an effective design of modulators or inhibitors of this efflux transporter. Towards this goal, in this study, we have employed a membrane environment based computational approach to predict the functionally critical residues of CaMdr1p. For this, information theoretic scores which are variants of Relative Entropy (Modified Relative Entropy REM) were calculated from Multiple Sequence Alignment (MSA) by separately considering distinct physico-chemical properties of transmembrane (TM) and inter-TM regions. The residues of CaMdr1p with high REM which were predicted to be significantly important were subjected to site-directed mutational analysis. Interestingly, heterologous host Saccharomyces cerevisiae, over-expressing these mutant variants of CaMdr1p wherein these high REM residues were replaced by either alanine or leucine, demonstrated increased susceptibility to tested drugs. The hypersensitivity to drugs was supported by abrogated substrate efflux mediated by mutant variant proteins and was not attributed to their poor expression or surface localization. Additionally, by employing a distance plot from a 3D deduced model of CaMdr1p, we could also predict the role of these functionally critical residues in maintaining apparent inter-helical interactions to provide the desired fold for the proper functioning of CaMdr1p. Residues predicted to be critical for function across the family were also found to be vital from other previously published studies, implying its wider application to other membrane protein families

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy

Alcohol use and burden for 195 countries and territories, 1990-2016 : a systematic analysis for the Global Burden of Disease Study 2016

Background Alcohol use is a leading risk factor for death and disability, but its overall association with health remains complex given the possible protective effects of moderate alcohol consumption on some conditions. With our comprehensive approach to health accounting within the Global Burden of Diseases, Injuries, and Risk Factors Study 2016, we generated improved estimates of alcohol use and alcohol-attributable deaths and disability-adjusted life-years (DALYs) for 195 locations from 1990 to 2016, for both sexes and for 5-year age groups between the ages of 15 years and 95 years and older. Methods Using 694 data sources of individual and population-level alcohol consumption, along with 592 prospective and retrospective studies on the risk of alcohol use, we produced estimates of the prevalence of current drinking, abstention, the distribution of alcohol consumption among current drinkers in standard drinks daily (defined as 10 g of pure ethyl alcohol), and alcohol-attributable deaths and DALYs. We made several methodological improvements compared with previous estimates: first, we adjusted alcohol sales estimates to take into account tourist and unrecorded consumption; second, we did a new meta-analysis of relative risks for 23 health outcomes associated with alcohol use; and third, we developed a new method to quantify the level of alcohol consumption that minimises the overall risk to individual health. Findings Globally, alcohol use was the seventh leading risk factor for both deaths and DALYs in 2016, accounting for 2.2% (95% uncertainty interval [UI] 1.5-3.0) of age-standardised female deaths and 6.8% (5.8-8.0) of age-standardised male deaths. Among the population aged 15-49 years, alcohol use was the leading risk factor globally in 2016, with 3.8% (95% UI 3.2-4-3) of female deaths and 12.2% (10.8-13-6) of male deaths attributable to alcohol use. For the population aged 15-49 years, female attributable DALYs were 2.3% (95% UI 2.0-2.6) and male attributable DALYs were 8.9% (7.8-9.9). The three leading causes of attributable deaths in this age group were tuberculosis (1.4% [95% UI 1. 0-1. 7] of total deaths), road injuries (1.2% [0.7-1.9]), and self-harm (1.1% [0.6-1.5]). For populations aged 50 years and older, cancers accounted for a large proportion of total alcohol-attributable deaths in 2016, constituting 27.1% (95% UI 21.2-33.3) of total alcohol-attributable female deaths and 18.9% (15.3-22.6) of male deaths. The level of alcohol consumption that minimised harm across health outcomes was zero (95% UI 0.0-0.8) standard drinks per week. Interpretation Alcohol use is a leading risk factor for global disease burden and causes substantial health loss. We found that the risk of all-cause mortality, and of cancers specifically, rises with increasing levels of consumption, and the level of consumption that minimises health loss is zero. These results suggest that alcohol control policies might need to be revised worldwide, refocusing on efforts to lower overall population-level consumption.Peer reviewe