Association of Depression With Myocardial Infarction With Non-obstructive Coronary Arteries: A Systematic Review.

This systematic review examined the association between depression and myocardial infarction with non-obstructive coronary arteries (MINOCA). A comprehensive literature search was conducted using electronic databases, resulting in the inclusion of six small case-control and cohort studies reported from Spain, Australia, China, and Pakistan. The studies included various study designs, such as cohort studies, case-control studies, and prospective cohort studies.

Enabling biomedical technologies for chronic myelogenous leukemia (CML) biomarkers detection.

Chronic myelogenous/myeloid leukemia (CML) is a type of cancer of bone marrow that arises from hematopoietic stem cells and affects millions of people worldwide. Eighty-five percent of the CML cases are diagnosed during chronic phase, most of which are detected through routine tests. Leukocytes, micro-Ribonucleic Acids, and myeloid markers are the primary biomarkers for CML diagnosis and are mainly detected using real-time reverse transcription polymerase chain reaction, flow cytometry, and genetic testing.

AQAFI: a bioanalytical method for automated KPIs quantification of fluorescent images of human leukocytes and micro-nano particles.

Micro-nanoparticle and leukocyte imaging find significant applications in the areas of infectious disease diagnostics, cellular therapeutics, and biomanufacturing. Portable fluorescence microscopes have been developed for these measurements, however, quantitative assessment of the quality of images (micro-nanoparticles, and leukocytes) captured using these devices remains a challenge. Here, we present a novel method for automated quality assessment of fluorescent images (AQAFI) captured using smartphone fluorescence microscopes (SFM).

Multi-modal sensing with integrated machine learning to differentiate specific leukocytes targeted by electrically sensitive hybrid particles.

The growing need for personalized, accurate, and non-invasive diagnostic technology has resulted in significant advancements, from pushing current mechanistic limitations to innovative modality developments across various disease-related biomarkers. However, there still lacks clinical solutions for analyzing multiple biomarkers simultaneously, limiting prognosis for patients suffering with complicated diseases or comorbidities. Here, we conceived, fabricated, and validated a multifrequency impedance cytometry apparatus with novel frequency-sensitive barcoded metal oxide Janus particles (MOJPs) as cell-receptor targeting agents.

Can antidepressant use be associated with emotional blunting in a subset of patients with depression? A scoping review of available literature.

Introduction: Despite frequent recognition of emotional blunting in the published literature, either as a primary symptom of depression or as an adverse effect of antidepressants, there is no systematic synthesis on this topic to our knowledge. We undertook this scoping review to assess the prevalence, clinical features, implicated causes and management of emotional blunting, outlining the phenomenological and clinical gaps in research.

Method: A systematic search was done until March 15, 2022, to include all original studies (i.

Point-of-Care Portable 3D-Printed Multispectral Sensor for Real-Time Enzyme Activity Monitoring in Healthcare Applications.

Absorbance spectroscopy finds many biomedical and physical applications ranging from studying the atomic and molecular details of the analyte to determination of unknown biological species and their concentration or activity in the samples. Commercially available laboratory-based spectrometers are usually bulky and require high power and laborious manual processing, making them unsuitable to be deployed in portable and space-constrained environments, thereby further limiting their utility for real-time on-site monitoring. To address these challenges, here we developed a portable 3D-printed multispectral spectrophotometer based on absorbance spectroscopy for real-time monitoring of enzyme molecular activity.

Digital filtering dissemination for optimizing impedance cytometry signal quality and counting accuracy.

Improving biosensor performance which utilize impedance cytometry is a highly interested research topic for many clinical and diagnostic settings. During development, a sensor's design and external factors are rigorously optimized, but improvements in signal quality and interpretation are usually still necessary to produce a sensitive and accurate product. A common solution involves digital signal processing after sample analysis, but these methods frequently fall short in providing meaningful signal outcome changes.

MACHINE LEARNING ENABLES QUANTIFYING CELL-JANUS PARTICLE CONJUGATES THROUGH MICROFLOWING IMPEDANCE SIGNALS.

In this work, we demonstrate the differentiation of demodulated multifrequency signals from impedance sensitive microparticles when targeting surface receptors on neutrophils in a microfluidic impedance cytometer. These scheme uses a single signal input and detection configuration, and machine learning can differentiate particle types with up to 82% accuracy.

Excitation modalities for enhanced micro and nanoparticle imaging in a smartphone coupled 3D printed fluorescent microscope.

Smartphone fluorescent microscopes (SFM) offer many functional characteristics similar to their benchtop counterparts at a fraction of the cost and have been shown to work for biomarker detection in many biomedical applications. However, imaging and quantification of bioparticles in the sub-micron and nanometer range remains challenging as it requires aggressive robustness and high-performance metrics of the building blocks of SFM. Here, we explored multiple excitation modalities and their performance on the imaging capability of an SFM.

Antibody-functionalized aluminum oxide-coated particles targeting neutrophil receptors in a multifrequency microfluidic impedance cytometer.

Personalized diagnostics of infectious diseases require monitoring disease progression due to their ever-changing physiological conditions and the multi-faceted organ system mechanisms involved in disease pathogenesis. In such instances, the recommended clinical strategies involve multiplexing data collection from critical biomarkers related to a patient's conditions along with longitudinal frequent patient monitoring. Numerous detection technologies exist both in research and commercial settings to monitor these conditions, however, they fail to provide biomarker multiplexing ability with design and data processing simplicity.

Aluminum Oxide-Coated Particle Differentiation Employing Supervised Machine Learning and Impedance Cytometry.

This article uses a supervised machine learning (ML) system for identifying groups of nanoparticles coated with metal oxides of varying thicknesses using a microfluidic impedance cytometer. These particles generate unique impedance signatures when probed with a multifrequency electric field and finds applications in enabling many multiplexed biosensing technologies. However, current experimental and data processing techniques are unable to sensitively differentiate different metal oxide coated particle types.

Bioelectronic Sensor with Magnetic Modulation to Quantify Phagocytic Activity of Blood Cells Employing Machine Learning.

Microbial infections result in activating an immune response in the human body, which triggers inflammatory pathways resulting in recognition and subsequent killing of the pathogens. Quantifying the blood cells' natural ability to kill pathogens, i.e.

Profiling single-cell level phagocytic activity distribution with blood lactate levels.

The ability to kill infecting microbes is an essential facet of our immune response to an infection. However, phagocytic ability is often overlooked as a part of immunological profile in infected patients' diagnosis, as the understanding of phagocytic capabilities in disease states is incomplete. In this work, we have evaluated for the first time the relationship between blood lactate level and the neutrophil phagocytic activity at a single-cell level.

Development of myocarditis and pericarditis after COVID-19 vaccination in adult population: A systematic review.

Objectives: A clear temporal relationship between myocarditis and pericarditis after COVID-19 vaccination has led to the belief that the vaccine may act as a trigger for these cardiologic complications. The aim of this systematic review is to explore the incidence, clinical presentation, management, and association between them.

Methods: We conducted a systematic literature search on Cochrane, MEDLINE, and EMBASE as per guidelines of PRISMA (Preferred Reporting Items for Systematic Reviews).

Deep Learning Application for Effective Classification of Different Types of Psoriasis.

Psoriasis is a chronic inflammatory skin disorder mediated by the immune response that affects a large number of people. According to latest worldwide statistics, 125 million individuals are suffering from psoriasis. Deep learning techniques have demonstrated success in the prediction of skin diseases and can also lead to the classification of different types of psoriasis.

De-novo fabrication of sunlight irradiated silver nanoparticles and their efficacy against E. coli and S. epidermidis.

Silver nanoparticles (AgNPs) gained significant attention due to their activity against microbial pathogens, cancer cells, and viral particles etc. Traditional fabrication methods require hazardous chemicals as reducing agents and their usage and disposal pose a significant hazard to environmental ecosystem. Here, a de novo, robust, cost effective and an eco-friendly method is reported to fabricate AgNPs irradiated with sunlight (SL) while using Salvadora persica root extract (SPE) as reducing agent.

Investigating Cell-Particle Conjugate Orientations in a Microfluidic Channel to Ameliorate Impedance-based Signal Acquisition and Detection.

Many biomedical experimental assays rely on cell-to-microparticle conjugation and their subsequent detection to quantify disease-related biomarkers. In this report, we investigated the effect of particle attachment position on a cell's surface to a signal acquired using impedance cytometry. We also present a novel configuration of independent coplanar microelectrodes positioned at the bottom and top of the microfluidic channel.

Frequency-Time Domain (FTD) Impedance Data Analysis to Improve Accuracy of Microparticle Enumeration in a Microfluidic Electronic Counter.

Experimental background noise present in biosensors' data hinders the ability for sensitive and accurate detection of critical biomarkers. Here, we report our digital signal processing analysis with respect to frequency and time domain (FTD) data to reduce noise in an experimental microfluidic impedance cytometer. We evaluated the effectiveness of employed noise filtering techniques independently, including baseline drift correction, high frequency noise filtering, and powerline interference mitigation.

Classification Framework for Healthy Hairs and Alopecia Areata: A Machine Learning (ML) Approach.

Alopecia areata is defined as an autoimmune disorder that results in hair loss. The latest worldwide statistics have exhibited that alopecia areata has a prevalence of 1 in 1000 and has an incidence of 2%. Machine learning techniques have demonstrated potential in different areas of dermatology and may play a significant role in classifying alopecia areata for better prediction and diagnosis.

Time-domain signal averaging to improve microparticles detection and enumeration accuracy in a microfluidic impedance cytometer.

Microfluidic impedance cytometry is a powerful system to measure micro and nano-sized particles and is routinely used in point-of-care disease diagnostics and other biomedical applications. However, small objects near a sensor's detection limit are plagued with relatively significant background noise and are difficult to identify for every case. While many data processing techniques can be utilized to reduce noise and improve signal quality, frequently they are still inadequate to push sensor detection limits.

Circular shaped microelectrodes for single cell electrical measurements for lab-on-a-chip applications.

The impedimetric sensing techniques for single cell characterization have witnessed growing interest due to their high sensitivity and widespread applications. However, adapting the method to different biological measurements in microfluidic environments under various input conditions can result in feeble signal detection leading to a drastic decrease in the sensor sensitivity. The reduced signal-to-noise ratio (SNR) hinders the signal differentiation, sensor accuracy and prohibits fully integrated point-of-care applications.

Biochip with multi-planar electrodes geometry for differentiation of non-spherical bioparticles in a microchannel.

A biosensor capable of differentiating cells or other microparticles based on morphology finds significant biomedical applications. Examples may include morphological determination in the cellular division process, differentiation of bacterial cells, and cellular morphological variation in inflammation and cancer etc. Here, we present a novel integrated multi-planar microelectrodes geometry design that can distinguish a non-spherical individual particle flowing along a microchannel based on its electrical signature.

A modular microscopic smartphone attachment for imaging and quantification of multiple fluorescent probes using machine learning.

Portable smartphone-based fluorescent microscopes are becoming popular owing to their ability to provide major functionalities offered by regular benchtop microscopes at a fraction of the cost. However, smartphone-based microscopes are still limited to a single fluorophore, fixed magnification, the inability to work with a different smartphones, and limited usability to either glass slides or cover slips. To overcome these challenges, here we present a modular smartphone-based microscopic attachment.

Salvadora persica mediated synthesis of silver nanoparticles and their antimicrobial efficacy.

Silver nanoparticles (AgNPs) exhibit strong antimicrobial properties against many pathogens. Traditionally employed chemical methods for AgNPs synthesis are toxic for the environment. Here, we report a quicker, simpler, and environmentally benign process to synthesize AgNPs by using an aqueous 'root extract' of Salvadora persica (Sp) plant as a reducing agent.