Availability Of Methods Research Articles

Designing a Compact Portable Electro-Cardio Device Enhanced by Machine Learning for Early Detection of Myocardial Infraction

Cardiovascular diseases remain one of the leading causes of death globally, underscoring the need for accessible, efficient, and accurate methods of heart health monitoring. Traditional heart monitoring tools, often confined to clinical settings, limit the ability to perform continuous, real-time assessments of heart function. This challenge has driven the demand for innovative, portable solutions that enable early detection and proactive management of heart conditions. The Heart Box project aims to revolutionize cardiac health monitoring through a portable, AI-integrated system that provides real-time analysis of electrocardiogram signals. Leveraging precision healthcare technology, this device enables early detection of heart conditions by continuous monitoring. Our ground breaking project encountered two primary challenges: establishing reliable hardware connections for data capture and sourcing appropriate datasets to train our Convolutional Neural Network model. Overcoming the intricacies of hardware integration demanded meticulous troubleshooting and innovative solutions to ensure seamless data acquisition. Additionally, identifying and curating high-quality datasets posed a significant hurdle, requiring exhaustive research and data pre-processing efforts. Despite these challenges, our perseverance and dedication led to the successful development of a robust Convolutional Neural Network model, revolutionizing cardiac assessment by leveraging cutting-edge technology and data-driven insights.

New Insights into the Antibacterial Activity of Hydroxytyrosol Extracted from Olive Leaves: Molecular Docking Simulations of its Antibacterial Mechanisms.

This study investigated the biological activities of a hydroxytyrosol-rich extract from Olea europaea leaves, particularly its ability to eradicate severe pathogenic bacteria producing Extended-Spectrum Beta-Lactamases (ESBLs). The latter bacteria are emerging microorganisms that pose significant challenges due to their resistance to a broad range of potent therapeutic drugs. The extract was prepared through an accessible acid hydrolysis method. In vitro and In silico analyses through MIC, MBC analysis and molecular docking were conducted to evaluate the antibacterial properties. The extract showed remarkable antioxidant activity and significant antibacterial potential against reference species and ESBL bacteria. MIC and MBC calculations confirmed the extract's capacity to kill bacteria rather than just inhibit their growth. Further in silico analyzes demonstrated the high binding affinity of HT to the active sites of the gyrase B subunit and the peptidoglycan DD-transpeptidase domain from proteins located in the cytoplasm and the cell wall of the bacteria, respectively. Results confirmed the structure-activity relationship and the ability of HT to disrupt essential bacterial functions. This study validates the debated antimicrobial potential of HT and highlights its importance as a potential therapeutic agent against resistant bacteria, which is a critical area of research given the global challenge of antibiotic resistance.

ВОЗМОЖНОСТИ ПРИМЕНЕНИЯ НЕИНВАЗИВНЫХ ОФТАЛЬМОЛОГИЧЕСКИХ МЕТОДОВ ДИАГНОСТИКИ ПАТОЛОГИЧЕСКИХ СОСТОЯНИЙ ПРОЦЕССА РАННЕГО СТАРЕНИЯ НА МОДЕЛИ ПРИМАТОВ

Currently, there is an urgent need for early diagnostics, searching for means of predicting the development of cognitive deficit, and assessing the dynamics of changes during treatment. Given the common origin of the brain and eye, which undergo the same onto- and phylogenetic development, some researchers have suggested that the eye can act as a “window” for easily accessible and non-invasive methods for studying brain pathology [3]. The popularity of fundamental research is growing, and interest in creating disease models in primates is becoming more relevant. The creation of such models will significantly expand the possibilities of obtaining the most reliable information, especially in the field of gerontology and brain pathology (cataract, senile ring, age-related changes in the fundus and other signs of early aging). In this part of the work, approaches to studying the fundus, physiological parameters of the structures and functions of the visual organ in rhesus macaques and hamadryas baboons of different ages are developed; potential ophthalmological biomarkers for diagnosing early aging processes are considered. A combined approach using ophthalmological markers, including non-invasive visualization of the retina and detection of pathological proteins in the intraocular fluid, lens or retina, in combination with biomarkers of oxidative stress (malondialdehyde, lipofuscin, protein carbonyls, 8-OH-deoxyguanosine) will allow us to develop a methodology for diagnosing the development and correction of early aging in humans on a primate model.

ВОЗМОЖНОСТИ ПРИМЕНЕНИЯ НЕИНВАЗИВНЫХ ОФТАЛЬМОЛОГИЧЕСКИХ МЕТОДОВ ДИАГНОСТИКИ ПАТОЛОГИЧЕСКИХ СОСТОЯНИЙ ПРОЦЕССА РАННЕГО СТАРЕНИЯ НА МОДЕЛИ ПРИМАТОВ

Currently, there is an urgent need for early diagnostics, searching for means of predicting the development of cognitive deficit, and assessing the dynamics of changes during treatment. Given the common origin of the brain and eye, which undergo the same onto- and phylogenetic development, some researchers have suggested that the eye can act as a “window” for easily accessible and non-invasive methods for studying brain pathology [3]. The popularity of fundamental research is growing, and interest in creating disease models in primates is becoming more relevant. The creation of such models will significantly expand the possibilities of obtaining the most reliable information, especially in the field of gerontology and brain pathology (cataract, senile ring, age-related changes in the fundus and other signs of early aging). In this part of the work, approaches to studying the fundus, physiological parameters of the structures and functions of the visual organ in rhesus macaques and hamadryas baboons of different ages are developed; potential ophthalmological biomarkers for diagnosing early aging processes are considered. A combined approach using ophthalmological markers, including non-invasive visualization of the retina and detection of pathological proteins in the intraocular fluid, lens or retina, in combination with biomarkers of oxidative stress (malondialdehyde, lipofuscin, protein carbonyls, 8-OH-deoxyguanosine) will allow us to develop a methodology for diagnosing the development and correction of early aging in humans on a primate model.

Photobiocatalytic Platform for the Efficient Enantio-Divergent Synthesis of β-Fluoromethylated Ketones.

β-Fluoromethyl (CH2F, CHF2, and CF3)-substituted chiral ketones are essential moieties and are vital building blocks in pharmaceutical and agrochemistry. However, general and convenient methods for enantio-diverse access to diverse β-fluoromethylated ketones are lacking, hindering the further development of these functional moieties. In this study, we developed an ene-reductase-based photobiocatalytic platform for efficient synthesis of enantio-divergent β-fluoromethylated chiral ketones. Our method highlights substrate-type diversity, excellent enantioselectivity, enzymatic enantio-divergent synthesis, as well as a dicyanopyrazine (DPZ)-type photosensitizer for biocompatible olefin E/Z isomerization in enzymatic stereoconvergent olefin asymmetric reduction, thereby providing a general photobiocatalytic solution to diverse β-fluoromethylated chiral ketones.

Comparison of Sternotomy Access Versus Thoracotomy Access in the Surgical Treatment of Aortic Coarctation: A Propensity Score-matched Study.

The search for the "Holy Grail" of surgical repair for aortic coarctation involves finding an access and correction method that successfully and consistently avoids aortic recoarctation and arterial hypertension. The controversy persists as there is still no consensus on the best surgical approach and technique to achieve these objectives. The objective of this study is to compare sternotomy and left thoracotomy as surgical techniques for treating patients with aortic coarctation and hypoplasia of the distal aortic arch. From January 2008 to December 2020, 103 surgical procedures were performed using an extended oblique anastomosis. The patients were divided into 2 groups: oblique, extended anastomosis from thoracotomy access (n-68; 66%) and oblique extended anastomosis from sternotomy access (n-35; 34%). After performing a propensity score analysis (1:1) for the entire sample, 25 patients from sternotomy access were matched with 25 patients from thoracotomy access. The mortality rate in the thoracotomy group was 4%, with 1 patient, while it was 8% with 2 patients in the sternotomy group, p > 0.99. There were no significant differences in early postoperative complications between the two groups. Recurrence of aortic coarctation was observed more frequently in the thoracotomy group (20.8% vs 0%). Low weight was identified as the only risk factor for aortic re-coarctation. The mortality rates and early postoperative complications associated with the surgical repair of aortic coarctation were similar between the sternotomy and thoracotomy approaches. Nevertheless, sternotomy approach may be beneficial in reducing aortic recoarctation.

Detection of ATTR cardiac amyloidosis using a novel artificial intelligence algorithm for wearable-adapted noisy single-lead electrocardiograms

Abstract Background Amyloid cardiomyopathy (ATTR-CM) is often underdiagnosed, with very few diagnosed before the onset of symptoms, leading to delayed use of disease-modifying therapies that could have substantial prognostic implications early in the disease course. In the US, the disease has a substantially higher prevalence among Black adults, who are further challenged by lower access to healthcare services and lower preventative care, further reducing the ability for clinician-led diagnosis. We sought to develop an approach for identifying ATTR-CM on noisy single-lead ECGs obtainable on portable and wearable ECG devices, allowing broad use in community screening. Purpose To develop a portable/wearable device-adapted AI-ECG algorithm capable of detecting ATTR-CM on noisy single-lead ECGs. Methods We identified patients with ATTR-CM from 2015 through June 2023 across 5 hospitals of a large U.S.-based hospital system using positive bone scintigraphy scans or pharmacotherapy with an approved transthyretin stabilizer. In the development cohort, we used lead I of 12 lead ECGs, commonly captured by portable and wearable ECG devices, to train a novel noise-adapted model explicitly designed to infer information against simulated real-world noises. For this, ECG signals were augmented using random Gaussian real-world noise within four frequency ranges at multiple signal-to-noise ratios. We tested the model in an independent set of cases and matched controls drawn from July through December 2023. Results The development cohort consisted of 1,011 ECGs from 234 patients (median age 79 years [IQR:70-85], 17% women), with 10:1 age- and sex-matched 10,110 controls with 10,110 ECGs (age 79 [IQR:70-85] years 17.7% female). In the independent test set of 139 ECGs from 47 patients (age 80 [75-86] years, 32% women) and matched controls (1390 ECGs and patients) from July through December 2023, the AUROC (area under the receiver operating characteristic curve) of the AI-ECG model for ATTR-CM was 0.90 [95%CI: 0.88-0.92] (A). The AUPRC was 0.44 [0.36-0.53]. The sensitivity and specificity of the model were 0.85 and 0.80. The positive predictive value ranged from 0.12 at a 3% prevalence level to 0.59 at a 25% prevalence (B). Conclusions A novel portable and wearable-devices adapted model demonstrates promising performance for detecting ATTR-CM on single-lead ECGs, representing a more accessible method for screening in community-dwelling adults.

Calculating potential impact fractions to assess the impact of tobacco control policies

Abstract This presentation will provide a straightforward and accessible method for evaluating the effectiveness of various tobacco control policies in reducing the smoking-associated disease burden. To estimate numbers and proportions of potentially avoidable disease cases under different policy intervention scenarios (such as cigarette tax/price increases, comprehensive marketing ban, and plain packaging), this approach entails the calculation of age- and sex-specific potential impact fractions, representing the percentage change in disease risk following changes in exposure to smoking. The approach can account for lag and latency periods between reductions in smoking prevalence and declines in disease excess risks. The future disease burden is estimated by combining data on the incidence or mortality of the disease of interest, published effect sizes of tobacco control policies, and national smoking prevalence data. Intervention scenarios are compared against a status quo scenario that assumes the continuation of recent smoking trends. The presentation will outline the approach, using the example of smoking-associated cancer, and discuss model assumptions, required data, and sensitivity analyses. This straightforward modelling framework enables the comparison of different health policy measures’ impacts, providing valuable insights for policymakers and public health officials regarding the potential public health impact of tobacco control policy measures.

Routine chest X-ray post cardiac implantable electronic device implantation: is it really required? Experience from a tertiary centre

Abstract Introduction The 2021 EHRA expert consensus statement on pacemaker (PPM) and transvenous implantable cardioverter defibrillator (ICD) implant technique proposes a chest X-ray (CXR) prior to hospital discharge in all patients. This recommendation serves the dual purpose of excluding pneumothorax following subclavian or axillary access (SAA) and documenting lead position. Purpose Given that cephalic venous access (CA) precludes pneumothorax, the utility of CXR as a tool for identifying early lead displacement (LD) in these patients warrants further examination. We studied the yield of post implant CXR, device interrogation (DI), or both for the identification of pneumothorax and LD in patients undergoing transvenous PPM or ICD implant via SAA or CA. Methods 4,051 consecutive PPM or ICD devices were implanted at a tertiary centre between 2016-2019. Lead position was documented fluoroscopically on conclusion of all procedures. A subsequent post implant CXR was performed in cases of SAA, plus in those CA patients requiring imaging for another indication (e.g. pulmonary oedema). Post implant DI was only performed in patients with a high anticipated burden of ventricular pacing (e.g. high-grade atrioventricular block). All patients underwent clinical review with repeat DI at 30 days post implant. LD was defined as a clinically significant translocation of a lead requiring repositioning or reprogramming. Results For the whole cohort, the incidence of LD at 30 days was 2.2% (n=89), and the incidence of pneumothorax (within the SAA group) was 0.9% (n=11). The venous access method used, post implant investigations, and the incidence of LD is shown in Figure 1. 1,282 patients (31.6%) required SAA; CXR was performed in all, and 9 LDs were identified (0.7%). However, four patients (0.3%) with ‘normal’ CXRs had evidence of LD on inpatient DI, and a further 17 LDs (1.3%) were confirmed on the 30 day DI despite a ‘normal’ CXR. 2,769 patients (68.4%) underwent CA; 309 (11%) required CXR. This identified LD in four patients (1.3%), with three further patients (1%) found to have LD on subsequent inpatient DI despite a ‘normal’ CXR. A further four LDs (1.3%) were diagnosed at 30 day DI. The majority (89%, n=2,460) of CA patients were discharged without CXR. 30% (n=825) of these patients required inpatient DI; this identified 13 LDs (1.6%). Accordingly, 59% (n=1,635) of CA patients had no post procedural investigations; 24 (1.5%) were found to have LD at 30 day DI. Conclusions In patients who developed LD within 30 days of a PPM or ICD implant, a post implant CXR identified this in only 32% of cases (n=13/41), and the overall sensitivity of CXR for LD was low (50%). Additionally, the majority of LDs (54%) occurred after hospital discharge. We suggest that the utility of post implant CXR for identifying LD is limited, and that CXR should not be considered an essential investigation in CA device implants.Figure 1

CHARACTERISTICS AND OUTCOME OF LEFT MAIN STEM PCI AMONG WOMEN

Left main stem disease is a severe condition affecting a major coronary artery, traditionally treated with coronary artery bypass grafting. Recent advancements in percutaneous coronary intervention have made it a viable option, particularly for women who face unique risks and outcomes compared to men. Objective: This study aims to evaluate the characteristics and outcomes of percutaneous coronary intervention for left main stem disease in female patients, focusing on procedural outcomes and changes in laboratory parameters before and after the intervention. Methods: A prospective observational study was conducted from January to July 2024 with 68 female patients, aged 18 and above, from Fauji Foundation Hospital Rawalpindi. Various percutaneous coronary intervention techniques were used, and data on ejection fractions, access methods, and procedural specifics were collected. Pre- and post-procedural laboratory outcomes including haemoglobin, creatinine, platelet counts, and alanine aminotransferase levels were analyzed. Results: The mean age of participants was 63.6 years. Diabetes, hypertension, and dyslipidemia were common comorbidities. Laboratory results showed stable haemoglobin levels, slight increases in creatinine, and consistent platelet counts and ALT levels post-procedure. Statistical analysis revealed no significant changes in these parameters, indicating minimal impact on anaemia, renal function, and platelet or liver function. Conclusion: percutaneous coronary intervention for the left main stem in females generally results in stable laboratory outcomes with minimal impact on anaemia, renal function, and platelet or liver function. The procedure is safe with a low mortality rate. Continuous monitoring is recommended to manage any potential complications effectively.

Establishing the Reliability of the GaitON® Motion Analysis System: A Foundational Study for Gait and Posture Analysis in a Healthy Population.

Gait and posture analysis plays a crucial role in understanding human movement, with significant applications in rehabilitation, sports science, and clinical settings. The GaitON® system, a 2D motion analysis tool, provides an accessible and cost-effective method for assessing gait and posture. However, its reliability in clinical practice, particularly for intra-rater consistency, remains to be evaluated. This study aims to assess the intra-rater reliability of the GaitON® system in a healthy population, focusing on gait and posture parameters. A total of 20 healthy participants (10 males and 10 females) aged 18 to 50 years were recruited for the study. Each participant underwent gait and posture assessments using the GaitON® system on two separate occasions, spaced one week apart. Video recordings from anterior and posterior views were used to analyze gait, while images from anterior, posterior, and lateral views were captured to assess posture with markers placed on key anatomical landmarks. The reliability of the measurements was analyzed using intraclass correlation coefficients (ICC), a standard error of measurement (SEM), and the smallest detectable difference (SDD) method. The GaitON® system demonstrated excellent intra-rater reliability across a wide range of gait and posture parameters. ICC values for gait parameters, including hip, knee, and ankle joint angles, ranged from 0.90 to 0.979, indicating strong consistency in repeated measurements. Similarly, ICC values for posture parameters, such as the head alignment, shoulder position, and ASIS alignment, were above 0.90, reflecting excellent reliability. SEM values were low across all parameters, with the smallest SEM recorded for the hip joint angle (0.37°), and SDD values further confirmed the precision of the system. The GaitON® system provides reliable and consistent measurements for both gait and posture analysis in healthy individuals. Its high intra-rater reliability and low measurement error make it a promising tool for clinical and sports applications. Further research is needed to validate its use in clinical populations and compare its performance to more complex 3D motion analysis systems.

Empowering Healthcare: TinyML for Precise Lung Disease Classification

Respiratory diseases such as asthma pose significant global health challenges, necessitating efficient and accessible diagnostic methods. The traditional stethoscope is widely used as a non-invasive and patient-friendly tool for diagnosing respiratory conditions through lung auscultation. However, it has limitations, such as a lack of recording functionality, dependence on the expertise and judgment of physicians, and the absence of noise-filtering capabilities. To overcome these limitations, digital stethoscopes have been developed to digitize and record lung sounds. Recently, there has been growing interest in the automated analysis of lung sounds using Deep Learning (DL). Nevertheless, the execution of large DL models in the cloud often leads to latency, dependency on internet connectivity, and potential privacy issues due to the transmission of sensitive health data. To address these challenges, we developed Tiny Machine Learning (TinyML) models for the real-time detection of respiratory conditions by using lung sound recordings, deployable on low-power, cost-effective devices like digital stethoscopes. We trained three machine learning models—a custom CNN, an Edge Impulse CNN, and a custom LSTM—on a publicly available lung sound dataset. Our data preprocessing included bandpass filtering and feature extraction through Mel-Frequency Cepstral Coefficients (MFCCs). We applied quantization techniques to ensure model efficiency. The custom CNN model achieved the highest performance, with 96% accuracy and 97% precision, recall, and F1-scores, while maintaining moderate resource usage. These findings highlight the potential of TinyML to provide accessible, reliable, and real-time diagnostic tools, particularly in remote and underserved areas, demonstrating the transformative impact of integrating advanced AI algorithms into portable medical devices. This advancement facilitates the prospect of automated respiratory health screening using lung sounds.

Forgotten Violence: Social and Cultural Contexts of Sibling Abuse in Afghanistan (A Case Study of Students - District 6 of Kabul)

Background and Purpose: The probability of a person being subjected to violence in the family is much higher than outside the family. The purpose of this research is to examine family violence, especially sibling violence among families in Kabul province, Afghanistan. Methodology: This research was conducted using a simple accessible sampling method with several 370 boys and girls in the families of Kabul using a researcher-made questionnaire. Findings and Conclusions: Descriptive findings show that among the families of Kabul city, physical abuse by siblings is 26.4%, psychological abuse by siblings is 26.4%, and lastly, siblings are sexually abused by 10.2%. is placed the age of the victim of sibling abuse was more than 16 years old in boys and girls (54.5%) had the highest frequency. 28% had the most sibling abuse in children 4 and later, 54.5% of Tajiks and 41.5% of early education had high levels of sibling abuse in their families. On the other hand, 37.6% of unemployed families, 36% of low-income families, 50% of parents living alone, and 54.4% of families with vehicles had more frequency of sibling abuse. Analytical findings show that the cultural and social characteristics of families, such as the level of parental education, poverty, and unemployment, have an effect on the level of violence between siblings, and students who have a lower level of family background than They told others, violence has become more prevalent among them. The results show that social factors and cultural levels within families are effective in the level of violence among children; therefore, if a society can strengthen the normative and cultural components of families through education, it can improve the health of families.

Cough2COVID-19 detection using an enhanced multi layer ensemble deep learning framework and CoughFeatureRanker

In response to the pressing requirement for precise and easily accessible COVID-19 detection methods, we present the Cough2COVID-19 framework, which is cost-effective, non-intrusive, and widely accessible. The conventional diagnostic methods, notably the PCR test, are encumbered by limitations such as cost and invasiveness. Consequently, the exploration of alternative solutions has gained momentum. Our innovative approach employs a multi-layer ensemble deep learning (MLEDL) framework that capitalizes on cough audio signals to achieve heightened efficiency in COVID-19 detection. This study introduces the Cough2COVID-19 framework, effectively addressing these challenges through AI-driven analysis. Additionally, this study proposed the CoughFeatureRanker algorithm, which delves into the robustness of pivotal features embedded within cough audios. The CoughFeatureRanker algorithm selects the most prominent features based on their optimal discriminatory performance from 15 features to detect COVID-19. The effectiveness of the CoughFeatureRanker algorithm within the ensemble framework is scrutinized, confirming its favorable influence on the accuracy of COVID-19 detection. The Cough2COVID-19 (MLEDL) framework achieves remarkable outcomes in COVID-19 detection through cough audio signals, boasting a specificity of 98%, sensitivity of 97%, accuracy of 98%, and an AUC score of 0.981. Our framework asserts its supremacy in precise non-invasive screening through an exhaustive comparison with cutting-edge methodologies. This groundbreaking innovation holds the potential to enhance urban resilience by transforming disease diagnosis, offering a significant approach to curtailing transmission risks and facilitating timely interventions in the ongoing battle against the pandemic.

Targeted nanopore sequencing using the Flongle device to identify mitochondrial DNA variants

Variants in mitochondrial genomes (mtDNA) can cause various neurological and mitochondrial diseases such as mitochondrial myopathy, encephalopathy, lactic acidosis, stroke-like episodes (MELAS). Given the 16 kb length of mtDNA, continuous sequencing is feasible using long-read sequencing (LRS). Herein, we aimed to show a simple and accessible method for comprehensive mtDNA sequencing with potential diagnostic applications for mitochondrial diseases using the compact and affordable LRS flow cell “Flongle.” Whole mtDNA amplification (WMA) was performed using genomic DNA samples derived from four patients with mitochondrial diseases, followed by LRS using Flongle. We compared these results to those obtained using Cas9 enrichment. Additionally, the accuracy of heteroplasmy rates was assessed by incorporating mtDNA variants at equimolar levels. Finally, mtDNA from 19 patients with Parkinson’s disease (PD) was sequenced using Flongle to identify disease risk-associated variants. mtDNA variants were detected in all four patients with mitochondrial diseases, with results comparable to those obtained from Cas9 enrichment. Heteroplasmy levels were accurately detected (r2 > 0.99) via WMA using Flongle. A reported variant was identified in three patients with PD. In conclusion, Flongle can simplify the traditionally cumbersome and expensive mtDNA sequencing process, offering a streamlined and accessible approach to diagnosing mitochondrial diseases.

Artificial intelligence for detection of retinal toxicity in chloroquine and hydroxychloroquine therapy using multifocal electroretinogram waveforms.

Chloroquine and hydroxychloroquine, while effective in rheumatology, pose risks of retinal toxicity, necessitating regular screening to prevent visual disability. The gold standard for screening includes retinal imaging and automated perimetry, with multifocal electroretinography (mfERG) being a recognized but less accessible method. This study explores the efficacy of Artificial Intelligence (AI) algorithms for detecting retinal damage in patients undergoing (hydroxy-)chloroquine therapy. We analyze the mfERG data, comparing the performance of AI models that utilize raw mfERG time-series signals against models using conventional waveform parameters. Our classification models aimed to identify maculopathy, and regression models were developed to predict perimetric sensitivity. The findings reveal that while regression models were more adept at predicting non-disease-related variation, AI-based models, particularly those utilizing full mfERG traces, demonstrated superior predictive power for disease-related changes compared to linear models. This indicates a significant potential to improve diagnostic capabilities, although the unbalanced nature of the dataset may limit some applications.

Development of an Electromyography Signal Acquisition Prototype and Statistical Validation Against a Commercial Device.

Electromyography (EMG) stands out as an accessible and inexpensive method for identifying muscle contractions on the surface and within deeper muscle tissues. Using specialized electronic circuits for amplification and filtering can help develop simple but effective systems for detecting and analyzing these signals. However, EMG devices developed by research teams frequently lack rigorous methodologies for validating the quality of the signals they record compared to those obtained by commercial systems that have undergone extensive testing and regulatory approval for market release. This underscores the critical need for standardized validation techniques to reliably assess the performance of experimental devices relative to established commercial equipment. Hence, this study introduces a methodology for the development and statistical validation of a laboratory EMG circuit compared with a professional device available on the market. The experiment simultaneously recorded the muscle electrical activity of 18 volunteers using two biosignal acquisition devices-a prototype EMG and a commercial system-both applied in parallel at the same recording site. Volunteers performed a series of finger and wrist extension movements to elicit myoelectric activity in these forearm muscles. To achieve this, it was necessary to develop not only the EMG signal conditioning board, but also two additional interface boards: one for enabling parallel recording on both devices and another for synchronizing the devices with the task programmatically controlled in Python that the volunteers were required to perform. The EMG signals generated during these tasks were recorded simultaneously by both devices. Subsequently, 22 feature indices commonly used for classifying muscular activity patterns were calculated from two-second temporal windows of the recordings to extract detailed temporal and spatial characteristics. Finally, the Mean Absolute Percentage Error (MAPE) was computed to compare the indices from the prototype with those from the commercial device, using this method as a validation system to assess the quality of the signals recorded by the prototype relative to the commercial equipment. A concordance of 87.6% was observed between the feature indices calculated from the recordings of both devices, suggesting high effectiveness and reliability of the EMG signals recorded by the prototype compared to the commercial device. These results validate the efficacy of our EMG prototype device and provide a solid foundation for the future evaluation of similar devices, ensuring their reliability, accuracy, and suitability for research or clinical applications.

Electrochemical Immunosensors on Laser-Induced Graphene Platforms for Monitoring of Anti-RBD Antibodies After SARS-CoV-2 Infection

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has posed a major challenge to global health. The development of fast, accurate, and accessible diagnostic methods is essential in controlling the disease and mitigating its impacts. In this context, electrochemical biosensors present themselves as promising tools for the efficient monitoring of SARS-CoV-2 infection. We have developed a highly specific biosensor for the detection of anti-SARS-CoV-2 antibodies in patient sera. The use of the RBD-S region as an antigen, although purified to minimize cross-linking, poses a specific challenge. The structural similarity between SARS-CoV-2 and other respiratory viruses, as well as the complexity of the serum matrix, hinders robust analytical strategies to ensure diagnostic accuracy. This work presents a novel immunosensor for COVID-19 diagnosis using laser-induced graphene (LIG) electrodes subjected to electrochemical reduction with graphene (named rGraphene-LIG). In the present study, we chose an initial approach focused on demonstrating the concept and evaluating the feasibility of the rGraphene-LIG sensor for SARS-CoV-2 detection. The rGraphene-LIG electrodes presented a notable current increase for the redox probe in the aqueous solution of a mixture of 5 mmol L−1 potassium ferricyanide/ferrocyanide ([Fe(CN)6]3−/[Fe(CN)6]4−) in 0.1 mol L−1 KCl (pH set at 7.4). As a proof of concept, the rGraphene-LIG electrode was applied for antibody determination in real samples using cyclic voltammetry, and a limit of detection (LOD) of 0.032 μg L−1 was achieved. When determining antigens in commercial samples, we obtained an LOD of 560 ηg mL−1 and a limit of quantification of 1677 ηg mL−1. The results of the electrochemical experiments were in accordance with the surface roughness obtained from atomic force microscopy images. Based on these results, the rGraphene-LIG electrode is shown to be an excellent platform for immunoglobulin detection when present in individuals after antigenic exposure caused by SARS-CoV-2.

IOT-Based Smart Door Selector For Double Security: Integration of Rfid and Blynk App for Economical Solution

This research aims to design and develop an Internet of Things (IoT)-based smart door lock system by utilizing Radio Frequency Identification (RFID) technology and the Blynk application. The system is designed to improve the level of home security by providing a variety of flexible access methods, including the use of RFID cards, smartphone apps, and emergency buttons. The prototype method is applied in system development, which involves creating a physical model to evaluate its performance and functionality. The test results showed that the system was operating properly, where RFID only responded to the registered card to unlock, thus increasing access security. Hardware design that is carried out with an economical approach and simple use of components makes this system more affordable compared to commercial smart door lock products on the market. This research shows great potential in increasing efficiency and comfort in managing home security systems and offers ease of implementation in various types of housing. Thus, this research makes a significant contribution to the development of innovative and practical solutions to improve home safety at a lower cost while providing a sense of security for its occupants.

Targeted and Untargeted Amine Metabolite Quantitation in Single Cells with Isobaric Multiplexing.

We developed a single cell amine analysis approach utilizing isobarically multiplexed samples of 6 individual cells along with analyte abundant carrier. This methodology was applied for absolute quantitation of amino acids and untargeted relative quantitation of amines in a total of 108 individual cells using nanoflow LC with high-resolution mass spectrometry. Together with individually determined cell sizes, this provides quantification of intracellularconcentrations within individual cells. The targeted method was partially validated for 10 amino acids with limits of detection in low attomoles, linear calibration range covering analyte amounts typically from 30 amol to 120 fmol, and correlation coefficients (R) above 0.99. This was applied with cell sizes recorded during dispensing to determine millimolar intracellular amino acid concentrations. The untargeted approach yielded 249 features that were detected in at least 25% of the single cells, providing modest cell type separation on principal component analysis. Using Greedy forward selection with regularized least squares, a sub-selection of 100 features explaining most of the difference was determined. These features were annotated using MS2 from analyte standards and accurate mass with library search. The approach provides accessible, sensitive, and high-throughput method with the potential to be expanded also to other forms of ultrasensitive analysis.