Early Diagnostics Research Articles

Nanomaterials-Based Field-Effect Transistor for Protein Sensing: New Advances.

It is crucial for early stage medical diagnostics to identify disease biomarkers at ultralow concentrations. A wide range of analytes can be identified using low-dimensional materials to build highly sensitive, targeted, label-free, field-effect transistor (FET) biosensors. Two-dimensional (2D) materials are preferable for high-performance biosensing because of their dramatic change in resistivity upon analyte adsorption or biomarker detection, tunable electronic properties, high surface activities, adequate stability, and layer-dependent semiconducting properties. We give a succinct overview of interesting applications for protein sensing with various architectural styles, such as 2D transition metal dichalcogenides (TMDs)-based FETs that include carbon nanotubes (CNTs), graphene (Gr), reduced graphene oxide (rGr), 2D transition-metal carbides (MXene), and Gr/MXene heterostructures. Because it might enable individuals to perform better, this review will be an important contribution to the field of medical science. These achievements demonstrate point-of-care diagnostics' abilities to detect biomarkers at ultrahigh performance levels. A summary of the present opportunities and challenges appears in the conclusion.

CRISPR-Powered Aptasensor for Diagnostics of Alzheimer's Disease.

Alzheimer's disease (AD) is a neurodegenerative disorder and the most common cause of dementia, characterized by the accumulation of amyloid beta (Aβ) peptides in the brain. Here, we present a simple, rapid, and affordable CRISPR-powered aptasensor for the quantitative detection of Aβ40 and Aβ42 biomarkers in cerebrospinal fluid (CSF) samples, enabling early and accurate diagnostics of AD patients. The aptasensor couples the high specificity of aptamers for Aβ biomarkers with CRISPR-Cas12a-based fluorescence detection. The CRISPR-powered aptasensor enables us to detect Aβ40 and Aβ42 in CSF samples within 60 min, achieving a detection sensitivity of 1 pg/mL and 0.1 pg/mL, respectively. To validate its clinical utility, we quantitatively detected Aβ40 and Aβ42 biomarkers in clinical CSF samples. Furthermore, by combining CSF Aβ42 levels with the c(Aβ42)/c(Aβ40) ratio, we achieved an accurate diagnostic classification of AD patients and healthy individuals, showing superior performance over the conventional ELISA method. We believe that our innovative aptasensor approach holds promise for the early diagnostic classification of AD patients.

Durable nonenzymatic electrochemical sensing using silver decorated multi-walled carbon nanotubes for uric acid detection

In this study, we demonstrate a facile, durable and inexpensive technique of producing silver nanoparticles-decorated multi-walled carbon nanotubes (MWCNT/AgNP) on the easy-to-use screen-printed carbon electrodes (SPCE) for non-enzymatic detection of uric acid (UA) in an electrochemical sensor. The developed sensors show great durability for three months in storage, and high specificity performance for preclinical study using spiked UA in a synthetic urine sample. A simple route for this hybrid nanocomposite was proposed through an oxidation–reduction with reflux (ORR) process. A significant increase in the electroactive surface area of SPCE was achieved by modifying it with MWCNT/AgNP. Scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy-dispersive x-ray spectroscopy (EDX), Fourier-transform infrared (FT-IR) spectroscopy, Raman spectroscopy, and x-ray diffraction (XRD) analysis confirmed this synthesis. The nanocomposite nanostructure electrodes achieved an outstanding UA detection with sensitivity of 0.1021 μA μM−1 and a wide dynamic range of 10–1000 μM. In phosphate-buffered saline (PBS), the measurements achieved a detection limit of 84.04 nM while in pure synthetic urine; it was 6.074 μM. The constructed sensor exhibits excellent stability and durability for several months, and great specificity against interfering compounds, including dopamine (DA), urea, and glucose. Overall, the present outcomes denote the potential of MWCNT/AgNP-decorated SPCE for early uric acid diagnostics tools in health monitoring.

Early diagnostics of prostate cancer based on a comprehensive analysis of risk factors

BACKGROUND: There is a trend of increasing incidence and mortality from prostate cancer. Existing methods for early diagnosis and screening of prostate cancer did not demonstrate enough efficiency.
 AIM: To improve the quality and efficiency of diagnostic measures for prostate cancer through a personalized approach to patients based on an analysis of a set of risk factors.
 MATERIALS AND METHODS: The methodology was developed using the data from the population-based Cancer Registry of the Altai Territory, created at the Altai Regional Oncology Center in Barnaul, Russia. At the moment the Cancer Registry included information on 308 550 patients with malignant neoplasms, including 15 167 males with prostate cancer. On the basis of the method of prostate cancer risk assessment proposed by Lazarev AF an automated program for early diagnosis of prostate cancer has been developed. Testing of this technique included assessment of 532 patients (aged between 23 and 82).
 RESULTS: 128 patients were determined to belong to the high risk pre-cancer group. A high-risk precancer registry was created, which included the patients having individual risk of developing prostate cancer either increased or high or absolute. Upon a set of in-depth examinations, 7 of 128 patients (9.3%) were diagnosed with prostate cancer, all at early stages (I or II). Thus, the incidence of prostate cancer among the patients from the high risk pre-cancer registry was significantly higher than the average observed during prophylactic examinations (0.01%).
 CONCLUSION: The automated program for early diagnosis of prostate cancer could be implemented with the aim to assign in-depth examination in more targeted manner to patients at high risk of prostate cancer and to contribute to detection the cancer in the early stages.

CONTEMPORARY PERSPECTIVES ON DIAGNOSING ACUTE PANCREATITIS

Acute pancreatitis represents a significant challenge in urgent surgery. It holds a prominent position among urgent surgical diseases, with a rising incidence of severe, complicated forms. The increase in the negative outcomes of acute pancreatitis treatment is linked to delayed diagnosis of severe forms and their complications, owing to the absence of a highly effective severity assessment scale. This deficiency contributes to unjustified surgical interventions. Early severe pancreatitis manifests with a rapidly progressive course leading to organ failure, hypoxia, necrotic foci formation, abdominal sepsis, and abdominal compartment syndrome. Specific scales for early diagnosis include the criteria scale for primary assessment of acute pancreatitis severity, the Ranson scale, and the BISAP scale. The modern diagnosis of severe acute pancreatitis relies on two key international classifications: the Revised Atlanta Classification (2012) and the Determinant-based classification (2012). To assess the intensity of pancreatic inflammation by CT scan, the Balthazar scale is used, which is included in the Computer Tomography Severity Index and enables to determine the area of pancreatic necrosis.
 The purpose of this study is to improve the diagnosis of acute severe pancreatitis.
 The study was carried out by assessing the medical records of 20 patients diagnosed with acute pancreatitis who received treatment at the Surgical Inpatient Department of the 2nd City Hospital, Poltava, from 2021 to 2022.
 The analysis revealed that conducting a computer tomography within the 48-72 hours from the onset of the disease enables a comprehensive assessment of its severity. This assessment significantly influences the subsequent treatment approach, reducing the necessity for surgical intervention and lowering the risk of patient mortality.
 Hence, in alignment with contemporary classifications and diagnostic perspectives on acute pancreatitis, MSCT assumes a pivotal role, significantly influencing the selection of treatment strategies in all cases. This inclusion facilitates timely surgical interventions and allows for adjustments to conservative therapy based on the severity of pancreatic inflammation. It is advisable, however, to conduct computed tomography 48-72 hours after the onset of pain to adequately assess the pancreatic condition. Morphological changes in the pancreas typically do not manifest within the initial day of the disease, making early CT diagnostics potentially misleading regarding the true state and severity of acute pancreatitis.

The Evolution of Artificial Intelligence in Biomedicine: Bibliometric Analysis

Background The utilization of artificial intelligence (AI) technologies in the biomedical field has attracted increasing attention in recent decades. Studying how past AI technologies have found their way into medicine over time can help to predict which current (and future) AI technologies have the potential to be utilized in medicine in the coming years, thereby providing a helpful reference for future research directions. Objective The aim of this study was to predict the future trend of AI technologies used in different biomedical domains based on past trends of related technologies and biomedical domains. Methods We collected a large corpus of articles from the PubMed database pertaining to the intersection of AI and biomedicine. Initially, we attempted to use regression on the extracted keywords alone; however, we found that this approach did not provide sufficient information. Therefore, we propose a method called “background-enhanced prediction” to expand the knowledge utilized by the regression algorithm by incorporating both the keywords and their surrounding context. This method of data construction resulted in improved performance across the six regression models evaluated. Our findings were confirmed through experiments on recurrent prediction and forecasting. Results In our analysis using background information for prediction, we found that a window size of 3 yielded the best results, outperforming the use of keywords alone. Furthermore, utilizing data only prior to 2017, our regression projections for the period of 2017-2021 exhibited a high coefficient of determination (R2), which reached up to 0.78, demonstrating the effectiveness of our method in predicting long-term trends. Based on the prediction, studies related to proteins and tumors will be pushed out of the top 20 and become replaced by early diagnostics, tomography, and other detection technologies. These are certain areas that are well-suited to incorporate AI technology. Deep learning, machine learning, and neural networks continue to be the dominant AI technologies in biomedical applications. Generative adversarial networks represent an emerging technology with a strong growth trend. Conclusions In this study, we explored AI trends in the biomedical field and developed a predictive model to forecast future trends. Our findings were confirmed through experiments on current trends.

Decision support system for the classification of Downey cells as a pre-diagnostic tool

Abstract Objectives Epstein–Barr virus (EBV) is a member of the herpes virus that causes infectious mononucleosis (IM). Downey cell is the atypical lymphocyte of IM and can be seen in various conditions. Peripheral blood smear (PBS) microscopic evaluation is used to identify Downey cells. A lack of experienced professionals or professional errors may obstruct early and accurate diagnostics for the microscopic evaluation. The main objective of this study is to create a decision support system by digitizing the PBS samples. A general tool providing an inexpensive and measurable solution is envisioned to analyze the PBS samples in detail to give alerting flags to prevent missing Downey cells in manual analysis. Methods The PBS dataset collected was split into Downey positives and negatives. The negative set consisted of 5 leucocyte subtypes. Mantiscope, a cloud-based slide scanner system, was used to collect images from the physical PBS samples. Clinically and laboratory-confirmed 35 IM patients and 124 healthy PBS slides were selected for this procedure. A number of cell counts were obtained after the application of annotation and augmentation methods, and a partially balanced dataset was created for the artificial intelligence (AI) network training. The verification steps included the calculation of sensitivity, specificity, and Cohen’s kappa metrics from the partitioned testing set that was not used during training. A validation process was also performed over the manually identified PBS samples to measure whether the algorithm noticed the samples or not. Results After testing this setup, we have observed 98 % sensitivity and 99 % specificity for Downey cells. According to the validation procedure of Downey positive and negative samples that were carried out by the physicians, a sensitivity of 57 %, specificity of 100 %, and Cohen’s kappa value of 0.5 were observed. Besides, the accuracy was found to be 66 % according to the physicians’ evaluations employing the digital images which were identified by Mantiscope, Conclusions Decision support systems can alert the physician for Downey cells and increase the rate of true diagnosis in PBS evaluation. A higher sensitivity and specificity for the detection of Downey cells would be achieved. However, the variance over the dataset is a constraint for effective diagnosis. As the annotation and AI development process continues to collect more data from patients, the model can be updated for future releases.

Current issues in the diagnostics of hip dysplasia in newborns in the regions of the Russian Federation

BACKGROUND:Early diagnostics of hip dysplasia in newborns are important medical and social problems because untimely treatment of these children leads to severe irreversible pathological disorders of the hip joint, dysplastic coxarthrosis, and consequently disability. Anamnesis data of patients from the Department of Hip Pathology of H. Turner National Medical Research Center for Children’s Orthopedics and Trauma Surgery show that late diagnoses of the dysplastic pathology of the hip joint are not rare, which does not tend to decrease in number.
 AIM:To identify and analyze the causes of the late diagnosis of dysplastic hip joint pathology in newborns in the Russian Federation.
 MATERIALS AND METHODS:Statistical data from 64 regions of the Russian Federation were collected and analyzed to study the causes of untimely diagnosis of dysplastic hip joint pathology in newborns.
 RESULTS:According to the information received, 3,456,207 children were born in 64 regions of the Russian Federation between 2019 and 2021. Of these children, 108,737 (3.1%) were diagnosed with hip dysplastic pathology of varying severity (acetabular dysplasia, subluxation, and dislocation), and 3,943 cases (3.6%) had untimely diagnosis.
 CONCLUSIONS: Late diagnosis is primarily caused by the untimely appearance of patients for ultrasound screening and initial examination by an orthopedist. The secondary reason is the understaffing of medical organizations with specialists–orthopedists and specialists in Doppler ultrasound examination. To reduce the number of cases of late diagnosed or missed dysplastic hip pathologies and improve the quality of orthopedic care for children in the Russian Federation, strengthening health education must be recommended among parents and staff of medical organizations with orthopedic and ultrasound specialists.

Piezopulsometric analysis of the functional state of human cardiovascular system

This paper expounds how to apply in practice the developed computer method of arterial piezopulsometry, with examples, for non-invasive express analysis of the functional state and the nature of autonomic regulation of the cardiovascular system in patients of different ages and health conditions. The participation and role of the contractile component of myocytes of the left ventricle of the heart is assessed as a universal muscle effector, providing both the basic pumping function of the heart and the implementation of the autonomic regulation of heart contraction using peripheral neurohumoral mechanisms that maintain the optimal level of cardiac hemodynamics. This analysis enhances the predictive value of early non-invasive pulsometric diagnostics of the individual functional state and nature of regulation of human cardiovascular system during ontogenesis. This knowledge helps identify the risk of reducing the ability of the cardiovascular system, which has developed during its evolution, to maintain dynamic balance, self-regulation and adaptation to changing environmental conditions, which is associated with a violation of the body’s homeostasis.

Targeting CDK6 in hormone receptor-positive breast cancer: inhibitor discovery for precision oncology through dynamics study

CDK6 is a critical protein involved in the regulation of the cell cycle, playing an important role in the progression from the G1 to S phase. In breast cancer, dysregulation of this protein is involved in tumour development and progression, particularly in hormone receptor-positive (HR+) breast cancers. The upregulation of CDK6 have been observed in a subset of breast cancers, leading to uncontrolled progression of the cell cycle and increased proliferation of cells. The purpose of this abstract is to provide an outline of CDK6's role. In breast cancer and the therapeutic strategies targeting CDK6 using specific selected inhibitors. To discover viable therapeutic candidates after competitive inhibition of CDK6 with a small molecular drug complex, high throughput screening and docking studies were used. Further, we carried the compounds based on ADMET properties and prediction of activity spectra for substances analysis. Finally, two different compounds were selected to carry out MD simulations. CDK6-IMPHY002642 and CDK6-IMPHY005260 are the two compounds that were identified. Overall, our results suggest that the CDK6-IMPHY002642 and CDK6-IMPHY005260 complex was relatively stable during the simulation. The compounds that have been found can also be further examined as potential therapeutic possibilities. The combined findings suggest that CDK6, together with their genetic changes, can be investigated in therapeutic interventions for precision oncology, leveraging early diagnostics and target-driven therapy. Communicated by Ramaswamy H. Sarma

Assessment of awareness of the female population of Gomel region about prevention and early diagnosis of breast cancer

Breast cancer is the most common malignancy in the female population. Early detection of breast cancer is crucial for reducing mortality from the disease. Late detection of breast cancer may have an unfavorable prognosis. Study Objective – To evaluate the awareness of the female population of the Gomel region concerning prevention and early diagnostics of breast cancer. The participants were 317 women, all of them were of full age, gave their consent to participate in the present study, and filled in a specially designed questionnaire.Absolute majority of respondents know that effectiveness of treatment of breast cancer depends on the stage of its detection, almost all respondents know who the doctor-mammologistis. According to respondents the main reasons of breast cancer development are genetic predisposition, bad ecology, breast trauma and smoking. Only 24,29% of respondents pointed out the correct frequency of mammography examinations at the age of over 50 years – once in 2 years. The fact that 77,6% of the respondents, in one form or another, received information about cancer prevention in the outpatient clinic seems extremely favorable. More than a half of the respondents are not aware of the breast cancer screening program in the Republic of Belarus. The absolute majority of the respondents would prefer to get information about prevention of malignant neoplasms from their doctor, mass media and the Internet.According to the data of the study, the majority of respondents have basic information about breast cancer and risk factors for its development. All participants also received information about mammography and its benefits in early detection of cancer. However, the level of public awareness is insufficient on a number of questions concerning the timing and frequency of examinations, screening, and early detection of cancer.

Advances in three dimensional metal enhanced fluorescence based biosensors using metal nanomaterial and nano-patterned surfaces.

Metal enhanced fluorescence (MEF) is a phenomenon that increases fluorescence signal through placement of metal near a fluorophore. For biosensing applications, MEF-based biosensors are becoming increasingly popular as it enables highly sensitive detection of molecules, important for early diagnosis. The structure and size of the metal influence the optical properties through enhancing the fluorophore photostability and light absorption and emission. In recent years, many metal nanostructures have been fabricated and examined for their effectiveness in developing MEF-based biosensors. This review focuses on the latest applications of three-dimensional nanostructures and nano-patterned surfaces used to develop and improve fluorescence sensing via MEF. Current reviews mostly discussed the applications of two dimensional MEF and metal-nanoparticles-based MEF with a focus on fabrication of nanoparticles and metal substrates. In this article, we focused more on the effect of the metal nanostructure and size on MEF and then provided an in-depth summary of the performance of the state-of-the-art three dimensional MEF-based biosensors. While more work is needed to demonstrate applicability for complex samples, it is evident that with the use of metal nanoparticles and three dimensional nano-patterns, the assay sensitivity of fluorescence-based detection can be greatly improved, making it suitable for use in early disease diagnostics.

Physics-based analytical model for trap assisted biosensing in dual cavity negative capacitance junctionless accumulation mode FET

The design of a ferroelectric-based biosensor for detecting various biomolecules like proteins and DNA has captivated the interest of researchers in early disease diagnostics and treatment monitoring. Doomed by this interest, an analytical model for trap-assisted biosensing in Dual Cavity Negative Capacitance Junctionless Accumulation Mode FET is proposed for detecting various biomolecules. The biomolecules are treated as traps that tunnel through the biosensor's ferroelectric layer. The analytical model is validated through extensive numerical device simulations. The device incorporates the detection of charged and uncharged biomolecules in terms of dielectric constants and biomolecule concentration. The nano-cavities are created inside the gate dielectric stack to collect biomolecules like proteins and DNA. As the biomolecules are infused as traps in cavity areas, electrical properties of the biosensor, like input characteristics, transconductance, threshold voltage, on-state current, and subthreshold swing change. The sensitivity obtained for the proposed biosensor is compared with the existing biosensor and found that it is best suited for susceptible biosensing applications.

Enhancement of Optical Coherence Tomography for Early Diagnostics Through Ag-Decorated ZnO Quantum Dots-Induced Motion Analysis

Optical Coherence Tomography (OCT) stands as a pivotal imaging modality in medical diagnostics, providing intricate insights into microstructural alterations within biological tissues. This research delves into the augmentative impact of nanostructures on OCT, with a specific emphasis on their potential applications in early diagnostic scenarios. The article introduces a novel composite material, Silver-Zinc Oxide (Ag-ZnO) nano-structures, synthesized through the amalgamation of zinc oxide (ZnO) quantum dots and silver (Ag) particles. The study scrutinizes the enhancement effect of these nanostructures on the depth imaging capability and diagnostic precision of OCT. Employing the finite difference time domain method, the research simulates and calculates the extinction spectrum enhancement effect of Ag-ZnO quantum dots in OCT. Comparative analyses are conducted to evaluate the effectiveness and diagnostic accuracy of OCT imaging when enhanced with Ag-ZnO quantum dots against Magnetic Resonance Imaging (MRI) technology. The outcomes manifest a noteworthy improvement in diagnostic accuracy with the integration of Ag-ZnO quantum dots in OCT, underscoring their efficacy in heightening imaging depth and diagnostic precision for early diagnostic applications. This study not only accentuates the pivotal role played by quantum dots in amplifying the capabilities of OCT but also paves the way for the advancement of sophisticated diagnostic tools within the realm of medical imaging.

Early instrumental diagnostics on playground of head trauma in sporting events: the role of biomarkers to improve sports medical protocols in recognition and treatment of brain concussion

Early instrumental diagnostics on playground of head trauma in sporting events: the role of biomarkers to improve sports medical protocols in recognition and treatment of brain concussion

Iodized household salt utilization and associated factors among households in East Africa: a multilevel modelling analysis using recent national health surveys

IntroductionIodine deficiency disorders (IDDs) are a significant global public health issue that affects the physical and mental development of every age group, with children and nursing mothers being the most vulnerable. Approximately 50 million of approximately 2 billion people with iodine deficiency (ID) globally exhibit clinical symptoms. Identifying iodine levels using various techniques is important when considering treatment choices. Screening programs and early ID diagnostics are crucial for the follow-up of pregnant women, especially in iodine-deficient nations. There have been calls for universal salt iodization programs, but only approximately 71% of people have access to them. The problem is more common in developing nations; however, there is a shortage of literature on the individual-, family-, and community-level factors influencing iodized salt use in East Africa. This study aimed to investigate individual- and community-level factors of household iodized salt usage in East Africa.MethodsUsing Stata 17, this study used the most recent demographic and health survey datasets from twelve East African countries. The survey included a weighted sample of 154,980 households. To assess factors related to iodized salt use in the region, bivariable and multivariable multilevel logistic regressions were used. P values less than or equal to 0.2, and < 0.05 were used in the binary regression, and to deem variables statistically significant in the final model respectively.ResultsAbout 87.73% (95% CI = 87.56,87.89) households have utilized iodized household salt. Secondary and above education (AOR = 1.23, 95% CI = 1.17–1.30), household heads with ages of 25–35 years, 36–45 years (AOR = 1.20, 95% CI = 1.12,1.28), 36–45 years (AOR = 1.16, 95% CI = 1.09,1.24), and more than 45 years (AOR = 1.18, 95% CI = 1.11,1.25), lower and middle wealth (AOR = 0.89, 95% CI = 0.76,0.89) and (AOR = 0.97, 95% CI = 0.81,0.93), media exposure (AOR = 1.10, 95% CI = 1.07–1.14), female household leaders (AOR = 1.08, 95% CI = 1.04–1.12), access to improved drinking water and better toilet facility (AOR = 2.26, 95% CI = 2.18–2.35) and (AOR = 1.50, 95% CI = 1.44,1.56), larger than five family members (AOR = 0.96, 95% CI = 0.93–0.99), high community level wealth (AOR = 1.54, 95% CI = 1.27–1.87), and low community education(AOR = 0.40, 95% CI = 0.33,0.49) were statistically associated with utilization of iodized household salt in East Africa respectively.ConclusionIn East Africa, household salt iodization is moderately good. To expand the use of iodized salt in the region, access to improved drinking water and toilet facilities, participating family leaders, using the opportunity of family planning services, media sources, and the improvement of the community’s socioeconomic level are all needed.

Imaging genetics based dementia risk prediction using deep survival neural network

AbstractBackgroundDementia has a large impact on people’s lives and is one of the leading causes of death. Therefore, accurate risk assessment is essential for improving early diagnostics and intervention. Previous studies have proposed various models for dementia risk prediction, based on imaging biomarkers and genetic factors. However, most of them have linear, unimodality or low‐dimensional nature due to model limitations and computational challenges of integrating such heterogeneous and high‐dimensional domains as images and genotype data. Recent development of Artificial Intelligence, specifically neural networks, enabled such integration, yet focused on classification problems, not risk prediction. We present a deep survival neural network to meet the purpose.MethodData were obtained from the Rotterdam Study. Individuals with complete data and available brain MRI before dementia diagnosis were included. MRI scans were pre‐processed using voxel‐based morphometry according to Good et al. into gray matter images. We included ApoE‐ε4 and 76 top‐risk SNPs based on most recently published GWAS (Bellenguez et al.). We developed the model combining Convolutional Neural Network and Survival Analysis, with the maximum‐likelihood estimator and C‐index of cox proportional hazard (Cox) model as the loss function and metric. To evaluate model performances, we used Cox model as comparisons: one baseline model with gender, age, genetic inputs and one extended model with additional extracted brain measurements. We performed 10 cross‐validation by stratified sampling to reveal the true differences.ResultTable1 shows little performance differences among the models, as expected age is already the strongest predictor. However, we see that raw gray matter and genetics inputs contribute to prediction additionally in all models. Furthermore, using the method from Tsang et al. we show strong interactions among image, age and genetic features (Fig.1). Fig.2 visualizes the most important brain regions for prediction on 3D image using GRAD‐CAM.ConclusionWe developed a deep survival model for dementia risk prediction which do not outperform classical model with brain measurements at present. However, our model is able to integrate high‐dimensional MRI and genetic data, detect and visualize the importance and interactions among input data, which may give additional insight for dementia prediction and etiologic research.

Simultaneous and sensitive detection of SARS-CoV-2 proteins spike and nucleocapsid based on long-range SERS biosensor

BackgroundEarly diagnosis of SARS-CoV-2 infection is still critical to control COVID-19 outbreak. Traditional polymerase chain reaction, enzyme-linked immunosorbent assay or lateral flow immunoassay performed poorly on detection times, sample preparation process and accuracy. Surface-enhanced Raman scattering (SERS)-based detection has emerged as a powerful analytical technique, which overcomes the above limitations. However, due to the near-field effect of traditional substrate, it is difficult to monitor the binding event of aptamers with proteins. It is obvious that a novel SERS substrate thatsupportedextended and stronger electromagnetic fields was required to hold long-range effects and allow for binding event testing. ResultsDriven by this challenge, we reported a long-range SERS-active substrate, which was built by inserting bowtie nanoaperture arrays in a refractive-index-symmetric environment and Au mirror surfaces, for SARS-CoV-2 protein binding event detection. Then, a double-π structure aptasensor was simply designed through the hybridization of spike (S) and nucleocapsid (N) proteins aptamers, and a corresponding complementary strand. This kind of double-π structure would dissociate when targets proteins S and N existed and led to the SERS responses decreased, which established the detection basis of our system. What's more, due to two Raman labels were involved, both proteins S and N can be sensed simultaneously. Our proposed method showed improved sensitivity with a low limit of detection for multiplex detection (1.6 × 10−16 g/mL for protein S and 1.0 × 10−16 g/mL for protein N) over a wide concentration range. SignificanceThis represents the first long-range SERS apatasensor platform for detection of S and N proteins simultaneously. Our method showed high sensitivity, selectivity, reproducibility, stability and remarkable recoveries in human in saliva and serum samples, which is particularly important for the early diagnostics of COVID as well as for future unknown coronavirus.

BC-Net: Early Diagnostics of Breast Cancer Using Nested Ensemble Technique of Machine Learning

BC-Net: Early Diagnostics of Breast Cancer Using Nested Ensemble Technique of Machine Learning

Features of mild duration asthma diagnostics in childbearing aged females

Asthma diagnostics especially of it’s mild forms, is considered to be a serious problem within primary health care unit due to the absence of exact evaluation algorithm of subjective, clinical and functional manifestations. Objective. The goal of our study was to create recommendation on early asthma diagnostics in childbearing aged females, based on particular analysis of complaints, clinical and functional test results and to make them available to all primary medical care units. Materials and methods. 2460 females of childbearing age with various severity of asthma duration were studied with complex of clinic and functional methods. Results. Patients with various grades of asthma severity could be compliant free, same time the can appear signs of bronchial obstruction during auscultation. The marked discordance leads to mismatch in therapy administration. PEF50 decrease while initial spirometry and its’ increase after probe with bronchodilator is considered to be the earliest sign of bronchial obstruction. Asthma diagnostics at prehospital stage is to based upon complete chart collection, complaints analysis, auscultation signs while forced expiration as well as on spirography data on volume and forced expiration dynamics and PEF50. Conclusion. Results of large population study on clinical feature of asthma duration and functional diagnostics of mild asthma duration could be recommended for primary health care physicians as method of early asthma diagnostics and therapy administration