Diagnostic System Research Articles

Bone marrow edema detection for diagnostic support of axial spondyloarthritis using MRI.

This study proposes a process for detecting slices with bone marrow edema (BME), a typical finding of axSpA, using MRI scans as the input. This process does not require manual input of ROIs and provides the results of the judgment of the presence or absence of BME on a slice and the location of edema as the rationale for the judgment. First, the signal intensity of the MRI scans of the sacroiliac joint was normalized to reduce the variation in signal values between scans. Next, slices containing synovial joints were extracted using a slice selection network. Finally, the BME slice detection network determines the presence or absence of the BME in each slice and outputs the location of the BME. The proposed method was applied to 86 MRI scans collected from 15 hospitals in Japan. The results showed that the average absolute error of the slice selection process was 1.49 slices for the misalignment between the upper and lower slices of the synovial joint range. The accuracy, sensitivity, and specificity of the BME slice detection network were 0.905, 0.532, and 0.974, respectively. This paper proposes a process to detect the slice with BME and its location as the rationale of the judgment from an MRI scan and shows its effectiveness using 86 MRI scans. In the future, we plan to develop a process for detecting other findings such as bone erosion from MR scans, followed by the development of a diagnostic support system.

Neuroprotective and Nootropic Evaluation of some Important Medicinal Plants in Dementia: A Review

: Dementia is a devitalising decline in neurological acuity, which burdens both high- and lowincome countries due to poor diagnostic systems and high healthcare costs. A growing population and rises in deleterious environmental and genetic anomalies aggravate new and aggressive cases of dementia and neurodegenerative disorders. The unparalleled capability of medicinal plants is constantly explored in treating neurological disorders, and some of these phytocompounds are used for treating diseases. However, some of these metabolites are neurotoxic and disrupt DNA polymerase functions. They possess lifethreatening side effects and are unable to prevent disease progression. Therefore, it is important to focus on discovering novel compounds from new medicinal plants. Our review encompasses some medicinal plant metabolites recently explored in treating this disease. We also summarized the molecular mechanism of action as determined in in-vitro: the toxic dose, nootropic and neuroprotective effects on neurophysiology and neurotransmitter levels, and the antioxidant effects.

Upgrade of the Lyman-alpha diagnostic system on DIII-D for main chamber edge neutral studies.

The LLAMA (Lyman Alpha Measurement Apparatus) pinhole camera diagnostic had previously been deployed on DIII-D to measure radial profiles of the Lyman-α (Ly-α) deuterium neutral line brightness across the plasma boundary in the lower chamber to infer neutral deuterium density and ionization rate profiles. This system has recently been upgraded with a new diagnostic head, named ALPACA, that also encloses two pinhole cameras and duplicates the LLAMA views in the upper chamber. Similar to LLAMA, ALPACA provides two times 20 lines of sight, viewing the plasma edge on the inboard and outboard sides with a radial resolution of ∼2.5 cm (FWHM) and an effective time resolution of ∼1 ms that allows for the investigation of inter-ELM dynamics. The extended Ly-α system provides better coverage to study neutrals in experiments with various plasma shapes utilizing both the upper and lower divertors. Furthermore, post-campaign calibration of the LLAMA diagnostic has successfully been demonstrated for the first time. This was facilitated by various upgrades to the calibration set-up and detailed measurements of the emissivity distribution of the Ly-α calibration source using a pinhole collimator. It was found that the sensitivity of the inboard LLAMA pinhole camera was reduced by a factor of 2.0 ± 0.2 over the course of six months of plasma operation in 2021. The upgraded Ly-α system, equipped with improved absolute calibration, will provide key input for neutral fueling and pedestal particle transport studies and for 2D edge transport code validation on the DIII-D tokamak.

Design and measurements of the diamagnetic loop in Aditya-U tokamak

A diamagnetic diagnostic system is designed, prepared and installed at Aditya Upgrade Tokamak. Also, the raw data is acquired in a specified acquisition system with suitable front-end electronics. The entire system consists of separate loops and is first calibrated in the presence of all coils, which conduct electricity during the plasma discharge. Then, the change in the toroidal magnetic field due to the plasma is measured using the diamagnetic system, and hence, several plasma parameters including poloidal beta, stored energy, etc. are estimated. The measurements were then repeated during various tests of the tokamak plasma, and several interesting facts were observed. The present paper covers the entire concept of designing magnetic diagnostics – from basic considerations to final data interpretation.

A microservice based control architecture for mobile robots in safety-critical applications

Mobile robots have become more and more common in public space. This increases the importance of meeting safety requirements of autonomous robots. Simple mechanisms, such as emergency braking, alone do not suffice in these highly dynamic situations. Moreover, actual robotic control approaches in literature and practice do not take safety particularly into account. A more sophisticated situational approach for assessment and planning is needed as part of the high-level process control. This paper presents the concept of a safety-critical Robot Control Architecture for mobile robots based on microservices and a Hierarchical Finite State Machine. It expands already existing architectures by drastically reducing the amount of centralized logic and thus increasing the overall system’s level of concurrency, interruptibility and fail-safety. Furthermore, it introduces new potential for code reuse that allows for straightforward implementation of safety mechanisms such as internal diagnostics systems. In doing so, this concept presents the template of a new type of state machine implementation. It is demonstrated with the application of a delivery robot, which was implemented and operated in real public during a broader research project.

Progress and challenges in the design of ITER's polarimetric Thomson scattering diagnostic system.

Polarimetric Thomson scattering (PTS) is a technique that allows for accurate measurements of electron temperature (Te) in very hot plasmas (Te > 10 keV, a condition expected to be regularly achieved in ITER). Under such conditions, the spectral region spanned by the TS spectrum is large and extends to low wavelengths, where the transmission of the collection optics decreases, available detectors are less efficient, and the high level of plasma background light perturbs the measurements. This work presents the recent developments in the design of a PTS system for ITER, along with the challenges posed by the complex machine design. The system performance is assessed for an updated geometry (with respect to previous publication), showing that, with a scattering angle θscat = 167°, the expected signal is strongly reduced. Potential alternatives are analyzed: (1) a system employing a different laser injection position, allowing for a more favorable scattering angle and (2) a recently proposed dual-polarization laser pulse technique. The latter is evaluated for the possible ITER geometry, again showing that a more favorable scattering angle is needed for a robust performance.

Design and development of the magnetic diagnostic systems for the first operational phase of the SMART tokamak.

A set of magnetic diagnostics has been designed, manufactured, and calibrated for the first operational phase of the small aspect ratio tokamak. The sensor suite comprises of Rogowski coils; 2D magnetic probes; and poloidal, saddle, and diamagnetic flux loops. A set of continuous Rogowski coils has been manufactured for the measurement of plasma current and induced eddy currents in conductive elements. A set of flux loops and magnetic probes will be used as input for the reconstruction of the magnetohydrodynamic equilibrium. The quantity and position of these sensors have been verified to be sufficient with synthetic equilibrium reconstructions using the equilibrium fitting code and baseline scenarios computed with the Fiesta code. These sensors will also be used as input for the real-time control system, and magnetic probes will be used for the detection of plasma instabilities. The calibration procedure for the magnetic probes is described, and the results are shown. The signal conditioning and data acquisition systems are described.

Collection optics of JT-60SA edge Thomson scattering diagnostic.

The mission of the JT-60SA project is to complement ITER's capabilities by addressing the fundamental physics and engineering challenges necessary to develop a practical and reliable fusion power plant. Diagnostics play a pivotal role in achieving this mission, especially the Thomson Scattering (TS) diagnostic systems developed by a collaborative Japan-EU team. The edge Thomson scattering of JT-60SA is tailored to measure the low field side outer region of the plasma, in particular, to resolve the electron temperature Te and density ne. The collection optics of the edge TS system have a critical role in meeting the required field of view and spatial resolution despite the limited space. This work presents a comprehensive optomechanical design of the optics assembly, whose main features are telecentricity and compactness, highlighting its capabilities. The tests undertaken to verify its performance: focal plane identification, thermal cycle, and magnification, are described.

Level of Knowledge of Medical Practitioner of Diagnosing and Managing Swallowing Difficulties in Hospitalized Setting of Dhaka City

Aim: To have a clear idea and explore the existing practice of medical practitioner about the proper diagnostic system and management of patient with adult dysphagia in a hospitalized setting. Materials and Methods: This is a quantitative type of cross sectional survey study where 60 samples were assigned purposively from in Dhaka Medical College Hospital, Shaheed Suhrawardy Medical College Hospital, Enam Medical College Hospital, BRB Medical College Hospital. The self-modified a questionnaire was used for the study. Statistical analysis: Data was analyzed by using descriptive statistical analysis (SPSS= Statistics package for social science) method. Results: On average, most of the participants 67% (40) are males and 33% (20) are females. The maximum number participants, 82% (49) in the age range (23-34) years. The researcher has showed the area of specialty of the respondents according to the inclusion criteria. In the study 11.7% (7) participants are gastroenterologist, 10% (6) are orthodontist, 16.7% (10) are ENT, 41.7% (25) are medicine specialist and 20% (12) are neurologist. Maximum number of participants working station 46% (28) are in Dhaka Medical College Hospital. The duration of the study of the respondents on dysphagia out of the 60 respondents 30 studied for 2-3 hours. The respondents first encounter a patient with dysphagia. 46.7% (28) have seen in the period of 2012-2016. The frequency of the participants dealing with a patient having dysphagia the responses were categorized 46.7% (28) responded sometimes. The participants understand the role of SLT on diagnosing and managing dysphagia 52% (31) responded with yes. Conclusions: Dysphagia has emerged as a common complication due to stroke. More than 50% persons are affected by dysphagia after stroke (Smithard et al., 2007). This study consisted with medical practitioner from neurology, medicine, ENT, orthodontics and gastroenterology department. Both specialist and interns were part of this study. The referral level to SLT is also not satisfactory. Among 60 participants only 11 refer to SLT whereas 49 never or seldom refer to SLT. This showcases the lack of awareness on the role of SLT in diagnosing and managing of dysphagia. So, the proper collaboration of medical practitioner and SLT should be made so that the patient can get a proper treatment that will improve their condition.

Reflectivity measurement of a silicon carbide mirror sample for ITER divertor vacuum ultraviolet spectrometer.

The first mirror is the front-end optic component that reflects light emitted from the plasma to the diagnostic system in fusion plasmas. Silicon carbide (SiC), known for its relatively high mechanical strength and radiation tolerance, has been selected as the substrate material for the first mirror in the ITER divertor vacuum ultraviolet (VUV) spectrometer. To measure the reflectivity of the ellipse cylindrical SiC mirror to be manufactured, a device for reflectivity measurement in the VUV wavelength range was developed. First, the reflectivity of a sample SiC mirror (15mm diameter × 10mm thick) was measured across the ITER-required incidence angles, and the results are reported in this study. A hollow cathode lamp with helium gas was used as the VUV light source in the wavelength range of 23-60nm, and a dedicated VUV spectrometer to select specific wavelengths was developed. The spectrometer utilized laminar-type replica diffraction gratings (Shimadzu 30-006) and two back-illuminated charge-coupled devices (BI-CCD, Andor DO 940P-BEN) for the grating and detector, respectively. A cropping technique with aperture was employed to precisely localize the VUV light's reflection onto the SiC mirror surface. The experimentally measured reflectivity values of SiC at the required incidence angles of VUV light were compared with theoretically calculated reflectivity curves. The oxidation layer (SiO2) formed on the SiC surface and the incidence angle of VUV light to the BI-CCD chip (E2V) would be the factors affecting the accuracy of the reflectivity.

Создание и развитие высоконадежных информационно-управляющих систем с элементами искусственного интеллекта для перспективных технологических комплексов

Methods and means of improving complex technological equipment are shown through the operational assessment of the quality of implemented technologies realtime. This is proved by the fact that implementing technological operations on universal equipment each time requires modeling dynamic processes and taking into account a large number of uncertainty factors that affect the geometry generation quality. It is not possible to be a priori aware of these factors. There is a need to create new information technologies with the possibilities of universal application for immediate understanding of various dynamic processes in diagnostic, identification and control systems. Standard computer systems for statistical analysis and optimization of dynamic processes with the possibilities of universal application for various implementations of modern technologies have been introduced. The possibility of using integrative criteria and methods of artificial intelligence for diagnostic systems, identification and control of advanced technological complexes is shown. The implementation of information systems for the management of complex objects of various technological purposes is presented. The proposed modeling methods and approaches have been tested at various machine-building enterprises when processing parts on turning, milling and grinding machines, both universal and CNC. The research results made it possible to implement new principles of automated control and optimal adjustment of technological processes in real time and create an automated system for evaluating their quality, which allows increasing the efficiency and reliability of management decisions by conducting optimization directly on operating equipment. Based on the methods and approaches described above, new results have been obtained in the implementation of plasma technologies for the modification of geometrically complex surfaces of mechanical engineering products aimed at increasing wear resistance, hardness and other technical characteristics of the working surfaces of precision engineering products. A fairly complete approbation of methods, approaches, procedures and decision-making criteria for various technologies allows them to be recommended for universal applicability.

Skin melanoma detection and classification with deep learning

Because of its rapid growth and high mortality rate, melanoma skin cancer is among the most dangerous types of skin cancer. Consequently, melanoma treatment relies heavily on early detection. Based on the U-Net architecture with VGG-16 encoder and semantic segmentation, we present a skin lesion segmentation approach for dermoscopic pictures in this study. Diagnostic imaging systems can assess the characteristics of the segmented skin lesion and assign them a classification based on those aspects. Even on computers without powerful GPUs, the training accuracy is still high enough (over 95%) using the suggested strategy, which uses fewer resources. We use the ISIC dataset, which contains dermoscopy images, to train the model in our trials. We compare the suggested skin lesion segmentation method to others that use deep learning and analyze the Sorensen-Dice and Jaccard scores to determine how well it performs. In terms of skin lesion segmentation, the experimental findings demonstrated that the proposed method outperformed the alternatives.

Intelligent Data Processing for Alzheimer's Disease Using Deep Learning

Background: With the advancement in Alzheimer's disease (AD) brain, cells start to deteriorate, which eventually creates physical dependency and mental instability that interferes with daily living. Presently, this disease is immedicable. Therefore, the only suitable treatment is early detection and prevention. Although many studies have investigated the usefulness of deep learning in AD detection, relatively few have focused on the necessary image preprocessing processes, which are essential to any computer-aided diagnostic system. Furthermore, an optimal classification strategy that takes into account a diverse handful of prominent features is required. Method: This paper focuses on improving MRI-based AD detection by incorporating image enhancement approaches and deep hybrid learning into a fused framework to harness the power of multiple Deep Learning (DL) architectures and Machine Learning (ML) classifiers. The deep features extracted from three heterogeneous CNN architectures, namely, VGG16, DensetNet169, and MobileNetV1, are fused to produce a more informative and discriminative hybrid feature. Furthermore, the mRMR approach was used to optimise the acquired features, followed by classification via a stack of multiple ML classifiers to predict the target class. Results: The proposed architecture based on feature fusion strategy and ensemble learning resulted in 99.53%(Accuracy), 99.73%(Precision),99.70%(Recall), and 99.72% (F1 score). The presented model outperformed individual deep CNN architectures. Conclusion: Lastly, we present a sobol-based sensitivity analysis that illustrates the concentration of the presented technique upon significant regions of the image and can assist medical professionals in decoding the decisions. The presented technique exeplifies the potency and constancy of categorizing Alzheimer's disease.

Building a system of diagnostics of problem areas in the management of business processes of the organization

Aim. To develop and formulate an algorithm that allows timely diagnosing and eliminating problems in business processes.Objectives. To consider and systematize the main signs of problems in business processes; to determine the main sources, causes of losses and inefficiency; to formulate an algorithm that allows to assess the effectiveness of the process; to test the proposed algorithm for assessing the effectiveness of business processes on the example of a specific organization.Methods. The authors used the methods of descriptive literature review and statistical data analysis. The research includes the study and analysis of scientific articles, reports of scientific conferences and other publications related to the subject of the article. The analysis of statistical data is based on the performance of the organization, in particular the operator of urban mobility services. All data used for calculations are of a conditional nature.Results. In the process of research the works of modern authors related to the analysis of efficiency and diagnostics of problems in business processes are considered and generalized. In the mentioned works some theoretical aspects of the topic are widely and in detail studied, which are then systematized by the authors of the present study. As a result, an algorithm has been formed that allows in practice to monitor the efficiency of business processes of the organization, as well as to diagnose possible problems in a timely manner. When testing this approach in practice, in the context of the analysis of business processes in the organization, the following results were obtained: a number of critical problems in business processes that have a negative impact on the result of activity were identified; a plan to improve their efficiency was developed and implemented.Conclusions. The process approach is one of the most successful tools to improve the efficiency of the organization. Optimal operation of all key and essential business processes ultimately affects the company’s result. The operating conditions of an organization are constantly changing under the influence of a dynamic business environment. One of the most important elements of the efficiency management system for business processes, and consequently for the business as a whole, is a properly formed set of indicative indicators reflecting the progress of each process. The choice of indicators should be based primarily on the strategic goals of the organization, cascaded down to the level of individual processes. The study confirmed the hypothesis that the risks of efficiency reduction can be identified in advance and in a timely manner with the help of a correctly constructed system of simple control indicators. For these purposes, taking into account the concept of lean production and continuous improvement, as well as on the basis of the classification of losses set out in it, a questionnaire for express analysis of business process efficiency has been developed.

Progress of the bolometer diagnostic system on EAST

Measurements of the total radiated power and its spatial distribution are crucial for fusion research. On the experimental advanced superconducting tokamak (EAST), both the metal foil resistive bolometer and the absolute extreme ultraviolet (AXUV) photodiodes have been used to quantify the radiated power. This article introduces the latest improvement of the bolometer diagnostic system on EAST. It also details the successful design and installation of new divertor AXUV cameras, which are dedicated to the investigation of divertor physics. The shielding components of the bolometer detector have been refined, and the article provides a detailed exposition of the double shielding structures that have been verified as effective in microwave shielding. Additionally, the changes in the radiated power distribution in the divertor region during the plasma detachment process are measured using the divertor AXUV camera. Finally, the radiated power measured by the AXUV detector and metal foil resistive bolometer are compared, and different detector performances are presented.

HQ-DCGAN: Hybrid quantum deep convolutional generative adversarial network approach for ECG generation

The class imbalance of electrocardiogram (ECG) data is a serious impediment to the development of diagnostic systems for heart disease. To address this issue, this paper proposes HQ-DCGAN, a hybrid quantum deep convolutional generative adversarial network, specifically designed for the generation of ECGs. The proposed algorithm employs different quantum convolutional layers for the generator and discriminator as feature extractors and utilizes parameterized quantum circuits (PQCs) to enhance computational capabilities, along with the model-feature mapping process. Moreover, this algorithm preserves the nonlinearity and scalability inherent to classical convolutional neural networks (CNNs), thereby optimizing the utilization of quantum resources, and ensuring compatibility with contemporary quantum devices. In addition, this paper proposes a novel evaluation metric, 1D Fréchet Inception Distance (1DFID), to assess the quality of the generated ECG signals. Simulation experiments show that HQ-DCGAN exhibits strong performance in ECG signal generation. Furthermore, the generated signals achieve an average classification accuracy of 82.2%, outperforming the baseline algorithms. It has been experimentally proven that HQ-DCGAN is friendly to currently noisy intermediate-scale quantum (NISQ) computers, in terms of both number of qubits and circuit depths, while improving the stability.

Program to optimise detecting growth hormone deficiency in children and increase adherence to replacement therapy

Background. The significance of this study lies in the fact that short stature is highly prevalent among children, affec­ting 1–5 % of the population and having diverse causes. The child’s growth potential in the long term is largely depends on the effectiveness of the diagnostic system and the level of adherence to the prescribed therapy. The purpose was to improve the diagnosis of growth hormone deficiency (GHD) in children and adherence to recombinant human growth hormone (rhGH) therapy based on information about the regional prevalence of the disease and barriers to adherence. Materials and methods. A follow-up study was conducted from 2012 to 2020 at the Odesa Regional Children’s Hospital. The cohort included 94 children with GHD. The prevalence was determined by calculating the ratio of the number of all detected GHD cases to the children population per 100,000. Adherence was measured using the Morisky Medication Adherence Scale. The statistical processing of the results was done using t-test and chi-square methods, and p-values less than 0.05 were considered statistically significant. Results. An assessment of GHD prevalence, the level of adherence and the frequency of continuity of rhGH therapy in children in the Odesa region at the end of 2014 revealed the incompleteness of regional diagnosis of the disease (in Odesa, 1 : 11,200; in the Odesa region, 1 : 10,800), as well as a low level of acceptable adhe­rence (in 57.4 %) and insufficient frequency of continuity of therapy (in 76.9 %). These data formed the basis of the regional program for optimizing the identification and management of GHD in children, which included organizational, medical and social measures. By the end of 2020, the prevalence of GHD in Odesa was 1 : 4,300, and in the Odesa region, 1 : 5,100. The rate of acceptable adherence to rhGH therapy increased to 80.0 %, and frequency of continuity of therapy to 91.1 %. Conclusions. The regional program designed to improve the detection and management of GHD in children has been found to improve the diagnosis of the disease, increase adhe­rence to rhGH therapy, and the frequency of continuity of treatment. High adherence to treatment is a bioethical issue because it signifies a good partnership between physicians, children, and parents and indicates respect for patient autonomy.

Residual Dizziness Characteristics of Idiopathic Sudden Sensorineural Hearing Loss Patients with Benign Paroxysmal Positional Vertigo.

ISSNHL, a common clinical condition, can be accompanied by vertigo. Initially, research on sudden deafness primarily focused on the hearing loss itself, with less emphasis on episodic vertigo. However, as vertigo research has advanced, it has been recognized that BPPV is a frequent accompaniment to ISSNHL-associated vertigo. Even after treatment, some patients may experience residual dizziness. This study investigates the characteristics of patients with ISSNHL accompanied by BPPV and the impact of residual dizziness on their lives. This study is being conducted on patients with ISSNHL accompanied by BPPV, analyzing the characteristics of such patients and the impact of residual dizziness on their lives. Overall, 54 adult inpatients with ISSNHL and BPPV were included in this study. All patients received 50 mg of intravenous prednisolone for 5 consecutive days and hemodilution agents for 10 days. At the same time, BPPV was treated with repositioning by the same therapist using the SRM-IV vertigo diagnostic and treatment system, and different repositioning methods were used for different types of otolithiasis. Patients were grouped according to the absence of residual dizziness when the nystagmus disappeared at the time of discharge. There were 24 cases in the group with residual symptoms, including 10 males and 14 females. The proportion of females was 58.33%, with an average age of 46.75 ± 13.80. The group without residual symptoms consisted of 30 cases, including 13 males and 17 females. The female proportion was 56.67%, with an average age of 45.77 ± 11.86. There is no statistical significance between the two groups in the pre-treatment hearing status and DHI scores. The HAMA (Hamilton Anxiety Rating Scale) scores before treatment were compared, revealing a significant statistical difference. ISSNHL-associated BPPV may be caused by vascular embolism or thrombosis in the cochlear or spiral modiolar artery. This disrupts blood flow, leading to ischemia in the otolithic membrane and subsequent detachment of otoconia. Because this detachment often occurs within 24 h of the initial event, patients experience positional vertigo early in the course of the disease.

A chatbot based question and answer system for the auxiliary diagnosis of chronic diseases based on large language model

In recent years, artificial intelligence has made remarkable strides, improving various aspects of our daily lives. One notable application is in intelligent chatbots that use deep learning models. These systems have shown tremendous promise in the medical sector, enhancing healthcare quality, treatment efficiency, and cost-effectiveness. However, their role in aiding disease diagnosis, particularly chronic conditions, remains underexplored. Addressing this issue, this study employs large language models from the GPT series, in conjunction with deep learning techniques, to design and develop a diagnostic system targeted at chronic diseases. Specifically, performed transfer learning and fine-tuning on the GPT-2 model, enabling it to assist in accurately diagnosing 24 common chronic diseases. To provide a user-friendly interface and seamless interactive experience, we further developed a dialog-based interface, naming it Chat Ella. This system can make precise predictions for chronic diseases based on the symptoms described by users. Experimental results indicate that our model achieved an accuracy rate of 97.50% on the validation set, and an area under the curve (AUC) value reaching 99.91%. Moreover, conducted user satisfaction tests, which revealed that 68.7% of participants approved of Chat Ella, while 45.3% of participants found the system made daily medical consultations more convenient. It can rapidly and accurately assess a patient’s condition based on the symptoms described and provide timely feedback, making it of significant value in the design of medical auxiliary products for household use.

Molecularly Imprinted Viral Protein Integrated Zn-Cu-In-Se-P Quantum Dots Superlattice for Quantitative Ratiometric Electrochemical Detection of SARS-CoV-2 Spike Protein in Saliva.

Solution-processable colloidal quantum dots (QDs) are promising materials for the development of rapid and low-cost, next-generation quantum-sensing diagnostic systems. In this study, we report on the synthesis of multinary Zn-Cu-In-Se-P (ZCISeP) QDs and the application of the QDs-modified electrode (QDs/SPCE) as a solid superlattice transducer interface for the ratiometric electrochemical detection of the SARS-CoV-2-S1 protein in saliva. The ZCISeP QDs were synthesized through the formation of In(Zn)PSe QDs from InP QDs, followed by the incorporation of Cu cations into the crystal lattice via cation exchange processes. A viral-protein-imprinted polymer film was deposited onto the QDs/SPCE for the specific binding of SARS-CoV-2. Molecular imprinting of the virus protein was achieved using a surface imprinting electropolymerization strategy to create the MIP@QDs/SPCE nanosensor. Characterization through spectroscopic, microscopic, and electrochemical techniques confirmed the structural properties and electronic-band state of the ZCISeP QDs. Cyclic voltammetry studies of the QDs/SPCE superlattice confirmed efficient electron transport properties and revealed an intraband gap energy state with redox peaks attributed to the Cu1+/2+ defects. Binding of SARS-CoV-2-S1 to the MIP@QDs/SPCE cavities induced a gating effect that modulated the Fe(CN)6 3-/4- and Cu1+/2+ redox processes at the nanosensor interface, producing dual off/on ratiometric electrical current signals. Under optimal assay conditions, the nanosensor exhibited a wide linear detection range (0.001-100 pg/mL) and a low detection limit (0.34 pg/mL, 4.6 fM) for quantitative detection of SARS-CoV-2-S1 in saliva. The MIP@QDs/SPCE nanosensor demonstrated excellent selectivity against nonspecific protein targets, and the integration with a smartphone-based potentiostat confirmed the potential for point-of-care applications.