System for the rapid detection of viral infections in blood samples

Abstract

A system for the rapid detection of viral infections in blood samples, consisting of: a microfluidic processing module configured to receive a microliter-scale whole blood sample and process it through sequential steps, including plasma separation, lysis of viral particles, and biomarker extraction; a biosensor array unit comprising a plurality of nanostructured sensor elements functionalized with pathogen-specific ligands, wherein the biosensor array is integrated into the microfluidic module to enable real-time detection of viral nucleic acids or antigens; a temperature-controlled amplification module embedded in the microfluidic channel and configured to perform isothermal nucleic acid amplification using recombinase polymerase amplification (RPA), loop-mediated isothermal amplification (LAMP), or helicase-dependent amplification (HDA); a signal acquisition subsystem comprising optical, electrochemical, or piezoelectric transducers coupled to the biosensor array, the subsystem converting molecular interaction data into digital signals; a machine learning processor operatively connected to the signal detection subsystem and configured to classify the infection status using pre-trained classification models with confidence scoring and anomaly marking; a wireless communication interface operatively connected to the machine learning processor to disseminate the results remotely, with the entire system housed in a portable enclosure operable via a graphical user interface or touchscreen.

Description

Field of the Invention:The present invention relates to the field of biomedical diagnostics and the detection of infectious diseases. More particularly, the invention relates to an integrated, rapid and automated system for detecting viral infections in blood samples using a microfluidic platform, biosensor nanotechnology and artificial intelligence (AI).BACKGROUND OF THE INVENTIONConventional diagnostic methods for detecting viral infections, such as RT-PCR, ELISA and culture-based assays, are time consuming, require specialized laboratory infrastructure and skilled personnel. These methods often meet their limits in outbreaking scenarios or at remote locations, as thermal cyclers, cooling chain transport, and multistage sample preparation are required. Although rapid diagnostic tests (RDTs) are faster, they tend to have lower sensitivity, limited exciter coverage, and qualitative results. In addition, current technologies typically target specific viruses and are not flexible enough to efficiently treat newly occurring or mutated viral strains. The urgent global need for a diagnostic solution that offers high sensitivity, rapid results, minimal user intervention, and adaptability to multiple viral pathogens has advanced the development of the present invention.The detection of viral infections in blood samples is an important part of modern diagnostics and enables timely clinical interventions, epidemiological monitoring and effective disease management. In recent decades, numerous methods for identifying viral pathogens in biological fluids, in particular blood, have been developed and standardized. The most widely used conventional methods include enzyme immunoassays (ELISA), polymerase chain reaction (PCR), reverse transcriptase PCR (RT-PCR), virus cultures, and lateral flow immunoassays (LFIA). Despite their established application and official approvals, these methods are subject to various restrictions with regard to time expenditure, infrastructure dependence, sensitivity, adaptability to new virus strains and suitability for use in low-resource environments.For example, RT-PCR has evolved to the gold standard for the molecular detection of many viruses such as SARS-CoV-2, HIV and hepatitis viruses because of its ability to amplify specific nucleic acid sequences to a detectable level. Although RT-PCR offers excellent sensitivity and specificity under ideal conditions, it requires thermal cyclers, cooling chain transport of reagents and highly skilled personnel for performing several protocol steps, including RNA extraction, reverse transcription, amplification and gel electrophoresis, or real-time quantification. In addition, the process is generally time-consuming and takes several hours from sample entry to result generation. In burstout scenarios or in emergency exposures, this time delay may result in delays in isolation, handling, and containment, which in turn results in increased morbidity and risk of transmission in the community. RT-PCR is also known to give false-negative results in early stages of infection due to improper sample handling, degradation of viral RNA or low viral load. These limitations emphasize the need for alternative or supplemental detection mechanisms that are faster, more portable, and less infrastructure dependent.Another widely used diagnostic instrument is the enzyme immunoassay (ELISA) which relies on antigen-antibody interactions to recognize viral proteins or antibodies produced by the host. ELISA is particularly useful to confirm past infections or immunological response to vaccination. Its use is, however, limited by the large laboratory analyzers required, manual pipetting and hour incubation steps. In addition, serological tests such as ELISA cannot detect infections in the early phase of virus challenge if the host has not yet produced antibodies. This "window" results in low sensitivity in detecting acute stage infections, rendering ELISA ineffective as a first choice diagnostic instrument in rapidly developing clinical situations. Cross reactivity between different viral proteins can also lead to false positive results, which is particularly problematic in regions where multiple endemic viruses such as dengue, zika and chikungunya viruses occur simultaneously.Virus culture was historically important but has become largely unsatisfactory for routine diagnostics because of its high labor, long processing time (days to weeks) and the need for laboratories of Biosecure Level 2 or 3. Although culture methods allow direct observation of virus replication, which may be useful for drug sensitivity studies and strain typing, they are impractical for point-of-care or emergency use. Because of the long incubation times, virus culture is unsuitable for time-critical clinical decisions, and handling live virus particles entails considerable risks for the biosecureness of medical professionals.Lateral flow immunoassays (LFIAs) have proven to be a fast and cost effective alternative to viral detection, particularly for on-site or home assays. They form the basis for many recipe-free COVID-19 and influenza test kits. LFIAs are attractive because they are easy to handle, do not require special instruments, and provide results within 15-30 minutes. However, their reliability is impaired by the relatively low sensitivity and specificity compared to molecular diagnostics. False negative results are common, especially at the early stage of infection when the antigen level is low. In addition, most lateral flow assays are qualitative rather than quantitative and provide binary positive / negative results without disruption of viral load or stage of infection. Their limited multiplexing ability also limits it to the detection of a single virus per test, making it inefficient in coinfections or when differential diagnosis is required.In response to these limitations, recent advances in microfluidics, nanotechnology, and biosensor development have advanced the development of next generation diagnostic systems. Microfluidic lab-on-chip platforms promising lower reagent consumption, accelerated reaction kinetics and automated liquid handling in compact formats. However, these systems still present integration problems when it is concerned to combine all the steps of sample preparation, such as lysis, purification and amplification, on a single chip, especially in complex biological matrices such as whole blood. Moreover, maintaining thermal and biochemical stability across microfluidic channels, particularly during isothermal nucleic acid amplification, requires a precise design that is not yet surface-covering available in commercial devices.Nanobiosensors, including those with graphene, gold nanoparticles or quantum dots, have shown enormous potential for highly sensitive and rapid detection of viral markers. These sensors utilize changes in electrical, optical or magnetic properties when target molecules bind to their functionalized surfaces. While laboratory prototypes yielded promising results for viruses such as Zika, Dengue and Influenza, their conversion into market-ready devices is still limited. Challenges include signal drift due to non-specific binding, short sensor lifetime, complex requirements for surface chemistry, and the need for sophisticated electronics to detect and interpret the nano scale interactions. Moreover, most nanobiosensors are designed for the detection of a single analyte and therefore do not have scalability or adaptability for the detection of multiple viruses.Another promising, but not yet mature, approach is CRISPR-based diagnostics. In this context, Cas proteins such as Cas12 or Cas13, controlled by RNA, identify specific viral sequences and generate detectable signals via bilateral cleavage activity. Technologies such as SHERLOCK and DETECTR have been proposed as highly sensitive and specific diagnostics for SARS-CoV-2 and other viruses. However, these tests still require preamplification steps (e.g., RPA or LAMP), temperature control, and fluorescence detection devices. Due to the complexity and cost of these new methods, they are currently unsuitable for use in low-resource environments.Moreover, existing diagnostic systems lack intelligent, adaptive interpretation levels that can take into account the variability of patient physiology, virus development, or contextual clinical factors. Most diagnostic platforms are rule-based and provide fixed thresholds for positivity and non-use machine learning or AI for deeper insight. In contrast, AI-integrated diagnostics can learn from large sets of virus signatures, patient history, and environmental metadata to provide differentiated probabilistic evaluations of the infection status. However, the integration of AI into point-of-care diagnostics is complicated by computational constraints, data protection concerns, and lack of standardization between platforms.The convergence of microfluidics, nanotechnology, biosensors and KI provides a forward path. However, current implementations focus either closely on a single key technology or remain trapped in research prototypes. There continues to be an urgent need for an integrated, integrated and commercially viable diagnostic system which overcomes the collective limitations of existing solutions and is optimized for rapid and reliable virus detection from blood samples in both central laboratories and in remote environments. This gap constitutes the technical motif for the present invention.SUMMARY OF THE INVENTIONThe present invention provides a compact, automated system for rapidly detecting viral infections in blood samples. It integrates a disposable microfluidic cartridge with functionalized nanobiosensors, integrated biochemical processing elements and a real-time machine learning-based data analysis unit. The system takes a small amount of capillary or venous blood and performs automated sample lysis, purification and nucleic acid amplification - followed by high resolution biosensorics with electrochemical, optical or plasmonic readings.The surfaces of the biosensors are provided with virus-specific antibodies, aptamers or CRISPR-based capture elements which can bind viral proteins or genetic material in a highly specific manner. Signal changes resulting from the interaction with viruses are detected and processed by an AI engine trained on large virological datasets to accurately determine the type and concentration of viral pathogens. The entire diagnostic procedure is completed in 10 to 15 minutes and is presented to the user via an intuitive touch screen surface. The modular system supports a broad range of viruses, including SARS-CoV-2, HIV, hepatitis B / C, Zika, Dengue, and influenza A / B, and is scalable to accommodate additional targets. Its portable structure, rechargeable battery and connectivity functions make it ideal for use in clinics, mobile healthcare facilities, airports, and military facilities.The primary object of the present invention is to provide a rapid, precise and portable system for detecting viral infections directly from blood samples. This significantly reduces the diagnostic processing time and enables early identification of pathogenic pathogens. A central goal is to develop a compact, integrated diagnostic instrument that combines automated sample preparation, viral biomarker recognition, and smart result interpretation in a single machine or structure, thus minimizing human intervention and laboratory dependence. The invention also aims to overcome the limitations of existing diagnostic methods such as RT-PCR, ELISA and lateral flow assays by introducing a hybrid approach that utilizes microfluidic sample handling, biosensor-based detection and KI-directed analytics.Another object of the invention is the real-time recognition of viral nucleic acids, proteins or other biomarkers without complex thermocycling or large scale instrumentation. The system is intended to be adaptable by the use of modular test cartridges with pathogen-specific reagents and sensors and to recognize a broad range of viral pathogens-including dengue, zika, HIV, hepatitis and SARS-CoV-2. Moreover, the invention is intended to facilitate diagnostics in resource constrained or field-based environments through battery operation, wireless connectivity for cloud-based analyses, and intuitive user interface.Another object of the invention is to provide multiparametric analysis capabilities that can detect coinfections or simultaneously detect multiple viruses in a single blood sample. This improves diagnostic throughput and clinical decision making. The system is also minimally invasive, requires only microliters of blood and can perform complete procedures, including lysis, extraction, amplification, signal detection and data processing, in a closed, sterile chamber to reduce contamination risks and operator burden.BRIEF DESCRIPTION OF THE FIGUREThese and other features, aspects and advantages of the present invention will become more fully understood by reading the following detailed description when taken in conjunction with the accompanying drawings, in which like numerals represent like parts throughout. The following applies here: Figure 1 shows a block diagram of a system for rapid detection of viral infections in blood samplesThose skilled in the art will also appreciate that the elements in the drawing are shown for simplicity and are not necessarily to scale. For example, the flowcharts illustrate the method using the key steps to improve understanding of aspects of the present disclosure. Also, as for the construction of the apparatus, individual or plural components of the apparatus may be represented by conventional symbols in the drawing. The drawing may only show the specific details relevant to understanding the embodiments of the present disclosure so as not to obscure the drawing with details readily apparent to those skilled in the art after the present description.DETAILED DESCRIPTION OF THE INVENTIONIn order to promote an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and will be described in an comprehensible manner. However, the scope of the invention is not limited thereby. Changes and further modifications of the illustrated system, as well as further applications of the principles of the invention, are possible, as would normally occur to a person skilled in the art.It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be limiting thereof.References throughout this specification to "one aspect," "another aspect," or similar language mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the phrases "in one embodiment," "in another embodiment," and similar phrases in this specification may or may not refer to the same embodiment.The terms "comprises," "comprising," or other variations thereof are intended to cover a non-exclusive inclusion, such that a process or method comprising a list of steps may include not only those steps, but also other steps not expressly listed or inherent in that process or method. Likewise, the phrase "comprises... for" one or more devices, subsystems, elements, structures, or components does not exclude, without further limitations, the existence of other devices, subsystems, elements, structures, components, or additional devices, subsystems, elements, structures, or components.Unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by one of ordinary skill in the art. The systems, methods, and examples provided herein are for illustrative purposes only and are not to be considered limiting.Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.Referring to the block diagram of Figure 1, a system for rapid detection of viral infections in blood samples is shown. The system 100 comprises: a microfluidic processing module (102) configured to receive and process a microliter-scale whole blood sample by sequential steps including plasma separation, lysis of viral particles, and biomarker extraction; a biosensor array unit (104) comprising a plurality of nanostructured sensor elements functionalized with pathogen-specific ligands, wherein the biosensor array is integrated into the microfluidic module to allow real-time detection of viral nucleic acids or antigens; a temperature controlled amplification module (106) embedded in the microfluidic channel and configured to perform isothermal nucleic acid amplification via recombinase polymerase amplification (RPA), loop-mediated isothermal amplification (LAMP), or helicase-dependent amplification (HDA); a signal acquisition subsystem (108) comprising optical, electrochemical or piezoelectric transducers coupled to the biosensor array, the subsystem converting molecular interaction data to digital signals; a machine learning processor (110) operatively connected to the signal acquisition subsystem and configured to classify the infection status using pre-trained classification models with confidence assessment and anomaly marking; a wireless communication interface (112) operatively connected to the machine learning processor (112a) for remotely distributing the results, the entire system being housed within a portable housing (112b) operable via a graphical user interface (112c) or a touch screen.In one embodiment, the microfluidic processing module (102) further comprises an active separation membrane incorporating on-chip capillary valves configured to direct the blood sample into separate plasma and cell compartments, wherein the plasma is directed to downstream lysis and detection pathways without the need for centrifugation or external actuation.In one embodiment, the biosensor array unit (104) comprises a patterned graphene oxide or gold nanoparticle matrix on a flexible substrate, wherein each sensor node is coated with single-stranded oligonucleotide probes complementary to viral target sequences, and wherein the nanostructure configuration enhances surface plasmon resonance or electron mobility to enhance binding signal sensitivity.In one embodiment, the gain module (106) is thermally controlled by an embedded micro-heater controlled via a feedback loop that includes a temperature sensor on the chip, and wherein the kinetics of the gain response are modulated by dynamically adjustable response times based on preliminary signal profiles from the biosensor array.In one embodiment, the signal acquisition subsystem (108) includes a multiplexed transduction circuit that switches between optical absorption, impedance spectroscopy and surface wave resonance measurements depending on the type of target biomarker, thus allowing recognition of hybrid biomarkers for both viral RNA and surface antigens within the same analysis cycle.In one embodiment, the machine learning processor (112a) is also configured to execute a Bayesian inference model trained based on historical blood profiles of patients, viral parent-specific biomarker kinetics, and geographic epidemiological trends, wherein the model dynamically adjusts classification thresholds based on the time of onset of infection and the viral mutation rates.In one embodiment, the wireless communication interface (112) is implemented via Bluetooth Low Energy (BLE) or NB-IoT protocols and is configured to synchronize patient data, test results, and sensor calibration metadata with a secure cloud-based server, with synchronization via end-to-end encrypted channels that conform to healthcare data protection standards.In one embodiment, the graphical user interface (112c) is programmed to display the multi-phase test history, estimated time to result, biomarker specific amplification curves, and AI-based interpretation cues, and is also configured to allow manual override for confirmatory retry tests in marked unsafe cases.In one embodiment, the portable housing (112b) is comprised of a shock resistant polymeric housing with an embedded Seamless Power Supply (UPS) that supports continuous operation for at least 8 hours, and wherein the housing contains sterile cartridge slots for disposable test modules to minimize cross-contamination.In one embodiment, the biosensor array (104) is replaced with a CRISPR-Cas12 / Cas13-based detection module comprising a lyophilized, optionally rehydrated CRISPR reagent matrix, wherein target-specific guide RNAs trigger visual fluorescence or lateral flow output upon cleavage of a reporter molecule, and wherein the output is digitized by an optical scanner integrated into the system.The detailed description of the invention, entitled "System for Rapid Detection of Viral Infections in Blood Samples," provides comprehensive insight into the architecture, components and operation of the integrated diagnostic platform, with emphasis being placed in particular on machine learning techniques used for intelligent result interpretation.The system consists of a compact, portable diagnostic instrument that enables rapid viral detection directly from microliter-scale whole blood samples. The system architecture includes a microfluidic processing module, a biosensor array unit, an isothermal gain chamber, a signal acquisition subsystem, and a machine learning based decision engine - all housed in a robust, portable housing. After the blood sample is introduced into a particular inlet port of a disposable test cartridge, the sample is automatically passed through a microfluidic channel system. A capillary-based plasma separation membrane embedded in the first stage of the cartridge separates plasma from cell contents using passive hydrodynamic principles, thereby obviating external centrifugation or vacuum forces. The plasma fraction is then passed into a lysis chamber where the viral envelopes are broken chemically or thermally and the desired nucleic acids or antigens are released.After lysis, the released viral RNA or proteins are delivered in two parallel ways. One way uses a nanostructured biosensor array of graphene oxide or surfaces functionalized with gold nanoparticles. These surfaces are structured with ligands such as single stranded oligonucleotide probes complementary to the viral target genomes or aptamers and monoclonal antibodies for antigen detection. The molecular interaction between the probes and the viral targets causes quantifiable changes in the local electrical, optical or piezoelectric environment, which are converted into analog signals via a multimodal detection system. Simultaneously, a portion of the lysate is introduced into a temperature controlled amplification module using an isothermal amplification method such as RPA, LAMP or HDA. The chamber contains lyophilized reagents that are rehydrated upon sample input and the reaction kinetics are monitored and controlled by an embedded micro-heater coupled to a digital thermal sensor and operating with a closed feedback mechanism.The analog signals received from the biosensor and the amplification modules are passed to a signal acquisition subsystem equipped with analog-to-digital converters and a multiplexer. Depending on the detected biomarker - nucleic acid or antigen - the system selects the appropriate detection modality (e.g. impedance, optical absorption or resonant frequency shift) to maximize sensitivity and specificity. The digitized output signals are fed to a machine learning processor which acts as a diagnostic brain of the system. This processor is preinstalled with a number of hierarchical classification techniques, including supervised learning models such as random forests, support vector machines (SVMs), and neural networks trained on large annotated data sets with signal patterns, concentration curves, amplification profiles, and infection states for a variety of viruses.The core of the diagnostic engine is a Bayesian probability model that combines real-time sensor data with clinical and epidemiological prior knowledge to estimate the probability of infection. This model takes into account signal noise, assay variability and known biomarker dynamics associated with the viral replication cycle. Confidence values are generated by calculating posterior probability of infection taking into account observed sensor features, assay time and amplification delay or plateau phases. In ambiguous or marginal cases, where the signal strength is near the decision threshold, the model accesses a second decision level that uses fuzzy logic and anomaly detection to either flag the result for manual verification or recommend a retest.Moreover, the processor is capable of adaptive learning through federated update mechanisms. In this case, anonymous user data can be transmitted securely to a cloud-based server in order to periodically retraining models without jeopardizing the privacy of the patients. The communication interface supports Bluetooth Low Energy (BLE) and NB-IoT protocols for synchronization with central health information systems. In parallel, a graphical user interface on the device presents the diagnostic results in a user-friendly manner, including real-time progress indicators, biomarker specific amplification diagrams, and technical annotations that clinically interpret the test results.The structure of the device comprises a robust polymer housing with shock absorption and an integrated seamless power supply allowing a permanent use in the field of eight hours. Sterile disposable cartridges are inserted into a front compartment of the main unit. Each cartridge contains integrated channels, sensor patches, reagent chambers, and waste containers. The design provides minimal servicing and eliminates the risk of cross-contamination by sealed fluidics and disposable flow paths. For detection scenarios with newly occurring or novel viruses, a CRISPR-based variant of the system is implemented. This includes a Cas12 or Cas13 detection module in which leader RNAs bind to viral RNA targets and activate a bilateral cleavage mechanism that separates a fluorescent reporter molecule. This visual change is detected by an optical scanner and interpreted via the same AI pipeline, enabling nucleic acid detection without conventional amplification.In summary, the described system combines real-time biosensor system, isothermal molecular amplification and artificial intelligence in order to enable a fully automatic, highly sensitive and portable diagnostic workflow for virus detection. The integration of adaptive machine learning models, clearable AI-based evaluation systems and modular test cartridges makes the system robust, versatile and suitable for use in clinics, field and emergency situations where timely and accurate diagnosis of viral infections is of greatest importance.The proposed system consists of a self-contained diagnostic device and a preloaded microfluidic disposable cartridge. The diagnostic device is accommodated in a robust thermoplastic housing with dimensions of approximately 30 cm×20 cm×15 cm and equipped with a high-resolution touchscreen, an integrated microcontroller, a temperature controller and a sensor display. The blood sample inlet is accessible from the outside and allows capillary blood samples to be taken with a lancet or venous blood samples to be taken with a syringe.The disposable cartridge is produced from biocompatible polymer substrates by means of micro milling or soft lithography and contains separate microfluidic channels for sample dosing, lysis, reagent mixing, amplification and detection. Integrated into the detection chambers are nanobiosensors made of graphene, gold nanoparticles or photonic crystal resonators functionalized with virus-specific ligands. The cartridge also contains reagent reservoirs with lyophilized or fluidic reagents for virus lysis, reverse transcription, isothermal amplification (e.g., LAMP or RPA), and enzymatic signal generation.After introduction of the sample, an internal peristaltic micropump or capillary driven system automates blood flow through the cartridge. Viral particles are lysed, their genetic material extracted and amplified under temperature controlled conditions assisted by microheaters and thermistors. The amplified nucleic acids or antigens subsequently bind to corresponding surface-functionalized biosensors. Real-time detection of molecular binding is via one or more transduction techniques, such as electrochemical impedance spectroscopy (EIS), fluorescence resonance energy transfer (FRET), or localized surface plasmon resonance (LSPR). These signals are amplified and digitized by integrated electronics.The digitized signal data is passed to an embedded AI-based processor unit, which consists of a neural inference engine or an FPGA (field programmable gate array). This employs pre-trained deep learning models to classify viral infection. The processor matches the sensor response curves with an encrypted, continuously updatable database of viral signatures. It performs probabilistic estimation of the viral presence and concentration and outputs confidence values that can be displayed on the touch screen in readable diagnostic reports and also shared over Wi-Fi, Bluetooth, or secure cloud APIs.The diagnostic process takes less than 15 minutes from sample introduction to result output. The disposable cartridge ensures biosecureness and prevents cross-contamination. The main device is equipped with an integrated UV-C sterilization unit for regular disinfection of the sample container. The power supply is effected via alternating current or an integrated lithium-ion battery which makes possible over 50 test cycles per charge.In one embodiment, the biosensor chamber is reconfigurable using tunable photonic crystal cavities that allow selective spectral recognition of mutant virus strains by adjusting resonant frequencies. In another embodiment, CRISPR-Cas recognition complexes are pre-immobilized with mutation specific leader RNAs to precisely identify viral RNA, allowing next generation precision virology.The modular construction of the system enables the extension by further analytes such as bacterial DNA, tumor biomarkers or autoimmune markers and is thus expandable beyond virology. This invention provides a fracturing solution for early, precise and rapid viral diagnostics, reduces central laboratory dependence and allows rapid medical care in both urban and rural areas.The drawings and the foregoing description show examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be divided into multiple functional elements. Elements of one embodiment may be added to another embodiment. For example, the order of the processes described herein may be changed and is not limited to the manner described herein. Moreover, the actions of a flow chart need not be performed in the order shown; nor do all actions necessarily need to be performed. Also, actions that are not dependent on other actions may be performed in parallel with the other actions. The scope of the embodiments is by no means limited by these specific examples. Numerous variations, whether or not explicitly stated in the specification, such as differences in structure, dimensions, and material use, are possible. The scope of the embodiments is at least as broad as recited in the following claims.Advantages, other advantages and solutions to problems have been described above with reference to specific embodiments. However, the advantages, merits, solutions to problems and any components that may result in an advantage, merit or solution being introduced or enhanced are not to be understood as critical, required or essential features or components of individual or all claims.REFERENCES100 System For Rapid Detection of Viral Infections in Blood Samples. 102 microfluidic processing module 104 biosensor array unit 106 temperature controlled gain module 108 signal acquisition subsystem 110 machine learning processor 112 wireless communication interface 112 aprocessor for machine learning 112 bmountable housing 112 c GRAFISCHE

Claims

A system for rapidly detecting viral infections in blood samples, comprising: a microfluidic processing module configured to receive and process a microliter-scale whole blood sample by successive steps including plasma separation, lysis of viral particles and extraction of biomarkers; a biosensor array unit comprising a plurality of nanostructured sensor elements functionalized with pathogen specific ligands, wherein the biosensor array is integrated into the microfluidic module to enable real-time detection of viral nucleic acids or antigens; a temperature controlled amplification module embedded in the microfluidic channel and configured to perform isothermal nucleic acid amplification using recombinase polymerase amplification (RPA), loop mediated isothermal amplification (LAMP), or helicase dependent amplification (HDA); a signal acquisition subsystem comprising optical, electrochemical, or piezoelectric transducers coupled to the biosensor array, the subsystem converting molecular interaction data into digital signals; a machine learning processor operatively connected to the signal acquisition subsystem and configured to classify the infection status using pre-trained classification models with confidence assessment and anomaly marking; a wireless communication interface operatively connected to the machine learning processor for remotely distributing results, wherein the entire system is housed within a portable housing operable via a graphical user interface or a touch screen.The system of claim 1, wherein the microfluidic processing module further comprises an active separation membrane equipped with integrated on-chip capillary valves configured to direct the blood sample into separate plasma and cell compartments, wherein the plasma is directed to downstream lysis and detection pathways without requiring centrifugation or external actuation.The system of claim 1, wherein the biosensor array unit comprises a patterned graphene oxide or gold nanoparticle matrix on a flexible substrate, wherein each sensor node is coated with single-stranded oligonucleotide probes complementary to viral target sequences, and wherein the nanostructure configuration enhances surface plasmon resonance or electron mobility to enhance binding signal sensitivity.The system of claim 1, wherein the temperature of the amplification module is controlled by an embedded micro-heater controlled via a feedback loop including a temperature sensor on the chip, and wherein the kinetics of the amplification reaction is modulated by a dynamically adjustable reaction time based on preliminary signal profiling from the biosensor array.The system of claim 1, wherein the signal acquisition subsystem comprises a multiplexed transduction circuit that switches between optical absorption, impedance spectroscopy and surface wave resonance measurements depending on the type of target biomarker, thereby allowing recognition of hybrid biomarkers for both viral RNA and surface antigens within the same analysis cycle.

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