Description:

Portable diagnostic device for early detection of Alzheimer’s disease biomarkers in cerebrospinal fluid
Field of the Invention
[0001] The present disclosure relates to portable diagnostic devices, more particularly, to detection of Alzheimer’s disease biomarkers in cerebrospinal fluid using integrated microfluidic and biosensing technologies.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Alzheimer’s disease is a progressive neurodegenerative disorder characterized by cognitive decline, memory impairment, and eventual loss of functional independence. Clinical diagnosis is often delayed until symptoms become severe, by which point significant neuronal damage has already occurred. Early diagnosis is critically important for therapeutic intervention, disease management, and clinical trial enrollment. Presently, established diagnostic methods include positron emission tomography imaging, cerebrospinal fluid biomarker assays, and neuropsychological assessments. However, existing techniques are limited by cost, accessibility, and invasiveness.
[0004] Positron emission tomography scans require specialized facilities and radiotracers, which are expensive and unavailable in many regions. Conventional cerebrospinal fluid assays for amyloid-beta, tau, and phosphorylated tau biomarkers are performed in centralized laboratories, requiring extensive processing and highly trained personnel. This results in delays between sample collection and clinical reporting, reducing the utility of biomarker analysis for real-time decision-making. Furthermore, laboratory-based immunoassays are prone to variability in sensitivity, limiting their reproducibility across diverse clinical environments.
[0005] Advances in microfluidics and biosensing technologies have enabled miniaturized devices for biomarker detection. Research prototypes incorporating electrochemical biosensors have demonstrated feasibility in detecting Alzheimer’s disease markers with enhanced sensitivity. However, existing systems are not fully portable, lack real-time analysis capabilities, and often depend on external laboratory support for data processing. Additionally, challenges remain in reliably separating low-abundance biomarkers from complex cerebrospinal fluid samples within a handheld platform.
[0006] Accordingly, there exists a need for a fully integrated portable diagnostic device capable of direct cerebrospinal fluid analysis. Such a device should incorporate microfluidic separation, nanomaterial biosensing, electrochemical signal transduction, and computational analysis into a single handheld platform. The system must provide real-time diagnostic results, enable wireless communication with external health record systems, and support point-of-care diagnostics for early intervention in Alzheimer’s disease.
Summary
[0007] The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
[0008] The following paragraphs provide additional support for the claims of the subject application.
[0009] The disclosure pertains to a portable diagnostic device for early detection of Alzheimer’s disease biomarkers in cerebrospinal fluid is disclosed. The system comprises a microfluidic sample collection chamber configured for controlled cerebrospinal fluid introduction, a biomarker separation unit configured with microchannel filtration and nanoscale affinity filters, and a biosensing array incorporating functionalized nanomaterial electrodes. The biosensing array detects amyloid-beta peptides, tau proteins, and phosphorylated tau fractions with high sensitivity. A signal transduction circuit translates antigen-antibody binding events into electrochemical signals, which are subsequently analyzed by an onboard data processing module.
[00010] The processing module incorporates microcontrollers and machine learning algorithms, enabling comparative analysis against diagnostic thresholds and integration with historical clinical data. Diagnostic outcomes are presented on a touchscreen interface and transmitted wirelessly to external devices for integration with electronic health record systems or telemedicine platforms. A calibration module incorporating synthetic biomarker standards enables validation of sensitivity prior to use.
[00011] The portable device is powered by a rechargeable battery and integrates power management systems to ensure prolonged operational use. Configurations allow real-time biomarker detection at the point of care without the need for centralized laboratory facilities. The system thereby facilitates early diagnosis of Alzheimer’s disease by enabling immediate biomarker analysis following lumbar puncture, supporting early intervention strategies, clinical trial recruitment, and personalized treatment monitoring.
Brief Description of the Drawings
[00012] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00013] FIG. 1 illustrates a system architecture diagram of the portable diagnostic device, depicting integration of the microfluidic collection chamber, biomarker separation unit, biosensing array, signal transduction circuit, data processing module, and communication interface within a handheld housing, in accordance with the embodiments of the present disclosure.
[00014] FIG. 2 illustrates a method flow diagram of the diagnostic process, beginning with cerebrospinal fluid collection, proceeding through biomarker enrichment, electrochemical detection, signal transduction, computational analysis, and presentation of diagnostic results, in accordance with the embodiments of the present disclosure.
[00015] FIG. 3 illustrates a neural network model diagram implemented within the data processing module, showing layered computational nodes configured to analyze electrochemical signals, identify Alzheimer’s biomarker patterns, and generate diagnostic classifications through supervised learning, in accordance with the embodiments of the present disclosure.
Detailed Description
[00016] In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
[00017] The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
[00018] Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
[00019] The disclosed portable diagnostic device for early detection of Alzheimer’s disease biomarkers in cerebrospinal fluid will now be described in expanded detail. The device integrates microfluidic fluid handling, biomarker separation, nanomaterial biosensing, electrochemical signal transduction, and computational analysis within a handheld platform. The overall operational flow begins with sample collection. A lumbar puncture interface introduces cerebrospinal fluid into a microfluidic chamber. Said chamber incorporates a pressure-regulated inlet valve and micropumping system to ensure controlled flow. Contamination is minimized through integrated sterilization filters and sealed channels. The chamber volume is optimized for small fluid quantities, enabling minimally invasive collection.
[00020] The collected cerebrospinal fluid passes into the biomarker separation unit. This unit comprises microchannel membranes and nanoscale affinity filters functionalized with ligands specific to amyloid-beta oligomers and phosphorylated tau proteins. Filtration separates target biomarkers from non-specific proteins and cellular debris. Affinity filters selectively capture low-abundance molecules while allowing non-target components to pass through. The separation unit thereby increases concentration of relevant biomarkers, improving sensitivity of subsequent detection.
[00021] The processed sample is directed to the biosensing array. Said array is fabricated from nanomaterials such as graphene, gold nanoparticles, or carbon nanotubes. Each electrode is functionalized with monoclonal antibodies immobilized through linker chemistry, configured to bind amyloid-beta, total tau, or phosphorylated tau specifically. Antigen-antibody binding events occur on the electrode surface, altering electrochemical properties. The binding reaction is detected with high sensitivity due to the enhanced surface-to-volume ratio of nanomaterials.
[00022] Signal transduction occurs through an integrated circuit connected to the biosensing array. Detection modalities include impedance spectroscopy, amperometry, and field-effect transistor sensing. Binding-induced changes are converted into measurable electrical signals, which are amplified and transmitted to the data processing module. The transduction circuit includes noise-reduction filters and calibration channels, enabling consistent signal quality across multiple tests.
[00023] The data processing module comprises a microcontroller programmed with diagnostic thresholds for Alzheimer’s biomarkers. Machine learning algorithms are employed to compare observed concentration profiles against clinical datasets. Statistical models improve specificity and sensitivity by identifying characteristic biomarker patterns. Data processing occurs in real time, generating diagnostic outcomes within minutes.
[00024] Diagnostic results are presented on a display interface. The display includes a touchscreen panel enabling user interaction and configuration. Results can be transmitted wirelessly through Bluetooth, Wi-Fi, or near-field communication modules. Integration with electronic health records supports direct incorporation into clinical workflows. Telemedicine compatibility allows remote consultation and monitoring.
[00025] The device is powered by a rechargeable lithium-polymer battery. Power management circuitry regulates consumption, enabling extended operation. Charging can be performed through USB or wireless charging modules. Low-power microcontrollers and sensors ensure prolonged battery life, supporting portability in both clinical and home environments.
[00026] In one embodiment, the device is optimized for rapid screening. The microfluidic chamber is designed for low-volume cerebrospinal fluid samples, with minimal processing time. The biosensing array incorporates high-affinity antibodies to achieve detection within minutes. This embodiment provides immediate diagnostic results suitable for clinical triage or trial enrollment.
[00027] In a second embodiment, the device is optimized for high sensitivity. The separation unit incorporates multi-stage nanoscale affinity filters to enrich low-abundance biomarkers. The biosensing array utilizes graphene electrodes with extended binding surfaces. The data processing module employs advanced algorithms trained on extensive datasets to improve accuracy in early disease stages. This embodiment is suited for research and long-term monitoring.
[00028] In a third embodiment, the device is configured for integrated telemedicine deployment. The display interface emphasizes wireless communication, enabling remote access to diagnostic data. The system incorporates cloud-based machine learning, allowing continuous updates to diagnostic models. Clinical data can be shared with specialists in real time, expanding access to expertise. This embodiment provides technical benefits in remote or resource-limited regions.
[00029] Operational flows may vary depending on clinical context. In acute settings, rapid biomarker analysis supports immediate decision-making. In longitudinal monitoring, repeated measurements are stored and compared over time, enabling tracking of disease progression. In multi-center clinical trials, standardized devices ensure consistent data collection across diverse populations.
[00030] Data processing flows are reiterated across contexts. Calibration using synthetic standards occurs prior to each use, ensuring reliability. Noise reduction filters process raw signals into normalized data streams. Machine learning algorithms analyze patterns, producing diagnostic outcomes. Each repetition of this process reinforces reproducibility and reduces variability.
[00031] Thus, the disclosed portable diagnostic device integrates sample handling, biomarker separation, biosensing, and computational analysis into a handheld platform. The multiplicity of embodiments demonstrates adaptability to different clinical needs. The system delivers technical benefits including real-time analysis, minimized invasiveness, enhanced sensitivity, wireless connectivity, and suitability for point-of-care use. Collectively, the disclosed system provides a transformative approach for early detection of Alzheimer’s disease, enabling earlier intervention, improved patient outcomes, and expanded research capabilities.
[00032] Figure 1 provides a system architecture diagram of the portable diagnostic device for Alzheimer’s biomarker detection. The diagram depicts sequential arrangement of hardware and fluidic subsystems. The microfluidic collection chamber is configured for controlled intake of cerebrospinal fluid. The collected sample is routed into a biomarker separation unit, where nanoscale affinity filters enrich target proteins. Downstream, the biosensing array receives enriched analytes, where immobilized antibodies capture amyloid-beta and tau proteins. Electrochemical changes are transmitted to the signal transduction circuit, which converts raw biochemical interactions into measurable electrical signals. The data processing module then analyzes signals in real time and

transmits results to the communication interface for display and wireless sharing. The system architecture demonstrates hierarchical integration of fluidic handling, sensing, transduction, and analysis within a compact platform. Each module is designed to function independently while maintaining seamless interconnection. The technical benefit of this configuration lies in its ability to consolidate sample processing, detection, and computation into a portable unit, thereby enabling point-of-care diagnosis.
[00033] Figure 2 provides a method flow diagram showing the operational sequence of the disclosed diagnostic process. The flow initiates with cerebrospinal fluid collection via a lumbar puncture interface connected to the microfluidic chamber. The sample then undergoes biomarker separation, where amyloid-beta and tau proteins are isolated through affinity filters. Following separation, enriched analytes are introduced to the biosensing array, where antibody-antigen binding events occur. Electrochemical variations are captured and conveyed to the signal transduction circuit. Converted electrical signals are transmitted to the data processing module, where algorithms quantify biomarker concentration levels. The diagnostic outcome is subsequently displayed on the communication interface and transmitted wirelessly to external health systems. This flow diagram illustrates linear progression of operations, where each stage provides input for the next, ensuring diagnostic integrity. The advantage of this arrangement lies in its structured pathway, which transforms raw biological samples into clinically actionable insights without external laboratory support.
[00034] Figure 3 provides a neural network model diagram illustrating the computational framework implemented within the data processing module. The neural network consists of an input layer receiving electrical signals from the transduction circuit, hidden layers comprising computational nodes configured for feature extraction and pattern recognition, and an output layer producing diagnostic classifications. Each hidden node is configured with weighted connections that learn from training datasets of biomarker profiles. The system applies supervised learning algorithms to distinguish between normal, mild cognitive impairment, and Alzheimer’s disease states. The neural network model enhances device accuracy by identifying subtle signal variations not easily discernible through static thresholding. The technical benefit lies in the adaptability of the learning framework, which continuously improves classification accuracy as additional patient data is incorporated, thereby reducing false positives and improving early detection.
[00035] Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
[00036] While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.
 

Claims
I/We Claim:
1. A portable diagnostic device for early detection of Alzheimer’s disease biomarkers in cerebrospinal fluid, comprising: a microfluidic sample collection chamber configured to receive cerebrospinal fluid through a minimally invasive lumbar puncture interface; a biomarker separation unit incorporating microchannel filtration membranes configured to isolate amyloid-beta peptides, tau proteins, and phosphorylated tau fractions; a biosensing array comprising functionalized nanomaterial electrodes immobilized with monoclonal antibodies specific to Alzheimer’s biomarkers; a signal transduction circuit configured to convert antigen-antibody binding events into measurable electrochemical outputs; a data processing module comprising an integrated microcontroller configured to analyze biomarker concentration profiles against pre-programmed diagnostic thresholds; and a display and communication interface configured to present diagnostic outcomes and transmit processed data to external devices, wherein the portable diagnostic device provides real-time analysis for early detection of Alzheimer’s disease.
2. The portable diagnostic device of claim 1, wherein the microfluidic sample collection chamber incorporates a pressure-regulated inlet valve and micro-pumping system, thereby enabling controlled introduction of cerebrospinal fluid and minimizing contamination during sample transfer.
3. The portable diagnostic device of claim 1, wherein the biomarker separation unit further comprises nanoscale affinity filters functionalized with peptide ligands specific to amyloid-beta oligomers, thereby selectively enriching low-abundance pathogenic biomarkers from cerebrospinal fluid samples.
4. The portable diagnostic device of claim 1, wherein the biosensing array is fabricated from graphene, gold nanoparticles, or carbon nanotube electrodes functionalized with monoclonal antibodies, thereby enhancing sensitivity of biomarker detection through increased surface-to-volume ratio.
5. The portable diagnostic device of claim 1, wherein the signal transduction circuit comprises impedance spectroscopy modules, amperometric sensors, or field-effect transistor interfaces, thereby enabling multi-modal detection of biomarker binding events across a range of concentration levels.
6. The portable diagnostic device of claim 1, wherein the data processing module incorporates machine learning algorithms configured to compare biomarker concentration profiles with historical clinical data, thereby improving diagnostic specificity and reducing false positives.
7. The portable diagnostic device of claim 1, wherein the display and communication interface comprises a touchscreen panel and wireless communication modules selected from Bluetooth, Wi-Fi, or near-field communication, thereby enabling integration with electronic health record systems and telemedicine platforms.
8. The portable diagnostic device of claim 1, wherein the device is powered by a rechargeable lithium-polymer battery integrated with a low-power management system, thereby ensuring operational portability and extended use during clinical and remote diagnostics.
9. The portable diagnostic device of claim 1, wherein the system further comprises a calibration module incorporating synthetic biomarker standards, thereby enabling on-site verification of device sensitivity and accuracy prior to clinical use.
10. The portable diagnostic device of claim 1, wherein integration of microfluidic separation, biosensing array, electrochemical transduction, and computational analysis within a handheld platform provides real-time, minimally invasive, and portable biomarker detection enabling early intervention in Alzheimer’s disease management.

Portable diagnostic device for early detection of Alzheimer’s disease biomarkers in cerebrospinal fluid
Abstract
A portable diagnostic device for early detection of Alzheimer’s disease biomarkers in cerebrospinal fluid is disclosed. The device comprises a microfluidic sample collection chamber, a biomarker separation unit with nanoscale filtration, and a biosensing array comprising functionalized nanomaterial electrodes. A signal transduction circuit converts antigen-antibody interactions into electrochemical outputs, while a data processing module analyzes biomarker profiles against diagnostic thresholds. Results are displayed on a touchscreen interface and transmitted wirelessly to external systems. A calibration module verifies sensitivity using synthetic standards. The device is battery powered with power management for portable use. Integration of microfluidics, biosensing, and computational analysis within a handheld platform enables real-time, point-of-care detection of amyloid-beta and tau proteins, providing an accessible tool for early intervention in Alzheimer’s disease.
Fig. 1

, Claims:Claims
I/We Claim:
1. A portable diagnostic device for early detection of Alzheimer’s disease biomarkers in cerebrospinal fluid, comprising: a microfluidic sample collection chamber configured to receive cerebrospinal fluid through a minimally invasive lumbar puncture interface; a biomarker separation unit incorporating microchannel filtration membranes configured to isolate amyloid-beta peptides, tau proteins, and phosphorylated tau fractions; a biosensing array comprising functionalized nanomaterial electrodes immobilized with monoclonal antibodies specific to Alzheimer’s biomarkers; a signal transduction circuit configured to convert antigen-antibody binding events into measurable electrochemical outputs; a data processing module comprising an integrated microcontroller configured to analyze biomarker concentration profiles against pre-programmed diagnostic thresholds; and a display and communication interface configured to present diagnostic outcomes and transmit processed data to external devices, wherein the portable diagnostic device provides real-time analysis for early detection of Alzheimer’s disease.
2. The portable diagnostic device of claim 1, wherein the microfluidic sample collection chamber incorporates a pressure-regulated inlet valve and micro-pumping system, thereby enabling controlled introduction of cerebrospinal fluid and minimizing contamination during sample transfer.
3. The portable diagnostic device of claim 1, wherein the biomarker separation unit further comprises nanoscale affinity filters functionalized with peptide ligands specific to amyloid-beta oligomers, thereby selectively enriching low-abundance pathogenic biomarkers from cerebrospinal fluid samples.
4. The portable diagnostic device of claim 1, wherein the biosensing array is fabricated from graphene, gold nanoparticles, or carbon nanotube electrodes functionalized with monoclonal antibodies, thereby enhancing sensitivity of biomarker detection through increased surface-to-volume ratio.
5. The portable diagnostic device of claim 1, wherein the signal transduction circuit comprises impedance spectroscopy modules, amperometric sensors, or field-effect transistor interfaces, thereby enabling multi-modal detection of biomarker binding events across a range of concentration levels.
6. The portable diagnostic device of claim 1, wherein the data processing module incorporates machine learning algorithms configured to compare biomarker concentration profiles with historical clinical data, thereby improving diagnostic specificity and reducing false positives.
7. The portable diagnostic device of claim 1, wherein the display and communication interface comprises a touchscreen panel and wireless communication modules selected from Bluetooth, Wi-Fi, or near-field communication, thereby enabling integration with electronic health record systems and telemedicine platforms.
8. The portable diagnostic device of claim 1, wherein the device is powered by a rechargeable lithium-polymer battery integrated with a low-power management system, thereby ensuring operational portability and extended use during clinical and remote diagnostics.
9. The portable diagnostic device of claim 1, wherein the system further comprises a calibration module incorporating synthetic biomarker standards, thereby enabling on-site verification of device sensitivity and accuracy prior to clinical use.
10. The portable diagnostic device of claim 1, wherein integration of microfluidic separation, biosensing array, electrochemical transduction, and computational analysis within a handheld platform provides real-time, minimally invasive, and portable biomarker detection enabling early intervention in Alzheimer’s disease management.