Protein detection device and protein detection method

Abstract

We provide a system and method configured for the non-invasive identification of proteins exhibiting specific structural conformations in vivo. [Solution] This system includes a light source that irradiates a target with light at a predetermined pulse period, a sound detection device that captures acoustic signals generated by photoacoustic effects, and an information processing unit that analyzes the detected acoustic signals to determine the presence or accumulation of the target protein. This technology enables the early detection of disease-related proteins such as amyloid-beta and degenerated α-synuclein fibers without requiring complex imaging diagnostics or invasive biopsy procedures.

Description

Technical Field

[0001] This application claims the benefit of priority of U.S. Provisional Application No. 63 / 689,693, filed on August 31, 2024, the entire content of which is incorporated herein by reference.

[0002] The present disclosure relates to a detection device and a detection method for detecting a protein having a predetermined structure in a subject.

Background Art

[0003] Early detection of diseases is highly desirable because timely intervention can improve clinical outcomes and quality of life. However, conventional diagnostic techniques often rely on invasive biopsies or complex imaging techniques, which are burdensome for patients and medical staff, especially at the early stages of diseases. Therefore, there is a strong demand for a simpler, cheaper, and less invasive method for detecting disease-related proteins at an early stage.

[0004] Detecting proteins with specific structural features in the organs and tissues of animals (including humans) has been shown to be effective in assessing the presence and progression of various diseases. For example, the detection of amyloid fibrils in the retina can contribute not only to the understanding of Alzheimer's disease (AD) but also to other diseases associated with abnormal protein aggregation. [[ID=ZI]]

[0005] Photoacoustic imaging is one of the known methods for detecting substances in living tissues. In this technique, light is irradiated onto the tissue, and the acoustic signals (ultrasound) generated from the target substance are detected to visualize the target. However, this method has limitations in sensitivity, and a certain amount of mass of the target substance is required to generate a detectable signal.

Prior Art Documents

Patent Documents

[0006]

Patent Document 1

Summary of the Invention

[0007] Conventional photoacoustic imaging may fail to detect target substances unless they are present in relatively large quantities within biological tissue. In other words, photoacoustic imaging has limitations in sensitivity, and if the accumulation of target substances is insufficient, it may not be possible to visualize them as an image. As a result, the detection of proteins with a specific structure using photoacoustic imaging is only possible after the disease has progressed and large amounts of protein have accumulated, which can hinder early diagnosis.

[0008] Furthermore, photoacoustic imaging systems tend to be complex and expensive because they require specialized components such as highly sensitive cameras and pulsed laser light sources. These technical and economic barriers limit their practical application to simple, early-stage disease screening.

[0009] In light of the above, the purpose of this disclosure is to provide a simple and cost-effective technology that can detect proteins even when they are present in trace amounts in living organisms. [Means for solving the problem]

[0010] To achieve the above objective, a protein detection device according to one aspect of this disclosure comprises the following: Light source: It is configured to irradiate a biological target with pulsed light at a fixed irradiation period, and this irradiation period refers to the number of pulses per minute.

[0011] Sound detection device: Placed near the target, it is configured to detect acoustic signals resulting from the photoacoustic effect that occurs when pulsed light is absorbed by tissue.

[0012] Information processing device: Configured to determine the presence of a protein having a predetermined structure within a target object based on the detected sound intensity.

[0013] In another embodiment, the protein detection method of the present invention includes irradiating a target object with light at a predetermined irradiation period, detecting a sound photoacoustically generated by a sound detection device, and determining the presence of a protein having a predetermined structure based on the magnitude of the sound. This method can be carried out using the aforementioned device and is particularly suitable for detecting degenerated or aggregated proteins such as amyloid-beta and α-synuclein fibers associated with Alzheimer's disease and Parkinson's disease. [Effects of the Invention]

[0014] The inventors of this disclosure have found that when light is shone on a target object at a predetermined interval, proteins with a specific structure contained in the target emit detectable sound through the photoacoustic effect. Notably, even in minute quantities, these proteins generate acoustic signals that can be detected by sound detection devices. Since the acquisition of acoustic signals can be performed with a simple equipment configuration, this apparatus and method enable the early detection of disease-related proteins non-invasively and at low cost. This facilitates the early diagnosis of diseases associated with these proteins and increases the feasibility of routine clinical screening. [Brief explanation of the drawing]

[0015] [Figure 1] This is a schematic diagram showing a protein detection device according to one embodiment of the present disclosure. [Figure 2] This flowchart shows a protein detection method according to one embodiment of the present disclosure. [Figure 3] This is a flow diagram conceptually illustrating calibration strategies in photoacoustic protein detection. [Figure 4] Experimental results show that brain slices from APP transgenic mice emitted stronger acoustic signals compared to wild-type mice, particularly in the 2,000–3,500 Hz range. [Figure 5] Preliminary findings from studies of human brain tissue are presented. These results confirm the specificity of this detection method for amyloid structures.

Mode for Carrying Out the Invention

[0016] Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. However, for the sake of avoiding redundancy and enhancing clarity, well-known or duplicate matters may be omitted. The drawings and the following description are provided to assist those skilled in the art in fully understanding the present invention and are not intended to limit the claims.

[0017] <Summary> As shown in FIG. 1, the protein detection device 100 of the present disclosure irradiates a target object W such as an animal tissue with light at a predetermined irradiation cycle to determine the presence or absence of a protein having a predetermined structure. The acoustic signal generated from the protein by the photoacoustic effect is detected by the acoustic detection device 3. Based on the magnitude of the detected sound, the information processing device 5 determines the presence of the target protein.

[0018] Detectable proteins include, but are not limited to, amyloid fibrils derived from misfolded proteins such as amyloid-β and synuclein. In the case of amyloid detection in the retina, a method of irradiating the eyeball with light from the outside or irradiating the excised tissue can be taken.

[0019] The retina is an anatomical site that is extremely suitable for non-invasively detecting disease-related proteins such as amyloid-β. As an extension of the central nervous system (CNS), the retina shares many biochemical and pathological characteristics with the brain and contains early accumulations of misfolded proteins associated with Alzheimer's disease and Parkinson's disease.

[0020] The retina is optically accessible through the pupil, and the retinal tissue can be irradiated and analyzed without surgical intervention by a light-based diagnostic technique such as the photoacoustic-based system disclosed herein. This non-invasive accessibility makes it suitable for daily and repeated evaluations. Furthermore, multiple studies have shown that amyloid-β deposits are present in the retinal layers of patients with early Alzheimer's disease, indicating that the retina can function as a "window" for brain pathology.

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[0022] Configuration

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[0025] The sound detection device 3 is implemented as a microphone and is positioned to detect sound SO (acoustic signal) generated from the target object W by the photoacoustic effect. This effect occurs when proteins with specific structures, such as amyloid fibers, absorb pulsed light and emit pressure waves. The microphone is designed to be a small and low-cost device capable of acquiring these acoustic signals with sufficient sensitivity.

[0026] The information processing device 5 includes a processor 51 and a storage device 53, and is configured to control the operation of the entire system. The processor 51 controls the light source 1 to pulse irradiation at a predetermined period (e.g., 10,000 to 20,000 rpm, preferably 12,000 to 15,000 rpm) to induce a detectable acoustic signal of 1,500 to 4,000 Hz within the target object. This frequency band is within the audible range and does not cause visual discomfort, thus enabling safe and effective signal detection. The processor 51 analyzes the acoustic signal acquired by the sound detection device 3 and determines the presence or accumulation of the target protein by comparing the magnitude of the detected sound with a preset threshold. Repeated measurements can also be used to evaluate the progression of the disease. The storage device 53 stores system parameters such as irradiation settings (INF1) and detection thresholds (INF2).

[0027] The configuration described above enables compact, low-cost, and non-invasive protein detection suitable for portable or wearable applications such as smart glasses and head-mounted displays (HMDs). The use of small components such as LEDs and microphones allows the system to be easily integrated into wearable devices. Mobile devices (e.g., smartphones, tablets) function as information processing units and can communicate with wearable devices via Bluetooth® or wired connections.

[0028] <Detection operation> Figure 2 is a flowchart showing an example of a detection method performed by the protein detection device 100.

[0029] Step S1: The processor 51 controls the light source 1 to emit pulsed irradiation light LI based on the irradiation period obtained from the irradiation parameter information INF1. The light is directed toward the target object W, which may be in vivo tissue (e.g., retina) or an in vivo sample.

[0030] Step S2: The sound detection device 3 detects the sound SO generated from the target object W in response to the light irradiation. The sound signal is transmitted to the processor 51.

[0031] Step S3: The processor 51 analyzes the detected sound SO and determines whether the components within a predefined frequency range (e.g., 1,500 to 4,000 Hz) exceed the first threshold INF2 stored in the memory device 53. Spectral decomposition processing such as FFT (Fast Fourier Transform) is used for the analysis.

[0032] Step S4: If the sound level is above a threshold (Yes in S3), the processor 51 determines that a protein with a predetermined structure is present in the target object W. If necessary, notification is given by light or sound. The amount of protein can also be calculated and displayed.

[0033] Step S5: If the sound level is below the threshold (No in S3), it is determined that the protein is not present or is not at a detectable level. Another signal may be output to notify the user.

[0034] Various modifications are possible without departing from the essence of the present invention. For example, the above procedure can be reordered or executed in parallel. Signal processing may be implemented by hardware such as analog bandpass filters. The information processing device is cloud-based, enabling remote storage and analysis of detection data. Light sources and optical filters can also be adapted if a particular wavelength is more effective for a particular target. Furthermore, additional configurations such as user operation buttons can be added to improve operability. It is also possible to estimate the amount of protein by associating the loudness of the sound with pre-acquired calibration data.

[0035] <Calibration and Comparative Analysis> Figure 3 is a flowchart illustrating two complementary calibration strategies used to improve diagnostic utility and reliability in photoacoustic-based protein detection. The nodes in the upper panel represent the establishment of a baseline acoustic signal obtained from the subject, preferably in the early or preclinical stages of the disease. This baseline can be utilized in the following two calibration strategies:

[0036] Absolute calibration: The detected signal is compared to a reference curve obtained from a known sample (e.g., wild-type or transgenic brain tissue, or an artificial phantom) to estimate the absolute load of disease-related proteins such as amyloid-beta fibers. This allows for comparisons between subjects and standardization of diagnostic thresholds.

[0037] Longitudinal calibration: By comparing acoustic signals acquired from the same subject over time with their own baseline profile, protein accumulation and disease progression can be individually monitored. For example, monthly evaluations can track changes in sound intensity and frequency characteristics. By analyzing trends in acoustic data (increase, stability, decrease), the progression of pathological burdens such as amyloid accumulation can be evaluated. Furthermore, if standardized reference values ​​or calibration data are available, comparisons between subjects become possible.

[0038] The lower nodes demonstrate that thresholds used to determine the presence or severity of a protein can be dynamically adjusted based on these calibration strategies. Furthermore, it is also possible to dynamically adjust detection thresholds and improve predictive accuracy using artificial intelligence (AI) calibration trained on large datasets of acoustic signatures and clinical metadata (e.g., age, sex, APOE genotype, cognitive score).

[0039] <Example 1: Verification of amyloid detection using mouse brain slices> Figure 4 shows representative acoustic signal data obtained through experimental validation using brain slices from wild-type (WT) mice and APP transgenic mice with amyloid-beta (Aβ) plaques (5xFAD strain, B6SJL-Tg(APPSwF1Lon,PSEN1M146L L286V)6799Vas / Mmjax). These transgenic mice have mutations in the human APP gene (Swedish, Florida, London types) and PSEN1 (M1146L, L286V), which induce early and significant Aβ plaque formation from approximately 3 months of age.

[0040] Frozen coronal brain sections (40 μm thick) obtained from 8-month-old mice were placed on glass slides and positioned on a soundproofed polystyrene foam substrate. A digital stroboscope equipped with a white LED that pulsed at approximately 12,000 rpm was used for illumination. A MEMS (micro-electromechanical system) microphone was positioned to avoid direct exposure to tissue and light sources, and acoustic signals generated by photoacoustic effects were collected. A control microphone was placed under conditions without tissue or light illumination to establish a background baseline.

[0041] The figure shows six frequency domain plots: the upper panel shows FFT (Fast Fourier Transform) spectra obtained from three WT mice (Control #1-3), and the lower panel shows FFT spectra obtained from three transgenic AD model mice (AD Model #1-3). In the WT samples, the signal amplitude was low and relatively flat across the entire frequency range. In contrast, the AD slices showed a clear increase in the 2,000-3,500 Hz range, shown as blue and yellow trails, indicating the detection of amyloid-specific acoustic signatures exceeding the background noise threshold.

[0042] Both WT and AD slices generated acoustic responses to light irradiation, but significantly higher signal amplitudes were consistently observed, particularly in AD slices compared to WT. The most pronounced intergroup differences were observed when the light source pulse period was in the range of 12,000–15,000 rpm. Importantly, these results support the idea that the detected acoustic signals are specific to amyloid accumulation, given that the only difference between WT and AD samples was the presence of Aβ plaques.

[0043] Data acquisition was performed under normal laboratory conditions, including ambient noise from air conditioning systems, equipment fans, and human activity. Even without the use of special equipment such as soundproof rooms, the detection system consistently identified Aβ-related acoustic signatures exceeding a defined threshold. This demonstrates that the proposed system enables reliable protein detection in real-world environments.

[0044] To further improve the signal-to-noise ratio (SNR), the information processing unit implements frequency-domain filtering to enhance periodic signals in the 2,000–3,500 Hz range and attenuate nonspecific background noise. These results demonstrate that the disclosed apparatus and method can detect the accumulation of pathological proteins even under normal environmental conditions.

[0045] <Example 2: Analysis of human brain slices> Figure 5 shows the frequency spectra of temporal lobe frozen sections (40 μm) of Alzheimer's disease (AD) patients aged 65 years or older and cognitively normal control subjects, using the photoacoustic detection apparatus described herein. Each plot shows the acoustic signal intensity (in arbitrary units) against frequency. The upper panel shows data from three control subjects (Control #1-3), and the lower panel shows data from three AD patients (AD #1-3).

[0046] Among the control samples, Control #3 showed a distinctly different spectral profile from the AD sample, with no increase in acoustic signal in the 2,000–3,500 Hz range. In contrast, Controls #1 and #2 showed frequency profiles similar to the AD sample, suggesting the possibility of early or preclinical amyloid-beta (Aβ) accumulation. This observation is consistent with previous reports that Aβ deposition can occur up to 15 years before the onset of clinical symptoms.

[0047] All human brain sections (controls and AD) showed low-frequency acoustic signals thought to be due to structural elements common to cortical tissue. However, only AD and preclinical control samples showed increased signal intensity in the 2,000–3,500 Hz range, consistent with the acoustic signature associated with Aβ plaques.

[0048] These results demonstrate that this photoacoustic detection system has high sensitivity in distinguishing between Aβ-positive and Aβ-negative tissues, supporting its clinical applicability as a non-invasive or minimally invasive diagnostic method for detecting asymptomatic or early Aβ accumulation in human brain tissue.

[0049] As described above, the present invention encompasses novel apparatuses and related methods for non-invasively detecting proteins having specific structures, such as amyloid-beta fibrils. Detectable target proteins include, but are not limited to, amyloid fibrils (amyloid-beta, α-synuclein, etc.) derived from denatured proteins commonly associated with neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease.

[0050] For example, when targeting amyloid in the retina, the light may be shone into the eye from the outside, or it may be shone towards the extracted retinal tissue.

[0051] Claim 1 defines a protein detection device comprising a light source, a sound detection unit, and an information processing unit configured to determine the presence or accumulation of a target protein based on an acoustic response induced by pulse irradiation (see Figure 1).

[0052] Claim 16 corresponds to a method using the apparatus described in Claim 1, and includes irradiating a biological object with light, detecting the resulting acoustic signal, and determining the presence or accumulation level of a protein based on the intensity and frequency of the detected sound (see Figure 2).

[0053] The method of claim 16 is intended to be carried out using the apparatus described in claim 1 and its dependent claims, and expands the scope of patent protection in terms of both system configuration and functional use.

[0054] <Technical advantages over conventional technologies (imaging method vs. non-imaging method)> While Patent Document 1 focuses on photoacoustic imaging for visualizing pathological changes in tissue, the present invention provides a non-imaging signal-based detection system for proteins such as amyloid-beta. This fundamental difference results in the following technical advantages:

[0055] Elimination of the need for image reconstruction: Conventional techniques required high-resolution transducers and complex algorithms to reconstruct images from multiple spatially resolved photoacoustic signals. In contrast, the present invention relies solely on the magnitude and frequency profile of a single-point acoustic signal, eliminating the need for spatial scanning, synchronization, and tomography.

[0056] Reduced hardware requirements: Because this invention does not require imaging, it eliminates the need for ultrasonic detection arrays, scanning actuators, high-speed A / D converters, and complex software such as beamforming and delayed addition methods. As a result, systems can be built using inexpensive, consumer-grade microphones and LEDs.

[0057] High-speed acquisition and interpretation: Simple acoustic signature analysis using FFT on short-duration signals enables real-time evaluation of protein accumulation without waiting for image acquisition or reconstruction. This makes it suitable for rapid screening at the point of care.

[0058] Compact, wearable, and mobile-compatible: This device can be integrated into mobile or wearable platforms such as smartphone accessories and eyeglass-mounted systems.

[0059] No precise spatial targeting required: This invention detects proteins based on overall signal changes in areas of interest such as the retina, thus eliminating the need for localization on the micrometer order or depth discrimination.

[0060] Widespread Adoption and Deployment: Conventional diagnostic imaging systems were limited to specialized medical facilities due to their high cost and complexity. This invention enables low-cost, high-volume screening and is suitable for primary care, homes, and resource-limited environments. Furthermore, this invention does not require special environments such as acoustically shielded rooms that photoacoustic imaging systems require, and functions effectively even in ambient noise. [Explanation of Symbols]

[0061] 100: Protein detection device 1:Light source 3: Sound detection device 5: Information Processing Device 51: Processor 53: Storage device INF1: Irradiation parameter information INF2: First threshold LI: Irradiation light SO: Sound W :Target object

Claims

1. A light source configured to irradiate an object with light in a predetermined irradiation cycle, A sound detection device configured to detect sound emitted from the object due to the photoacoustic effect generated by the aforementioned irradiation, The system comprises an information processing device configured to determine the presence of disease-related proteins based on the detected sound intensity, The information processing device includes a non-volatile memory that stores a classification algorithm learned to identify the signature of a disease-related protein from the characteristics of an acoustic signal. A protein detection device characterized by the following features.

2. The protein detection device according to claim 1, wherein the information processing device determines that the protein is present when the detected sound level is equal to or greater than a predetermined threshold stored in the non-volatile memory.

3. The protein detection device according to claim 1, wherein the information processing device compares the detected sound intensity with sound data recorded with time information to evaluate the tendency of protein accumulation in the object.

4. The protein detection apparatus according to claim 1, wherein the irradiation cycle is in the range of 10,000 rpm to 20,000 rpm and is optimized for amplification of acoustic signals.

5. The protein detection apparatus according to claim 1, wherein the light source irradiates broadband white light including a wavelength range of 400 nm to 700 nm.

6. The protein detection apparatus according to claim 1, wherein the sound detection device is a capacitive or piezoelectric microphone covering a frequency band of at least 1,000 Hz to 5,000 Hz.

7. The protein detection device according to claim 1, wherein the information processing device is configured to calculate an estimated concentration of the disease-related protein based on the amplitude modulation and frequency shift analysis of the detected sound.

8. The protein detection device according to claim 1, wherein the disease-related protein includes amyloid fibers, and the information processing device is configured to output a diagnostic alert when amyloid accumulation in the retina exceeds a diagnostic threshold indicating a neurodegenerative disease.

9. The protein detection apparatus according to claim 1, wherein the object includes human or animal retina or brain tissue.

10. The protein detection device according to claim 1, wherein the object is the retina of an animal, and the light source is configured to illuminate the retina through the sclera or through the pupil.

11. The protein detection device according to claim 1, wherein the sound detection device is positioned externally, acoustically coupled to the eye, and detects sounds in the frequency range of 1,500 Hz to 4,000 Hz.

12. The protein detection apparatus according to claim 1, wherein the information processing device is integrated into a wearable terminal capable of real-time data processing.

13. The protein detection device according to claim 12, wherein the wearable device is selected from the group consisting of a smartphone, a smartwatch, a tablet, smart glasses, or a head-mounted display.

14. The protein detection device according to claim 12, further comprising a wireless communication module configured to transmit detected sound data to a cloud-based system for centralized analysis and longitudinal tracking.

15. The protein detection apparatus according to claim 12, wherein the information processing device compares and displays the currently detected sound with past trend lines.

16. A method for detecting disease-related proteins in an object, The process of irradiating the object with light in a predetermined irradiation cycle, A step of detecting sound emitted from the object by photoacoustic effect, The process involves analyzing the sound using a machine learning classifier trained to identify the signature of disease-related proteins, A method comprising the step of determining the presence of the disease-related protein based on at least one of the detected sound intensity or frequency component.

17. The method according to claim 16, further comprising the step of comparing the detected sound intensity with a predetermined threshold stored in a memory device.

18. The method according to claim 16, further comprising the step of evaluating the progress of protein accumulation by comparing the detected sound intensity with previously recorded sound intensity.

19. The method according to claim 16, wherein the disease-related protein includes amyloid fibrils, and the method includes the step of detecting the accumulation of amyloid fibrils in the retina and outputting a diagnostic flag when the amount of accumulation exceeds a threshold indicating a neurodegenerative disease.

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