US-12622634-B2 - Disease early diagnosis system based on sebum gas analysis

US12622634B2US 12622634 B2US12622634 B2US 12622634B2US-12622634-B2

Abstract

In response to the difficulty in disease early diagnosis and low diagnostic accuracy in the prior art, a disease early diagnosis system based on sebum gas analysis is provided, which combines a micro-electro-mechanical system, a novel two-dimensional material and a metal surface plasma resonance technology to provide a device for sebum gas collection and infrared spectrum enhancement, which collects human body trace sebum gas and enhances its infrared spectrum by metal plasma resonance, and then the sebum gas metal-plasma-enhanced infrared spectrum is inputted into a disease early diagnosis model for analysis, and ultimately achieved early diagnoses of diseases. The method is a truly non-invasive disease diagnosis method, which is non-invasive, simple, efficient, and has the advantages of no pollution to the environment and no ecological damage.

Inventors

Huimin Hao
Huijing Hao

Assignees

Huimin Hao

Dates

Publication Date: 20260512
Application Date: 20240407
Priority Date: 20230424

Claims (10)

1 . A disease early diagnosis system based on sebum gas analysis, comprising: a sebum gas collection and infrared spectrum enhancement device, a FTIR (Fourier Transform Infrared) Microscope, a disease early diagnosis model, and a computer; wherein the sebum gas collection and infrared spectrum enhancement device is configured to be attached to a forehead or a back of the human body for 3-5 minutes to collect sebum gas of the human body, and the FTIR Microscope scans a plasma-enhanced infrared spectrum of the sebum gas; then the plasma-enhanced infrared spectrum of the sebum gas is inputted into the disease early diagnosis model installed in the computer; and the disease early diagnosis model analyzes and outputs a diagnostic result of whether a disease is developed.
2 . The disease early diagnosis system, as recited in claim 1 , wherein the sebum gas collection and infrared spectrum enhancement device comprises a fixing strap, a fixing frame provided on the fixing strap, and a nano gas-sensitive core provided in the fixing frame.
3 . The disease early diagnosis system, as recited in claim 2 , wherein the nano gas-sensitive core comprises a substrate made of an optical window material with an infrared transmittance rate of no less than 90%, wherein a monolayer or multiple layers of a two-dimensional gas-sensitive material is attached to a top surface of the substrate, and a nano-metallic array is provided on the top surface of the substrate.
4 . The disease early diagnosis system, as recited in claim 3 , wherein the substrate is made of a CaF 2 crystal.
5 . The disease early diagnosis system, as recited in claim 3 , wherein the nano-metallic array is a metal array formed by one kind of unit structures, or a metal array formed by multiple kinds of unit structures; a metal adopted is gold or silver, each of the unit structures is a bow-tie structure formed by a pair of isosceles triangles, an elongated rectangular structure, or other structures with a plasma resonance peak at 4.7 μm-10.5 μm; a height of the unit structures is 80 nm-120 nm.
6 . The disease early diagnosis system, as recited in claim 2 , wherein a breathable protective layer is provided on the fixing frame, which is a breathable protective gauze bonded to an external edge of the fixing frame.
7 . The disease early diagnosis system, as recited in claim 3 , wherein the two-dimensional gas-sensitive material attached to the top surface of the substrate of the nano gas-sensitive core is molybdenum disulfide, graphene, carbon nanotubes, or other two-dimensional materials with gas-sensitive properties.
8 . The disease early diagnosis system, as recited in claim 2 , wherein the fixing strap is an elongated tape.
9 . The disease early diagnosis system, as recited in claim 1 , wherein the disease early diagnosis model is built by using neural networks, principal component regression, partial least squares regression, kernel methods, random forests, deep learning, or other effective spectral analysis methods, and creation of the disease early diagnosis model comprises steps of: 1) respectively collecting more than 5000 sebum gas samples from subjects with and without a certain disease by using the sebum gas collection and infrared spectrum enhancement device, and establishing a sample database; 2) if there are less than 2000 sebum gas samples from the subjects with the certain disease in the step 1), expanding the collected sebum gas samples of the subjects with the certain disease through a sample expanding method; and 3) selecting 80% of the sebum gas samples in the sample database for training, and testing with remaining 20% of the sebum gas samples, and optimizing model parameters, thus completing the creation of the disease early diagnosis model.
10 . The disease early diagnosis system, as recited in claim 9 , wherein the sample expanding method of the step 2) comprises specific steps of: after adsorbing sebum gas from a patient, removing a nano gas-sensitive core of the sebum gas collection and infrared spectrum enhancement device, and placing a bottom surface of the nano gas-sensitive core on a heating plate in an airtight heatable gas chamber, wherein a nano-metallic array is located right above a central hole of the heating plate; placing the airtight heatable gas chamber on a sample platform of the FTIR Microscope, so that infrared light of the FTIR Microscope penetrates through upper and lower infrared transmittance windows of the heatable gas chamber as well as the central hole of the heating plate; opening an exhaust valve of the airtight heatable gas chamber, and inputting nitrogen gas of 99.99% purity into the airtight heatable gas chamber to purge for 2-3 minutes; and then rapidly and linearly increasing a temperature of the heating plate in the airtight heatable gas chamber to 100° C. by a heating controller, then increasing the temperature to 110° C., 120° C., 130° C., 140° C., 150° C., 160° C., 170° C., 180° C. and 190° C. with a 10° C. increment each time; inputting 150 sccm nitrogen of 99.99% purity into the airtight heatable gas chamber at starting of the heating controller; obtaining expanded samples of an original sample under corresponding step temperatures, and directly scanning a metal plasma-enhanced infrared spectrum of each of the expanded samples.

Description

CROSS REFERENCE OF RELATED APPLICATION The present invention claims priority under 35 U.S.C. 119(a-d) to CN 202310448155.0, filed Apr. 24, 2023; and CN 202310479097.8, filed Apr. 28, 2023. BACKGROUND OF THE PRESENT INVENTION Field of Invention The present invention relates to a technical field of early diagnosis of diseases, and more particularly to a disease early diagnosis system based on sebum gas analysis. Description of Related Arts Material-energy metabolism is one of the characteristics of living organisms, and the products of human metabolism can reflect abnormal changes in the body. When a human has a certain disease, its sweat and sebum metabolic products will emit a specific odor. For example, in diabetic patients, fat is oxidized in the liver to produce ketone bodies, which emit the odor of rotten apples; in patients with chronic nephritis or liver disease, ammonia is emitted due to the retention of urea nitrogen and creatinine in the blood; and in patients with Parkinson's disease (PD), their sebum odor contain perillic aldehyde and other organic gases, which leads to a “musky” body odor; Alzheimer's disease patients emit formaldehyde from their bodies, etc. The development of many chronic diseases, such as cancer, Parkinson's disease, and Alzheimer's disease, is a slow progression, with some lasting up to 10 years or more before symptoms appear. Taking Parkinson's disease as an example, in 2015, the British Broadcasting Corporation reported that Joy Milne, a nurse, smelled a special odor emanating from her PD-afflicted husband's body 12 years before his signs and symptoms appeared. In 2019, Dr. Tilo Kunath, a renowned Parkinson's disease expert at the University of Edinburgh in the United Kingdom, also researched and confirmed that hippuric acid (C9H9NO3), perillic aldehyde (C10H14O), eicosane (C20H42) and octadecanal (C18H38), which are contained in the sebum of the human body, are closely related to PD. If these specific gases can be timely detected at the early stage of the patient's disease, it will provide a basis for the diagnosis and treatment of some diseases, thus realizing the early diagnosis and treatment of disease. However, it is difficult to detect gases released by the human body. First of all, what forms the body odor is a mixture of gases released from human body sweat, sebum, and other sources, which contains organic volatile components, and its content is very small and difficult to collect. Conventional human sebum gas collection methods collect sebum by wiping gauze on the human skin and then obtain gases through thermal desorption. The collection process is cumbersome and complex, the equipment requirements are very high, and the gas in the desorption process will be mixed with impurities that cannot be removed, which seriously affects the detection accuracy. Secondly, gas chromatography will be inevitably used for sebum gas analysis, which is expensive and complex in operation and greatly increases the threshold and the difficulty of sebum gas analysis. Thirdly, the human sebum gas has a complex composition, which is volatile organic compounds (VOC), wherein conventional VOC detection methods can only test the total concentration of VOC but cannot determine the content of its components. Conventionally, scholars have begun to pay attention to the relationship between human sebum odor and diseases, trying to diagnose diseases through analyzing the sebum gas. Chen Xing's group developed a portable “electronic nose” combining gas chromatography and surface acoustic wave sensing to analyze human sebum gas, so as to diagnose PD, which exhibited 91.6% specificity and 91.7% sensitivity. Japan's Kinji OHNO team analyzed skin gases and obtained 90.2% analytical accuracy and 85.2% specificity, and concluded that gases on the skin may serve as biomarkers for PD. Nobutaka Hattori's team identified PD after analyzing sebum RNA, age, and gender information. Prof. Hossam Haik's team analyzed the respiratory samples of patients with PD by using chemoresistive sensors and silicon nanowire sensors, as well as gas chromatography-mass spectrometry, and exhibited 76% accuracy, 77% sensitivity, and 73% specificity. In summary, the analysis of human sebum gas all over the world is still limited by the sebum sampling method, detection instruments, and analytical methods to achieve satisfactory results. SUMMARY OF THE PRESENT INVENTION In response to the difficulty in disease early diagnosis and low diagnostic accuracy in the prior art, an object of the present invention is to provide a disease early diagnosis system based on sebum gas analysis, which combines a micro-electro-mechanical system (MEMS), a novel two-dimensional material and a metal surface plasma resonance technology to provide a device for sebum gas collection and infrared spectrum enhancement, which collects human body trace sebum gas and enhances its infrared spectrum by metal plasma resonance. And then, the metal-plasma-enhance