Therapeutic effect evaluation method and system for diseases and related biomarkers

Abstract

The invention provides a curative effect evaluation method and system for diseases and a related biomarker, and the method comprises the following steps: obtaining a first detection object in a first state, and detecting the biomarker in the first detection object; acquiring a second detection object in a second state, and detecting a biomarker in the second detection object; comparing the biomarker in the first detection object with the biomarker in the second detection object to obtain a comparison result; and determining an evaluation result according to the comparison result.

Description

Technical Field

[0001] This disclosure relates to the field of therapeutic detection technology, and in particular to a method, system and related biomarkers for evaluating the efficacy of treatment for diseases. Background Technology

[0002] Humans suffer from neurodegenerative diseases, such as Alzheimer's disease (AD), which is the most prevalent type of neurodegenerative dementia worldwide, accounting for approximately 60%-70% of all dementia cases. Currently, there are over 55 million dementia patients globally, a number projected to increase to 150 million by 2050, with AD patients making up the vast majority. While some anti-amyloid monoclonal antibodies have been approved for current treatments, they are only applicable to early-stage disease, and their clinical benefit-risk ratio remains controversial, failing to meet the treatment needs of the vast majority of AD patients (especially those in the middle and late stages). Therefore, safe and effective non-pharmacological interventions are urgently needed.

[0003] The core pathological features of Alzheimer's disease (AD) include β-amyloid (Aβ) deposition and tau protein hyperphosphorylation, accompanied by abnormal brain network connectivity, neuroinflammatory activation, and progressive cognitive decline. Current research indicates that elevated levels of glial fibrillary acidic protein (GFAP, primarily expressed in astrocytes), a neuroinflammatory marker, are closely related to AD progression, and its plasma levels can predict Aβ pathology and cognitive decline. While neurofilament light chain protein (NfL), a marker of axonal injury, is associated with AD progression, current interventions struggle to effectively regulate its expression. Furthermore, AD patients exhibit weakened functional connectivity in the default mode network (DMN), increased delta-band power spectral density (PSD) in EEG signals, and decreased neural signal complexity (such as Lempel-Ziv complexity LZC and multiscale entropy MSE). These indicators are all positively correlated with the degree of cognitive impairment, but currently, there is a lack of intervention strategies to simultaneously improve these pathological, electrophysiological, and cognitive indicators related to protein regulation.

[0004] Currently, transcranial focused ultrasound (tFUS), as a novel non-invasive neuromodulation technology, has shown the potential to improve brain network connectivity and cognitive function in Alzheimer's disease (AD) patients. However, existing research mainly focuses on optimizing the technical parameters of tFUS, without clarifying the key protein regulatory targets on which its therapeutic effect depends, nor has it developed a protein combination that can be used for efficacy assessment or adjuvant therapy. Furthermore, existing AD biomarkers (such as Aβ)... 40 / 42 While p-tau217 can be used for diagnosis, it cannot reflect changes in efficacy after tFUS neuromodulation in real time. There is an urgent need for a protein combination that is directly related to the efficacy of tFUS to fill the technical gap of "efficacy-protein biomarker" correlation in non-pharmacological interventions for Alzheimer's disease. Summary of the Invention

[0005] This disclosure provides a method, system, and related biomarkers for evaluating the efficacy of treatment for diseases, in order to at least address the above-mentioned technical problems existing in the prior art.

[0006] In a first aspect, embodiments of this disclosure provide a machine-executed method for evaluating the efficacy of treatment for a disease, the method comprising: Acquire the first sample in the first state, and detect the biomarkers in the first sample; Acquire a second sample in a second state, and detect biomarkers in the second sample; The biomarkers in the first test substance and the biomarkers in the second test substance are compared to obtain a comparison result; the evaluation result is determined based on the comparison result.

[0007] Secondly, embodiments of this disclosure provide a system for evaluating the efficacy of treatment for a disease, the system comprising: The first detection module is used to acquire a first detection object in a first state and detect biomarkers in the first detection object; The second detection module is used to acquire the second detection item in the second state and detect the biomarkers in the second detection item; The comparison module is used to compare the biomarkers in the first detectable and the biomarkers in the second detectable to obtain a comparison result; and to determine the evaluation result based on the comparison result.

[0008] Thirdly, embodiments of this disclosure provide the application of a biomarker and / or a reagent for detecting the biomarker in the preparation and / or screening of products, wherein the use of the products includes at least one of the following: 1) predicting the therapeutic efficacy of a disease, 2) preventing and / or treating a disease, and 3) assessing the prognosis of a disease; The biomarkers include at least one of the following proteins or their encoding genes: P13987, Q9BSL1, Q9BYC5, Q9UBV7, Q9Y275, P25311, and GFAP.

[0009] This disclosure provides a method, system, and related biomarkers for evaluating the efficacy of treatment for a disease. The method includes: acquiring a first test substance in a first state and detecting biomarkers in the first test substance; acquiring a second test substance in a second state and detecting biomarkers in the second test substance; comparing the biomarkers in the first test substance and the biomarkers in the second test substance to obtain a comparison result; and determining an evaluation result based on the comparison result. The biomarkers include at least one of the following proteins or their encoding genes: P13987, Q9BSL1, Q9BYC5, Q9UBV7, Q9Y275, P25311, and GFAP. Thus, by using at least one protein or its encoding gene from P13987, Q9BSL1, Q9BYC5, Q9UBV7, Q9Y275, P25311, and GFAP as a protein or encoding gene that can be used for efficacy assessment, the evaluation can reflect the changes in efficacy before and after treatment (such as after tFUS neuromodulation) in real time. This fills the technical gap in the correlation between "efficacy-protein biomarkers" in non-pharmacological interventions or "efficacy-protein biomarkers" in pharmacological interventions for diseases such as Alzheimer's disease, and solves the problem that existing biomarkers for diseases such as Alzheimer's disease cannot be used to assess the efficacy of non-pharmacological interventions or pharmacological interventions in real time.

[0010] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0011] Figure 1 A schematic flowchart illustrating a machine-executed method for evaluating the efficacy of treatment for a disease, provided as an embodiment of this disclosure; Figure 2 This is a schematic diagram of a disease efficacy evaluation system provided in an embodiment of the present disclosure; Figure 3 A comparative schematic diagram of protein expression provided in an embodiment of this disclosure; Figure 4 This is a schematic diagram showing a comparison before and after tFUS intervention, provided as an embodiment of this disclosure. Detailed Implementation

[0012] To make the objectives, features, and advantages of this disclosure more apparent and understandable, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.

[0013] Figure 1 This is a schematic flowchart illustrating a machine-executed method for evaluating the efficacy of treatment for a disease, as provided in an embodiment of this disclosure. Figure 1 As shown, the machine can be any electronic device, which can perform the efficacy assessment method. The electronic device can be a server, computer, laptop, or other smart device, etc., and the method includes: Step 101: Obtain the first test item in the first state, and detect the biomarkers in the first test item; Step 102: Obtain the second test substance in the second state, and detect the biomarkers in the second test substance; Step 103: Compare the biomarkers in the first test substance and the biomarkers in the second test substance to obtain the comparison results; determine the evaluation results based on the comparison results.

[0014] In some embodiments, the biomarker includes at least one of the following proteins or their encoding genes: P13987, Q9BSL1, Q9BYC5, Q9UBV7, Q9Y275, P25311, GFAP.

[0015] In one example, any one of P13987, Q9BSL1, Q9BYC5, Q9UBV7, Q9Y275, P25311, and GFAP can be used as a biomarker for comparison to obtain the comparison results and then determine the evaluation results.

[0016] In another example, any two or more of P13987, Q9BSL1, Q9BYC5, Q9UBV7, Q9Y275, P25311, and GFAP (multiple means two or more, with a maximum of five here) can be used as biomarkers for comparison to obtain the comparison results and then determine the evaluation results.

[0017] In some embodiments, the biomarker includes at least one of the following proteins or their encoding genes: P13987, Q9BSL1, Q9BYC5, Q9UBV7, Q9Y275.

[0018] In one example, any one of P13987, Q9BSL1, Q9BYC5, Q9UBV7, and Q9Y275 can be used as a biomarker for comparison to obtain the comparison results and then determine the evaluation results.

[0019] In another example, any two or more of P13987, Q9BSL1, Q9BYC5, Q9UBV7, and Q9Y275 (multiple means two or more, with a maximum of five here) can be used as biomarkers for comparison to obtain the comparison results and then determine the evaluation results.

[0020] Here, the proteins mentioned above are represented by UniProt names. That is, P13987, Q9BSL1, etc., are unique identifiers for proteins in the UniProt database, used to distinguish various proteins. Each protein is described below.

[0021] For example, the P13987 protein corresponds to CD59 (CD59 glycoprotein).

[0022] The Q9BSL1 protein corresponds to UBAC1 (Ubiquitin-associated domain-containing protein 1).

[0023] The Q9BYC5 protein corresponds to FUT8 (Alpha-(1,6)-fucosyltransferase).

[0024] The Q9UBV7 protein corresponds to B4GALT7 (Beta-1,4-galactosyltransferase 7).

[0025] The Q9Y275 protein corresponds to TNFSF13B (Tumor necrosis factor ligand superfamily member 13B).

[0026] In some embodiments, the biomarker includes the following protein or its encoding gene: P13987; Alternatively, the biomarker includes at least one of the following proteins or their encoding genes: Q9BSL1, Q9BYC5, Q9UBV7, Q9Y275; Alternatively, the biomarkers may include the following proteins or their encoding genes: Q9BSL1, Q9BYC5, Q9UBV7, Q9Y275; Alternatively, the biomarker may include the following protein or its encoding gene: P13987, and at least one of the following proteins or their encoding genes: Q9BSL1, Q9BYC5, Q9UBV7, Q9Y275; Alternatively, the biomarkers may include the following proteins or their encoding genes: P13987, Q9BSL1, Q9BYC5, Q9UBV7, Q9Y275.

[0027] Here, at least one characterization can be one or more, and multiple characterizations can represent two or more. In this manner, several proteins or combinations of their encoding genes are provided as biomarkers.

[0028] In some embodiments, the biomarker further includes at least one of the following proteins or their encoding genes: P25311, glial fibrillary acidic protein (GFAP).

[0029] Here, P25311 is the unique identifier for the protein in the UniProt database, used to distinguish various proteins. P25311 corresponds to AZGP1 (Zinc-alpha-2-glycoprotein).

[0030] In some embodiments, the therapeutic effect includes the efficacy of non-pharmacological interventions and / or the efficacy of pharmacological interventions.

[0031] In some embodiments, the disease includes diseases related to brain synapses and / or neuroplasticity.

[0032] In some embodiments, the disease includes neurodegenerative diseases.

[0033] In some embodiments, the disease includes neurological disorders.

[0034] In some embodiments, the disease includes diseases caused by degenerative changes in the central nervous system.

[0035] In some embodiments, the disease includes at least one of Alzheimer's disease (AD), Parkinson's disease, dementia with Lewy bodies (DLB), Huntington's disease, amyotrophic lateral sclerosis (ALS), multiple system atrophy, frontotemporal dementia, spinocerebellar ataxia, vascular dementia, and traumatic brain injury.

[0036] Here, the neurodegenerative diseases mentioned include acute neurodegenerative diseases and chronic neurodegenerative diseases. The former mainly includes cerebral ischemia (CI), brain injury (BI), and epilepsy; the latter includes Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), amyotrophic lateral sclerosis (ALS), different types of spinocerebellar ataxia (SCA), Pick's disease, etc.

[0037] The neurological disorders mentioned include: vascular dementia, traumatic brain injury, etc.

[0038] In some embodiments, the non-pharmacological intervention may include: transcranial focused ultrasound stimulation (tFUS) modulation; Accordingly, the first state is the state before transcranial focused ultrasound stimulation modulation; The second state is the state after transcranial focused ultrasound stimulation and modulation in the first state.

[0039] Specifically, an example is provided of identifying at least one protein or its encoding gene through personalized tFUS neural regulation experiments. The procedure for tFUS neural regulation experiments may include: Based on resting-state functional magnetic resonance imaging (rs-fMRI) to locate the most impaired functional connectivity areas in the brains of AD patients as target points, tFUS modulation was employed, with the specific modulation parameters as follows: Frequency: 500kHz, average peak spatial pulse intensity: 8.17W / cm², stimulation duration: 15 minutes / session, 5 times per week, total number of sessions: 10.

[0040] By comparing proteins that show significant changes before and after tFUS treatment, this study investigates the relationship between the protein combination and information such as cognition, default mode network, EEG signal complexity, plasma glial fibrillary acidic protein (GFAP), and the reduction of pathological changes in AD patients compared to normal individuals. The aim is to identify at least one protein or its encoding gene directly associated with the efficacy of tFUS, namely at least one of the following: P13987, Q9BSL1, Q9BYC5, Q9UBV7, Q9Y275, P25311, and GFAP. In some embodiments, determining the evaluation result based on the comparison result includes: The evaluation result is determined as the first result if at least one of the following conditions is met: The decline in P13987 exceeded the first threshold; The rise of Q9BSL1 exceeds the second threshold; The rise of Q9BYC5 exceeded the third threshold; The rise of Q9UBV7 exceeded the fourth threshold; The rise of Q9Y275 exceeded the fifth threshold.

[0041] Here, the first result indicates that the therapeutic effect is effective. If the above conditions are not met, the evaluation result is considered to be the second result, which indicates that the therapeutic effect is ineffective.

[0042] As an example, assuming tFUS regulation is used as an intervention, the effectiveness of tFUS regulation is assessed by observing changes in proteins or their encoding genes in biomarkers of the same individual before and after tFUS regulation. The state before tFUS regulation is denoted as the first state; the state after tFUS regulation is denoted as the second state.

[0043] Biomarkers before and after tFUS regulation can be obtained by detecting blood samples before and after treatment. It is easy to understand that the pretreatment methods and the amount of blood sample used for the detection should be the same before and after tFUS regulation.

[0044] The specific explanations for each of the above conditions are as follows.

[0045] It should be understood that the conditions considered in the judgment are different due to the different proteins or their encoding genes used, and there is a corresponding relationship. For example, if the biomarker includes P13987, then the judgment condition should also consider P13987; if the biomarker includes Q9BSL1, then the judgment condition should also consider Q9BSL1; these will not be elaborated here.

[0046] In one example, the degree of decline exceeding a threshold (such as a first threshold) could mean that: the decline in the P13987 protein exceeds a certain proportion, or the decline exceeds a certain threshold; or, the overall protein score reaches a target range (in the case of a combination of multiple proteins). The threshold value can vary depending on the judgment criteria, i.e., the comparison method.

[0047] The increase exceeding a threshold (such as a second threshold, a third threshold, etc.) can refer to: the increase in protein value (such as Q9BSL1, Q9BYC5, Q9UBV7, Q9Y275) exceeding a certain proportion, or the increase exceeding a certain threshold; or, the overall protein score reaching the target range (in the case of a combination of multiple proteins). The threshold value can vary depending on the judgment criteria, i.e., the comparison method.

[0048] In another example, the degree of increase or decrease can also be assessed by calculating logFC, FC, Beta value and / or p value. For example, the changes in the protein before and after tFUS regulation can be reflected by logFC, FC, Beta value and / or p value.

[0049] For example, logFC and FC can reflect the magnitude of changes in protein expression; For example, if FC>1: it indicates an increase in protein content; if FC<1: it indicates a decrease in protein content; if FC=1: it indicates no change in protein content.

[0050] For example, if logFC>0: it means the protein level is increased; if logFC<0: it means the protein level is decreased; if logFC=0: it means the protein level remains unchanged.

[0051] Beta values ​​can reflect the impact of interventions (such as tFUS regulation) on proteins. For example, a beta value > 0 indicates an increase in protein quantity after regulation. A beta value < 0 indicates a decrease in protein quantity after regulation. A beta value = 0 indicates that regulation has no significant effect on protein quantity.

[0052] The p-value assesses the statistical significance of a change. For example, a p-value less than 0.05 (usually less than 0.05 or 0.01) indicates that the change is significant. A p-value greater than 0.05 indicates that the change may be due to random factors and the difference is not significant.

[0053] Thus, by judging whether the conditions are met, it is determined whether the product is valid.

[0054] Here, the study found that the P13987 protein is significantly elevated in people with diseases (such as Alzheimer's disease), but significantly reduced after intervention (such as tFUS regulation), indicating that it can reverse the pathological changes of related proteins in people with diseases such as Alzheimer's disease. Therefore, this protein can be used to evaluate whether the intervention (such as tFUS regulation) is effective.

[0055] Studies have found that proteins such as Q9BSL1, Q9BYC5, Q9UBV7, and Q9Y275 are significantly decreased in people with diseases (such as Alzheimer's disease), but significantly increased after intervention (such as tFUS treatment). This indicates that the pathological changes of related proteins in people with diseases such as Alzheimer's disease can be reversed. Therefore, at least one of Q9BSL1, Q9BYC5, Q9UBV7, and Q9Y275 can be used to evaluate the effectiveness of intervention (such as tFUS regulation).

[0056] Therefore, based on the above research results, we provide the aforementioned proteins or their encoding genes, as well as the corresponding judgment conditions.

[0057] In some embodiments, if the biomarker includes at least one of the following proteins or their encoding genes: P25311, and / or GFAP, determining the evaluation result based on the comparison result includes: The evaluation result is considered the first result if at least one of the following conditions is met: The decrease in P25311 exceeded the sixth threshold; The decrease in GFAP exceeded the seventh threshold.

[0058] Here, the study found that the P25311 protein is significantly elevated in people with diseases (such as Alzheimer's disease), but significantly reduced after intervention (such as tFUS regulation), indicating that it can reverse the pathological changes of related proteins in people with diseases such as Alzheimer's disease. Therefore, this protein can be used to evaluate whether the intervention (such as tFUS regulation) is effective.

[0059] Plasma levels of glial fibrillary acidic protein (GFAP) were significantly reduced after intervention (such as tFUS regulation) (p<0.05), and the magnitude of the reduction was positively correlated with improvements in cognitive function (MMSE score) and enhanced DMN functional connectivity. Therefore, it is proposed that P25311 and glial fibrillary acidic protein can be combined for evaluation in addition to the above proteins.

[0060] The increase or decrease can be reflected by the difference in protein levels before and after treatment, the ratio of the difference to the protein level before treatment, or the aforementioned logFC, FC, Beta values ​​and / or p values. For P25311 and glial fibrillary acidic protein, the values ​​of the sixth and seventh thresholds may differ under different circumstances, but the principle is the same, both used to reflect whether the protein level has decreased significantly after intervention (such as tFUS regulation).

[0061] It should be noted that the values ​​of the first, second, third, fourth, fifth, sixth, and seventh thresholds mentioned above are all related to the patient's pre-treatment condition, age, gender, and disease duration. Different threshold values ​​are used for judgment in different situations and can be flexibly adjusted based on individual patient characteristics, pre-treatment condition, historical data, and population standards. No specific values ​​are limited here. Of course, changes in the corresponding protein before and after intervention (such as tFUS regulation) can also be assessed by calculating logFC, FC, Beta value, and / or p-value, etc.

[0062] In this embodiment, based on the improvement of the patient's phenotype (physiological or cognitive state, including behavior, cognitive ability, and neurological function) after intervention (such as tFUS regulation), the manifestations may include: improved cognition (MMSE (Mini-Mental State Examination) score), enhanced tFUS neural modulation connectivity strength, increased multiple indicators in EEG signal complexity, and decreased plasma GFAP levels; simultaneously, regarding the expression of proteins related to Alzheimer's disease (AD), such proteins are gradually reduced, and their expression tends to be similar to the corresponding levels in normal individuals. Therefore, it is proposed that any combination of the above proteins be formed based on related proteins as a therapeutic response protein for efficacy evaluation.

[0063] In some embodiments, the method may further include: Obtain the cognitive assessment results in the first state, and obtain the cognitive assessment results in the second state; Determine the changes in cognitive assessment results in the first state and the second state; Accordingly, based on the comparison results, the evaluation results are determined, including: Based on the comparison results and the change information, the evaluation result is determined; The cognitive assessment results include at least one of the following evaluation indicators: cognitive function assessment results, brain imaging assessment results, electroencephalogram (EEG) signal assessment results, and neural signal assessment results.

[0064] In one example, the cognitive function assessment results can be obtained based on the MMSE (Mini-Mental State Examination); Brain imaging assessment results can be obtained based on DMN (Default Mode Network) functional connectivity assessment; The results of EEG signal assessment can be obtained using delta-band PSD (Delta Wave Power Spectral Density, a method for analyzing the power distribution of low-frequency waves (delta band)); The results of neural signal assessment can be obtained using LZC (Lempel-Ziv Complexity, a method for assessing dynamic changes in brain activity by measuring the complexity of signals).

[0065] In addition to protein analysis, the evaluation can also combine pre-treatment (i.e., first state) and post-treatment (i.e., second state) cognitive assessment results. Cognitive assessment results can include various other indicators, such as MMSE score, DMN functional connectivity, delta-band PSD, and LZC.

[0066] Accordingly, a specific evaluation threshold can be set for each indicator. For example, if the MMSE score increases by ≥2 points, DMN functional connectivity improves by ≥15%, delta band PSD decreases by ≥20%, and LZC increases by ≥10%, the treatment can be considered effective based on a comprehensive assessment of these factors. Of course, the above values ​​are just one example, and other values ​​can be used in actual applications; no limitation is made here.

[0067] In some embodiments, the same detection method is used to detect biomarkers in the first test substance and to detect biomarkers in the second test substance.

[0068] To provide an example, taking a protein combination as a biomarker, tFUS as the intervention method, and AD as the disease, the detection method includes: Sample collection: Peripheral venous blood was collected from AD patients before tFUS intervention (baseline) and after intervention (on the day of the end of 10 treatments). The blood was treated with ethylenediaminetetraacetic acid (EDTA) for anticoagulation, and the plasma was separated by centrifugation (centrifugation conditions: 3000 rpm, 10 minutes, 4°C). The plasma was stored at -80°C for subsequent testing and analysis. Protein detection: The protein content in the plasma can be measured using any method. Detection methods can include mass spectrometry (MS), enzyme-linked immunosorbent assay (ELISA), and advanced detection methods based on the ELISA principle, such as Quanterix Simoa assay, MSD assay, and Lumipulse assay. For detection, specific recognition elements can be used through nucleic acid recognition, antibody recognition, mass spectrometry, etc.; detection principles can include mass spectrometry, electrochemiluminescence, fluorescence detection, etc.

[0069] Efficacy assessment criteria: For several prognostic factors, including single proteins and combinations thereof, and combinations of proteins with plasma GFAP concentrations, a significant increase or decrease in the corresponding individual protein concentrations compared to baseline (meaning a significant increase in protein concentration in AD patients, but a significant decrease after tFUS treatment) is considered indicative of significant tFUS neuromodulation efficacy. In addition, other indicators such as an increase of ≥2 points in MMSE score, an enhancement of ≥15% in DMN functional connectivity, a decrease of ≥20% in delta-band PSD, and an increase of ≥10% in LZC can be used for comprehensive assessment.

[0070] This disclosure provides an application of biomarkers and / or reagents for detecting said biomarkers in the preparation and / or screening of products, wherein the products are used for at least one of the following: 1) predicting the therapeutic efficacy of a disease, 2) preventing and / or treating a disease, and 3) assessing the prognosis of a disease; The biomarkers include at least one of the following proteins or their encoding genes: P13987, Q9BSL1, Q9BYC5, Q9UBV7, Q9Y275, P25311, and GFAP.

[0071] In some embodiments, the biomarker includes at least one of the following proteins or their encoding genes: P13987, Q9BSL1, Q9BYC5, Q9UBV7, Q9Y275; Alternatively, the biomarker may include the following protein or its encoding gene: P13987; Alternatively, the biomarker includes at least one of the following proteins or their encoding genes: Q9BSL1, Q9BYC5, Q9UBV7, Q9Y275; Alternatively, the biomarkers may include the following proteins or their encoding genes: Q9BSL1, Q9BYC5, Q9UBV7, Q9Y275; Alternatively, the biomarker may include the following protein or its encoding gene: P13987, and at least one of the following proteins or their encoding genes: Q9BSL1, Q9BYC5, Q9UBV7, Q9Y275; Alternatively, the biomarkers may include the following proteins or their encoding genes: P13987, Q9BSL1, Q9BYC5, Q9UBV7, Q9Y275.

[0072] In some embodiments, the therapeutic effect includes the efficacy of non-pharmacological interventions and / or the efficacy of pharmacological interventions.

[0073] In some embodiments, the disease includes diseases related to brain synapses and / or neuroplasticity.

[0074] In some embodiments, the disease includes neurodegenerative diseases.

[0075] In some embodiments, the disease includes neurological disorders.

[0076] In some embodiments, the disease includes diseases caused by degenerative changes in the central nervous system.

[0077] In some embodiments, the disease includes at least one of Alzheimer's disease, Parkinson's disease, Lewy body dementia, Huntington's disease, amyotrophic lateral sclerosis, multiple system atrophy, frontotemporal dementia, spinocerebellar ataxia, vascular dementia, and traumatic brain injury.

[0078] In some embodiments, the non-pharmacological intervention includes transcranial focused ultrasound stimulation modulation.

[0079] This disclosure also provides a protein combination for evaluating the efficacy of transcranial focused ultrasound stimulation (tFUS), the protein combination including the following protein: P13987; or, The protein combination includes at least one of the following proteins: Q9BSL1, Q9BYC5, Q9UBV7, and Q9Y275; or, The protein combination includes the following proteins: P13987, and at least one of the following: Q9BSL1 protein, Q9BYC5 protein, Q9UBV7 protein, and Q9Y275 protein.

[0080] In some embodiments, the protein combination further includes at least one of the following proteins: P25311 protein, GFAP.

[0081] This disclosure provides another intervention target for a disease, the intervention target including the following protein: P13987; or, The intervention target is a protein including at least one of the following: Q9BSL1, Q9BYC5, Q9UBV7, Q9Y275; or, The intervention targets include the P13987 protein, and at least one of the following proteins: Q9BSL1, Q9BYC5, Q9UBV7, and Q9Y275.

[0082] In some embodiments, the disease includes diseases related to brain synapses and / or neuroplasticity.

[0083] In some embodiments, the disease includes neurodegenerative diseases.

[0084] In some embodiments, the disease includes neurological disorders.

[0085] In some embodiments, the disease includes diseases caused by degenerative changes in the central nervous system.

[0086] In some embodiments, the disease includes at least one of Alzheimer's disease, Parkinson's disease, Lewy body dementia, Huntington's disease, amyotrophic lateral sclerosis, multiple system atrophy, frontotemporal dementia, spinocerebellar ataxia, vascular dementia, and traumatic brain injury.

[0087] In some embodiments, the intervention target may also include at least one of the following proteins: P25311, GFAP.

[0088] Figure 2 This is a schematic diagram of a disease efficacy evaluation system provided in an embodiment of the present disclosure; as shown below. Figure 2 As shown, the system can be applied to any electronic device, such as a server, computer, laptop, or other smart device. The system may include: The first detection module is used to acquire a first detection object in a first state and detect biomarkers in the first detection object; The second detection module is used to acquire the second detection item in the second state and detect the biomarkers in the second detection item; The comparison module is used to compare the biomarkers in the first detectable and the biomarkers in the second detectable to obtain a comparison result; and to determine the evaluation result based on the comparison result.

[0089] In some embodiments, the biomarker includes at least one of the following proteins or their encoding genes: P13987, Q9BSL1, Q9BYC5, Q9UBV7, Q9Y275, P25311, GFAP.

[0090] In some embodiments, the biomarker includes at least one of the following proteins or their encoding genes: P13987, Q9BSL1, Q9BYC5, Q9UBV7, Q9Y275.

[0091] In some embodiments, the biomarker includes the following protein or its encoding gene: P13987; Alternatively, the biomarker includes at least one of the following proteins or their encoding genes: Q9BSL1, Q9BYC5, Q9UBV7, Q9Y275; Alternatively, the biomarkers may include the following proteins or their encoding genes: Q9BSL1, Q9BYC5, Q9UBV7, Q9Y275; Alternatively, the biomarker may include the following protein or its encoding gene: P13987, and at least one of the following proteins or their encoding genes: Q9BSL1, Q9BYC5, Q9UBV7, Q9Y275; Alternatively, the biomarkers may include the following proteins or their encoding genes: P13987, Q9BSL1, Q9BYC5, Q9UBV7, Q9Y275.

[0092] In some embodiments, the biomarker further includes at least one of the following proteins or their encoding genes: P25311, GFAP.

[0093] In some embodiments, the therapeutic effect includes the efficacy of non-pharmacological interventions and / or the efficacy of pharmacological interventions.

[0094] In some embodiments, the disease includes diseases related to brain synapses and / or neuroplasticity.

[0095] In some embodiments, the disease includes neurodegenerative diseases.

[0096] In some embodiments, the disease includes neurological disorders.

[0097] In some embodiments, the disease includes diseases caused by degenerative changes in the central nervous system.

[0098] In some embodiments, the disease includes at least one of Alzheimer's disease (AD), Parkinson's disease, dementia with Lewy bodies (DLB), Huntington's disease, amyotrophic lateral sclerosis (ALS), multiple system atrophy, frontotemporal dementia, spinocerebellar ataxia, vascular dementia, and traumatic brain injury.

[0099] In some embodiments, the non-pharmacological intervention includes: transcranial focused ultrasound stimulation modulation; Accordingly, the first state is the state before transcranial focused ultrasound stimulation modulation; The second state is the state after transcranial focused ultrasound stimulation and modulation in the first state.

[0100] In some embodiments, the comparison module is configured to consider the evaluation result as the first result if at least one of the following conditions is met: The decline in P13987 exceeded the first threshold; The rise of Q9BSL1 exceeds the second threshold; The rise of Q9BYC5 exceeded the third threshold; The rise of Q9UBV7 exceeded the fourth threshold; The rise of Q9Y275 exceeded the fifth threshold.

[0101] In some embodiments, the comparison module is configured to consider the evaluation result as the first result if at least one of the following conditions is met: The decrease in P25311 exceeded the sixth threshold; And / or, The decrease in GFAP exceeded the seventh threshold.

[0102] In some embodiments, the first detection module is used to obtain the cognitive assessment result in a first state and the cognitive assessment result in a second state; The second detection module is used to determine the changes in the cognitive assessment results in the first state and the cognitive assessment results in the second state; Accordingly, the comparison module is used to determine the evaluation result based on the comparison result and the change information; Optionally, the cognitive assessment results include at least one of the following evaluation indicators: cognitive function assessment results, brain imaging assessment results, electroencephalogram (EEG) signal assessment results, and neural signal assessment results.

[0103] It is understood that the efficacy evaluation system for diseases provided in the above embodiments can, when implementing the corresponding efficacy evaluation methods, allocate the above processing to different program modules as needed to complete all or part of the processing described above. Furthermore, the system and corresponding method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process is detailed in the method embodiments, which will not be repeated here.

[0104] The above-mentioned methods for assessing efficacy using changes in the aforementioned proteins or combinations of their encoding genes have been validated. A specific validation example is provided below.

[0105] Specifically, taking AD as the disease and tFUS as the intervention method as an example, other diseases can be excluded in advance (this exclusion is to avoid the influence of other diseases on the AD validation results; other diseases can be excluded to improve the accuracy and reliability of the validation). The validation process is as follows: (I) Twenty-four AD patients (including mild cognitive impairment (MCI) and AD dementia) meeting the diagnostic criteria of the National Institute on Aging-Alzheimer's Association (NIA-AA) were selected. All patients were confirmed to be Aβ-positive by 18F-Aβ PET or plasma p-tau217 testing, with an MMSE score ≤30 and a MoCA score <26, and were aged 50-85 years. Patients with other neurological diseases (such as epilepsy, stroke), intracranial metal implants, severe systemic diseases, and those who were pregnant or lactating were excluded.

[0106] Patients were randomly assigned 1:1 to either the tFUS group (n=12) or the sham surgery group (n=12). Baseline demographics (age, sex, years of education, APOE-ε4 carrier rate) and plasma protein levels (GFAP, Aβ) were compared between the two groups. 40 Aβ 42 There were no significant differences in p-tau217 and NfL (p>0.05), as shown in Table 1 below:

[0107] Table 1 Among them, GFAP is a glial fibrillary acidic protein and a marker of nerve damage; Aβ 40 and Aβ 42 β-amyloid protein is commonly found in Alzheimer's disease. p-tau217 is phosphorylated tau protein and is currently the most sensitive plasma marker for early diagnosis of Alzheimer's disease. NfL is neurofilament light chain protein and is a marker of nerve damage.

[0108] (ii) Using the tFUS intervention program Target localization: Resting-state fMRI data of patients were acquired using a 3.0T GE MRI scanner. A whole-brain functional connectivity matrix was constructed using the AAL90 brain region template. The data were compared with functional connectivity data from multiple age-matched cognitively normal (CU) individuals (51 cases in total) to screen out the brain regions with the most significant functional connectivity reduction (such as the medial prefrontal cortex mPFC and the posterior cingulate cortex PCC) as tFUS targets.

[0109] tFUS Equipment and Parameters: The tFUS system was used, including hardware (STM32F103RCT6 main control module, LM5106 driver module, Olympus V391 focusing transducer) and software (signal generation and parameter monitoring modules). The control parameters were: frequency 500kHz, tone burst duration 0.5ms, pulse repetition frequency 100Hz, duty cycle 5%, stimulation duration 0.5s, stimulation interval 7s, spatial peak pulse average intensity 8.17W / cm², each intervention lasting 15 minutes, 5 times per week, for a total of 10 interventions. The sham surgery group used the same equipment but did not output ultrasound energy.

[0110] (III) Procedures for Plasma Protein Detection Sample collection and processing: 5 mL of peripheral venous blood was collected at baseline (1 day before intervention) and after intervention (on the day of the end of the 10th treatment). The blood was anticoagulated with EDTA, centrifuged at 3000 rpm for 10 minutes at 4°C, and the plasma was separated, aliquoted and stored at -80°C to avoid repeated freeze-thaw cycles. Plasma proteomics analysis: Whole-sample protein analysis was performed on patient plasma before and after treatment. Diluted according to each patient's protein levels to ensure consistent loading volume for each plasma sample, with plasma consumption between 4-10 μL. Before loading, samples underwent pretreatment including removal of high-abundance proteins and enzymatic digestion. Each sample was then loaded individually into a liquid chromatography-mass spectrometry (LC-MS) system for peptide separation and mass spectrometry analysis. The obtained data were then compared against a database to identify the proteins present in the samples.

[0111] Simoa assay: Remove frozen plasma, thaw at room temperature, and dilute according to the kit instructions (Aβ). 40 Aβ 42 NfL and GFAP were diluted 1:4, and p-tau217 was diluted 1:3. The kit (Quanterix 104570, 104465) was loaded onto the HD-X analyzer, and the detection parameters were set (incubation time 30 minutes, detection temperature 37℃). Each sample was tested twice, and the average value was taken as the final concentration. Standards (concentrations of 0.1, 1, 10, 100, 1000 pg / mL) and quality control samples (low, medium, and high concentrations) were added to each batch of tests. The correlation coefficient R² of the standards must be >0.99, and the CV of the quality control samples must be <10%. Otherwise, the test should be repeated.

[0112] (iv) Testing of efficacy evaluation indicators, including the following tests: Cognitive function: The MMSE scale was used to assess cognitive function at baseline and 3 months after intervention. The scores were independently given by two trained neurologists and the average value was taken. Brain imaging: rs-fMRI data were acquired using a 3.0T GE MRI scanner. The GRETNA tool was used for box preprocessing (removal of the first 10 volumes, slice time correction, and head motion correction), and the functional connectivity strength (AUC value) of the DMN was calculated. EEG signals: Resting-state EEG was collected for 10 minutes using a 64-channel EEG cap (Brain Products GmbH) (sampling rate 1000Hz). After preprocessing, the delta band (1-4Hz) PSD and LZC (frontal and parietal lobe regions) were analyzed.

[0113] (V) Results Analysis Regarding changes in plasma proteins: Based on paired t-tests, plasma GFAP concentration was significantly reduced in the tFUS group after intervention (p<0.05), while Aβ concentration was significantly increased. 40 Aβ 42 No significant changes were observed in p-tau217 and NfL (p>0.05); no significant changes were observed in the concentrations of any protein in the sham ultrasound group (p>0.05).

[0114] Regarding improvements in cognition and brain function: Based on paired t-tests, the MMSE score, DMN functional connectivity strength, delta band PSD, and parietal lobe LZC were significantly increased in the tFUS group 3 months after intervention (p<0.05); the above indicators did not change significantly in the sham surgery group (p>0.05).

[0115] Regarding changes in plasma protein composition: Plasma proteomics employed a generalized linear model (GLM) to compare changes in differentially expressed proteins in patients after ultrasound treatment compared to before treatment, adjusting for age and sex. The model was: log(differentially expressed protein) ~ pre- and post-treatment + age + sex. Furthermore, the fold change (FC) of protein expression after treatment was calculated using the limma package. After tFUS ultrasound stimulation treatment, six proteins showed significant changes, all associated with improvement in AD pathology. Specific results are shown in Table 2. Two proteins exhibited expression inhibition: Uniprot protein P13987 had a Beta value of -0.621 (mild inhibition) and an FC of 0.65; and protein P25311 had a Beta value of -1.1 (moderate inhibition, higher than P13987) and an FC of 0.47. Four proteins showed upregulated expression: Q9BSL1 (Beta=0.94, moderately upregulated), Q9BYC5 (Beta=0.688, mildly upregulated), Q9UBV7 (Beta=1.11, moderately upregulated, with the highest degree of upregulation), and Q9Y275 (Beta=0.974, moderately upregulated, with an upregulation level close to that of Q9BSL1) (p<0.05). The corresponding functional groups (FCs) were: Q9BSL1 1.92, Q9BYC5 1.59, Q9UBV7 2.15, and Q9Y275 1.84.

[0116] In Table 2, the Beta value (β value) is a statistical indicator used to quantify the "strength and direction of the association" between the independent variable (feature) and the dependent variable (response variable). Here, it corresponds to the relative average change in the logarithmic value of the protein after treatment. p < 0.05 indicates that these results are statistically significant.

[0117] `negLogP` is the negative logarithm of the p-value, providing a visual representation of the significance of the difference; the larger the value, the more significant the difference. `AveExpr` is the average expression level, reflecting the overall expression intensity.

[0118]

[0119] Table 2 Figure 3 A comparative schematic diagram of protein expression provided in an embodiment of this disclosure; Figure 3 In this context, the proteins specifically refer to P13987, Q9BSL1, Q9BYC5, Q9UBV7, and Q9Y275 proteins; CN stands for Cognitive normal, AD stands for Alzheimer's disease; Ref stands for reference sequence; and Proteinexpression indicates the protein expression level, which usually refers to the protein content in the sample.

[0120] Figure 4 A comparative diagram of tFUS intervention before and after an embodiment of this disclosure; Figure 4 In this context, BL represents the baseline level, i.e., the protein content in the sample before treatment; Post represents the post-treatment level, i.e., the protein content in the sample after tFUS intervention. Standardized protein expression levels refer to the standardized protein expression levels used to eliminate differences between experiments.

[0121] The above verifies the role of at least one of the following proteins—P13987, Q9BSL1, Q9BYC5, Q9UBV7, and Q9Y275—in efficacy assessment. Based on the above proteins, the method provided in this disclosure has the following beneficial effects: Precise assessment of tFUS efficacy: The above protein combination is used as a specific biomarker for the efficacy of tFUS neuromodulation. The corresponding increase or decrease directly reflects the degree of improvement in cognitive function (MMSE) and EEG signals (δ band PSD, LZC), which solves the problem that existing AD biomarkers cannot assess the efficacy of non-drug interventions in real time.

[0122] Improving multidimensional pathological indicators of AD: By combining the expression of the above proteins, it is possible to simultaneously improve related pathological indicators such as enhanced cognitive function, enhanced brain network connectivity, increased complexity of EEG signals, and inhibition of neuroinflammation.

[0123] Potential intervention targets: By using the above protein combinations as potential intervention targets, it is hoped that direct regulation of one or more of the above targets can be achieved to alleviate AD-related pathology and improve cognition.

[0124] With a wide range of applications: the above protein combination can be used for indication screening of AD patients before tFUS treatment (e.g., patients with plasma GFAP elevation ≥30% are more suitable for tFUS intervention), efficacy monitoring during treatment, and prognostic assessment after treatment; filling the gap in efficacy assessment of tFUS intervention for AD patients at the protein and molecular levels.

[0125] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0126] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this disclosure, "a plurality of" means two or more, unless otherwise explicitly specified.

[0127] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.

Claims

1. A machine-executed method for evaluating the efficacy of treatment for a disease, characterized in that, The method includes: Acquire the first sample in the first state, and detect the biomarkers in the first sample; Acquire a second sample in a second state, and detect biomarkers in the second sample; The biomarkers in the first test substance and the biomarkers in the second test substance are compared to obtain a comparison result; the evaluation result is determined based on the comparison result.

2. The method according to claim 1, characterized in that, The biomarkers include at least one of the following proteins or their encoding genes: P13987, Q9BSL1, Q9BYC5, Q9UBV7, Q9Y275, P25311, and GFAP.

3. The method according to claim 1, characterized in that, The biomarkers include at least one of the following proteins or their encoding genes: P13987, Q9BSL1, Q9BYC5, Q9UBV7, Q9Y275.

4. The method according to claim 1, characterized in that, The biomarkers include the following proteins or their encoding genes: P13987; Alternatively, the biomarker includes at least one of the following proteins or their encoding genes: Q9BSL1, Q9BYC5, Q9UBV7, Q9Y275; Alternatively, the biomarkers may include the following proteins or their encoding genes: Q9BSL1, Q9BYC5, Q9UBV7, Q9Y275; Alternatively, the biomarker may include the following protein or its encoding gene: P13987, and at least one of the following proteins or their encoding genes: Q9BSL1, Q9BYC5, Q9UBV7, Q9Y275; Alternatively, the biomarkers may include the following proteins or their encoding genes: P13987, Q9BSL1, Q9BYC5, Q9UBV7, Q9Y275.

5. The method according to claim 3 or 4, characterized in that, The biomarkers also include at least one of the following proteins or their encoding genes: P25311, GFAP.

6. The method according to claim 1, characterized in that, The therapeutic effects include the effects of non-pharmacological interventions and / or the effects of pharmacological interventions; And / or, the disease includes diseases related to brain synapses and / or neural plasticity; and / or, the disease includes neurodegenerative diseases; and / or, the disease includes neurodegenerative diseases; and / or, the disease includes diseases caused by degenerative changes in the central nervous system; and / or, the disease includes at least one of Alzheimer's disease, Parkinson's disease, Lewy body dementia, Huntington's disease, amyotrophic lateral sclerosis, multiple system atrophy, frontotemporal dementia, spinocerebellar ataxia, vascular dementia, and traumatic brain injury.

7. The method according to claim 6, characterized in that, The non-pharmacological interventions include: transcranial focused ultrasound stimulation modulation; Accordingly, the first state is the state before transcranial focused ultrasound stimulation modulation; The second state is the state after transcranial focused ultrasound stimulation and modulation in the first state.

8. The method according to claim 3, characterized in that, The step of determining the evaluation result based on the comparison result includes: The evaluation result is determined as the first result if at least one of the following conditions is met: The decline in P13987 exceeded the first threshold; The rise of Q9BSL1 exceeds the second threshold; The rise of Q9BYC5 exceeded the third threshold; The rise of Q9UBV7 exceeded the fourth threshold; The rise of Q9Y275 exceeded the fifth threshold.

9. The method according to claim 5, characterized in that, Based on the comparison results, the evaluation results are determined, including: The evaluation result is considered the first result if at least one of the following conditions is met: The decrease in P25311 exceeded the sixth threshold; The decrease in GFAP exceeded the seventh threshold.

10. The method according to claim 1, characterized in that, The method further includes: Obtain the cognitive assessment results in the first state, and obtain the cognitive assessment results in the second state; Determine the changes in cognitive assessment results in the first state and the second state; Accordingly, based on the comparison results, the evaluation results are determined, including: Based on the comparison results and the change information, the evaluation result is determined; The cognitive assessment results include at least one of the following evaluation indicators: cognitive function assessment results, brain imaging assessment results, electroencephalogram (EEG) signal assessment results, and neural signal assessment results.

11. A system for evaluating the efficacy of treatment for a disease, characterized in that, The system includes: The first detection module is used to acquire a first detection object in a first state and detect biomarkers in the first detection object; The second detection module is used to acquire the second detection item in the second state and detect the biomarkers in the second detection item; The comparison module is used to compare the biomarkers in the first detectable and the biomarkers in the second detectable to obtain a comparison result; and to determine the evaluation result based on the comparison result.

12. The use of biomarkers and / or reagents for detecting said biomarkers in the preparation and / or screening of products, characterized in that, The product is used for at least one of the following purposes: 1) predicting the therapeutic efficacy of a disease, 2) preventing and / or treating a disease, and 3) assessing the prognosis of a disease. The biomarkers include at least one of the following proteins or their encoding genes: P13987, Q9BSL1, Q9BYC5, Q9UBV7, Q9Y275, P25311, and GFAP.

13. The application according to claim 12, characterized in that, The therapeutic effects include the effects of non-pharmacological interventions and / or the effects of pharmacological interventions; And / or, the disease includes diseases related to brain synapses and / or neural plasticity; and / or, the disease includes neurodegenerative diseases; and / or, the disease includes neurodegenerative diseases; and / or, the disease includes diseases caused by degenerative changes in the central nervous system; and / or, the disease includes at least one of Alzheimer's disease, Parkinson's disease, Lewy body dementia, Huntington's disease, amyotrophic lateral sclerosis, multiple system atrophy, frontotemporal dementia, spinocerebellar ataxia, vascular dementia, and traumatic brain injury.

Images