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The integration of morphological attributes extracted from histopathological images and genomic data holds significant importance in advancing tumor diagnosis, prognosis, and grading. Histopathological images are acquired through microscopic examination of tissue slices, providing valuable insights into cellular structures and pathological features. On the other hand, genomic data provides information about tumor gene expression and functionality. The fusion of these two distinct data types is crucial for gaining a more comprehensive understanding of tumor characteristics and progression. In the past, many studies relied on single-modal approaches for tumor diagnosis. However, these approaches had limitations as they were unable to fully harness the information from multiple data sources. To address these limitations, researchers have turned to multi-modal methods that concurrently leverage both histopathological images and genomic data. These methods better capture the multifaceted nature of tumors and enhance diagnostic accuracy. Nonetheless, existing multi-modal methods have, to some extent, oversimplified the extraction processes for both modalities and the fusion process. In this study, we presented a dual-branch neural network, namely SG-Fusion. Specifically, for the histopathological modality, we utilize the Swin-Transformer structure to capture both local and global features and incorporate contrastive learning to encourage the model to discern commonalities and differences in the representation space. For the genomic modality, we developed a graph convolutional network based on gene functional and expression level similarities. Additionally, our model integrates a cross-attention module to enhance information interaction and employs divergence-based regularization to enhance the model's generalization performance. Validation conducted on glioma datasets from the Cancer Genome Atlas unequivocally demonstrates that our SG-Fusion model outperforms both single-modal methods and existing multi-modal approaches in both survival analysis and tumor grading.

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Tumors pose a major threat to human health, accounting for nearly one-sixth of global deaths annually. The primary treatments include surgery, radiotherapy, chemotherapy, and immunotherapy, each associated with significant side effects. This has driven the search for new therapies with fewer side effects and greater specificity. Nanotechnology has emerged as a promising field in this regard, particularly nanomolecular machines at the nanoscale. Nanomolecular machines are typically constructed from biological macromolecules like proteins, DNA, and RNA. These machines can be programmed to perform specialized tasks with precise instructions. Recent research highlights their potential in tumor diagnostics-identifying susceptibility genes, detecting viruses, and pinpointing tumor markers. Nanomolecular machines also offer advancements in tumor therapy. They can reduce traditional treatment side effects by delivering chemotherapy drugs and enhancing immunotherapy, and they support innovative treatments like sonodynamic and phototherapy. Additionally, they can starve tumors by blocking blood vessels, and eliminate tumors by disrupting cell membranes or lysosomes. This review categorizes and explains the latest achievements in molecular machine research, explores their models, and practical clinical uses in tumor diagnosis and treatment. It aims to broaden the research perspective and accelerate the clinical adoption of these technologies.

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DNA nanotechnology plays a crucial role in precise cancer medicine. Currently, molecular logic circuits are applied to detect tumor-specific biomarkers and control the release of therapeutic drugs. However, these systems lack self-learning capabilities for intelligent diagnostics in biological samples, and their data processing capabilities are limited. Here, a molecular learning vector quantization neural network (LVQNN) model based on DNA strand displacement (DSD) technology for breast tumor diagnosis is developed. Compared to previous work, the molecular LVQNN boasts powerful computing abilities, handling high-dimensional data for intelligent cancer diagnosis. To verify the feasibility and versatility of the network, two distinct typical datasets are selected: one from a single source with cell morphology data from 569 cases, and a more extensive one spanning different populations and ages, with miRNA gene expression data from 1881 cases. By using the molecular LVQNN, diagnostic experiments are conducted on 50 and 120 public individuals from these two datasets, respectively, achieving accuracy rates of 94% and 97.5%. This study demonstrates that the LVQNN model exhibits significant advantages in breast cancer diagnosis and enhances diagnostic accuracy while introducing new approaches for intelligent cancer diagnosis, anticipated to bring significant breakthroughs and application prospects to precise cancer medicine.

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Objective: The prognosis of colorectal cancer has continuously improved in recent years thanks to continuous progress in both the therapeutic and diagnostic fields. The specific objective of this study is to contribute to the diagnostic field through the evaluation of the correlation between superior hemorrhoidal vein (SHV) ectasia detected on computed tomography (CT) and Tumor (T), Node (N), and distant metastasis (M) examination and mesorectal fascia (MRF) invasion in the preoperative staging of rectal cancer. Methods: Between January 2018 and April 2022, 46 patients with histopathological diagnosis of rectal cancer were retrospectively enrolled, and the diameter of the SHV was evaluated by CT examination. The cutoff value for SHV diameter used is 3.7 mm. The diameter was measured at the level of S2 during portal venous phase after 4× image zoom to reduce the interobserver variability. The parameters evaluated were tumor location, detection of MRF infiltration (defined as the distance < 1 mm between the tumor margins and the fascia), SHV diameter, detection of mesorectal perilesional lymph nodes, and detection of metastasis. Results: A total of 67.39% (31/46) of patients had SHV ectasia. All patients with MRF infiltration (4/46, 7.14%) presented SHV ectasia (average diameter of 4.4 mm), and SHV was significantly related with the development of liver metastases at the moment of primary staging and during follow-up. Conclusion: SHV ectasia may be related to metastasis and MRF involvement; therefore, it could become a tool for preoperative staging of rectal cancer.

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T cells play an important role in adaptive immunity. Mature T cells specifically recognize antigens on major histocompatibility complex molecules through T-cell receptors (TCRs). As the TCR repertoire is highly diverse, its analysis is vital in the assessment of T cells. Advances in sequencing technology have provided convenient methods for further investigation of the TCR repertoire. In the present review, the TCR structure and the mechanisms by which TCRs function in tumor recognition are described. In addition, the potential value of the TCR repertoire in tumor diagnosis is reviewed. Furthermore, the role of the TCR repertoire in tumor immunotherapy is introduced, and the relationships between the TCR repertoire and the effects of different tumor immunotherapies are discussed. Based on the reviewed literature, it may be concluded that the TCR repertoire has the potential to serve as a biomarker for tumor prognosis. However, a wider range of cancer types and more diverse subjects require evaluation in future research to establish the TCR repertoire as a biomarker of tumor immunity.

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Quantum dots (QDs) represent a class of nanoscale wide bandgap semiconductors, and are primarily composed of metals, lipids, or polymers. Their unique electronic and optical properties, which stem from their wide bandgap characteristics, offer significant advantages for early cancer detection and treatment. Metal QDs have already demonstrated therapeutic potential in early tumor imaging and therapy. However, biological toxicity has led to the development of various non-functionalized QDs, such as carbon QDs (CQDs), graphene QDs (GQDs), black phosphorus QDs (BPQDs) and perovskite quantum dots (PQDs). To meet the diverse needs of clinical cancer treatment, functionalized QDs with an array of modifications (lipid, protein, organic, and inorganic) have been further developed. These advancements combine the unique material properties of QDs with the targeted capabilities of biological therapy to effectively kill tumors through photodynamic therapy, chemotherapy, immunotherapy, and other means. In addition to tumor-specific therapy, the fluorescence quantum yield of QDs has gradually increased with technological progress, enabling their significant application in both in vivo and in vitro imaging. This review delves into the role of QDs in the development and improvement of clinical cancer treatments, emphasizing their wide bandgap semiconductor properties.

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Papillary glioneuronal tumors (PGNTs), classified as Grade I by the WHO in 2016, present diagnostic challenges due to their rarity and potential for malignancy. Xiaodan Du et al.'s recent study of 36 confirmed PGNT cases provides critical insights into their imaging characteristics, revealing frequent presentation with headaches, seizures, and mass effect symptoms, predominantly located in the supratentorial region near the lateral ventricles. Lesions often appeared as mixed cystic and solid masses with septations or as cystic masses with mural nodules. Given these complexities, artificial intelligence (AI) and machine learning (ML) offer promising advancements for PGNT diagnosis. Previous studies have demonstrated AI's efficacy in diagnosing various brain tumors, utilizing deep learning and advanced imaging techniques for rapid and accurate identification. Implementing AI in PGNT diagnosis involves assembling comprehensive datasets, preprocessing data, extracting relevant features, and iteratively training models for optimal performance. Despite AI's potential, medical professionals must validate AI predictions, ensuring they complement rather than replace clinical expertise. This integration of AI and ML into PGNT diagnostics could significantly enhance preoperative accuracy, ultimately improving patient outcomes through more precise and timely interventions.

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Quinoline-based fibroblast activation protein (FAP) inhibitors (FAPIs) have recently emerged as a focal point in global nuclear medicine, underscored by their promising applications in cancer theranostics and the diagnosis of various nononcological conditions. This review offers an in-depth summary of the existing literature on the evolution and use of FAPI tracers in China, tracing their journey from preclinical to clinical research. Moreover, this review also assesses the diagnostic accuracy of FAPI PET for the most common cancers in China, analyzes its impact on oncologic management paradigms, and investigates the potential of FAP-targeted radionuclide therapy in patients with advanced or metastatic cancer. This review also summarizes studies using FAPI PET for nononcologic disorders in China. Thus, this qualitative overview presents a snapshot of China's engagement with FAPI tracers, aiming to guide future research endeavors.

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Extracellular vesicles (EVs) are important carriers of signaling molecules, such as nucleic acids, proteins, and lipids, and have become a focus of increasing interest due to their numerous physiological and pathological functions. For a long time, most studies on EV components focused on noncoding RNAs; however, in recent years, extracellular vesicle proteins (EVPs) have been found to play important roles in diagnosis, treatment, and drug resistance and thus have been considered favorable biomarkers and therapeutic targets for various tumors. In this review, we describe the general protocols of research on EVPs and summarize their multifaceted roles in precision medicine applications, including cancer diagnosis, dynamic monitoring of therapeutic efficacy, drug resistance research, tumor microenvironment interaction research, and anticancer drug delivery.

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Hyperparameter tuning plays a pivotal role in the accuracy and reliability of convolutional neural network (CNN) models used in brain tumor diagnosis. These hyperparameters exert control over various aspects of the neural network, encompassing feature extraction, spatial resolution, non-linear mapping, convergence speed, and model complexity. We propose a meticulously refined CNN hyperparameter model designed to optimize critical parameters, including filter number and size, stride padding, pooling techniques, activation functions, learning rate, batch size, and the number of layers. Our approach leverages two publicly available brain tumor MRI datasets for research purposes. The first dataset comprises a total of 7,023 human brain images, categorized into four classes: glioma, meningioma, no tumor, and pituitary. The second dataset contains 253 images classified as "yes" and "no." Our approach delivers exceptional results, demonstrating an average 94.25% precision, recall, and F1-score with 96% accuracy for dataset 1, while an average 87.5% precision, recall, and F1-score, with accuracy of 88% for dataset 2. To affirm the robustness of our findings, we perform a comprehensive comparison with existing techniques, revealing that our method consistently outperforms these approaches. By systematically fine-tuning these critical hyperparameters, our model not only enhances its performance but also bolsters its generalization capabilities. This optimized CNN model provides medical experts with a more precise and efficient tool for supporting their decision-making processes in brain tumor diagnosis.

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Nitric oxide (NO) exhibits both pro- and anti-tumor effects. Therefore, real-time in vivo imaging and quantification of tumor NO dynamics are essential for understanding the conflicting roles of NO played in pathophysiology. The current molecular probes, however, cannot provide high-resolution imaging in deep tissues, making them unsuitable for these purposes. Herein, we designed a photoacoustic probe with an absorption maximum beyond 1000 nm for high spatial quantitative imaging of in vivo tumor NO dynamics. The probe exhibits remarkable sensitivity, selective ratiometric response behavior, and good tumor-targeting abilities, facilitating ratiometric imaging of tumor NO throughout tumor progression in a micron-resolution level. Using the probe as the imaging agent, we successfully quantified NO dynamics in tumor, liver and kidney. We have pinpointed an essential concentration threshold of around 80 nmol/cm3 for NO, which plays a crucial role in the "double-edged-sword" function of NO in tumors. Furthermore, we revealed a reciprocal relationship between the NO concentration in tumors and that in the liver, providing initial insights into the possible NO-mediated communication between tumor and the liver. We believe that the probe will help resolve conflicting aspects of NO biology and guide the design of imaging agents for tumor diagnosis and anti-cancer drug screening.

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This study proposes using magnetically induced currents in medical infrared imaging to increase the temperature contrast due to the electrical conductivity differences between tumors and healthy tissues. There are two objectives: (1) to investigate the feasibility of this active method for surface and deep tumors using numerical simulations, and (2) to demonstrate the use of this method through different experiments conducted with phantoms that mimic breast tissues. Tumorous breasts were numerically modeled and simulated in active and passive modes. At 750 kHz, the applied current was limited for breast tissue-tumor conductivities (0.3 S/m and 0.75 S/m) according to the local specific absorption rate limit of 10 W/kg. Gelatin-based and mashed potato phantoms were produced to mimic tumorous breast tissues. In the simulation studies, the induced current changed the temperature contrast on the imaging surface, and the tumor detection sensitivity increased by 4 mm. An 11-turn 70-mm-long solenoid coil was constructed, 20 A current was applied for deep tumors, and a difference of up to 0.4  ∘ C was observed in the tumor location compared with the temperature in the absence of the tumor. Similarly, a 23-turn multi-layer coil was constructed, and a temperature difference of 0.4  ∘ C was observed. The temperature contrast on the body surface changed, and the tumor detection depth increased with the induced currents in breast IR imaging. The proposed active thermal imaging method was validated using numerical simulations and in vitro experiments.

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Significance: Deep learning enables label-free all-optical biopsies and automated tissue classification. Endoscopic systems provide intraoperative diagnostics to deep tissue and speed up treatment without harmful tissue removal. However, conventional multi-core fiber (MCF) endoscopes suffer from low resolution and artifacts, which hinder tumor diagnostics. Aim: We introduce a method to enable unpixelated, high-resolution tumor imaging through a given MCF with a diameter of around 0.65 mm and arbitrary core arrangement and inhomogeneous transmissivity. Approach: Image reconstruction is based on deep learning and the digital twin concept of the single-reference-based simulation with inhomogeneous optical properties of MCF and transfer learning on a small experimental dataset of biological tissue. The reference provided physical information about the MCF during the training processes. Results: For the simulated data, hallucination caused by the MCF inhomogeneity was eliminated, and the averaged peak signal-to-noise ratio and structural similarity were increased from 11.2 dB and 0.20 to 23.4 dB and 0.74, respectively. By transfer learning, the metrics of independent test images experimentally acquired on glioblastoma tissue ex vivo can reach up to 31.6 dB and 0.97 with 14 fps computing speed. Conclusions: With the proposed approach, a single reference image was required in the pre-training stage and laborious acquisition of training data was bypassed. Validation on glioblastoma cryosections with transfer learning on only 50 image pairs showed the capability for high-resolution deep tissue retrieval and high clinical feasibility.

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BACKGROUND: Cancer is a major disease that threatens human life and health. Raman spectroscopy can provide an effective detection method. OBJECTIVE: The study aimed to introduce the application of Raman spectroscopy to tumor detection. We have introduced the current mainstream Raman spectroscopy technology and related application research. METHODS: This article has first introduced the grim situation of malignant tumors in the world. The advantages of tumor diagnosis based on Raman spectroscopy have also been analyzed. Secondly, various Raman spectroscopy techniques applied in the medical field are introduced. Several studies on the application of Raman spectroscopy to tumors in different parts of the human body are discussed. Then the advantages of combining deep learning with Raman spectroscopy in the diagnosis of tumors are discussed. Finally, the related problems of tumor diagnosis methods based on Raman spectroscopy are pointed out. This may provide useful clues for future work. CONCLUSION: Raman spectroscopy can be an effective method for diagnosing tumors. Moreover, Raman spectroscopy diagnosis combined with deep learning can provide more convenient and accurate detection results.

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Progesterone and AdipoQ Receptor 3 (PAQR3) is a member of the AdipoQ receptor. Our previous studies have found that PAQR3 plays a role as a candidate inhibitor in cardiac adenocarcinoma, breast cancer, gastric cancer and colorectal cancer, but the systematic analysis of PAQR3 in tumors is currently lacking. The objective of this study was to investigate the prognostic and therapeutic value of PAQR3 in 31 tumors. Through the analysis of TCGA, UALCAN, GEO, GEPIA2, TIMER, Kaplan-Meier plotter, TISIDB and other databases, it was found that the expression level of PAQR3 changed significantly in different tumor types, and the expression level of Neuroblastoma was very high. And the level of Prostate adenocarcinoma is low. In addition, the expression level of PAQR3 in Cholangiocarcinoma, Esophageal carcinoma, Head and neck squamous carcinoma, Liver Hepatocellular Carcinoma, Lung Adenocarcinoma and Lung squamous cell carcinoma was significantly higher than that in normal tissues. However, the expression level of PAQR3 in Breast Cancer, Kidney Renal Clear Cell Carcinoma, Kidney renal papillary cell carcinoma, Prostate Adenocarcinoma, Rectum Adenocarcinoma, Thyroid Cancer and Uterine Corpus Endometrial Carcinoma was lower than that in normal tissues. Subsequently, we explored the value of PAQR3 as a prognostic indicator of cancer. In Acute Myeloid Leukemia, Lower-grade Glioma and Glioblastoma, Pediatric Low-grade Gliomas, Kidney Chromophobe, and Thyroid Cancer, PAQR3 expression was positively correlated with OS and DSS, while in Rectum Adenocarcinoma, PAQR3 expression was negatively correlated with OS. PAQR3 high expression group Lower-grade Glioma and Glioblastoma, Pediatric Low-grade Gliomas, Uveal Melanoma, Kidney Chromophobe and DFI were positively correlated. PAQR3 can be used as a risk factor for the prognosis of multiple tumors. Then, we discussed the correlation between PAQR3 and immunology, and found that PAQR3 has a wide range of mutations in various tumor types, the most common mutation type is missense mutation, and common mutation types also include amplification, depth deletion, splicing, truncation and structural variation. Among the tumor samples with PAQR3 alterations, mutation occurred in all tumor samples except prostate adenocarcinoma and adrenal cortical carcinoma, head and neck squamous cell carcinoma, brain low-grade glioma, and kidney clear cell carcinoma, while esophageal adenocarcinoma had the highest total alteration frequency. PAQR3 was strongly associated with CNV in 18 tumors, particularly in Ovarian cancer, Lung squamous cell carcinoma, and Adenoid cystic carcinoma. On the other hand, PAQR3 has a higher SNV frequency in Uterine Corpus Endometrial Carcinoma, Skin Cutaneous Melanoma and Lung Adenocarcinoma, among which Uterine Corpus Endometrial Carcinoma has the highest SNV frequency. These results showed that PAQR3 expression levels were significantly correlated with tumor mutation load, microsatellite instability, neoantigens, and purity. In summary, PAQR3 can affect the tumor microenvironment and has potential for chemotherapy. Finally, we investigated the role of PAQR3 in tumor resistance and found that the expression of PAQR3 affects the efficacy of multiple chemotherapy drugs. Based on these studies, we found that PAQR3 plays an important role in cancer and has potential in tumor diagnosis and prognosis.

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Calcium carbonate (CaCO3), a natural common inorganic material with good biocompatibility, low toxicity, pH sensitivity, and low cost, has a widespread use in the pharmaceutical and chemical industries. In recent years, an increasing number of CaCO3-based nano-drug delivery systems have been developed. CaCO3 as a drug carrier and the utilization of CaCO3 as an efficient Ca2+ and CO2 donor have played a critical role in tumor diagnosis and treatment and have been explored in increasing depth and breadth. Starting from the CaCO3-based nano-drug delivery system, this paper systematically reviews the preparation of CaCO3 nanoparticles and the mechanisms of CaCO3-based therapeutic effects in the internal and external tumor environments and summarizes the latest advances in the application of CaCO3-based nano-drug delivery systems in tumor therapy. In view of the good biocompatibility and in vivo therapeutic mechanisms, they are expected to become an advancing biomedicine in the field of tumor diagnosis and treatment.

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Exosomal proteins from the parental cells are considered to be promising biomarker sets for precise tumor diagnostics and monitoring. However, the accurate quantitative analysis of low-abundance exosomal proteins remains challenging due to the heterogeneity of clinical samples. Here, we standardized the exosomal concentration with a fluorogenic membrane probe and developed an aptamer-bivalent-cholesterol-mediated Proximity Entropy-driven Exosomal Protein Reporter (PEEPR). The proposed PEEPR enables the in-situ analysis of multiple exosomal proteins by integrating bivalent cholesterol anchor (exosomal lipid bilayer) and aptamer (exosomal proteins) with a proximity entropy-driven circuit. Based on this strategy, we successfully achieved detection limits of 3.9 pg/mL exosomal GPC-3 and 3.4 pg/mL exosomal PD-L1. Notably, the standardization of exosome concentrations is designed to avoid errors due to biological heterogeneity. The results showed that evaluating the levels of exosomal GPC-3 and PD-L1 in clinical samples via this strategy could accurately differentiate healthy individuals, hepatitis B patients, and hepatocellular carcinoma patients. In summary, PEEPR is a promising clinical diagnostic strategy for the quantitative analysis of a variety of tumor-associated exosomal proteins for the precise diagnosis and personalized treatment monitoring of tumors.

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Tumor cell-targeting molecules play a vital role in cancer diagnosis, targeted therapy, and biomarker discovery. Aptamers are emerging as novel targeting molecules with unique advantages in cancer research. In this work, we have developed several DNA aptamers through cell-based systematic evolution of ligands by exponential enrichment (Cell-SELEX). The selected SYL-6 aptamer can bind to a variety of cancer cells with high signal. Tumor tissue imaging demonstrated that SYL-6-Cy5 fluorescent probe was able to recognize multiple clinical tumor tissues but not the normal tissues, which indicates great potential of SYL-6 for clinical tumor diagnosis. Meanwhile, we identified prohibitin 2 (PHB2) as the molecular target of SYL-6 using mass spectrometry, pull-down and RNA interference assays. Moreover, SYL-6 can be used as a delivery vehicle to carry with doxorubicin (Dox) chemotherapeutic agents for antitumor targeted chemotherapy. The constructed SYL-6-Dox can not only selectively kill tumor cells in vitro, but also inhibit tumor growth with reduced side effects in vivo. This work may provide a general tumor cell-targeting molecule and a potential biomarker for cancer diagnosis and targeted therapy.

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@#Cancer diagnosis and treatment has always been a hot spot in clinical and basic scientific research. In recent years,scientists have developed a large number of tumor diagnosis and treatment methods based on bacteria combined with nanotechnology. Compared with pure bacterial diagnosis and treatment,bacterial diagnosis and treatment combined with nanotechnology can produce multiple synergistic effects,thereby improving the efficacy of tumor diagnosis and treatment.The characteristics of bacteria such as environmental sensitivity,tropism,motility and hypoxia growth combined organically with nanotechnology can increase the solubility of insoluble drugs,promote drug lysosomal escape,and avoid phagocytosis and clearance of the reticuloendothelial system to construct a new type of bacterial micro/nano diagnosis and treatment platform,thereby achieving the precise tumor diagnosis and controlled drug release. This paper reviewed the research progress of bacteria combined with nanotechnology for tumor diagnosis and treatment in recent years and the challenges and possible solutions,so as to provide reference for promoting the rapid development of tumor diagnosis and treatment research.

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The application of Raman spectroscopy in the field of laboratory medicine is making continuous progress and development. The biosensor platform based on Raman spectroscopy provides a new means for accurate molecular diagnosis of diseases. In particular, as a fast and non-destructive detection method, surface-enhanced Raman scattering has the advantages of simple sample preparation, little interference from water and real-time detection, and shows great application potential in the field of medical examination. At the same time, with the integration of SERS and other technologies, including electrochemistry, new nano-materials, microfluidic, biochip, DNA nano-machine, artificial intelligence and machine learning, it will play a more and more important role in the field of medical laboratory. With the deepening of SERS research and the cross-integration between multiple disciplines, it will be widely used in biomedical detection and is expected to become an important technology platform for the next generation of precision diagnosis.