Recently, the team of Professor Guan Jian from the Department of Radiation Oncology at Nanfang Hospital, Southern Medical University, published a cover article in the internationally renowned journal Advanced Science (IF: 15.1), entitled ＂VLDL and LDL Subfractions Enhance the Risk Stratification of Individuals Who Underwent Epstein-Barr Virus-Based Screening for Nasopharyngeal Carcinoma: A Multicenter Cohort Study.＂Based on nuclear magnetic resonance (NMR) spectroscopy,the study performed a comprehensive metabolomic analysis of plasma samples from hundreds of nasopharyngeal carcinoma patients and Epstein-Barr virus (EBV)-infected individuals across multiple centers to explore potential risk factors for nasopharyngeal carcinoma, and further developed a risk stratification model based on an NMR risk score (NRS) for individualized risk assessment of people at high risk of nasopharyngeal carcinoma.The paper specifically acknowledges ProteinT for its support in NMR testing and analysis.Nasopharyngeal carcinoma (NPC) is a highly metastatic and invasive head and neck cancer originating from the nasopharyngeal epithelium. In Southeast Asia and southern China, its incidence has risen sharply to 20–40 cases per 100,000 person-years. Early-stage NPC is difficult to diagnose because its symptoms are insidious and non-specific, so most patients are already at an advanced stage when diagnosed. Epstein-Barr virus (EBV) is one of the important causative factors of NPC. EBV antibody serological testing is commonly used for NPC screening, but its positive predictive value (PPV) is only about 4%. There is therefore an urgent need for a test that combines high specificity with cost-effectiveness in order to screen effectively and accurately identify individuals at high risk of NPC.｜Methods｜From June 2019 to September 2022, Nanfang Hospital recruited 297 NPC patients (NPC cohort) and 149 healthy controls with positive EBV serology (EBV cohort) as the discovery cohort.NMR was used to quantitatively analyze 38 plasma metabolites and 112 lipoprotein subfraction parameters in participants＇ plasma samples, comprehensively characterizing the metabolic profile changes in the two cohorts, screening for potential metabolic biomarkers closely associated with NPC, and developing a risk stratification model for nasopharyngeal carcinoma.At the same time, 84 NPC patients and 53 EBV-positive controls were recruited at two hospitals in a high-incidence area of Guangdong Province as a validation cohort for independent external multi-center validation.｜Results｜Significant differences were found between the metabolic profiles of the NPC and EBV groups, with lipid metabolism particularly prominent; the small, dense very low-density lipoprotein (VLDL-5) subfraction particle components and the large, buoyant low-density lipoprotein (LDL-1) subfraction particle components are important cancer-promoting factors in the development of nasopharyngeal carcinoma.Both VLDL-5 (triglycerides, TG; cholesterol, CH; free cholesterol, FC; phospholipids, PL) and LDL-1 (triglycerides, TG; cholesterol, CH; free cholesterol, FC; phospholipids, PL; apolipoprotein B, ApoB) particles showed significant VIP values (VIP > 1) and were clearly increased in the plasma of NPC patients. Correlation analysis with metabolites showed that LDL and VLDL subfraction particles were positively correlated with metabolites involved in carbohydrate or amino acid metabolism, suggesting that they may participate directly or indirectly in these metabolic processes, although the specific mechanisms remain to be explored. ROC analysis of the structural components of LDL-1 and VLDL-5 particles showed satisfactory AUC values, indicating great potential as biomarkers for nasopharyngeal carcinoma. The discovery cohort also revealed alterations in plasma metabolite profiles and metabolic reprogramming in the NPC cohort, and eight differential metabolic markers were screened out that could significantly distinguish the NPC cohort from the EBV cohort; enrichment analysis indicated that they mainly cause metabolic disorders related to glucose metabolism, such as the Warburg effect, pyruvate metabolism and glycolysis.Based on the characteristic metabolic markers identified in the discovery dataset, five metabolites (glucose, lactate, pyruvate, methionine, ornithine), four components of VLDL-5 particles (triglycerides, cholesterol, free cholesterol and phospholipids) and three components of LDL-1 particles (triglycerides, phospholipids and ApoB) were finally selected as model factors toconstruct a risk stratification model based on the NMR risk score (NRS). The AUC in the discovery cohort was 0.985 (95% CI: 0.977–0.994). Validation using the independent validation cohort gave an AUC of 0.836 (95% CI: 0.748–0.924). To evaluate the potential of NRS for NPC screening, NRS was combined with EBV antibody serological testing; when the NRS median (N50) was used as the cut-off value, the combined NRS performed best (AUC: 0.841, 95% CI: 0.811–0.871), with both sensitivity and specificity above 0.80 (PPV: 0.7008; NPV: 0.9271; Youden index: 0.6817).｜Conclusions｜This study reveals for the first time the close association between plasma lipid metabolic disturbance and tumorigenesis in nasopharyngeal carcinoma patients. At the same time, it innovatively reports a risk stratification model built on characteristic plasma metabolites and lipoprotein subfractions which, when combined with standard EBV antibody serological testing, can stratify the risk of nasopharyngeal carcinoma patients effectively and accurately. It offers a new direction for early screening and prevention of nasopharyngeal carcinoma and is expected to bring better clinical benefit to people at high risk of the disease.ProteinT NMR Testing ServiceNMR is an ideal analytical technique that allows non-invasive, non-destructive and quantitative investigation of molecular structure, dynamic processes and chemical reactions. A large body of literature confirms that NMR is an irreplaceable tool for detecting low-density lipoprotein particle (LDL-P) subfractions, and the role of LDL-P in the early warning and treatment monitoring of atherosclerotic cardiovascular disease (ASCVD) has been extensively reported: the smaller and more numerous the lipoprotein particles, the higher the risk of atherosclerosis. This technology has been approved abroad and is recommended by authoritative guidelines and consensus statements in China and internationally.ProteinT is the first to introduce internationally leading NMR spectroscopy technology into China and apply it clinically, addressing the current pain point that conventional lipid markers lack precision.Thanks to its stable performance, ProteinT has established a fully automated standard platform featuring simple sample preparation, automatic data acquisition and processing, automatic generation of quantitative reports, and no requirement for specialized NMR expertise.ProteinT NMR testing items and their clinical applicationsAt the same time, NMR can be applied to the analysis of urinary metabolite information. Supported by biomedical expertise and exclusive database management, it enables the creation of personal health management records and comprehensive personal health management from multiple perspectives—body, psychology, food, disease and lifestyle. This approach provides a powerful research tool for clinical studies of metabolic syndrome, cancer, diabetes, kidney disease and other conditions, and serves as strong support for ProteinT＇s gene–protein–metabolism multi-omics platform.

On 21 April 2024, the Chinese Conference on Tumor Biomarkers (CCTB) came to a successful close at the Nanjing International Youth Cultural Centre. As a leader in clinical proteomics, ProteinT was invited to join colleagues from across the industry for this academic gathering, sharing in the feast of science and discussing breakthroughs and the future of cancer prevention and treatment.Highlights in reviewThe ProteinT booth drew steady crowds. Its precision clinical solutions for cancer, cardiovascular and cerebrovascular diseases built on proteomics and other multi-omics approaches, together with its range of approved kit products, attracted the attention of numerous experts, scholars and peers, who stopped by for in-depth discussion with our technical team. At the same time, the conference provided the occasion to launch a sincere gift marking our tenth anniversary — the ＂Free T Program＂ — designed to support the application and translation of precision medicine projects.Innovation awardThe lung cancer risk screening product built on ProteinT＇s independently developed n-LAPE/MSTMplatformreceived the ＂2023 Tumor Biomarker Innovative Technology Product Award＂. The product screens for protein biomarkers associated with the occurrence and development of lung cancer and, through a big-data lung cancer risk model, calculates a risk score to assess the subject＇s lung cancer risk — full recognition of the innovative and advanced nature of ProteinT＇s technology.Product portfolioA novel biomarker discovery platform for liquid biopsy based on next-generation nano-scale low-abundance protein enrichment mass spectrometry n-LAPE/MSTMMultiple self-developed products certified by NMPASeveral of ProteinT＇s self-developed, mass-spectrometry-platform-based proteomicsfull-process testing products have obtained NMPA registration certificates, laying a solid foundation for bringing proteomics into clinical service and leading clinical mass spectrometry testing in proteomics into an era of standardization.A generous tenth-anniversary gift — Free TThe Free T Program is still under way. Principal investigators and experts working in the relevant research fields are welcome to scan the QR code and take part. We look forward to working with you to advance the application and translation of precision medicine projects.

A new chapter written by the Qinhuai River, a new journey begun in the city of Jinling. From 19 to 21 April 2024, the Chinese Conference on Tumor Biomarkers (CCTB) will be held at the Nanjing International Youth Cultural Centre. This year＇s conference is hosted by the Tumor Marker Professional Committee of the Chinese Anti-Cancer Association together with Nanjing Medical University.As a leader in clinical proteomics, ProteinT will make a strong appearance at this gathering, bringing technological breakthroughs and major products including assay kits, and presenting a comprehensive picture of its precision clinical solutions for the diagnosis and treatment of diseases using tumor biomarkers based on proteomics and other multi-omics approaches. Coinciding with the company＇s tenth anniversary, ProteinT will also release the ＂Free T Program＂ to support the application and translation of precision medicine projects — no gimmicks! Fee reductions! Matching support! Experts and scholars are warmly invited to visit the ProteinT booth, 5F-A08, to exchange ideas and discuss breakthroughs and the future of cancer prevention and treatment. Booth information Dates: 19–21 April 2024Venue: Nanjing International Youth Cultural Centre(No. 8 Yecheng Road, Shuangzha Subdistrict, Jianye District, Nanjing)Booth: 5F-A08 n-LAPE/MS™ platformProteinT has long been deeply engaged in proteomics and other multi-omics fields. After years of technical accumulation and breakthroughs, its exclusively self-developed platform for discovering novel liquid biopsy biomarkers — n-LAPE/MS™, based on next-generation nano-scale low-abundance protein enrichment mass spectrometry — has reached new heights. Combined with the latest 4D mass spectrometry platform, a single body fluid sample can yield detection of as many as 8,000+ proteins within an effective half-hour gradient, and tens of thousands of clinical body fluid samples have already been analyzed. At the same time, several self-developed products have been approved and brought to market, including a fully automated sample processing system (Tianjin medical device filing No. 20240319) and reagents for plasma protein and peptide extraction or purification (Tianjin medical device filing No. 20230397). Free T ProgramTo support clinical medical research and to explore the discovery of protein biomarkers for early screening, early diagnosis and disease course monitoring, ProteinT has joined forces with the Tianjin Key Laboratory of Clinical Multi-Omics to launch the ＂Free T Program.＂ The programme focuses on supporting large-cohort projects whose primary research object is body fluid proteomics. For eligible projects in the relevant fields, it offers a range of support packages worth up to RMB 10 million. Submit your basic details and project information to apply; after review by the expert committee, a dedicated colleague will contact you. We look forward to working with you to advance the application and translation of precision medicine projects. Conference programme

Alzheimer＇s disease (AD) is a chronic neurodegenerative disease, and early diagnosis plays a crucial role in improving patients＇ quality of life. Diagnosis currently relies mainly on imaging and cerebrospinal fluid biomarkers, and 67% of patients are already at the moderate-to-severe stage by the time they are diagnosed, missing the optimal window for intervention[1].As medical research advances, body fluid testing has shown great potential in AD diagnosis. Products based on cerebrospinal fluid or plasma biomarkers have been launched both in China and abroad, improving the detection rate and compliance of AD diagnosis and greatly accelerating research progress in developing body fluid-based AD biomarkers. High-impact papers continue to emerge from this line of research, further advancing the clinical translation of body fluid biomarkers for early AD diagnosis.Case 1Large-scale plasma proteomic analysis identifies a high-performance biomarker panel for AD screening and staging[2]The team of Academician Ip Nancy Yuk-Yu analyzed 1,160 plasma proteins in screening and validation AD cohort samples and systematically characterized the plasma proteome of AD, identifying 19 core plasma protein biomarkers associated with AD. On this basis, the team developed a biomarker model that accurately identifies AD and determines disease severity. Compared with existing blood biomarkers used for AD classification (such as plasma ATN biomarkers, plasma p-tau181 and p-tau217), the 19-protein panel identified in this study achieved higher accuracy, with an AUC of 0.98; seven of these proteins were associated with AD stage and could be used for disease staging. The team also developed a novel blood test that enables non-invasive detection, advancing early AD diagnosis by 5–10 years, with the additional ability to assess disease stage—a development of great value for both the diagnosis and treatment of the disease.AD classification based on plasma ATNbiomarkers and the 19-protein biomarker panelOn February 19, 2024, Academician Ip＇s team announced that the blood test they had developed simultaneously measures changes in the levels of 21 AD-related blood protein biomarkers, enabling early detection of AD and mild cognitive impairment with accuracies exceeding 96% and 87% respectively—higher than existing blood tests based on Aβ42/40, NfL or p-Tau181. This represents critical progress in the clinical translation of blood proteomic biomarkers for AD.Case 2Biomarker changes during the 20 years preceding AD diagnosis[3]The team of Professor Jia Jianping at Xuanwu Hospital, Capital Medical University, conducted a multicenter nested case–control study of AD biomarkers among cognitively normal participants in the China Cognition and Aging Study from January 2000 to December 2020. A subgroup of these participants underwent cerebrospinal fluid (CSF) testing, cognitive assessment and brain imaging every two to three years. A total of 648 AD patients were matched with 648 cognitively normal participants, and the temporal trajectories of CSF biomarker concentrations, cognitive test results and imaging findings were analyzed in both groups. Levels of CSF and imaging biomarkers in the AD group diverged from those of the cognitively normal group well before diagnosis: amyloid-β (Aβ) 42 at 18 years; the Aβ42/Aβ40 ratio at 14 years; phosphorylated tau 181 at 11 years; total tau at 10 years; neurofilament light chain at 9 years; hippocampal volume at 8 years; and cognitive decline at 6 years. As cognitive impairment progressed, the rate of change in CSF biomarker levels in the AD group initially accelerated and then slowed.Trajectories and rates of change of biomarkers before diagnosis of AD This study revealed for the first time the dynamic patterns of change in CSF and imaging biomarkers as AD progresses from the asymptomatic to the symptomatic stage, clarifying the key time points at which the pathophysiological changes of AD emerge. It lays a solid foundation for early AD screening and provides guidance on the therapeutic time window for new anti-AD drugs targeting Aβ and other relevant pathological proteins.Case 3Accuracy of a plasma p-tau217 immunoassay for the identification of AD[4] This study drew on three cohorts: TRIAD (Translational Biomarkers in Aging and Dementia), WRAP (the Wisconsin Registry for Alzheimer＇s Prevention) and SPIN (the São Paulo Neurodegenerative Diseases Initiative); longitudinal follow-up data came from the TRIAD and WRAP cohorts. A total of 786 participants were enrolled, with a mean age of 66.3 years and 35.9% male. All participants had complete Aβ data, obtained by PET in TRIAD and WRAP and by CSF Aβ42/Aβ40 in SPIN.Accuracy of plasma p-tau217 in detecting Aβ and tau positivity and in distinguishing the A+T− group from the A+T+ group Based on baseline characteristics, participants were divided into three groups: Aβ-negative and tau-negative A−T− (n = 297), A+T− (n = 66) and A+T+ (n = 29). The results showed that analyses across all three independent cohorts demonstrated the accuracy of plasma p-tau217 in identifying abnormal Aβ and tau signals, underscoring the important role of plasma p-tau217 as an initial screening tool in the management of cognitive impairment and confirming the feasibility of translating AD plasma biomarkers into clinical practice.Case 4Blood AD biomarker tests perform as well as or better than cerebrospinal fluid testing[5]The team of Oskar Hansson measured plasma %p-tau217 by liquid chromatography–tandem high-resolution mass spectrometry (LC-MS/HRMS) in independent cohorts in Sweden (BioFINDER-2) and the United States (Knight ADRC), and compared its diagnostic performance with that of CSF assays approved by the FDA and used in clinical practice (Fujirebio＇s CSF Aβ42/40 and Roche＇s p-tau181/Aβ42).Accuracy of plasma %p-tau217 in the clinical differential diagnosis of ADThe results showed that plasma %p-tau217 performed comparably to the FDA-approved CSF assays in classifying Aβ PET status, and superiorly in classifying tau PET status. High-quality AD cohorts using different technological approaches have confirmed the clinical performance of blood protein biomarkers, demonstrating their robustness. Plasma AD biomarker testing is poised to enter genuine clinical use—an advance that would not only reduce reliance on PET or CSF testing, but also raise the detection rate of AD and allow more patients to benefit from targeted therapy.As a leader in clinical proteomics and a provider of multi-omics technology services, ProteinT offers solutions for body fluid biomarker discovery.A novel liquid biopsy biomarker discovery platform based on next-generation nanoparticle-enabled low-abundance protein enrichment coupled with mass spectrometryn-LAPE/MSTMDrawing on years of accumulated expertise in mass spectrometry, ProteinT has successfully developed n-LAPE/MSTM, a novel liquid biopsy biomarker discovery platform built on next-generation nanoparticle-enabled low-abundance protein enrichment coupled with mass spectrometry, with fully independent intellectual property rights. The company has built a library of dozens of self-synthesized, surface-modified nanomaterials that enable specific and efficient enrichment of low-abundance proteins in body fluids. Using only minute sample volumes, the platform effectively removes high-abundance proteins and enriches low-abundance ones, markedly increasing both the number of proteins identified and the quantitative signal intensity in proteomic analysis. It can process 96 samples within five hours; combined with the latest mass spectrometry platforms, more than 8,000 proteins can be detected per sample within an effective 30-minute gradient. This breaks through the bottlenecks of body fluid proteomics research and can be widely applied across clinical research and exploration in cancer, cardiovascular and cerebrovascular disease, drug target discovery and beyond, driving innovative discoveries for body fluid proteomics in liquid biopsy.

The wind of AI has finally reached the healthcare industry.Healthcare has always been a large and highly typical industry scenario in urgent need of AI support. AI technologies represented by large models are now converging deeply with biopharmaceuticals and medical diagnosis and treatment, setting off a new wave of artificial intelligence. This article offers an in-depth reading of the 2023 Healthcare AI Large Model Industry Research Report for our readers.Healthcare AI large models are a branch of artificial intelligence in medicine. By learning from vast amounts of data, they generate new data instances and show strong application potential in medical imaging, drug discovery, assisted prevention and medical text analysis. They can improve both the quality and the accessibility of medical services while driving innovation across the entire healthcare industry.Development history1In 1956, the concept of ＂artificial intelligence＂ was first put forward. As mathematical tools such as Bayes＇ theorem and Boolean algebra were applied in medicine, an era of machine learning driven by statistical algorithms and prior knowledge began. The growth of clinical knowledge bases, electronic medical records and AI+imaging, together with the establishment of systematic standard datasets, pushed China＇s medical AI into the era of deep learning under a data-driven model, shifting from single-modality data to multimodal data fusion. After 2017 it evolved into the era of large models built on the Transformer framework and incorporating new algorithms. In recent years, major universities have launched research and development of healthcare AI large models, providing a solid technical foundation for the industry and helping China＇s AI+healthcare move into a stage of stable development.Clinical application scenarios Application scenarios for healthcare AI large modelsMedical imagingOffering differential diagnosis for patients and diagnostic suggestions for physiciansUsing image recognition and deep learning, intelligent image recognition helps physicians quickly extract the disease information presented in an image, improving diagnostic accuracy and efficiency, while providing clinical decision support and helping physicians formulate appropriate treatment and follow-up plans.Drug discoveryRaising the probability of success and lowering development costsAI large models extract relevant information from scientific publications, linking genes, diseases, compounds and biological processes, and deeply analyzing disease molecular mechanisms and biological activity in the disease context to support drug target discovery. AI drug screening models can rapidly and accurately mine and screen suitable compounds, using computer simulation to predict drug activity and safety, improving the efficiency of drug development, reducing costs, and enhancing accuracy and personalized treatment.Assisted preventionApplication to disease prevention holds enormous potentialPredicting an individual＇s risk of chronic diseases such as diabetes and heart disease based on factors including genetics, lifestyle habits and environment.Medical text processingDelivering accurate, fast and personalized diagnosis and recommendationsBuilt on AI large models, medical text can be processed quickly and accurately: automating the handling of clinical records and building personalized health management plans; parsing medical literature to support medical research; helping convert clinical services into standardized medical billing codes, improving billing efficiency and facilitating operations management and decision support; and enabling intelligent forecasting of epidemics, reducing national disease prevention and control costs.Analysis of test resultsAs healthcare AI large models attract widespread attention, a growing number of model testing studies have emerged. In an examination of 10 sets of images by the Royal College of Radiologists, the overall accuracy of AI was 79.5%, compared with 84.8% for radiologists. GPT-4V achieved 62% accuracy on the United States Medical Licensing Examination. Among the seven AI models tested against China＇s medical licensing examination, six platforms scored above 60%, even surpassing the general level of real candidates (whose overall written examination pass rate is 20%–25%). The tests also exposed the shortcomings of AI large models: when faced with non-standardized questions requiring comprehension or logical analysis — such as questions with images, scenario-based questions or ＂select the incorrect answer＂ questions — AI large models show relatively high error rates, lacking physicians＇ deep contextual understanding, flexible adaptability and interpersonal communication skills, particularly in traditional Chinese medicine diagnosis and treatment settings.Future development and challengesAccording to forecasts by EqualOcean, 2023–2027 will be a period of concentrated expansion for healthcare AI large models, for the following reasons:01 Improved R&D efficiencyAI models can automatically generate content, code and data analysis reports, supporting the rapid development and testing of R&D models;02 More accessible API servicesImproving the accessibility of AI large models and allowing non-technical medical professionals to integrate systems with ease;03Large pre-trained models ready for direct useDevelopers can fine-tune or optimize models for specific application scenarios without training from scratch, lowering the cost of data collection and computing resources.Market size and growth rate of China＇s healthcare AI large model products, 2023–2030Healthcare AI large models show broad promise. They can improve the efficiency and quality of medical services and bring patients better treatment and prevention options. Yet along the path to commercialization, beyond the technical layer, they also face multiple challenges including data privacy, intellectual property, safety and ethics. To overcome these challenges, in addition to strengthening technological R&D, it is necessary to introduce relevant legislation, establish standardized evaluation processes, data management and privacy protection mechanisms, and reinforce ethical norms and regulatory oversight.