A semantic feature enhanced YOLOv5-based network for polyp detection from colonoscopy images.

Colorectal cancer (CRC) is a common digestive system tumor with high morbidity and mortality worldwide. At present, the use of computer-assisted colonoscopy technology to detect polyps is relatively mature, but it still faces some challenges, such as missed or false detection of polyps. Therefore, how to improve the detection rate of polyps more accurately is the key to colonoscopy. To solve this problem, this paper proposes an improved YOLOv5-based cancer polyp detection method for colorectal cancer. The method is designed with a new structure called P-C3 incorporated into the backbone and neck network of the model to enhance the expression of features. In addition, a contextual feature augmentation module was introduced to the bottom of the backbone network to increase the receptive field for multi-scale feature information and to focus on polyp features by coordinate attention mechanism. The experimental results show that compared with some traditional target detection algorithms, the model proposed in this paper has significant advantages for the detection accuracy of polyp, especially in the recall rate, which largely solves the problem of missed detection of polyps. This study will contribute to improve the polyp/adenoma detection rate of endoscopists in the process of colonoscopy, and also has important significance for the development of clinical work.

Analysis of the correlation between the systemic inflammatory response index and the severity of coronary vasculopathy.

This study aims to analyze the correlation between Systemic Inflammatory Response Index (SIRI) and the severity of coronary artery stenosis in patients with coronary heart disease (CHD). It also aims to assess the predictive value of SIRI for the severity of coronary artery stenosis. A total of 2990 patients who underwent coronary angiography were included in this study. The Gensini score was used to estimate the severity of coronary vascular lesions. The predictive ability of SIRI for CHD was evaluated using receiver operating characteristic (ROC) curves. Binary multivariate logistic regression analysis was used to predict the likelihood of CHD based on the SIRI index. The results showed that people with higher SIRI index were more likely to have CHD (P < 0.001). After controlling for other risk factors, the highest quartile had a significantly higher incidence of coronary artery disease compared to the lowest quartile (odds ratio [OR] 2.25, 95% confidence interval [CI] 1.73-3.92, P < 0.001). Furthermore, the Gensini score was significantly higher in the fourth quartile group (T4) compared to the first (T1) and second (T2) quartile groups (P < 0.001). Additionally, the SIRI index was significantly higher in the group with severe coronary artery lesions compared to the mild and moderate groups (P < 0.001). The SIRI index also showed a higher predictive ability for the extent of coronary lesions under the ROC curve compared to other commonly used markers, including platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), and neutrophil-to-lymphocyte ratio (NLR) (P < 0.001). Therefore, SIRI index positively correlates with coronary artery stenosis in CHD patients, serving as an effective early screening marker for assessing stenosis severity.

Diagnosis of epilepsy by machine learning of high-performance plasma metabolic fingerprinting.

Epilepsy is a chronic neurological disorder that causes a major threat to public health and the burden of disease worldwide. High-performance diagnostic tools for epilepsy need to be developed to improve diagnostic accuracy and efficiency while still missing. Herein, we utilized nanoparticle-enhanced laser desorption/ionization mass spectrometry (NELDI MS) to acquire plasma metabolic fingerprints (PMFs) from epileptic and healthy individuals for timely and accurate screening of epilepsy. The NELDI MS enabled high detection speed (∼30 s per sample), high throughput (up to 384 samples per run), and favorable reproducibility (coefficients of variation <15 %), acquiring high-performed PMFs. We next constructed an epilepsy diagnostic model by machine learning of PMFs, achieving desirable diagnostic capability with the area under the curve (AUC) value of 0.941 for the validation set. Furthermore, four metabolites were identified as a diagnostic biomarker panel for epilepsy, with an AUC value of 0.812-0.860. Our approach provides a high-performed and high-throughput platform for epileptic diagnostics, promoting the development of metabolic diagnostic tools in precision medicine.

Vacancy-Driven High-Performance Metabolic Assay for Diagnosis and Therapeutic Evaluation of Depression.

Depression is one of the most common mental illnesses and is a well-known risk factor for suicide, characterized by low overall efficacy (<50%) and high relapse rate (40%). A rapid and objective approach for screening and prognosis of depression is highly desirable but still awaits further development. Herein, a high-performance metabolite-based assay to aid the diagnosis and therapeutic evaluation of depression by developing a vacancy-engineered cobalt oxide (Vo-Co3O4) assisted laser desorption/ionization mass spectrometer platform is presented. The easy-prepared nanoparticles with optimal vacancy achieve a considerable signal enhancement, characterized by favorable charge transfer and increased photothermal conversion. The optimized Vo-Co3O4 allows for a direct and robust record of plasma metabolic fingerprints (PMFs). Through machine learning of PMFs, high-performance depression diagnosis is achieved, with the areas under the curve (AUC) of 0.941-0.980 and an accuracy of over 92%. Furthermore, a simplified diagnostic panel for depression is established, with a desirable AUC value of 0.933. Finally, proline levels are quantified in a follow-up cohort of depressive patients, highlighting the potential of metabolite quantification in the therapeutic evaluation of depression. This work promotes the progression of advanced matrixes and brings insights into the management of depression.

A non-antibiotic erythromycin derivative improves muscle endurance by regulating endogenous anti-fatigue protein orosomucoid in mice.

At present, there are no official approved drugs for improving muscle endurance. Our previous research found acute phase protein orosomucoid (ORM) is an endogenous anti-fatigue protein, and macrolides antibiotics erythromycin can elevate ORM level to increase muscle bioenergetics and endurance parameters. Here, we further designed, synthesized and screened a new erythromycin derivative named HMS-01, which lost its antibacterial activity in vitro and in vivo. Data showed that HMS-01 could time- and dose-dependently prolong mice forced-swimming time and running time, and improve fatigue index in isolated soleus muscle. Moreover, HMS-01 treatment could increase the glycogen content, mitochondria number and function in liver and skeletal muscle, as well as ORM level in these tissues and sera. In Orm-deficient mice, the anti-fatigue and glycogen-elevation activity of HMS-01 disappeared. Therefore, HMS-01 might act as a promising small molecule drug targeting ORM to enhance muscle endurance.

Application of trauma-focused cognitive behavioral therapy among children and adolescents with childhood household dysfunction. / 创伤聚焦的认知行为疗法在童年家庭功能障碍儿童和青少年中的应用.

Childhood household dysfunction (CHD) is a common adverse childhood experience, which brings the heavy physical and mental afflictions to children and adolescents. Trauma-focused cognitive behavioral therapy (TF-CBT) is an evidence-based psychotherapy that helps children and adolescents who have experienced childhood trauma with traumatic memories. It aims to enhance the coping abilities of CHD children and adolescents, thereby improving the negative effects caused by trauma and effectively reducing psychological burden. TF-CBT can effectively improve post-traumatic stress disorder, emotional and behavioral problems, and family function in children and adolescents with CHD. It is recommended to conduct high-quality original research in the future, develop targeted TF-CBT intervention plans based on potential predictive factors, adopt a combination of online and offline methods, and construct TF-CBT interventions suitable for the Chinese CHD population to meet the mental health service needs of CHD children and adolescents.

Colorectal image analysis for polyp diagnosis.

Colorectal polyp is an important early manifestation of colorectal cancer, which is significant for the prevention of colorectal cancer. Despite timely detection and manual intervention of colorectal polyps can reduce their chances of becoming cancerous, most existing methods ignore the uncertainties and location problems of polyps, causing a degradation in detection performance. To address these problems, in this paper, we propose a novel colorectal image analysis method for polyp diagnosis via PAM-Net. Specifically, a parallel attention module is designed to enhance the analysis of colorectal polyp images for improving the certainties of polyps. In addition, our method introduces the GWD loss to enhance the accuracy of polyp diagnosis from the perspective of polyp location. Extensive experimental results demonstrate the effectiveness of the proposed method compared with the SOTA baselines. This study enhances the performance of polyp detection accuracy and contributes to polyp detection in clinical medicine.

Designing Peptide-Based Nanoinhibitors of Programmed Cell Death Ligand 1 (PD-L1) for Enhanced Chemo-immunotherapy.

The combination of immune checkpoint blockade (ICB) and chemotherapy has shown significant potential in the clinical treatment of various cancers. However, circulating regeneration of PD-L1 within tumor cells greatly limits the efficiency of chemo-immunotherapy and consequent patient response rates. Herein, we report the synthesis of a nanoparticle-based PD-L1 inhibitor (FRS) with a rational design for effective endogenous PD-L1 suppression. The nanoinhibitor is achieved through self-assembly of fluoroalkylated competitive peptides that target PD-L1 palmitoylation. The FRS nanoparticles provide efficient protection and delivery of functional peptides to the cytoplasm of tumors, showing greater inhibition of PD-L1 than nonfluorinated peptidic inhibitors. Moreover, we demonstrate that FRS synergizes with chemotherapeutic doxorubicin (DOX) to boost the antitumor activities via simultaneous reduction of PD-L1 abundance and induction of immunogenic cell death in murine colon tumor models. The nano strategy of PD-L1 regulation present in this study is expected to advance the development of ICB inhibitors and overcome the limitations of conventional ICB-assisted chemo-immunotherapy.

Deciphering the metabolic heterogeneity of hematopoietic stem cells with single-cell resolution.

Metabolic status is crucial for stem cell functions; however, the metabolic heterogeneity of endogenous stem cells has never been directly assessed. Here, we develop a platform for high-throughput single-cell metabolomics (hi-scMet) of hematopoietic stem cells (HSCs). By combining flow cytometric isolation and nanoparticle-enhanced laser desorption/ionization mass spectrometry, we routinely detected >100 features from single cells. We mapped the single-cell metabolomes of all hematopoietic cell populations and HSC subpopulations with different division times, detecting 33 features whose levels exhibited trending changes during HSC proliferation. We found progressive activation of the oxidative pentose phosphate pathway (OxiPPP) from dormant to active HSCs. Genetic or pharmacological interference with OxiPPP increased reactive oxygen species level in HSCs, reducing HSC self-renewal upon oxidative stress. Together, our work uncovers the metabolic dynamics during HSC proliferation, reveals a role of OxiPPP for HSC activation, and illustrates the utility of hi-scMet in dissecting metabolic heterogeneity of immunophenotypically defined cell populations.

Serum Metabolic Fingerprints Characterize Systemic Lupus Erythematosus.

Metabolic fingerprints in serum characterize diverse diseases for diagnostics and biomarker discovery. The identification of systemic lupus erythematosus (SLE) by serum metabolic fingerprints (SMFs) will facilitate precision medicine in SLE in an early and designed manner. Here, a discovery cohort of 731 individuals including 357 SLE patients and 374 healthy controls (HCs), and a validation cohort of 184 individuals (SLE/HC, 91/93) are constructed. Each SMF is directly recorded by nano-assisted laser desorption/ionization mass spectrometry (LDI MS) within 1 minute using 1 µL of native serum, which contains 908 mass to charge features. Sparse learning of SMFs achieves the SLE identification with sensitivity/specificity and area-under-the-curve (AUC) up to 86.0%/92.0% and 0.950 for the discovery cohort. For the independent validation cohort, it exhibits no performance loss by affording the sensitivity/specificity and AUC of 89.0%/100.0% and 0.992. Notably, a metabolic biomarker panel is screened out from the SMFs, demonstrating the unique metabolic pattern of SLE patients different from both HCs and rheumatoid arthritis patients. In conclusion, SMFs characterize SLE by revealing its unique metabolic pattern. Different regulation of small molecule metabolites contributes to the precise diagnosis of autoimmune disease and further exploration of the pathogenic mechanisms.

Designed Nanomaterials-Assisted Proteomics and Metabolomics Analysis for In Vitro Diagnosis.

In vitro diagnosis (IVD) is pivotal in modern medicine, enabling early disease detection and treatment optimization. Omics technologies, particularly proteomics and metabolomics, offer profound insights into IVD. Despite its significance, omics analyses for IVD face challenges, including low analyte concentrations and the complexity of biological environments. In addition, the direct omics analysis by mass spectrometry (MS) is often hampered by issues like large sample volume requirements and poor ionization efficiency. Through manipulating their size, surface charge, and functionalization, as well as the nanoparticle-fluid incubation conditions, nanomaterials have emerged as a promising solution to extract biomolecules and enhance the desorption/ionization efficiency in MS detection. This review delves into the last five years of nanomaterial applications in omics, focusing on their role in the enrichment, separation, and ionization analysis of proteins and metabolites for IVD. It aims to provide a comprehensive update on nanomaterial design and application in omics, highlighting their potential to revolutionize IVD.

Frailty and medication adherence among older adult patients with hypertension: a moderated mediation model.

Objective: Medication adherence has a critical impact on the well-being of older adult patients with hypertension. As such, the current study aimed to investigate the mediating role of health literacy between frailty and medication adherence and the moderating role of educational level. Methods: This cross-sectional study included patients admitted to the geriatric unit of a hospital. Participants were interviewed using the four-item Morisky Medication Adherence Scale, the Frailty Phenotype Scale, and the Health Literacy Management Scale. Spearman's correlation coefficients were used to assess the association between variables. Mediation and moderated mediation analyses were performed using Process version 4.1 via Model 4 and 14, respectively. Results: Data from 388 participants were analyzed. The median (IQR [P25-P75]) score for medication adherence was 4.00 (2.00-4.00). Results revealed that after controlling for age, sex, hypertension complication(s) and body mass index, frailty significantly contributed to medication adherence (ßtotal -0.236 [95% confidence interval (CI) -0.333 to -0.140]). Medication adherence was influenced by frailty (ßdirect -0.192 [95% CI -0.284 to -0.099]) both directly and indirectly through health literacy (ßindirect -0.044 [95% CI -0.077 to -0.014]). Educational level moderated the pathway mediated by health literacy; more specifically, the conditional indirect effect between frailty and medication adherence was significant among older adult hypertensive patients with low, intermediate, and high educational levels (effect -0.052 [95% CI -0.092 to -0.106]; effect -0.041 [95% CI -0.071 to -0.012]; effect -0.026 [95% CI -0.051 to -0.006]). The relationship between frailty and medication adherence in older adult patients with hypertension was found to have mediating and moderating effects. Conclusion: A moderated mediation model was proposed to investigate the effect of frailty on medication adherence. It was effective in strengthening medication adherence by improving health literacy and reducing frailty. More attention needs to be devoted to older adult patients with hypertension and low educational levels.

Self-assembly of alkylated lysine-dendron oxytocin amphiphiles for enhanced stability and sustained pharmacological activity.

Herein, we designed alkylated lysine-dendron oxytocin amphiphiles (ALOAs) 1G-OTK and 2G-OTK, which were self-assembled into spherical nanoparticles and nanostrips, respectively, and showed superior stability compared to native oxytocin. We found similar trends in the functional activity of ALOAs and native OT for human oxytocin receptor. This work may inspire the development of peptide drugs for clinical applications.

Case Report: A de novo NLRP3 variant resulting in autoinflammatory disease in a Chinese newborn.

Background: Cryopyrin-associated periodic syndromes (CAPS) have been considered autoinflammatory diseases resulting from NLRP3 gene mutations. In recent years, these conditions have been redefined as NLRP3-associated autoinflammatory diseases (NLRP3-AID). Our previous study highlighted a case of a Chinese individual carrying the de novo NLRP3 mutation. Results: A female child carrying a de novo variant (c.1718T>G, p. L573W) in the NLRP3 gene was presented in this work. The patient manifested various symptoms, including recurrent fever, a rash resembling urticaria, arthritis, physical growth retardation, a notable prominence of the forehead, and a flat nose bridge. Additionally, inflammatory markers, like WBC count, PLT count, CRP, ESR, and IL-6 showed elevated levels. Additionally, we observed interstitial pulmonary disease in the patient, which is not frequently mentioned in previous studies. Notably, the proband did not present with any ocular, auditory, or neurological symptoms. After 12 weeks of subcutaneous canakinumab injection, there was a clear improvement in the patient's clinical manifestations and inflammatory markers. Conclusion: Our study contributes to broadening the clinical spectrum of established pathogenic variants of NLRP3 gene, which are related to NLRP3-AID.

Astragaloside IV derivative HHQ16 ameliorates infarction-induced hypertrophy and heart failure through degradation of lncRNA4012/9456.

Reversing ventricular remodeling represents a promising treatment for the post-myocardial infarction (MI) heart failure (HF). Here, we report a novel small molecule HHQ16, an optimized derivative of astragaloside IV, which effectively reversed infarction-induced myocardial remodeling and improved cardiac function by directly acting on the cardiomyocyte to reverse hypertrophy. The effect of HHQ16 was associated with a strong inhibition of a newly discovered Egr2-affiliated transcript lnc9456 in the heart. While minimally expressed in normal mouse heart, lnc9456 was dramatically upregulated in the heart subjected to left anterior descending coronary artery ligation (LADL) and in cardiomyocytes subjected to hypertrophic stimulation. The critical role of lnc9456 in cardiomyocyte hypertrophy was confirmed by specific overexpression and knockout in vitro. A physical interaction between lnc9456 and G3BP2 increased NF-κB nuclear translocation, triggering hypertrophy-related cascades. HHQ16 physically bound to lnc9456 with a high-affinity and induced its degradation. Cardiomyocyte-specific lnc9456 overexpression induced, but knockout prevented LADL-induced, cardiac hypertrophy and dysfunction. HHQ16 reversed the effect of lnc9456 overexpression while lost its protective role when lnc9456 was deleted, further confirming lnc9456 as the bona fide target of HHQ16. We further identified the human ortholog of lnc9456, also an Egr2-affiliated transcript, lnc4012. Similarly, lnc4012 was significantly upregulated in hypertrophied failing hearts of patients with dilated cardiomyopathy. HHQ16 also specifically bound to lnc4012 and caused its degradation and antagonized its hypertrophic effects. Targeted degradation of pathological increased lnc4012/lnc9456 by small molecules might serve as a novel promising strategy to regress infarction-induced cardiac hypertrophy and HF.

The impact of risk perception and preference on farmland transfer-out: Evidence from a survey of farm households in China.

This study examines the impact of farmers' risk perceptions on their decision to transfer-out farmland, as well as the variations in this influence based on different risk preferences. The research utilizes survey data from 1389 farmers in rural China. By expanding the decision-making model for farmer households within the context of risk perception, this paper analyzes the equilibrium point of farmers' decision-making using mathematical models. Employing the Probit and Tobit models with instrumental variable methods, we discovered that farmers' perceptions of land revenue risk, land use risk, and non-farm employment risk significantly hindered the decision and rate of farmland transfer-out. Notably, the inhibitory effect decreased progressively. Moreover, risk preference was found to moderates the negative impacts. However, it is worth noting that risk perception remains a crucial factor impeding farmland transfer, primarily due to the prevalence of neutral or risk-averse risk preferences among most farmers. This study presents a novel perspective and explanatory framework for development of the farmland circulation market, aiming to overcome existing challenges from a risk-oriented standpoint. Consequently, it is essential for the government to carefully address all risks associated with farmland transfer, enhance the income guarantee system for farmers post-transfer, and offer non-farm employment training and job opportunities to facilitate the healthy and rapid development of the agricultural land transfer market.

Machine learning model of imipenem-resistant Klebsiella pneumoniae based on MALDI-TOF-MS platform: An observational study.

Background and Aim: Machine learning is an important branch and supporting technology of artificial intelligence, we established four machine learning model for the drug sensitivity of Klebsiella pneumoniae to imipenem based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and compared their diagnostic effect. Methods: The data of MALDI-TOF-MS and imipenem sensitivity of 174 cases of K. pneumoniae isolated from clinical specimens in the laboratory of microbiology department of Tianjin Haihe Hospital from 2019 January to 2020 December were collected. The mass spectrometry and imipenem sensitivity of 70 cases of imipenem-sensitive and 70 resistant cases were randomly selected to establish the training set model, 17 cases of sensitive and 17 cases of resistant cases were randomly selected to establish the test set model. Mass spectral peak data were subjected to orthogonal partial least squares discriminant analysis (OPLS-DA), the training set data model was established by machine learning least absolute shrinkage and selection operator (LASSO) algorithm, logistic regression (LR) algorithm, support vector machines (SVM) algorithm, neural network (NN) algorithm, the area under the curve (AUC) and confusion matrix of training set and test set model were calculated and selected by Grid search and 3-fold Cross-validation respectively, the accuracy of the prediction model was verified by test set confusion matrix. Results: The R²Y and Q² of OPLS-DA were 0.546 and 0.0178. The AUC of the best training set and test set models were 0.9726 and 0.9100, 1.0000 and 0.8581, 0.8462 and 0.6263, 1.0000 and 0.7180 evaluated by LASSO, LR, SVM and NN model respectively. The accuracy of the LASSO, LR, SVM and NN model were 87%, 79%, 62%, and 68% in test set, respectively. Conclusion: The LASSO prediction model of K. pneumoniae sensitivity to imipenem established in this study has a high accuracy rate and has potential clinical decision support ability.

Fully 3D-printed tortoise-like soft mobile robot with muti-scenario adaptability.

Soft robotic systems are well suited to unstructured, dynamic tasks and environments, owing to their ability to adapt and conform without damaging themselves or their surroundings. These abilities are crucial in areas such as human-robot interaction, simplification of control system and weight reduction. At present, the existing soft mobile robots still have the disadvantages of single motion mode and application scenario, difficult manufacturing and low energy conversion efficiency. Based on the current shortcomings of soft robots, this paper designs and proposes a fully 3D-printed tortoise-like soft mobile robot with muti-scenarios adaptability. The robot uses a Bionic Tortoise Leg Actuator structure that enables simultaneous bending of the actuator in both directions, simplifying robot control and increasing the maximum bending angle achievable. In addition, a reconfiguration design solution has been proposed to enable the robot to implement two bionic modes for land and sea turtles, adapting to move on hard and soft surfaces and in water, enabling it to move in amphibious and complex environments. The performance of the pneumatic soft actuator is also improved by an improved Digital Light Processing method that enhances the maximum strain of the 3D printed soft material. The prototype was tested to give maximum movement speeds for different gaits and environments, demonstrating that the fully 3D printed tortoise-like soft-mobile robot designed in this paper is highly adaptable to multiple scenarios. The robot studied in this paper has a wide range of applications, with potential applications including navigation in a variety of domain environments, inspection of large underground oil and gas pipelines, and navigation in high temperature, high humidity and strong magnetic field environments or in military alert conditions.

Immune Cell Infiltration Analysis Based on Bioinformatics Reveals Novel Biomarkers of Coronary Artery Disease.

Background: Coronary artery disease (CAD) is a multifactorial immune disease, but research into the specific immune mechanism is still needed. The present study aimed to identify novel immune-related markers of CAD. Methods: Three CAD-related datasets (GSE12288, GSE98583, GSE113079) were downloaded from the Gene Expression Integrated Database. Gene ontology annotation, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis and weighted gene co-expression network analysis were performed on the common significantly differentially expressed genes (DEGs) of these three data sets, and the most relevant module genes for CAD obtained. The immune cell infiltration of module genes was evaluated with the CIBERSORT algorithm, and characteristic genes accompanied by their diagnostic effectiveness were screened by the machine-learning algorithm least absolute shrinkage and selection operator (LASSO) regression analysis. The expression levels of characteristic genes were evaluated in the peripheral blood mononuclear cells of CAD patients and healthy controls for verification. Results: A total of 204 upregulated and 339 downregulated DEGs were identified, which were mainly enriched in the following pathways: "Apoptosis", "Th17 cell differentiation", "Th1 and Th2 cell differentiation", "Glycerolipid metabolism", and "Fat digestion and absorption". Five characteristic genes, LMAN1L, DOK4, CHFR, CEL and CCDC28A, were identified by LASSO analysis, and the results of the immune cell infiltration analysis indicated that the proportion of immune infiltrating cells (activated CD8 T cells and CD56 DIM natural killer cells) in the CAD group was lower than that in the control group. The expressions of CHFR, CEL and CCDC28A in the peripheral blood of the healthy controls and CAD patients were significantly different. Conclusion: We identified CHFR, CEL and CCDC28A as potential biomarkers related to immune infiltration in CAD based on public data sets and clinical samples. This finding will contribute to providing a potential target for early noninvasive diagnosis and immunotherapy of CAD.

Hollow multishelled heterostructures with enhanced performance for laser desorption/ionization mass spectrometry based metabolic diagnosis.

High-performance metabolic diagnosis-based laser desorption/ionization mass spectrometry (LDI-MS) improves the precision diagnosis of diseases and subsequent treatment. Inorganic matrices are promising for the detection of metabolites by LDI-MS, while the structure and component impacts of the matrices on the LDI process are still under investigation. Here, we designed a multiple-shelled ZnMn2O4/(Co, Mn)(Co, Mn)2O4 (ZMO/CMO) as the matrix from calcined MOF-on-MOF for detecting metabolites in LDI-MS and clarified the synergistic impacts of multiple-shells and the heterostructure on LDI efficiency. The ZMO/CMO heterostructure allowed 3-5 fold signal enhancement compared with ZMO and CMO with the same morphology. Furthermore, the ZMO/CMO heterostructure with a triple-shelled hollow structure displayed a 3-fold signal enhancement compared to its nanoparticle counterpart. Taken together, the triple-shelled hollow ZMO/CMO exhibits 102-fold signal enhancement compared to the commercial matrix products (e.g., DHB and DHAP), allowing for sensitive metabolic profiling in bio-detection. We directly extracted metabolic patterns by the optimized triple-shelled hollow ZMO/CMO particle-assisted LDI-MS within 1 s using 100 nL of serum and used machine learning as the readout to distinguish hepatocellular carcinoma from healthy controls with the area under the curve value of 0.984. Our approach guides us in matrix design for LDI-MS metabolic analysis and drives the development of a nanomaterial-based LDI-MS platform toward precision diagnosis.