Receptor Ligand-Free Mesoporous Silica Nanoparticles: A Streamlined Strategy for Targeted Drug Delivery across the Blood-Brain Barrier.

Mesoporous silica nanoparticles (MSNs) represent a promising avenue for targeted brain tumor therapy. However, the blood-brain barrier (BBB) often presents a formidable obstacle to efficient drug delivery. This study introduces a ligand-free PEGylated MSN variant (RMSN25-PEG-TA) with a 25 nm size and a slight positive charge, which exhibits superior BBB penetration. Utilizing two-photon imaging, RMSN25-PEG-TA particles remained in circulation for over 24 h, indicating significant traversal beyond the cerebrovascular realm. Importantly, DOX@RMSN25-PEG-TA, our MSN loaded with doxorubicin (DOX), harnessed the enhanced permeability and retention (EPR) effect to achieve a 6-fold increase in brain accumulation compared to free DOX. In vivo evaluations confirmed the potent inhibition of orthotopic glioma growth by DOX@RMSN25-PEG-TA, extending survival rates in spontaneous brain tumor models by over 28% and offering an improved biosafety profile. Advanced LC-MS/MS investigations unveiled a distinctive protein corona surrounding RMSN25-PEG-TA, suggesting proteins such as apolipoprotein E and albumin could play pivotal roles in enabling its BBB penetration. Our results underscore the potential of ligand-free MSNs in treating brain tumors, which supports the development of future drug-nanoparticle design paradigms.

Preparation of immunomagnetic composite nanostructures with bifunctional four-arm PEG derivatives as linkers for the ultrafast enrichment of zearalenone and its metabolites.

It is challenging to prepare sample pretreatment materials with simple use, strong selectivity and satisfactory enrichment performance. In this study, the antibody (3D4) that can specifically recognize zearalenone (ZEN) and its metabolites was immobilized on the surface of gold-coated magnetic Fe3O4 nanoparticles (GMN) by streptavidin (SA)-biotin interaction using GMN as the substrate and our designed four-arm PEG derivative (HS-4ARMPEG10K-(CM)3) as the linker. The immunomagnetic nanoparticles (GMN-4ARMPEG10K-SA-3D4) prepared by this strategy can achieve rapid enrichment (only 5 min) of analytes directly in the matrix, and higher enrichment capacity compared with the previous immunomagnetic particles. The sensitive and accurate analysis of ZEN and its metabolites can be achieved coupled with HPLC-MS/MS. The LODs and LOQs were 0.02-0.05 µg/kg and 0.05-0.10 µg/kg, respectively. The recoveries were 84.13%-112.67%, and the RSDs were 1.09%-9.39%. The method can provide a powerful tool for highly sensitive and rapid monitoring of mycotoxins in complex matrices due to its' strong selectivity and resistance to matrix interference.

Proteomic Analyses in Diverse Populations Improved Risk Prediction and Identified New Drug Targets for Type 2 Diabetes.

OBJECTIVE: Integrated analyses of plasma proteomics and genetic data in prospective studies can help assess the causal relevance of proteins, improve risk prediction, and discover novel protein drug targets for type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS: We measured plasma levels of 2,923 proteins using Olink Explore among ∼2,000 randomly selected participants from China Kadoorie Biobank (CKB) without prior diabetes at baseline. Cox regression assessed associations of individual protein with incident T2D (n = 92 cases). Proteomic-based risk models were developed with discrimination, calibration, reclassification assessed using area under the curve (AUC), calibration plots, and net reclassification index (NRI), respectively. Two-sample Mendelian randomization (MR) analyses using cis-protein quantitative trait loci identified in a genome-wide association study of CKB and UK Biobank for specific proteins were conducted to assess their causal relevance for T2D, along with colocalization analyses to examine shared causal variants between proteins and T2D. RESULTS: Overall, 33 proteins were significantly associated (false discovery rate < 0.05) with risk of incident T2D, including IGFBP1, GHR, and amylase. The addition of these 33 proteins to a conventional risk prediction model improved AUC from 0.77 (0.73-0.82) to 0.88 (0.85-0.91) and NRI by 38%, with predicted risks well calibrated with observed risks. MR analyses provided support for the causal relevance for T2D of ENTR1, LPL, and PON3, with replication of ENTR1 and LPL in Europeans using different genetic instruments. Moreover, colocalization analyses showed strong evidence (pH4 > 0.6) of shared genetic variants of LPL and PON3 with T2D. CONCLUSIONS: Proteomic analyses in Chinese adults identified novel associations of multiple proteins with T2D with strong genetic evidence supporting their causal relevance and potential as novel drug targets for prevention and treatment of T2D.

Dietary factors and patterns in relation to risk of later-onset ulcerative colitis in Chinese: A prospective study of 0.5 million people.

BACKGROUND: There is limited evidence on the associations of dietary factors and patterns with risk of later-onset ulcerative colitis (UC) in Chinese adults. AIMS: To investigate the associations of dietary factors and patterns with risk of later-onset UC in Chinese. METHODS: The prospective China Kadoorie Biobank cohort study recruited 512,726 participants aged 30-79. Dietary habits were assessed using food frequency questionnaires. Dietary patterns were derived by factor analysis with a principal component method. Cox regression analysis was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). RESULTS: During a median follow-up of 12.1 years, 312 cases of newly diagnosed UC were documented (median age of diagnosis 60.1 years). Egg consumption was associated with higher risk of UC (HR for daily vs. never or rarely: 2.29 [95% CI: 1.26-4.16]), while spicy food consumption was inversely associated with risk of UC (HR: 0.63 [0.45-0.88]). The traditional northern dietary pattern, characterised by high intake of wheat and low intake of rice, was associated with higher risk of UC (HR for highest vs. lowest quartile of score: 2.79 [1.93-4.05]). The modern dietary pattern, characterised by high intake of animal-origin foods and fruits, was associated with higher risk of UC (HR: 2.48 [1.63-3.78]). Population attributable fraction was 13.04% (7.71%-19.11%) for daily/almost daily consumption of eggs and 9.87% (1.94%-18.22%) for never/rarely consumption of spicy food. CONCLUSIONS: The findings highlight the importance of evaluating dietary factors and patterns in the primary prevention of later-onset UC in Chinese adults.

The palliative care needs and experiences of patients with advanced Parkinson's disease: a qualitative scoping review.

Aim: To determine the experiences and needs of palliative care in patients with advanced Parkinson's disease (PD). Methods: A scoping literature review methodology, as described by the Joanna Briggs Institute, was employed to search for relevant literature. An electronic search of studies published in English was conducted across five databases from inception to 10 September 2023. Results: The search yielded a total of 1,205 articles, with 20 meeting the inclusion criteria. The findings were organized into four themes: (1) unmet emotional and informational needs; (2) needs for effective coordination of care; (3) planning for the future; and (4) symptom management. This scoping review highlights the intricate nature of palliative care for patients with PD and sheds light on issues within current palliative care healthcare systems. The findings emphasize the necessity for individualized interventions and services to address the diverse unmet palliative care needs of people with PD. Conclusion: The study reveals the complex landscape of palliative care for individuals with advanced PD, emphasizing the inadequacies within existing healthcare systems. The identified themes underscore the importance of tailored interventions to address the varied unmet palliative care needs of this population.

MRUNet-3D: A multi-stride residual 3D UNet for lung nodule segmentation.

Obtaining an accurate segmentation of the pulmonary nodules in computed tomography (CT) images is challenging. This is due to: (1) the heterogeneous nature of the lung nodules; (2) comparable visual characteristics between the nodules and their surroundings. A robust multi-scale feature extraction mechanism that can effectively obtain multi-scale representations at a granular level can improve segmentation accuracy. As the most commonly used network in lung nodule segmentation, UNet, its variants, and other image segmentation methods lack this robust feature extraction mechanism. In this study, we propose a multi-stride residual 3D UNet (MRUNet-3D) to improve the segmentation accuracy of lung nodules in CT images. It incorporates a multi-slide Res2Net block (MSR), which replaces the simple sequence of convolution layers in each encoder stage to effectively extract multi-scale features at a granular level from different receptive fields and resolutions while conserving the strengths of 3D UNet. The proposed method has been extensively evaluated on the publicly available LUNA16 dataset. Experimental results show that it achieves competitive segmentation performance with an average dice similarity coefficient of 83.47 % and an average surface distance of 0.35 mm on the dataset. More notably, our method has proven to be robust to the heterogeneity of lung nodules. It has also proven to perform better at segmenting small lung nodules. Ablation studies have shown that the proposed MSR and RFIA modules are fundamental to improving the performance of the proposed model.

DeepKEGG: a multi-omics data integration framework with biological insights for cancer recurrence prediction and biomarker discovery.

Deep learning-based multi-omics data integration methods have the capability to reveal the mechanisms of cancer development, discover cancer biomarkers and identify pathogenic targets. However, current methods ignore the potential correlations between samples in integrating multi-omics data. In addition, providing accurate biological explanations still poses significant challenges due to the complexity of deep learning models. Therefore, there is an urgent need for a deep learning-based multi-omics integration method to explore the potential correlations between samples and provide model interpretability. Herein, we propose a novel interpretable multi-omics data integration method (DeepKEGG) for cancer recurrence prediction and biomarker discovery. In DeepKEGG, a biological hierarchical module is designed for local connections of neuron nodes and model interpretability based on the biological relationship between genes/miRNAs and pathways. In addition, a pathway self-attention module is constructed to explore the correlation between different samples and generate the potential pathway feature representation for enhancing the prediction performance of the model. Lastly, an attribution-based feature importance calculation method is utilized to discover biomarkers related to cancer recurrence and provide a biological interpretation of the model. Experimental results demonstrate that DeepKEGG outperforms other state-of-the-art methods in 5-fold cross validation. Furthermore, case studies also indicate that DeepKEGG serves as an effective tool for biomarker discovery. The code is available at https://github.com/lanbiolab/DeepKEGG.

LGCDA: Predicting CircRNA-Disease Association Based on Fusion of Local and Global Features.

CircRNA has been shown to be involved in the occurrence of many diseases. Several computational frameworks have been proposed to identify circRNA-disease associations. Despite the existing computational methods have obtained considerable successes, these methods still require to be improved as their performance may degrade due to the sparsity of the data and the problem of memory overflow. We develop a novel computational framework called LGCDA to predict circRNA-disease associations by fusing local and global features to solve the above mentioned problems. First, we construct closed local subgraphs by using k-hop closed subgraph and label the subgraphs to obtain rich graph pattern information. Then, the local features are extracted by using graph neural network (GNN). In addition, we fuse Gaussian interaction profile (GIP) kernel and cosine similarity to obtain global features. Finally, the score of circRNA-disease associations is predicted by using the multilayer perceptron (MLP) based on local and global features. We perform five- fold cross validation on five datasets for model evaluation and our model surpasses other advanced methods. The code is available at https://github.com/lanbiolab/LGCDA.

Deep Imputation Bi-stochastic Graph Regularized Matrix Factorization for Clustering Single-cell RNA-sequencing Data.

By generating massive gene transcriptome data and analyzing transcriptomic variations at the cell level, single-cell RNA-sequencing (scRNA-seq) technology has provided new way to explore cellular heterogeneity and functionality. Clustering scRNA-seq data could discover the hidden diversity and complexity of cell populations, which can aid to the identification of the disease mechanisms and biomarkers. In this paper, a novel method (DSINMF) is presented for single cell RNA sequencing data by using deep matrix factorization. Our proposed method comprises four steps: first, the feature selection is utilized to remove irrelevant features. Then, the dropout imputation is used to handle missing value problem. Further, the dimension reduction is employed to preserve data characteristics and reduce noise effects. Finally, the deep matrix factorization with bi-stochastic graph regularization is used to obtain cluster results from scRNA-seq data. We compare DSINMF with other state-of-the-art algorithms on nine datasets and the results show our method outperformances than other methods.

Dual-Track Multifunctional Bimetallic Metal-Organic Frameworks for Antibiotic Enrichment and Detection.

The improper use and overuse of antibiotics have led to significant burdens and detrimental effects on the environment, food supply, and human health. Herein, a magnetic solid-phase extraction program and an optical immunosensor based on bimetallic Ce/Zr-UiO 66 for the detection of antibiotics are developed. A magnetic Fe3O4@SiO2@Ce/Zr-UiO 66 metal-organic framework (MOF) is prepared to extract and enrich chloramphenicol from fish, wastewater, and urine samples, and a horseradish peroxidase (HRP)-Ce/Zr-UiO 66@bovine serum protein-chloramphenicol probe is used for the sensitive detection of chloramphenicol based on the dual-effect catalysis of Ce and HRP. In this manner, the application of Ce/Zr-UiO 66 in integrating sample pretreatment and antibiotic detection is systematically investigated and the associated mechanisms are explored. It is concluded that Ce/Zr-UiO 66 is a versatile dual-track material exhibiting high enrichment efficiency (6.37 mg g-1) and high sensitivity (limit of detection of 51.3 pg mL-1) for chloramphenicol detection and serving as a multifunctional MOF for safeguarding public health and hygiene.

Overcoming the Blood-Brain Tumor Barrier with Docetaxel-Loaded Mesoporous Silica Nanoparticles for Treatment of Temozolomide-Resistant Glioblastoma.

While temozolomide (TMZ) has been a cornerstone in the treatment of newly diagnosed glioblastoma (GBM), a significant challenge has been the emergence of resistance to TMZ, which compromises its clinical benefits. Additionally, the nonspecificity of TMZ can lead to detrimental side effects. Although TMZ is capable of penetrating the blood-brain barrier (BBB), our research addresses the need for targeted therapy to circumvent resistance mechanisms and reduce off-target effects. This study introduces the use of PEGylated mesoporous silica nanoparticles (MSN) with octyl group modifications (C8-MSN) as a nanocarrier system for the delivery of docetaxel (DTX), providing a novel approach for treating TMZ-resistant GBM. Our findings reveal that C8-MSN is biocompatible in vitro, and DTX@C8-MSN shows no hemolytic activity at therapeutic concentrations, maintaining efficacy against GBM cells. Crucially, in vivo imaging demonstrates preferential accumulation of C8-MSN within the tumor region, suggesting enhanced permeability across the blood-brain tumor barrier (BBTB). When administered to orthotopic glioma mouse models, DTX@C8-MSN notably prolongs survival by over 50%, significantly reduces tumor volume, and decreases side effects compared to free DTX, indicating a targeted and effective approach to treatment. The apoptotic pathways activated by DTX@C8-MSN, evidenced by the increased levels of cleaved caspase-3 and PARP, point to a potent therapeutic mechanism. Collectively, the results advocate DTX@C8-MSN as a promising candidate for targeted therapy in TMZ-resistant GBM, optimizing drug delivery and bioavailability to overcome current therapeutic limitations.

Predictive value of 8-year blood pressure measures in intracerebral hemorrhage risk over 5 years.

AIMS: The relationships between long-term blood pressure (BP) measures and intracerebral hemorrhage (ICH), as well as their predictive ability on ICH, were unclear. We aimed to investigate the independent associations of multiple BP measures with subsequent 5-year ICH risk, as well as the incremental value of these measures over a single-point BP measurement in ICH risk prediction. METHODS: We included 12,398 participants from the China Kadoorie Biobank (CKB) who completed three surveys every four to five years. The following long-term BP measures were calculated: mean, minimum, maximum, standard deviation, coefficient of variation, average real variability, and cumulative BP exposure (cumBP). Cox proportional hazard models were used to examine the associations between these measures and ICH. The potential incremental value of these measures in ICH risk prediction was assessed using Harrell's C statistics, continuous net reclassification improvement (cNRI), and relative integrated discrimination improvement (rIDI). RESULTS: The hazard ratios (95% confidence intervals) of incident ICH associated with per SD increase in cumSBP and cumDBP were 1.62 (1.25, 2.10) and 1.59 (1.23, 2.07), respectively. When cumBP was added to the conventional 5-year ICH risk prediction model, the C-statistic change was 0.009 (-0.001, 0.019), the cNRI was 0.267 (0.070, 0.464), and the rIDI was 18.2% (5.8%, 30.7%). Further subgroup analyses revealed a consistent increase in cNRI and rIDI in men, rural residents, and participants without diabetes. Other long-term BP measures showed no statistically significant associations with incident ICH and generally did not improve model performance. CONCLUSION: The nearly 10-year cumBP was positively associated with an increased 5-year risk of ICH and could significantly improve risk reclassification for the ICH risk prediction model that included single-point BP measurement.

This prospective cohort study of Chinese adults investigated the independent associations of multiple blood pressure (BP) measures with subsequent 5-year intracerebral hemorrhage (ICH) risk, as well as the incremental value of these measures over a single-point BP measurement in ICH risk prediction. The cumulative BP exposure (cumBP) was positively associated with subsequent 5-year risk of ICH, independent of the recent single-point SBP and DBP levels.The cumBP could improve the risk reclassification of the conventional 5-year ICH risk prediction model that included single-point BP measurement for all participants, as well as for men, rural residents, and participants without diabetes.

Correlates and consequences of atrial fibrillation in a prospective study of 25 000 participants in the China Kadoorie Biobank.

Aims: The prevalence of atrial fibrillation (AF) is positively correlated with prior cardiovascular diseases (CVD) and CVD risk factors but is lower in Chinese than Europeans despite their higher burden of CVD. We examined the prevalence and prognosis of AF and other electrocardiogram (ECG) abnormalities in the China Kadoorie Biobank. Methods and results: A random sample of 25 239 adults (mean age 59.5 years, 62% women) had a 12-lead ECG recorded and interpreted using a Mortara VERITAS™ algorithm in 2013-14. Participants were followed up for 5 years for incident stroke, ischaemic heart disease, heart failure (HF), and all CVD, overall and by CHA2DS2-VASc scores, age, sex, and area. Overall, 1.2% had AF, 13.6% had left ventricular hypertrophy (LVH), and 28.1% had ischaemia (two-thirds of AF cases also had ischaemia or LVH). The prevalence of AF increased with age, prior CVD, and levels of CHA2DS2-VASc scores (0.5%, 1.3%, 2.1%, 2.9%, and 4.4% for scores <2, 2, 3, 4, and ≥5, respectively). Atrial fibrillation was associated with two-fold higher hazard ratios (HR) for CVD (2.15; 95% CI, 1.71-2.69) and stroke (1.88; 1.44-2.47) and a four-fold higher HR for HF (3.79; 2.21-6.49). The 5-year cumulative incidence of CVD was comparable for AF, prior CVD, and CHA2DS2-VASc scores ≥ 2 (36.7% vs. 36.2% vs. 37.7%, respectively) but was two-fold greater than for ischaemia (19.4%), LVH (18.0%), or normal ECG (14.1%), respectively. Conclusion: The findings highlight the importance of screening for AF together with estimation of CHA2DS2-VASc scores for prevention of CVD in Chinese adults.

A metal-organic framework signaling probe-mediated immunosensor for the economical and rapid determination of enrofloxacin in milk.

Ensuring the safety of animal-derived foods requires the reliable and swift identification of enrofloxacin residues to monitor the presence of antibiotics. In this regard, we synthesized, tuned, and investigated the optical properties of a bimetallic metal-organic framework (Ce/Zr-UiO 66). The investigation was facilitated by employing a polydopamine-coated pipette tip with high adsorption efficiency, serving as an immunoreactive carrier. Subsequently, an immunofunctionalized variant of Ce/Zr-UiO 66, referred to as Ce/Zr-UiO 66@ Bovine serum albumin-enrofloxacin, was developed as an optical probe for the rapid and sensitive identification of enrofloxacin across a variety of samples. The method can accurately detect enrofloxacin at concentrations as low as 0.12 ng/mL, with a determination time of under 15 min; furthermore, it demonstrates exceptional efficacy when applied to food, environmental, and clinical samples. The implementation of this methodology offers a valuable means for cost-effective, rapid, and on-site enrofloxacin determination.

Segregation of morphogenetic regulatory function of Shox2 from its cell fate guardian role in sinoatrial node development.

Shox2 plays a vital role in the morphogenesis and physiological function of the sinoatrial node (SAN), the primary cardiac pacemaker, manifested by the formation of a hypoplastic SAN and failed differentiation of pacemaker cells in Shox2 mutants. Shox2 and Nkx2-5 are co-expressed in the developing SAN and regulate the fate of the pacemaker cells through a Shox2-Nkx2-5 antagonistic mechanism. Here we show that simultaneous inactivation of Nkx2-5 in the SAN of Shox2 mutants (dKO) rescued the pacemaking cell fate but not the hypoplastic defects, indicating uncoupling of SAN cell fate determination and morphogenesis. Single-cell RNA-seq revealed that the presumptive SAN cells of Shox2-/- mutants failed to activate pacemaking program but remained in a progenitor state preceding working myocardium, while both wildtype and dKO SAN cells displayed normal pacemaking cell fate with similar cellular state. Shox2 thus acts as a safeguard but not a determinant to ensure the pacemaking cell fate through the Shox2-Nkx2-5 antagonistic mechanism, which is segregated from its morphogenetic regulatory function in SAN development.

Multilayered Fe3O4@(ZIF-8)3 combined with a computer-vision-enhanced immunosensor for chloramphenicol enrichment and detection.

The misuse and overuse of chloramphenicol poses severe threats to food safety and human health. In this work, we developed a magnetic solid-phase extraction (MSPE) pretreatment material coated with a multilayered metal-organic framework (MOF), Fe3O4 @ (ZIF-8)3, for the separation and enrichment of chloramphenicol from fish. Furthermore, we designed an artificial-intelligence-enhanced single microsphere immunosensor. The inherent ultra-high porosity of the MOF and the multilayer assembly strategy allowed for efficient chloramphenicol enrichment (4.51 mg/g within 20 min). Notably, Fe3O4 @ (ZIF-8)3 exhibits a 39.20% increase in adsorption capacity compared to Fe3O4 @ZIF-8. Leveraging the remarkable decoding abilities of artificial intelligence, we achieved the highly sensitive detection of chloramphenicol using a straightforward procedure without the need for specialized equipment, obtaining a notably low detection limit of 46.42 pM. Furthermore, the assay was successfully employed to detect chloramphenicol in fish samples with high accuracy. The developed immunosensor offers a robust point-of-care testing tool for safeguarding food safety and public health.

Click Reaction-Mediated Fluorescent Immunosensor Based on Cu-MOF Nanoparticles for Ultrasensitive and High-Throughput Detection of Aflatoxin B1 in Food Samples.

Due to the high toxicity of aflatoxin B1 and its risks to human health, we developed a click reaction-mediated automated fluorescent immunosensor (CAFI) for sensitive detection of aflatoxin B1 based on the Cu(I)-catalyzed click reaction. With its large specific surface area, a copper-based metal-organic framework (Cu-MOF) was synthesized to adsorb and enrich the copper ion (Cu(II)) and then load the complete antigen (BSA-AFB1). After the immunoreaction, Cu(II) inside the Cu-MOF-Antigen conjugate would be reduced to Cu(I) in the presence of sodium ascorbate, which triggered the click reaction between the fluorescent donor-modified DNA and the receptor-modified complementary DNA to lead to a fluorescence signal readout. The whole reaction steps were finished by the self-developed automated immunoreaction device. This CAFI method showed a limit of detection (LOD) of 0.48 pg/mL as well as a 670-fold enhancement in sensitivity compared to conventional ELISA, revealing its great potential in practical applications and automated detection.

Association between social capital and quality of life in older adults with subjective cognitive decline: A cross-sectional study.

BACKGROUND: Subjective cognitive decline (SCD) is increasingly recognized as a clinical and medical risk factor for mild cognitive impairment (MCI) and dementia. Currently, there is little evidence regarding the quality of life (QoL) in older adults with SCD and the impact of social capital on their QoL. AIMS: To examine the perceptions of social capital and QoL among older adults with SCD. METHODS: A total of 325 participants (92.9 % response rate) with a self-reported diagnosis of SCD completed the Chinese version of the 36-item Short-Form Health Survey, the Chinese Shortened Social Capital Scale and the Generalized Anxiety Disorder Scale. A t-test was used to compare the QoL score of our sample with the Chinese norm. Pearson correlation analysis and multivariate linear regression analysis were used to assess the association of social capital with QoL. RESULTS: Social capital were strongly correlated with the total QoL, as well as its physical component summary and mental component summary. The QoL score of older adults with SCD was significantly lower than the Chinese norm (P < 0.001). Multivariate analysis showed that social capital, physical activity, nutrition and anxiety symptoms were factors associated with QoL among older SCD population (P < 0.05). CONCLUSION: The findings of the current study suggest that older adults with SCD may experience lower QoL. Social capital is associated with the QoL in older adults with SCD. These findings have implications for clinicians who work with older adults with SCD.

A Wash-Free Spheres-on-Sphere Strategy for On-Site and Multiplexed Biosensing.

Respiratory infections and food contaminants pose severe challenges to global health and the economy. A rapid on-site platform for the simultaneous detection of multiple pathogens is crucial for accurate diagnosis, appropriate treatment, and a reduced healthcare burden. Herein, we present a spheres-on-sphere (SOS) platform for multiplexed detection using a portable Coulter counter, which employs millimeter- and micron-sized spheres coupled with antibodies as multitarget probes. The assay allows for quantitative detection of multiple analytes within 20 min by simple mixing, enabling on-site detection. The platform shows high accuracy in identifying three respiratory viruses (SARS-CoV-2, influenza A virus, and parainfluenza virus) from throat swab samples, with LOD of 50.7, 32.4, and 49.1 pg/mL. It also demonstrates excellent performance in quantifying three mycotoxins (aflatoxin B1, deoxynivalenol, and ochratoxin A) from food samples. The SOS platform offers a rapid on-site approach with high sensitivity and specificity for applications in resource-limited settings.