Zirconia Hybrid Nanoshells for Nutrient and Toxin Detection

Early Diagnosis and Prognosis Prediction of Pancreatic Cancer Using Engineered Hybrid Core-Shells in Laser Desorption/Ionization Mass Spectrometry

Effective detection of bio-molecules relies on the precise design and preparation of materials, particularly in laser desorption/ionization mass spectrometry (LDI-MS). Despite significant advancements in substrate materials, the performance of single-structured substrates remains suboptimal for LDI-MS analysis of complex systems. Herein, designer Au@SiO2@ZrO2 core-shell substrates are developed for LDI-MS-based early diagnosis and prognosis of pancreatic cancer (PC). Through controlling Au core size and ZrO2 shell crystallization, signal amplification of metabolites up to 3 orders is not only achieved, but also the synergistic mechanism of the LDI process is revealed. The optimized Au@SiO2@ZrO2 enables a direct record of serum metabolic fingerprints (SMFs) by LDI-MS. Subsequently, SMFs are employed to distinguish early PC (stage I/II) from controls, with an accuracy of 92%. Moreover, a prognostic prediction scoring system is established with enhanced efficacy in predicting PC survival compared to CA19-9 (p < 0.05). This work contributes to material-based cancer diagnosis and prognosis.

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.

Ferric particle-assisted LDI-MS platform for metabolic fingerprinting of diabetic retinopathy

OBJECTIVES

To explore the metabolic fingerprints of diabetic retinopathy (DR) in individuals with type 2 diabetes using a newly-developed laser desorption/ionization mass spectrometry (LDI-MS) platform assisted by ferric particles.

METHODS

Metabolic fingerprinting was performed using a ferric particle-assisted LDI-MS platform. A nested population-based case-control study was performed on 216 DR cases and 216 control individuals with type 2 diabetes.

RESULTS

DR cases and control individuals with type 2 diabetes were comparable for a list of clinical factors. The newly-developed LDI-MS platform allowed us to draw the blueprint of plasma metabolic fingerprints from participants with and without DR. The neural network afforded diagnostic performance with an average area under curve value of 0.928 for discovery cohort and 0.905 for validation cohort (95 % confidence interval: 0.902-0.954 and 0.845-0.965, respectively). Tandem MS and Fourier transform ion cyclotron resonance MS with ultrahigh resolution identified seven specific metabolites that were significantly associated with DR in fully adjusted models. Of these metabolites, dihydrobiopterin, phosphoserine, N-arachidonoylglycine, and 3-methylhistamine levels in plasma were first reported to show the associations.

CONCLUSIONS

This work advances the design of metabolic analysis for DR and holds the potential to promise as an efficient tool for clinical management of DR.

Plasmonic alloys for quantitative determination and reaction monitoring of biothiols

Biothiols participate in numerous physiological and pathological processes in an organism. Quantitative determination and reaction monitoring of biothiols have important implications for evaluating human health. Herein, we synthesized plasmonic alloys as the matrix to assist the laser desorption and ionization (LDI) process of biothiols in mass spectrometry (MS). Plasmonic alloys were constructed with mesoporous structures for LDI enhancement and trimetallic (PdPtAu) compositions for noble metal-thiol hybridization, toward enhanced detection sensitivity and selectivity, respectively. Plasmonic alloys enabled direct detection of biothiols from complex biosamples without any enrichment or separation. We introduced internal standards into the quantitative MS system, achieving accurate quantitation of methionine directly from serum samples with a recovery rate of 103.19% ± 6.52%. Moreover, we established a rapid monitoring platform for the oxidation-reduction reaction of glutathione, consuming trace samples down to 200 nL with an interval of seconds. This work contributes to the development of molecular tools based on plasmonic materials for biothiol detection toward real-case applications.

Efficient and easily scaled-up biosorbent based on natural and chemically modified macauba (Acrocomia aculeata) to remove Al3+, Mn2+ and Fe3+ from surface water contaminated with iron mining tailings

The ruptures of tailings mine dams in the cities of Mariana and Brumadinho contaminated local Brazilian Rivers with toxic metals. Herein, we describe a scaled-up biosorbent based on natural macauba endocarp (NTE) and macauba endocarp chemically treated (TE) to remove Al³⁺, Mn²⁺ and Fe³⁺ from aqueous solutions. For the TE material: the variation of pH and temperature of water did not cause significant sorption interferences; the kinetics studies suggest a pseudo-second-order model; the adsorption isotherms revealed that the Langmuir equation was the best fit for Al³⁺ and Mn²⁺, while the Freundlich equation best described the Fe³⁺; and the maximum adsorption capacities were between 0.268 mg g^-1 and 1.379 mg g^-1. A scaled-up was carried out using an adsorption column to remove the metals from Rio Paraopeba River water samples and the results showed that both NTE and TE are potentially low cost biosorbents for removing Al³⁺, Mn²⁺ and Fe³⁺.

Deep Learning of Dual Plasma Fingerprints for High-Performance Infection Classification

Infection classification is the key for choosing the proper treatment plans. Early determination of the causative agents is critical for disease control. Host responses analysis can detect variform and sensitive host inflammatory responses to ascertain the presence and type of the infection. However, traditional host-derived inflammatory indicators are insufficient for clinical infection classification. Fingerprints-based omic analysis has attracted increasing attention globally for analyzing the complex host systemic immune response. A single type of fingerprints is not applicable for infection classification (area under curve (AUC) of 0.550-0.617). Herein, an infection classification platform based on deep learning of dual plasma fingerprints (DPFs-DL) is developed. The DPFs with high reproducibility (coefficient of variation <15%) are obtained at low sample consumption (550 nL native plasma) using inorganic nanoparticle and organic matrix assisted laser desorption/ionization mass spectrometry. A classifier (DPFs-DL) for viral versus bacterial infection discrimination (AUC of 0.775) and coronavirus disease 2019 (COVID-2019) diagnosis (AUC of 0.917) is also built. Furthermore, a metabolic biomarker panel of two differentially regulated metabolites, which may serve as potential biomarkers for COVID-19 management (AUC of 0.677-0.883), is constructed. This study will contribute to the development of precision clinical care for infectious diseases.

Mass Spectrometry as an Analytical Tool for Detection of Microplastics in the Environment

Plastic particles smaller than 5 mm accumulate in aqueous, terrestrial, and atmospheric environments and their discovery has been a serious concern when it comes to eco-toxicology and human health risk assessment. In the following review, the potential of mass spectrometry (MS) for the detection of microplastic (MP) pollutants has been elaborately reviewed. The use of various mass spectrometric techniques ranging from gas chromatography–mass spectrometry (GC-MS), liquid chromatographic mass spectrometric (LC-MS) to matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS), including their variants, have been reviewed. The lapses in the detection system have been addressed and future recommendations proposed. The challenges facing microplastics and their detection have been discussed and future directions, including mitigation methods, have been presented.

Integrative Serum Metabolic Fingerprints Based Multi-Modal Platforms for Lung Adenocarcinoma Early Detection and Pulmonary Nodule Classification

Identification of novel non-invasive biomarkers is critical for the early diagnosis of lung adenocarcinoma (LUAD), especially for the accurate classification of pulmonary nodule. Here, a multiplexed assay is developed on an optimized nanoparticle-based laser desorption/ionization mass spectrometry platform for the sensitive and selective detection of serum metabolic fingerprints (SMFs). Integrative SMFs based multi-modal platforms are constructed for the early detection of LUAD and the classification of pulmonary nodule. The dual modal model, metabolic fingerprints with protein tumor marker neural network (MP-NN), integrating SMFs with protein tumor marker carcinoembryonic antigen (CEA) via deep learning, shows superior performance compared with the single modal model Met-NN (p < 0.001). Based on MP-NN, the tri modal model MPI-RF integrating SMFs, tumor marker CEA, and image features via random forest demonstrates significantly higher performance than the clinical models (Mayo Clinic and Veterans Affairs) and the image artificial intelligence in pulmonary nodule classification (p < 0.001). The developed platforms would be promising tools for LUAD screening and pulmonary nodule management, paving the conceptual and practical foundation for the clinical application of omics tools.

Diagnosing, Typing, and Staging of Renal Cell Carcinoma by Designer Matrix-Based Urinary Metabolic Analysis

Molecular diagnosing, typing, and staging have been considered to be the ideal alternatives of imaging-based detection methods in clinics. Designer matrix-based analytical tools, with high speed, throughout, efficiency and low/noninvasiveness, have attracted much attention recently for in vitro metabolite detection. Herein, we develop an advanced metabolic analysis tool based on highly porous metal oxides derived from available metal-organic frameworks (MOFs), which elaborately inherit the morphology and porosity of MOFs and newly incorporate laser adsorption capacity of metal oxides. Through optimized conditions, direct high-quality fingerprinting spectra in 0.5 μL of urine are acquired. Using these fingerprinting spectra, we can discriminate the renal cell carcinoma (RCC) from healthy controls with higher than 0.99 of area under the curve (AUC) values (R²Y(cum) = 0.744, Q² (cum) = 0.880), as well, from patients with other tumors (R²Y(cum) = 0.748, Q²(cum) = 0.871). We also realize the typing of three RCC subtypes, including clear cell RCC, chromophobe RCC (R²Y(cum) = 0.620, Q²(cum) = 0.656), and the staging of RCC (R²Y(cum) = 0.755, Q²(cum) = 0.857). Moreover, the tumor sizes (threshold value is 3 cm) can be remarkably recognized by this advanced metabolic analysis tool (R²Y(cum) = 0.710, Q²(cum) = 0.787). Our work brings a bright prospect for designer matrix-based analytical tools in disease diagnosis, typing and staging.

Water promoted 9-fluorenylmethyloxycarbonyl detachment from amino acids in charged microdroplets

Aqueous microdroplets exhibit unique properties and can trigger reactions that do not occur in bulk solution. Herein, we have demonstrated that water, in microdroplets, can reduce the energy barrier for the lone H transfer of 9-fluorenylmethyloxycarbonyl and promote its detachment from the amino group. This strategy works on various amino acids and opens opportunities of aqueous microdroplets in triggering organic reactions.

ALOX15-launched PUFA-phospholipids peroxidation increases the susceptibility of ferroptosis in ischemia-induced myocardial damage

Myocardial ischemia/reperfusion (I/R) injury is a classic type of cardiovascular disease characterized by injury to cardiomyocytes leading to various forms of cell death. It is believed that irreversible myocardial damage resulted from I/R occurs due to oxidative stress evoked during the reperfusion phase. Here we demonstrate that ischemia triggers a specific redox reaction of polyunsaturated fatty acids (PUFA)-phospholipids in myocardial cells, which acts as a priming signaling that initiates the outbreak of robust oxidative damage in the reperfusion phase. Using animal and in vitro models, the crucial lipid species in I/R injury were identified to be oxidized PUFAs enriched phosphatidylethanolamines. Using multi-omics, arachidonic acid 15-lipoxygenase-1 (ALOX15) was identified as the primary mediator of ischemia-provoked phospholipid peroxidation, which was further confirmed using chemogenetic approaches. Collectively, our results reveal that ALOX15 induction in the ischemia phase acts as a “burning point” to ignite phospholipid oxidization into ferroptotic signals. This finding characterizes a novel molecular mechanism for myocardial ischemia injury and offers a potential therapeutic target for early intervention of I/R injury.

In Vitro Diagnostic Examination and Prognosis Surveillance by Hierarchical Heterojunction-Assisted Metabolic Analysis

High-throughput metabolic analysis based on laser desorption/ionization mass spectrometry exhibits broad prospects in the field of large-scale precise medicine, for which the assisted ionization ability of the matrix becomes a determining step. In this work, the gold-decorated hierarchical metal oxide heterojunctions (dubbed Au/HMOHs) are proposed as a matrix for extracting urine metabolic fingerprints (UMFs) of primary nephrotic syndrome (PNS). The hierarchical heterojunctions are simply derived from metal-organic framework (MOF)-on-MOF hybrids, and the native built-in electric field from heterojunctions plus the extra Au decoration provides remarkable ionization efficiency, attaining high-quality UMFs. These UMFs are employed to realize precise diagnosis, subtype classification, and effective prognosis evaluation of PNS by appropriate machine learning, all with 100% accurate ratios. Moreover, a high-confidence marker panel for PNS diagnosis is constructed. Interestingly, all panel metabolite markers present obviously uniform downregulation in PNS compared to healthy controls, shedding light on mechanism exploration and pathway analysis. This work drives the application of metabolomics toward precision medicine.

Nanomaterials-assisted metabolic analysis toward in vitro diagnostics

In vitro diagnostics (IVD) has played an indispensable role in healthcare system by providing necessary information to indicate disease condition and guide therapeutic decision. Metabolic analysis can be the primary choice to facilitate the IVD since it characterizes the downstream metabolites and offers real-time feedback of the human body. Nanomaterials with well-designed composition and nanostructure have been developed for the construction of high-performance detection platforms toward metabolic analysis. Herein, we summarize the recent progress of nanomaterials-assisted metabolic analysis and the related applications in IVD. We first introduce the important role that nanomaterials play in metabolic analysis when coupled with different detection platforms, including electrochemical sensors, optical spectrometry, and mass spectrometry. We further highlight the nanomaterials-assisted metabolic analysis toward IVD applications, from the perspectives of both the targeted biomarker quantitation and untargeted fingerprint extraction. This review provides fundamental insights into the function of nanomaterials in metabolic analysis, thus facilitating the design of next-generation diagnostic devices in clinical practice.

Cationic and Anionic Antimicrobial Agents Co-Templated Mesostructured Silica Nanocomposites with a Spiky Nanotopology and Enhanced Biofilm Inhibition Performance

HIGHLIGHTS

A ‘dual active templating’ strategy is firstly reported, using cationic and anionic bactericidal agents as co-templates for the preparation of antibacterial silica nanocomposite with spiky nanotopography. The spiky nanocomposite exhibited enhanced antibacterial and biofilm inhibition performance, compared to pure antimicrobial cationic agent templated smooth silica nanocomposite.

ABSTRACT

Silica-based materials are usually used as delivery systems for antibacterial applications. In rare cases, bactericidal cationic surfactant templated silica composites have been reported as antimicrobial agents. However, their antibacterial efficacy is limited due to limited control in content and structure. Herein, we report a “dual active templating” strategy in the design of nanostructured silica composites with intrinsic antibacterial performance. This strategy uses cationic and anionic structural directing agents as dual templates, both with active antibacterial property. The cationic-anionic dual active templating strategy further contributes to antibacterial nanocomposites with a spiky surface. With controllable release of dual active antibacterial agents, the spiky nanocomposite displays enhanced anti-microbial and anti-biofilm properties toward Staphylococcus epidermidis. These findings pave a new avenue toward the designed synthesis of novel antibacterial nanocomposites with improved performance for diverse antibacterial applications. [Image: see text]

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s40820-022-00826-4.

Healing Diabetic Ulcers with MoO3-X Nanodots Possessing Intrinsic ROS-Scavenging and Bacteria-Killing Capacities

Diabetic ulcers (DUs) appearing as chronic wounds are difficult to heal due to the oxidative stress in the wound microenvironment and their high susceptibility to bacterial infection. A routine treatment combining surgical debridement with anti-infection therapy is widely used for treating DUs in the clinic, but hardly offers a satisfying wound healing outcome. It is known that a long-term antibiotic treatment may also lead to the drug resistance of pathogens. To address these challenges, new strategies combining both reactive oxygen species (ROS) scavenging and bacterial sterilization have been proposed for fighting against DUs. Following this idea, oxygen deficient molybdenum-based nanodots (MoO_3-X ) for healing the DUs are reported. The ROS scavenging ability of MoO_3-X nanodots is investigated and the antibacterial property of the nanodots is also demonstrated. The systematic cell and animal experimental results indicate that the MoO_3-X nanodots can effectively reduce inflammation, promote epithelial cell regeneration, accelerate angiogenesis, and facilitate DUs recovery. Most importantly, they present excellent capacity to diminish infection of methicillin-resistant Staphylococcus aureus, manifesting the potent application prospect of MoO_3-X nanodots for diabetic wound therapy.

Porous Inorganic Materials for Bioanalysis and Diagnostic Applications

Porous inorganic materials play an important role in adsorbing targeted analytes and supporting efficient reactions in analytical science. The detection performance relies on the structural properties of porous materials, considering the tunable pore size, shape, connectivity, etc. Herein, we first clarify the enhancement mechanisms of porous materials for bioanalysis, concerning the detection sensitivity and selectivity. The diagnostic applications of porous material-assisted platforms by coupling with various analytical techniques, including electrochemical sensing, optical spectrometry, and mass spectrometry, etc., are then reviewed. We foresee that advanced porous materials will bring far-reaching implications in bioanalysis toward real-case applications, especially as diagnostic assays in clinical settings.

Ultra-Fast Label-Free Serum Metabolic Diagnosis of Coronary Heart Disease via a Deep Stabilizer

Although mass spectrometry (MS) of metabolites has the potential to provide real-time monitoring of patient status for diagnostic purposes, the diagnostic application of MS is limited due to sample treatment and data quality/reproducibility. Here, the generation of a deep stabilizer for ultra-fast, label-free MS detection and the application of this method for serum metabolic diagnosis of coronary heart disease (CHD) are reported. Nanoparticle-assisted laser desorption/ionization-MS is used to achieve direct metabolic analysis of trace unprocessed serum in seconds. Furthermore, a deep stabilizer is constructed to map native MS results to high-quality results obtained by established methods. Finally, using the newly developed protocol and diagnosis variation characteristic surface to characterize sensitivity/specificity and variation, CHD is diagnosed with advanced accuracy in a high-throughput/speed manner. This work advances design of metabolic analysis tools for disease detection as it provides a direct label-free, ultra-fast, and stabilized platform for future protocol development in clinics.

Material-assisted mass spectrometric analysis of low molecular weight compounds for biomedical applications

Low molecular weight compounds play an important role in encoding the current physiological state of an individual. Laser desorption/ionization mass spectrometry (LDI MS) offers high sensitivity with low cost for molecular detection, but it is not able to cover small molecules due to the drawbacks of the conventional matrix. Advanced materials are better alternatives, showing little background interference and high LDI efficiency. Herein, we first classify the current materials with a summary of compositions and structures. Matrix preparation protocols are then reviewed, to enhance the selectivity and reproducibility of MS data better. Finally, we highlight the biomedical applications of material-assisted LDI MS, at the tissue, bio-fluid, and cellular levels. We foresee that the advanced materials will bring far-reaching implications in LDI MS towards real-case applications, especially in clinical settings.

Ultrasound-Targeted Microbubble Destruction Mediates Gene Transfection for Beta-Cell Regeneration and Glucose Regulation

Ultrasound-targeted microbubble destruction (UTMD) mediates gene transfection with high biosafety and thus has been promising toward treatment of type 1 diabetes. However, the potential application of UTMD in type 2 diabetes (T2D) is still limited, due to the lack of systematic design and dynamic monitoring. Herein, an efficient gene delivery system is constructed by plasmid deoxyribonucleic acid (DNA) encoding glucagon-like peptide 1 (GLP-1) in ultrasound-induced microbubbles, toward treatment of T2D in macaque. The as designed UTMD afforded enhancement of cell membrane penetration and GLP-1 expression in macaque, which is characterized by ultrasound-guided biopsy to monitor the dynamic process of islet cells for 6 months. Also, improvement of pancreatic beta cell regeneration, and regulation of plasma glucose in macaque with T2D is achieved. The approach would serve as promising alternatives for the treatment of T2D.

Sustainable Carbon Materials toward Emerging Applications

It is desirable for a sustainable society that the production and utilization of renewable materials are net-zero in terms of carbon emissions. Carbon materials with emerging applications in CO₂ utilization, renewable energy storage and conversion, and biomedicine have attracted much attention both academically and industrially. However, the preparation process of some new carbon materials suffers from energy consumption and environmental pollution issues. Therefore, the development of low-cost, scalable, industrially and economically attractive, sustainable carbon material preparation methods are required. In this regard, the use of biomass and its derivatives as a precursor of carbon materials is a major feature of sustainability. Recent advances in the synthetic strategy of sustainable carbon materials and their emerging applications are summarized in this short review. Emphasis is made on the discussion of the original intentions and various sustainable strategies for producing sustainable carbon materials. This review provides basic insights and significant guidelines for the further design of sustainable carbon materials and their emerging applications in catalysis and the biomedical field.

Design of Multi-Shelled Hollow Cr2 O3 Spheres for Metabolic Fingerprinting

Schizophrenia (SZ) detection enables effective treatment to improve the clinical outcome, but objective and reliable SZ diagnostics are still limited. An ideal diagnosis of SZ suited for robust clinical screening must address detection throughput, low invasiveness, and diagnosis accuracy. Herein, we built a multi-shelled hollow Cr₂ O₃ spheres (MHCSs) assisted laser desorption/ionization mass spectrometry (LDI MS) platform for the direct metabolic profiling of biofluids towards SZ diagnostics. The MHCSs displayed strong light absorption for enhanced ionization and microscale surface roughness with stability for the effective LDI of metabolites. We profiled urine and serum metabolites (≈1 μL) with the enhanced LDI efficacy in seconds. We discriminated SZ patients (SZs) from healthy controls (HCs) with the highest area under the curve (AUC) value of 1.000 for the blind test. We identified four compounds with optimal diagnostic power as a simplified metabolite panel for SZ and demonstrated the metabolite quantification for clinic use. Our approach accelerates the growth of new platforms toward a precision diagnosis in the near future.

Synthesis and Spectrophotometric Analysis of Microcapsules Containing Immortelle Essential Oil

In this study, microcapsules were prepared by solvent evaporation technique using ethyl cellulose component as wall and essential oil as core material. The synthesis of microcapsules was carried out using different oil masses. The analysis of the microcapsules was carried out using field emission scanning electron microscope (FE-SEM) and UV spectrophotometric analysis using absorption spectrophotometer. The obtained results confirm the regular spherical shape and size of the synthesized microcapsules. The qualitative and quantitative spectrophotometric analysis of the microencapsulated immortelle oil was measured at the wavelength of 265 nm. The calibration diagram was used to calculate the unknown concentrations of the microencapsulated oil. The obtained results confirm the application of the presented method as relevant for the possible determination of microencapsulated oil on textile materials.

Crystal Phase-Related Toxicity of One-Dimensional Titanium Dioxide Nanomaterials on Kidney Cells

One-dimensional (1D) nanomaterials are widely used in different fields, and the increased application of 1D nanomaterials has drawn concerns about their unknown toxicity. 1D titanium oxide (TiO₂) nanomaterials in different crystal phases are commonly applied in environmental remediation and solar energy conversion fields, but these materials pose a threat to human health, especially to the kidneys, an organ with abundant blood flow. To systematically evaluate the cytotoxicity to the kidneys, TiO₂ nanofibers with TiO₂(B), anatase, and rutile phases, as well as nanorods with anatase and rutile phases were synthesized and added to the culture medium of HK2 cells. Cell counting kit-8 assay, 2',7'-dichlorofluorescin diacetate assay, Hoechst 33342 staining experiments, and quantitative real-time reverse transcription polymerase chain reaction tests were used to explore the renal effects of the as-prepared TiO₂ nanomaterials in the short term or long term. In the short-term evaluation, all the added TiO₂ nanomaterials were toxic to HK2 cells, and the cytotoxicity was dose-dependent. Rutile TiO₂ can widely attach to the cell surface and displays the most serious cell-killing and proapoptotic ability, while anatase induces the most serious oxidative stress. In long-term evaluation, all the as-prepared TiO₂ nanomaterials led to epithelial mesenchymal transition (EMT), a mechanism of renal fibrosis. Consistent with the short-term results, rutile induced the most serious EMT. This study indicated that the renal toxicity of 1D TiO₂ nanomaterials is crystal phase-dependent and that rutile induced the most significant renal cell injury. Oxidative stress is a crucial but not the only contributor to the renal toxicity of TiO₂ nanomaterials in the short term.

Metal-Organic Framework Hybrids Aid Metabolic Profiling for Colorectal Cancer

Colorectal cancer (CRC) is the third most common fatal cancer worldwide, accounting for ≈10% of cancer-related mortality. Metabolic shift occurs from the very early stage during the development of CRC, which is of significant etiological and diagnostic importance toward precision medicine. Here, an advanced molecular tool to characterize the metabolic alterations in CRC, based on metal-organic framework (MOF) hybrids is reported. Consuming only 500 nL of plasma without any sample pretreatment, MOF hybrids yield direct metabolic fingerprints by laser desorption/ionization mass spectrometry in seconds. A diagnostic prediction model by a machine learning algorithm is constructed, to discriminate CRC patients from normal controls with an average area under the curve of 0.947 for the discovery cohort and 0.912 for the independent validation cohort. In addition, CRC-specific metabolic signature consisting of 34 potential biomarkers, based on the aforementioned diagnostic model is identified. The results advance the design of nanomaterial-based platforms for metabolic analysis and establish a new liquid biopsy tool for CRC screening compatible with the current clinical workflow in practice.

Plasmonic Alloys Reveal a Distinct Metabolic Phenotype of Early Gastric Cancer

Gastric cancer (GC) is a multifactorial process, accompanied by alterations in metabolic pathways. Non-invasive metabolic profiling facilitates GC diagnosis at early stage leading to an improved prognostic outcome. Herein, mesoporous PdPtAu alloys are designed to characterize the metabolic profiles in human blood. The elemental composition is optimized with heterogeneous surface plasmonic resonance, offering preferred charge transfer for photoinduced desorption/ionization and enhanced photothermal conversion for thermally driven desorption. The surface structure of PdPtAu is further tuned with controlled mesopores, accommodating metabolites only, rather than large interfering compounds. Consequently, the optimized PdPtAu alloy yields direct metabolic fingerprints by laser desorption/ionization mass spectrometry in seconds, consuming 500 nL of native plasma. A distinct metabolic phenotype is revealed for early GC by sparse learning, resulting in precise GC diagnosis with an area under the curve of 0.942. It is envisioned that the plasmonic alloy will open up a new era of minimally invasive blood analysis to improve the surveillance of cancer patients in the clinical setting.

Development of a miniature time-of-flight mass spectrometer coupled with an improved substrate-enhanced laser-induced acoustic desorption source (SE-LIAD/TOF-MS)

A novel, compact and sensitive SE-LIAD/TOF-MS has been described. It facilitates fast sample preparation, and a full mass spectrum is acquired efficiently and sensitively. More importantly, it features the detection of non-acidic and non-basic or non-polar species, which is not suitable for determination by ESI and MALDI techniques. In this technique, standard samples, carbazole and melamine, are prepared on a Ti foil with a quartz plate attached to the backside of the Ti foil to perform a laser-induced acoustic desorption experiment (SE-LIAD) coupled to TOF-MS for analysis. Enhanced signals are observed with about 5.6 to 13.8 times higher than that obtained in the standard LIAD method, dependent on different ionization techniques. Compared to the EI spectra, the PI spectra for both species show intact and sharp molecular peaks. The limits of detection (LOD) of melamine were evaluated experimentally in the range from ∼2-6 pg (EI/MS mode) to ∼0.3-0.5 ng (VUV-SPI/MS mode). Thus, the method in this study exhibits rapid qualitative and quantitative analysis with good sensitivity, being free of the complex matrix influences.