Straightforward Creation of Multishell Hollow Hybrids for an Integrated Metabolic Monitoring System in Disease Management.

Multidimensional metabolic analysis has become a new trend in establishing efficient disease monitoring systems, as the constraints associated with relying solely on a single dimension in refined monitoring are increasingly pronounced. Here, coordination polymers are employed as derivative precursors to create multishell hollow hybrids, developing an integrated metabolic monitoring system. Briefly, metabolic fingerprints are extracted from hundreds of serum samples and urine samples, encompassing not only membranous nephropathy but also related diseases, using high-throughput mass spectrometry. With optimized algorithm and initial feature selection, the established combined panel demonstrates enhanced accuracy in both subtype differentiation (over 98.1%) and prognostic monitoring (over 95.6%), even during double blind test. This surpasses the serum biomarker panel (≈90.7% for subtyping, ≈89.7% for prognosis) and urine biomarker panel (≈94.4% for subtyping, ≈76.5% for prognosis). Moreover, after attempting to further refine the marker panel, the blind test maintains equal sensitivity, specificity, and accuracy, showcasing a comprehensive improvement over the single-fluid approach. This underscores the remarkable effectiveness and superiority of the integrated strategy in discriminating between MN and other groups. This work has the potential to significantly advance diagnostic medicine, leading to the establishment of more effective strategies for patient management.

Multiscale Structured Trimetal Oxide Heterojunctions for Urinary Metabolic Phenotype-Dependent Screening of Early and Small Hepatocellular Carcinoma.

Developing a standardized screening tool for the detection of early and small hepatocellular carcinoma (HCC) through urinary metabolic analysis poses a challenging yet intriguing research endeavor. In this study, a range of intricately interlaced 2D rough nanosheets featuring well-defined sharp edges is fabricated, with the aim of constructing diverse trimetal oxide heterojunctions exhibiting multiscale structures. By carefully engineering synergistic effects in composition and structure, including improved adsorption, diffusion, and other surface-driven processes, the optimized heterojunctions demonstrate a substantial enhancement in signal intensity compared to monometallic or bimetallic oxides, as well as fragmented trimetallic oxides. Additionally, optimal heterojunctions enable the extraction of high-quality urinary metabolic fingerprints using high-throughput mass spectrometry. Leveraging machine learning, discrimination of HCC patients from high-risk and healthy populations achieves impressive performance, with area under the curve values of 0.940 and 0.916 for receiver operating characteristic and precision-recall curves, respectively. Six crucial metabolites are identified, enabling accurate detection of early, small-tumor, alpha-fetoprotein-negative HCC (93.3%-97.3%). A comprehensive screening strategy tailored to clinical reality yields precision metrics (accuracy, precision, recall, and F1 score) exceeding 95.0%. This study advances the application of cutting-edge matrices-based metabolic phenotyping in practical clinical diagnostics.

Peripheral apolipoprotein is an independent factor for enlarged perivascular space in small vessel disease.

OBJECTIVE: The purpose of this study is to the relationship between peripheral apolipoproteins and cerebral small vessel disease (CSVD) imaging markers. METHODS: We reviewed the data of a population that above 40 years old with CSVD, while free of known dementia or acute stroke. We evaluated CSVD imaging markers, including white matter hyperintensities (WMHs), enlarged perivascular spaces (EPVS), lacunas, microbleeds by MRI scans, and measured peripheral apolipoproteins. RESULTS: After adjusting for age, sex and vascular risk factors,1) apoB and apoB/apoA-1 were related to grade of EPVS in basal ganglia(apoB:r=0.196,p<0.001;apoB/apoA-1:r=0.208,p<0.001), apoE was related to grade of EPVS in centrum semiovale (r=0.125,p=0.040); 2) apoB(OR=1.739, 95%CI=1.357-2.061, p<0.001), apoB/apoA-1(OR=1.116, 95%CI=1.037-1.761, p=0.005) and apoE(OR=1.287, 95%CI=1.036-1.599, p=0.023) were independent factors of presence of severer EPVS in basal ganglia, apoE was an independent factor of presence of severer EPVS in centrum semiovale (OR=1.235, 95%CI=1.021-1.494, p=0.029). CONCLUSION: Our findings demonstrated peripheral apolipoproteins, including apoB, apoB/apoA-1, and apoE, were independent factor for EPVS in CSVD.

High-throughput detection allied with machine learning for precise monitoring of significant serum metabolic changes in Helicobacter pylori infection.

High-throughput detection of large-scale samples is the foundation for rapidly accessing massive metabolic data in precision medicine. Machine learning is a powerful tool for uncovering valuable information hidden within massive data. In this work, we achieved the extraction of a single fingerprinting of 1 µL serum within 5 s through a high-throughput detection platform based on functionalized nanoparticles. We quickly obtained over a thousand serum metabolic fingerprintings (SMFs) including those of individuals with Helicobacter pylori (HP) infection. Combining four classical machine learning models and enrichment analysis, we attempted to extract and confirm useful information behind these SMFs. Based on all fingerprint signals, all four models achieved area under the curve (AUC) values of 0.983-1. In particular, orthogonal partial least squares discriminant analysis (OPLS-DA) model obtained value of 1 in both the discovery and validation sets. Fortunately, we identified six significant metabolic features, all of which can greatly contribute to the monitoring of HP infection, with AUC values ranging from 0.906 to 0.985. The combination of these six significant metabolic features can enable the precise monitoring of HP infection in serum, with over 95 % of accuracy, specificity and sensitivity. The OPLS-DA model displayed optimal performance and the corresponding scatter plot visualized the clear distinction between HP and HC. Interestingly, they exhibit a consistent reduction trend compared to healthy controls, prompting us to explore the possible metabolic pathways and potential mechanism. This work demonstrates the potential alliance between high-throughput detection and machine learning, advancing their application in precision medicine.

Composition, distribution, health risks, and drivers of phthalates in typical red paddy soils.

The accelerated accumulation of phthalate esters (PAEs) in paddy soils poses a serious threat to human health. However, related studies mainly focus on facility vegetable fields, drylands, and orchards, and little is known about paddy soils. In this study, 125 samples were collected from typical red paddy fields to investigate the pollution characteristics, sources, health risks, and main drivers of PAEs. Soil physicochemical properties, enzyme activity, and bacterial community composition were also measured simultaneously. The results showed that eight PAE congeners were detected ranging from 0.17 to 1.97 mg kg-1. Di-n-butyl phthalate (DBP), di-(2-ethylhexyl) phthalate (DEHP), and di-isobutyl phthalate (DIBP) were the most abundant PAE congeners, accounting for 81% of the total PAEs. DEHP exhibited a potential carcinogenic risk to humans through the intake route. The main PAEs were positively correlated with soil organic matter (SOM) and soil water content (SWC) contents. Low levels of PAEs increased bacterial abundance. Furthermore, most PAE congeners were positively correlated with hydrolase activity. Soil acidity and nutrient dynamics played a dominant role in the bacterial community composition, with PAE congeners playing a secondary role. These findings suggest that there may be a threshold response between PAEs and organic matter and nutrient transformation in red paddy soils, and that microbial community should be the key driver. Overall, this study deepens the understanding of ecological risks and microbial mechanisms of PAEs in red paddy soils.

Construction of Porous Perovskite Oxide Microrods with Au Nanoparticle Anchor for Precise Metabolic Diagnosis of Alzheimer's Disease.

Alzheimer's disease (AD) is a progressive illness, and early diagnosis and treatment can help delay its progression. However, clinics still lack high-throughput, low-invasive, precise, and objective diagnostic strategies. Herein, the Au nanoparticles anchored porous perovskite oxide microrods (CTO@Au) with designed superior properties is developed to construct a high-throughput detection platform. Specifically, a single metabolic fingerprinting is obtained from only 30 nL of serum within seconds, enabling the rapid acquisition of 239 × 8 high-quality fingerprints in ≈ 2 h. AD is distinguish from health controls and Parkinson's disease with an area under the curve (AUC) of 1.000. Moreover, eight specific metabolites are identified as a biomarker panel, based on which precise diagnosis of AD is achieved, with an AUC of 1.000 in blind test. The possible relevant pathways and potential mechanism involved in these biomarkers are investigated and discussed. This work provides a high-performance platform for metabolic diagnostic analysis.

Hollow Core-Shell Metal Oxide Heterojunctions for the Urinary Metabolic Fingerprint-Based Noninvasive Diagnostic Strategy.

Urine is a preferred object for noninvasive diagnostic strategies. Urinary metabolic analysis is speculatively regarded as an ideal tool for screening diseases closely related to the genitourinary system in view of the intimate relationship between metabolomics and phenotype. Herein, we propose a urinary metabolic fingerprint-based noninvasive diagnostic strategy by designing hollow core-shell metal oxide heterojunctions (denoted as MOHs). With outstanding light absorption and electron-hole separation ability, MOHs aid in the extraction of high-performance urine metabolic fingerprints. Coupled with optimized machine learning algorithms, we establish a metabolic marker panel for accurate diagnosis of prostate cancer (PCa), which is the most common malignant tumor of the male genitourinary system, achieving accuracies of 84.72 and 83.33% in the discovery and validation sets, respectively. Furthermore, metabolite variations and related pathway analyses confirm the credibility and change correlation of key metabolic features in PCa. This work tends to advance the noninvasive diagnostic strategy toward clinical realities.

Copper and cadmium co-contamination affects soil bacterial taxonomic and functional attributes in paddy soils.

Microorganisms inhabiting heavy metal-contaminated soils have evolved specific metabolic capabilities to survive, which has the potential for effective bioremediation. However, the ecological consequence of copper (Cu) and cadmium (Cd) on bacterial taxonomic and functional attributes of rice field remains unclear. Here, we selected paddy soils along a polluted river in southern China to evaluate the role of Cu and Cd contaminant fractions in regulating bacterial co-occurrence patterns. We also assessed the effects of these heavy metal fractions on the relative abundance of functional genes using shotgun metagenomic analysis. Soil Cu and Cd concentrations in paddy soils gradually decreased from upstream to downstream of the river, and had a greater impact on bacterial communities and metabolic potentials than soil general properties. Soil Cu and Cd contamination led to drastic changes in the cumulative relative abundance of ecological modules in bacterial co-occurrence networks. Bacteria associated with AD3, HSB_OF53-F07 (both belonging to Chloroflexi), Rokubacteriales, and Nitrospira were identified as tolerant to Cu and Cd contamination. The Cu and Cd contaminant fractions were positively correlated with the genes involved in metal resistance, carbon (C) fixation, nitrification, and denitrification, but negatively correlated with the genes related to nitrogen (N) fixation. These results indicated that soil Cu and Cd pollution not only enriched metal resistant genes, but also affected genes related to microbial C and N cycling. This is critical for facilitating microbiome bioremediation of metal-contaminated paddy soils.

Multiscale Element-Doped Nanowire Array-Coupled Machine Learning Reveals Metabolic Fingerprints of Nonreversible Liver Diseases.

Timely detection of nonreversible liver diseases contributes greatly to reasonable therapy and quality of life. Given the current situation, minimally invasive high-specificity molecular diagnosis based on body fluid can be a good choice. Herein, a mesoporous superstructure is designed using silicon atom-doped nanowire arrays to uniformly load Pt nanoparticles on the surface to produce a desirable ionization effect. We apply the multiscale element-doped nanowire arrays to efficiently assist extraction of high-quality metabolic fingerprints from only 35 nL of serum within seconds. Using different machine learning algorithms, we establish specific biomarker panels to distinguish different liver diseases from the healthy control, with more than 90% accuracy, sensitivity, and specificity. Moreover, from established biomarker panels, we further determine key metabolites of significant difference (p < 0.01) via group comparison to realize the discrimination of different liver diseases with 100% sensitivity. Our work confirms the design protocol of an advanced diagnosis tool and lays a robust foundation for metabolic molecular diagnosis in large-scale clinical application.

The Mechanism of Anticorrosion Performance and Mechanical Property Differences between Seawater Sea-Sand and Freshwater River-Sand Ultra-High-Performance Polymer Cement Mortar (UHPC).

There are abundant sea-sand resources on the earth. Traditional sea-sand concrete faced various problems relating to insufficient anticorrosion ability. In this paper, artificial seawater, sea sand, industrial waste, steel fiber, and polycarboxylate superplasticizer were used to prepare ultra-high-performance polymer cement mortar (SSUHPC). At the same time, freshwater river-sand ultra-high-performance polymer cement mortar (FRUHPC) with the same mixing ratio was prepared for comparative study. The compressive strength of SSUHPC reached 162.1 MPa, while the that of FRUHPC reached 173.3 MPa, which was slightly higher. Meanwhile, SSUHPC showed excellent anticorrosion characteristics in terms of carbonization, frost resistance and chloride resistance, and especially for sulfate resistance. The composition of SSUHPC was separated into three parts: mortar, pore and steel fiber, and the performance difference mechanisms of SSUHPC and FRUHPC were investigated by X-ray computed tomography (X-CT), mercury intrusion porosimetry (MIP), scanning electron microscopy (SEM), and X-ray diffraction (XRD). The hydration degree of mortar in SSUHPC was higher, with higher content of CSH and CH, and its better optimized gel pore characteristics gave SSUHPC better corrosion resistance. The mechanical properties of SSUHPC were slightly poor due to the uneven dispersion of steel fibers and air pores, with an- air pore porosity of 1.52% (above 200 µm) that was twice that of FRUHPC (0.6%). In this paper, the mechanics and anticorrosion performance of ultra-high-performance polymer cement mortar prepared with seawater sea sand were comprehensively evaluated, and the mechanism of performance difference between SSUHPC and FRUHPC was revealed, conducive to the targeted improvement of sea sand concrete.

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.

Sulphate Corrosion Mechanism of Ultra-High-Performance Concrete (UHPC) Prepared with Seawater and Sea Sand.

The lack of river sand is becoming increasingly serious. In this study, we consider how to use sea sand to prepare innovative construction and building materials with excellent mechanical and durability properties. Sulphate corrosion causes expansion, cracking and spalling of concrete, resulting in the reduction or even loss of concrete strength and cementation force. In this paper, artificial seawater, sea sand, industrial waste, steel fiber and polycarboxylate superplasticizer were used to prepare ultra-high-performance polymer cement mortar (SSUHPC), and the sulphate corrosion mechanism was investigated. The strength and cementation force of mortar on the SSUHPC surface decreased and flaked off with the development of sulphate erosion, and the steel fiber rusted and fell off. A 3D model was established based on X-ray computed tomography (X-CT), and the results showed that SSUHPC maintained excellent internal structural characteristics despite severe sulphate erosion on the surface. Mercury intrusion porosimetry (MIP), scanning electron microscopy (SEM) and X-ray diffraction (XRD) techniques were adopted to investigate the sulphate corrosion mechanism of SSUHPC. We found a transition zone within 1-5 mm of the surface of SSUHPC. The Vickers hardness of mortar in this area was increased by 5~15%, and the porosity was reduced to 3.8489%. Obvious structural damage did not occur in this area, but a high content of gypsum appeared. UHPC prepared with seawater sea sand was found to have better sulphate resistance than that prepared with freshwater river sand, which supports the development and utilization of sea sand in concrete.

Linkages between the molecular composition of dissolved organic matter and soil microbial community in a boreal forest during freeze-thaw cycles.

Soil dissolved organic matter (DOM) plays a vital role in biogeochemical processes. Global warming leads to increased freeze-thaw cycles (FTCs) in boreal forest soils, which can change DOM production and consumption. However, the interactions between the chemical composition of DOM molecules and the microbial communities that drive C decomposition in the context of freeze-thaw are poorly understood. Here, a FTCs incubation experiment was conducted. Combined with pyrolysis gas chromatography-mass spectrometry and high-throughput sequencing techniques, the relationships between DOM chemodiversity and microbial community structure were assessed. Results indicated that both low-frequency (2FTCs) and high-frequency freeze-thaw cycles (6FTCs) significantly increased soil dissolved organic carbon (DOC) contents in the surface (0-10 cm) and subsurface (50-60 cm) soil layers. In the topsoil, FTCs significantly reduced the relative abundance of aromatic compounds, but increased the relative proportions of alkanes, phenols, fatty acid methyl esters (Me) and polysaccharides in the DOM. In the subsuface soil layer, only the relative abundance of Me in the 6FTCs treatment increased significantly. The response of bacterial communities to FTCs was more sensitive than that of fungi, among which only the relative abundance of Gammaproteobacteria increased by FTCs. Moreover, the relative abundance of these taxa was positively correlated with the increment of DOC. Co-occurrence networks confirmed DOM-bacterial interactions, implying that specific microorganisms degrade specific substrates. At class level, Gammaproteobacteria were significantly positively correlated with labile C (polysaccharides and alkanes), whereas other bacterial classes such as Actinobacteria, Alphaproteobacteria, and Thermoleophilia were significantly positively correlated with aromatic compounds in the topsoil. Collectively, FTCs tended to activate DOM and enhance its biodegradability of DOM, potentially hampering DOC accumulation and C sequestration. These findings highlight the potential of DOM molecular mechanisms to regulate the functional states of soil bacterial communities under increased FTCs.

Research on the Effect of Desert Sand on Pore Structure of Fiber Reinforced Mortar Based on X-CT Technology.

Concrete is a multi-phase, porous system. The pore structure has an important influence on the properties of the concrete. In this paper, a kind of fiber reinforced mortar was prepared with desert sand and its pore structure was studied. The MIP technique was used to investigate the pore structure characteristics between 1 nm and 500 µm (in diameter). Meanwhile, the µX-CT technique was used to study the pore structure characteristics above 200 µm. It was found that the total porosity tends to decrease first and then increase as the dosage of desert sand increased. The porosity decreased gradually from the upper to bottom area inside the sample, and the diameter of the air voids near the upper area became larger. After curing for 28 days, the compressive strength of fiber reinforced mortar reached the maximum when the content of desert sand was 50%. In conclusion, the appropriate amount of desert sand can reduce the porosity of the fiber reinforced mortar to some extent and the number of large size air voids can be significantly reduced, which improves the pore structure and the mechanical properties of the fiber reinforced mortar.

Diagnostic ion filtering targeted screening and isolation of anti-inflammatory iridoid glycosides from Hedyotis diffusa.

Efficient and targeted screening and isolation of bioactive compounds from complex natural products is still a challenging work. Herein, diagnostic ion filtering based high-performance liquid chromatography-quadrupole time-of-flight-tandem mass spectrometry was firstly developed to screen six main iridoid glycosides from Hedyotis diffusa. Then, online extraction-high-speed counter current chromatography was proposed for targeted enrichment and preparative isolation using ethyl acetate/n-butanol/water (4.5:0.5:5, v/v/v) as solvent system. After that, Sephadex LH-20 column chromatography using methanol as solvent system was selected for further purification of six iridoid glycosides with purities over 98%. They were finally identified as monotropein, desacetylasperuloside acid, asperuloside, 6-O-(Z)-p-coumaroyl scandoside methyl ester, 6-O-(Z)-feruloyl scandoside methyl ester, and 6-O-(E)-p-coumaroyl scandoside methyl ester. And their anti-inflammatory activities were evaluated and confirmed by lipopolysaccharide activated RAW 264.7 macrophages. Obviously, the results provide a scientific basis for the potential applications of H. diffusa, and the developed methodology is efficient and reliable for targeted screening and isolation of bioactive compounds from natural products.

Homoisoflavonoids profiling of Ophiopogon japonicus by off-line coupling high-speed countercurrent chromatography with high-performance liquid chromatography-diode array detector-quadrupole time-of-flight tandem mass spectrometry.

Roots of Ophiopogon japonicus have been used as a functional food ingredient and traditional Chinese medicine for a long time in China. Homoisoflavonoids are one of the major kinds of bioactive compounds in O. japonicus; however, literature data about its homoisoflavonoids profile are scarce because of the complex ingredients with low abundance. Here, homoisoflavonoid fraction was prepared by petroleum ether extraction. Then, a high-speed countercurrent chromatography off-line coupling with high-performance liquid chromatography-diode array detector-quadrupole time-of-flight tandem mass spectrometry was developed for systematic identification of homoisoflavonoids. After that, 39 homoisoflavonoids, including 29 homoisoflavanone and 10 homoisoflavone, were unambiguously or tentatively identified, while 12 of them were reported in O. japonicus for the first time. Finally, eight available homoisoflavonoids were sensitively, precisely, and accurately determined by standard calibration curves, with limit of detection and limit of quantification in the range of 0.05-0.30 µg/mL and 0.12-0.66 µg/mL, relative standard deviation less than 7.3% for intra- and interday variations, and recovery at 94.5-105.2%. Collectively, our developed method is efficient, reliable, and valuable to profile chemical components of complex natural products.

Profiling of tyrosinase inhibitors in mango leaves for a sustainable agro-industry.

Although mango leaves are the main ingredients in some traditional Chinese medicine preparations and folk tea, they with considerable quantities are usually discarded as agricultural waste. Thus, to extend their potential, reverse ultrafiltration-HPLC-DAD-QTOF-MS/MS combining with key ion filtering strategy was proposed to efficiently fish and systematically identify tyrosinase inhibitors in ethyl acetate fraction of mango leaves, which has the highest total phenolic content (40.00 ± 0.84 mg GAE/g DW) and tyrosinase inhibition activity (IC50, 17.62 ± 1.26 µg/mL). Finally, 36 polyphenolic tyrosinase inhibitors were unambiguously characterized or tentatively identified, and three of them were found in mango leaves for the first time. Results suggested that the proposed strategy was powerful for effective identification of bioactive compounds in complex mixtures (e.g. food, agricultural and sideline products), and the findings would lay a foundation for potential applications of mango leaves in pharmaceutical, cosmetic, and food industrial fields.

Edaravone improves survival and neurological outcomes after CPR in a ventricular fibrillation model of rats.

OBJECTIVE: Overproduction of free radicals is a main factor contributing to cerebral injury after cardiac arrest (CA)/cardiopulmonary resuscitation (CPR). We sought to evaluate the impact of edaravone on the survival and neurological outcomes after CA/CPR in rats. METHODS: Rats were subjected to CA following CPR. For survival study, the rats with restoration of spontaneous circulation (ROSC) were randomly allocated to one of the two groups (edaravone and saline group, n=20/each group) to received Edaravone (3 mg/kg) or normal saline. Another 10 rats without experiencing CA and CPR served as the sham group. Survival was observed for 72 hours and the neurological deficit score (NDS) was calculated at 12, 24, 48, and 72 hours after ROSC. For the neurological biochemical analysis study, rats were subjected to the same experimental procedures. Then, edaravone group (n=24), saline group (n=24) and sham group (n=16) were further divided into 4 subgroups according to the different time intervals (12, 24, 48, and 72 hours following ROSC). Brain tissues were harvested at relative time intervals for evaluation of oxidative stress, TUNEL staining and apoptotic gene expression. RESULTS: Edaravone improved postresuscitative survival time and neurological deficit, decreased brain malonylaldehyde level, increased superoxide dismutase activities, decreased proapoptotic gene expression of capase-8, capase-3, and Bax, and increased antiapoptotic Bcl-2 expression at 12, 24, 48, and 72 hours after ROSC. CONCLUSIONS: Edaravone improves survival and neurological outcomes following CPR via antioxidative and antiapoptotic effects in rats.

The benefits of respective and combined use of green tea polyphenols and ERK inhibitor on the survival and neurologic outcomes in cardiac arrest rats induced by ventricular fibrillation.

BACKGROUND: Cerebral injury is a main factor contributing to a high mortality after cardiac arrest (CA)/cardiopulmonary resuscitation (CPR). OBJECTIVE: We sought to evaluate the effect of green tea polyphenols (GTPs) and ERK1/2 inhibitor PD98059 (PD) on the survival and neurologic outcomes after CA/CPR in rats. METHODS: First, rats were subjected to CA after CPR. The rats that restored spontaneous circulation were blindly allocated to the saline group (saline, IV, n = 12), the GTP group (GTPs, 10 mg/kg, IV, n = 12), the PD group (PD, 0.3 mg/kg, IV, n = 12), and the GTPs + PD group (GTPs, 10 mg/kg; PD, 0.3 mg/kg, IV, n = 12). Another 12 rats without experiencing CA and CPR were served as a sham group. Survival and the neurologic deficit score were observed for 72 hours after restoration of spontaneous circulation. Second, same experimental procedures were performed, and in 1 of 5 groups, animals were divided into 4 subgroups further according to the different time points (12, 24, 48, and 72 hours after restoration of spontaneous circulation [ROSC], n = 6/group). Brain tissues were harvested at relative time points for the morphologic evaluation as well as reactive oxygen species (ROS), malonylaldehyde, and superoxide dismutase (SOD) measurement. RESULTS: Green tea polyphenols, PD, and a combination of GTPs and PD used after ROSC alleviated the morphologic changes of the cerebrum. These 3 treatments also decreased the productions of ROS and malonylaldehyde, increased SOD activities in cerebral tissues, and improved the neurologic deficit and survival rates at 12, 24, 48, and 72 hours after ROSC. CONCLUSIONS: Administration of GTPs and PD after ROSC can alleviate cerebral injury, improve the survival and neurologic outcomes via reduction of ROS, and increase of SOD activity in a rat CA/CPR model.