All-atom RNA structure determination from cryo-EM maps.

Many methods exist for determining protein structures from cryogenic electron microscopy maps, but this remains challenging for RNA structures. Here we developed EMRNA, a method for accurate, automated determination of full-length all-atom RNA structures from cryogenic electron microscopy maps. EMRNA integrates deep learning-based detection of nucleotides, three-dimensional backbone tracing and scoring with consideration of sequence and secondary structure information, and full-atom construction of the RNA structure. We validated EMRNA on 140 diverse RNA maps ranging from 37 to 423 nt at 2.0-6.0 Å resolutions, and compared EMRNA with auto-DRRAFTER, phenix.map_to_model and CryoREAD on a set of 71 cases. EMRNA achieves a median accuracy of 2.36 Å root mean square deviation and 0.86 TM-score for full-length RNA structures, compared with 6.66 Å and 0.58 for auto-DRRAFTER. EMRNA also obtains a high residue coverage and sequence match of 93.30% and 95.30% in the built models, compared with 58.20% and 42.20% for phenix.map_to_model and 56.45% and 52.3% for CryoREAD. EMRNA is fast and can build an RNA structure of 100 nt within 3 min.

Tumor-derived lactate promotes resistance to bevacizumab treatment by facilitating autophagy enhancer protein RUBCNL expression through histone H3 lysine 18 lactylation (H3K18la) in colorectal cancer.

Bevacizumab plays an important role in the first and second line treatment for metastatic colorectal cancer (CRC). And induction of hypoxia and the tumors response to it plays an important role in determining the efficacy of antiangiogenic therapy while the connection between them remains unclear. Here, we found that lactate accumulated in the tumor environment of CRC and acted as substrates for histone lactylation, and this process was further induced by cellular enhanced glycolysis in hypoxia. We determined that CRC patients resistant to bevacizumab treatment presented with elevated levels of histone lactylation and inhibition of histone lactylation efficiently suppressed CRC tumorigenesis, progression and survival in hypoxia. Histone lactylation promoted the transcription of RUBCNL/Pacer, facilitating autophagosome maturation through interacting with BECN1 (beclin 1) and mediating the recruitment and function of the class III phosphatidylinositol 3-kinase complex, which had a crucial role in hypoxic cancer cells proliferation and survival. Moreover, combining inhibition of histone lactylation and macroautophagy/autophagy with bevacizumab treatment demonstrated remarkable treatment efficacy in bevacizumab-resistance patients-derived pre-clinical models. These findings delivered a new exploration and important supplement of metabolic reprogramming-epigenetic regulation, and provided a new strategy for improving clinical efficacy of bevacizumab in CRC by inhibition of histone lactylation.Abbreviations: 2-DG: 2-deoxy-D-glucose; BECN1: beclin 1; CQ: chloroquine; CRC: colorectal cancer; DMOG: dimethyloxalylglycine; H3K18la: histone H3 lysine 18 lactylation; MAP1LC3B/LC3B: microtubule associated protein 1 light chain 3 beta; Nala: sodium lactate; PDO: patient-derived orgnoid; PDX: patient-derived xenograft; RUBCNL/Pacer: rubicon like autophagy enhancer; SQSTM1/p62: sequestosome 1.

Impact of AlphaFold on structure prediction of protein complexes: The CASP15-CAPRI experiment.

We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo-trimers, 13 heterodimers including 3 antibody-antigen complexes, and 7 large assemblies. On average ~70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21 941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their five best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High-quality models were produced for about 40% of the targets compared to 8% two years earlier. This remarkable improvement is due to the wide use of the AlphaFold2 and AlphaFold2-Multimer software and the confidence metrics they provide. Notably, expanded sampling of candidate solutions by manipulating these deep learning inference engines, enriching multiple sequence alignments, or integration of advanced modeling tools, enabled top performing groups to exceed the performance of a standard AlphaFold2-Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem.

Neoadjuvant chemotherapy weakens the prognostic value of the pathological tumor burden score for colorectal cancer liver metastases.

BACKGROUND: The pathological tumor burden score (TBS) has been proven to be a better risk stratification tool for liver metastasis of colorectal cancer than the traditional clinical risk score (CRS). The aim of this study was to evaluate the prognostic value of the pathological tumor burden score in patients with or without neoadjuvant chemotherapy (NAC). METHODS: A total of 348 patients with colorectal liver metastases (CRLM) who underwent curative hepatic resection were retrospectively enrolled from September 1999 to December 2016. Univariable and multivariable Cox regression analyses were conducted to identify the independent predictors of prognosis. Kaplan-Meier curves and log-rank tests were used to determine whether TBS has enough discriminatory ability under certain grouping. RESULTS: Patients who received NAC had a higher median TBS than patients who did not receive NAC (4.07 vs. 2.69, P < 0.001). Among patients who did not receive NAC, those with TBS > 3 showed a significantly worse 3-year RFS (41.1% vs. 63.6%, P < 0.001) and 3-year OS rate (73.3% vs. 84.1%, P = 0.003) than those with TBS ≤ 3. Among the patients who received NAC, those with TBS ≤ 3 or TBS > 3 showed comparable 3-year RFS (33.3% vs. 26.4%, P = 0.400) and 3-year OS rates (76.5% vs. 58.2%, P = 0.064) to those who did not. Regardless of the regimen and response to NAC, there was no significant difference about 3-year RFS and 3-year OS rates between the TBS ≤ 3 and TBS > 3 groups. CONCLUSION: Pathological TBS can be applied to predict the RFS and OS of patients suffering from CRLM who did not receive NAC. However, pathological TBS might not be regard as prognosis in patients who did receive NAC.

Improvement of cryo-EM maps by simultaneous local and non-local deep learning.

Cryo-EM has emerged as the most important technique for structure determination of macromolecular complexes. However, raw cryo-EM maps often exhibit loss of contrast at high resolution and heterogeneity over the entire map. As such, various post-processing methods have been proposed to improve cryo-EM maps. Nevertheless, it is still challenging to improve both the quality and interpretability of EM maps. Addressing the challenge, we present a three-dimensional Swin-Conv-UNet-based deep learning framework to improve cryo-EM maps, named EMReady, by not only implementing both local and non-local modeling modules in a multiscale UNet architecture but also simultaneously minimizing the local smooth L1 distance and maximizing the non-local structural similarity between processed experimental and simulated target maps in the loss function. EMReady was extensively evaluated on diverse test sets of 110 primary cryo-EM maps and 25 pairs of half-maps at 3.0-6.0 Å resolutions, and compared with five state-of-the-art map post-processing methods. It is shown that EMReady can not only robustly enhance the quality of cryo-EM maps in terms of map-model correlations, but also improve the interpretability of the maps in automatic de novo model building.

Advances in Bi2WO6-Based Photocatalysts for Degradation of Organic Pollutants.

With the rapid development of modern industries, water pollution has become an urgent problem that endangers the health of human and wild animals. The photocatalysis technique is considered an environmentally friendly strategy for removing organic pollutants in wastewater. As an important member of Bi-series semiconductors, Bi2WO6 is widely used for fabricating high-performance photocatalysts. In this review, the recent advances of Bi2WO6-based photocatalysts are summarized. First, the controllable synthesis, surface modification and heteroatom doping of Bi2WO6 are introduced. In the respect of Bi2WO6-based composites, existing Bi2WO6-containing binary composites are classified into six types, including Bi2WO6/carbon or MOF composite, Bi2WO6/g-C3N4 composite, Bi2WO6/metal oxides composite, Bi2WO6/metal sulfides composite, Bi2WO6/Bi-series composite, and Bi2WO6/metal tungstates composite. Bi2WO6-based ternary composites are classified into four types, including Bi2WO6/g-C3N4/X, Bi2WO6/carbon/X, Bi2WO6/Au or Ag-based materials/X, and Bi2WO6/Bi-series semiconductors/X. The design, microstructure, and photocatalytic performance of Bi2WO6-based binary and ternary composites are highlighted. Finally, aimed at the existing problems in Bi2WO6-based photocatalysts, some solutions and promising research trends are proposed that would provide theoretical and practical guidelines for developing high-performance Bi2WO6-based photocatalysts.

High-Resolution Femtosecond Laser-Induced Carbon and Ag Hybrid Structure for Bend Sensing.

Miniaturized resistance-based portable bending sensors have been widely used for human health monitoring in recent years. Their sensitivities are defined by their resistance variations (ΔR/R), which strongly rely on the conductivity and minimum line width of the sensing unit. Laser-induced carbonization is a fast and simple method to fabricate porous-sensing structures. However, the fabrication resolution of conductive and deformation-sensitive structures is limited by the thermal effect of commonly used laser sources. With the assistance of femtosecond laser temporal shaping, plasma ejection confinement, and silver nitrate doping, the sheet resistance of the sensing structure was improved from 15 to 0.0004 Ω/□. A thin line with a lateral resolution of 6.5 µm is fabricated as the sensing unit. The fFabricated structures are characterized by electron microscopy, Raman spectroscopy, energy-dispersive spectroscopy, X-ray scattering, and time-resolved images. The strain sensor demonstrates a ΔR/R of 25.8% with a rising edge of 109 ms in the cyclic bending test. The sensor is further applied for detecting human pulse and finger bending.

The Pathologic Complete Response Ratio of Liver Metastases Represents a Valuable Prognostic Indicator.

Background and Objectives: The aim of this study was to evaluate the role of the pathologic complete response ratio of liver metastases (PCRRLM) in predicting the prognosis and recurrence of colorectal cancer liver metastases (CRLM). Methods: A total of 305 CRLM patients who underwent preoperative chemotherapy followed by hepatectomy were included. PCRRLM was defined as the number of liver metastases exhibiting pathologic complete response (PCR) divided by the number of total resected liver metastases. The Kaplan-Meier method was used to calculate survival, and differences were examined by the log-rank test. Univariate and multivariate analyses were performed to identify the predictors of PCRRLM, recurrence-free survival (RFS) and overall survival (OS). Results: Among the 305 included patients, 44 (14.4%) achieved a PCRRLM ≥0.50 (including PCRRLM = 1), and 261 (85.6%) achieved a PCRRLM <0.50 (including PCRRLM = 0). Patients of an older age (≥55 years old) and those with higher carcinoembryonic antigen (CEA) levels (≥5 ng/ml) were less likely to achieve a PCRRLM ≥0.50. In the multivariate analysis, PCRRLM≥ 0.50 (vs. < 0.50, HR [95% CI]: 0.67 [0.46-0.99], p = 0.043) was associated with better RFS. Positive lymph node status (vs. negative, HR [95% CI]: 1.46 [1.04-2.05], p = 0.028) and TBS ≥5 (vs. < 5, HR [95% CI]: 1.44 [1.02-2.04], p = 0.038) were associated with worse RFS. Conclusion: PCRRLM was significantly associated with long-term RFS after preoperative chemotherapy and CRLM resection. Thus, it may be a valuable indicator of recurrence in CRLM patients.

Model building of protein complexes from intermediate-resolution cryo-EM maps with deep learning-guided automatic assembly.

Advances in microscopy instruments and image processing algorithms have led to an increasing number of cryo-electron microscopy (cryo-EM) maps. However, building accurate models into intermediate-resolution EM maps remains challenging and labor-intensive. Here, we propose an automatic model building method of multi-chain protein complexes from intermediate-resolution cryo-EM maps, named EMBuild, by integrating AlphaFold structure prediction, FFT-based global fitting, domain-based semi-flexible refinement, and graph-based iterative assembling on the main-chain probability map predicted by a deep convolutional network. EMBuild is extensively evaluated on diverse test sets of 47 single-particle EM maps at 4.0-8.0 Å resolution and 16 subtomogram averaging maps of cryo-ET data at 3.7-9.3 Å resolution, and compared with state-of-the-art approaches. We demonstrate that EMBuild is able to build high-quality complex structures that are comparably accurate to the manually built PDB structures from the cryo-EM maps. These results demonstrate the accuracy and reliability of EMBuild in automatic model building.

Effect of Ho Addition on the Glass-Forming Ability and Crystallization Behaviors of Zr54Cu29Al10Ni7 Bulk Metallic Glass.

The effect of holmium (Ho) addition on the glass-forming ability (GFA) and crystallization behaviors of Zr54Cu29Al10Ni7 bulk metallic glass (BMGs) were studied by employing differential scanning calorimetry (DSC), X-ray diffraction (XRD), and transmission electron microscopy (TEM). The characteristic temperatures and activation energies of crystallization were obtained from DSC data. Classical kinetic modes were used to evaluate the crystallization processes of Zr54Cu29Al10Ni7 and Zr48Cu29Ni7Al10Ho6 BMGs. The results showed that Ho addition reduces the activation energy in the original crystallization period of Zr-based BMG and improves the nucleation, which is due to the formation of simpler compounds, such as CuZr2, Cu2Ho, and Al3Zr5.

TRScore: a 3D RepVGG-based scoring method for ranking protein docking models.

MOTIVATION: Protein-protein interactions (PPI) play important roles in cellular activities. Due to the technical difficulty and high cost of experimental methods, there are considerable interests towards the development of computational approaches, such as protein docking, to decipher PPI patterns. One of the important and difficult aspects in protein docking is recognizing near-native conformations from a set of decoys, but unfortunately, traditional scoring functions still suffer from limited accuracy. Therefore, new scoring methods are pressingly needed in methodological and/or practical implications. RESULTS: We present a new deep learning-based scoring method for ranking protein-protein docking models based on a 3D RepVGG network, named TRScore. To recognize near-native conformations from a set of decoys, TRScore voxelizes the protein-protein interface into a 3D grid labeled by the number of atoms in different physicochemical classes. Benefiting from the deep convolutional RepVGG architecture, TRScore can effectively capture the subtle differences between energetically favorable near-native models and unfavorable non-native decoys without needing extra information. TRScore was extensively evaluated on diverse test sets including protein-protein docking benchmark 5.0 update set, DockGround decoy set, as well as realistic CAPRI decoy set and overall obtained a significant improvement over existing methods in cross-validation and independent evaluations. AVAILABILITY AND IMPLEMENTATION: Codes available at: https://github.com/BioinformaticsCSU/TRScore.

Docking and scoring for nucleic acid-ligand interactions: Principles and current status.

Nucleic acid (NA)-ligand interactions have crucial roles in many cellular processes and, thus, are increasingly attracting therapeutic interest in drug discovery. Molecular docking is a valuable tool for studying molecular interactions. However, because NAs differ significantly from proteins in both their physical and chemical properties, traditional docking algorithms and scoring functions for protein-ligand interactions might not be applicable to NA-ligand docking. Therefore, various sampling strategies and scoring functions for NA-ligand interactions have been developed. Here, we review the basic principles and current status of docking algorithms and scoring functions for DNA/RNA-ligand interactions. We also discuss challenges and limitations of current docking and scoring approaches.

SSTR2 is a prognostic factor and a promising therapeutic target in glioma.

Gliomas are the most prevalent primary malignant central nervous system tumors among all tumors occurring in the brain and spinal cord. The poor outcome of glioma requires the discovery of novel biomarkers with potential therapeutic value. Somatostatin receptor subtype 2 (SSTR2) represents a diagnostic biomarker and potential therapeutic target in many cancers, such as meningioma and neuroendocrine tumors (NETs). However, the relationship of SSTR2 and glioma was unclear. Therefore, this study aimed to investigate the expression of SSTR2 and assess its prognostic and potential therapeutic value in a large cohort of patients with WHO grade I to IV glioma from a single Chinese center. Immunohistochemical analysis revealed that SSTR2 was highly expressed in 23.84% (72 of 302) of glioma (I-IV grade) samples. Among all glioma subtypes, high SSTR2 expression was detected mainly in oligodendroglioma, anaplastic oligodendroglioma, and astrocytoma, whereas SSTR2 was expressed at a low level, or not at all, in glioblastoma. Western blotting also confirmed the low expression of SSTR2 in glioblastoma cell lines. Statistical analysis showed that SSTR2 protein expression correlated significantly with WHO grade, the location of the tumor, epilepsy syndrome, mitosis (PHH3), proliferation index (Ki-67), IDH and 1p/19q-codeleted status. Kaplan-Meier analysis indicated that SSTR2 high expression was a good prognostic factor in glioma. In summary, this study demonstrated that SSTR2 might be a valuable prognostic factor and therapeutic target in certain glioma subtypes.

Platelet biomarkers identifying mild cognitive impairment in type 2 diabetes patients.

Type 2 diabetes mellitus (T2DM) is an independent risk factor of Alzheimer's disease (AD). Therefore, identifying periphery biomarkers correlated with mild cognitive impairment (MCI) is of importance for early diagnosis of AD. Here, we performed platelet proteomics in T2DM patients with MCI (T2DM-MCI) and without MCI (T2DM-nMCI). Pearson analysis of the omics data with MMSE (mini-mental state examination), Aß1-42/Aß1-40 (ß-amyloid), and rGSK-3ß(T/S9) (total to Serine-9-phosphorylated glycogen synthase kinase-3ß) revealed that mitophagy/autophagy-, insulin signaling-, and glycolysis/gluconeogenesis pathways-related proteins were most significantly involved. Among them, only the increase of optineurin, an autophagy-related protein, was simultaneously correlated with the reduced MMSE score, and the increased Aß1-42/Aß1-40 and rGSK-3ß(T/S9), and the optineurin alone could discriminate T2DM-MCI from T2DM-nMCI. Combination of the elevated platelet optineurin and rGSK-3ß(T/S9) enhanced the MCI-discriminating efficiency with AUC of 0.927, specificity of 86.7%, sensitivity of 85.3%, and accuracy of 0.859, which is promising for predicting cognitive decline in T2DM patients.

CHI3L2 Is a Novel Prognostic Biomarker and Correlated With Immune Infiltrates in Gliomas.

CHI3L2 (Chitinase-3-Like Protein 2) is a member of chitinase-like proteins (CLPs), which belong to the glycoside hydrolase 18 family. Its homologous gene, CHI3L1, has been extensively studied in various tumors and has been shown to be related to immune infiltration in breast cancer and glioblastoma. High CHI3L2 expression was reported to be associated with poor prognosis in breast cancer and renal cell carcinoma. However, the prognostic significance of CHI3L2 in glioma and its correlation between immune infiltration remains unclear. In this study, we examined 288 glioma samples by immunohistochemistry to find that CHI3L2 is expressed in tumor cells and macrophages in glioma tissues and highly expressed in glioblastoma and IDH wild-type gliomas. Relationships between CHI3L2 expression and clinical features (grade, age, Ki67 index, P53, PHH3 (mitotic figures), ATRX, TERTp, MGMTp, IDH, and 1p/19q co-deleted status) were evaluated. Kaplan-Meier survival was conducted to show high CHI3L2 expression in tumor cells (TC) and macrophage cells (MC) indicated poor prognosis in diffusely infiltrating glioma (DIG), lower-grade glioma (LGG), and IDH wild-type gliomas (IDH-wt). The overall survival time was higher in patients with dual-low CHI3L2 expression in TC and MC compared to those in patients with non-dual CHI3L2 expression and dual high expression in DIG and IDH wild-type gliomas. By univariate and multivariate analysis, we found that high CHI3L2 expression in tumor cells was an independent unfavorable prognostic factor in glioma patients. Moreover, we used two datasets (TCGA and CGGA) to verify the results of our study and explore the potential functional role of CHI3L2 by GO and KEGG analyses in gliomas. TIMER platform analysis indicated CHI3L2 expression was closely related to diverse marker genes of tumor immune infiltrating cells, including monocytes, TAMs, M1 macrophages, M2 macrophages, TGFß1+ Treg and T cell exhaustion in GBM and LGG. Western Blot validated CHI3L2 is expressed in glioma cells and microglia cells. The results of flow cytometry showed that CHI3L2 induces the apoptosis of CD8+ T cells. In conclusion, these results demonstrate CHI3L2 is related to poor prognosis and immune infiltrates in gliomas, suggesting it may serve as a promising prognostic biomarker and represent a new target for glioma patients.

Platelet biomarkers for a descending cognitive function: A proteomic approach.

Memory loss is the most common clinical sign in Alzheimer's disease (AD); thus, searching for peripheral biomarkers to predict cognitive decline is promising for early diagnosis of AD. As platelets share similarities to neuron biology, it may serve as a peripheral matrix for biomarkers of neurological disorders. Here, we conducted a comprehensive and in-depth platelet proteomic analysis using TMT-LC-MS/MS in the populations with mild cognitive impairment (MCI, MMSE = 18-23), severe cognitive impairments (AD, MMSE = 2-17), and the age-/sex-matched normal cognition controls (MMSE = 29-30). A total of 360 differential proteins were detected in MCI and AD patients compared with the controls. These differential proteins were involved in multiple KEGG pathways, including AD, AMP-activated protein kinase (AMPK) pathway, telomerase RNA localization, platelet activation, and complement activation. By correlation analysis with MMSE score, three positively correlated pathways and two negatively correlated pathways were identified to be closely related to cognitive decline in MCI and AD patients. Partial least squares discriminant analysis (PLS-DA) showed that changes of nine proteins, including PHB, UQCRH, CD63, GP1BA, FINC, RAP1A, ITPR1/2, and ADAM10 could effectively distinguish the cognitively impaired patients from the controls. Further machine learning analysis revealed that a combination of four decreased platelet proteins, that is, PHB, UQCRH, GP1BA, and FINC, was most promising for predicting cognitive decline in MCI and AD patients. Taken together, our data provide a set of platelet biomarkers for predicting cognitive decline which may be applied for the early screening of AD.

Full-length de novo protein structure determination from cryo-EM maps using deep learning.

MOTIVATION: Advances in microscopy instruments and image processing algorithms have led to an increasing number of Cryo-electron microscopy (cryo-EM) maps. However, building accurate models for the EM maps at 3-5 Å resolution remains a challenging and time-consuming process. With the rapid growth of deposited EM maps, there is an increasing gap between the maps and reconstructed/modeled three-dimensional (3D) structures. Therefore, automatic reconstruction of atomic-accuracy full-atom structures from EM maps is pressingly needed. RESULTS: We present a semi-automatic de novo structure determination method using a deep learning-based framework, named as DeepMM, which builds atomic-accuracy all-atom models from cryo-EM maps at near-atomic resolution. In our method, the main-chain and Cα positions as well as their amino acid and secondary structure types are predicted in the EM map using Densely Connected Convolutional Networks. DeepMM was extensively validated on 40 simulated maps at 5 Å resolution and 30 experimental maps at 2.6-4.8 Å resolution as well as an Electron Microscopy Data Bank-wide dataset of 2931 experimental maps at 2.6-4.9 Å resolution, and compared with state-of-the-art algorithms including RosettaES, MAINMAST and Phenix. Overall, our DeepMM algorithm obtained a significant improvement over existing methods in terms of both accuracy and coverage in building full-length protein structures on all test sets, demonstrating the efficacy and general applicability of DeepMM. AVAILABILITY AND IMPLEMENTATION: http://huanglab.phys.hust.edu.cn/DeepMM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

EMNUSS: a deep learning framework for secondary structure annotation in cryo-EM maps.

Cryo-electron microscopy (cryo-EM) has become one of important experimental methods in structure determination. However, despite the rapid growth in the number of deposited cryo-EM maps motivated by advances in microscopy instruments and image processing algorithms, building accurate structure models for cryo-EM maps remains a challenge. Protein secondary structure information, which can be extracted from EM maps, is beneficial for cryo-EM structure modeling. Here, we present a novel secondary structure annotation framework for cryo-EM maps at both intermediate and high resolutions, named EMNUSS. EMNUSS adopts a three-dimensional (3D) nested U-net architecture to assign secondary structures for EM maps. Tested on three diverse datasets including simulated maps, middle resolution experimental maps, and high-resolution experimental maps, EMNUSS demonstrated its accuracy and robustness in identifying the secondary structures for cyro-EM maps of various resolutions. The EMNUSS program is freely available at http://huanglab.phys.hust.edu.cn/EMNUSS.

Trace carbonyl analysis in water samples by integrating magnetic molecular imprinting and capillary electrophoresis.

In order to obtain high derivatization efficiency, the overuse of derivative agent 2,4-dinitrophenylhydrazine (2,4-DNPH) is necessary for carbonyl detection. But, the 2,4-DNPH residue will cause background interferences and limit the pre-concentration factor of the target analytes. In order to overcome the bottleneck problems, the magnetic molecularly imprinted polymer based solid-phase extraction (MMIPs-SPE) method was developed with 2,4-dinitroaniline (2,4-DNAN) as the dummy template. The characteristics and selectivity of the MMIPs were investigated. Under the optimized conditions, the enrichment of carbonyls-DNPH derivatives with simultaneous removal of the surplus 2,4-DNPH was achieved. By coupling with capillary electrophoresis (CE), a satisfactory analytical performance was obtained with the detection limit ranging from 1.2 to 8.7 µg L-1 for 8 carbonyls. The MMIPs-SPE-CE method was applied successfully for the carbonyl assessment in stream water, tap water and bottled water. In addition, the migration of carbonyls in bottled drinking water was investigated under UV irradiation and heating.

Determination of aldehydes in water samples by coupling magnetism-reinforced molecular imprinting monolith microextraction and non-aqueous capillary electrophoresis.

Excess 2, 4-dinitrophenylhydrazine (2, 4-DNPH) is often applied for the derivatization of aldehyde prior to the chromatographic analysis. However, the residual 2, 4-DNPH may cause background interference and limit the enrichment efficiency of trace aldehydes. To overcome the above bottle-neck problems, improve the extraction efficiency and omit the manipulation for changing the polarity of solvent for the hydrophobic analytes separation, a new method combining magnetism-reinforced in-tube solid phase microextraction (IT-SPME) technique with non-aqueous capillary electrophoresis (NACE) was developed. The monolithic extraction cartridge was prepared in situ inside a capillary and doped with magnetic molecular imprinting polymers (mMIPs). The selective and efficient extraction of the derived analytes with simultaneous removal of the superfluous derivatization agent was achieved owing to the combined effect of molecular imprinting and magnetism reinforcement. By coupling with NACE, the highly hydrophobic effluent can be analyzed directly. The LODs of the method are between 0.0032~0.0049 mg L-1 and the recoveries are between 87.3~99.8% for the tested aldehydes. The developed approach is sensitive enough for detection of surface (drinking) water. The aldehydes in real water samples have been detected by this method, showing results that are in good agreement with the standard SPE-HPLC method.