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1.
J Am Chem Soc ; 146(31): 21948-21959, 2024 Aug 07.
Artigo em Inglês | MEDLINE | ID: mdl-39075033

RESUMO

Single-molecule spectroscopy offers state-resolved measurements on charge-transfer reactions of single semiconductor nanocrystals, leading to the discovery of up to six single-charge transfer reactions with seven transient states for single CdSe/CdS core/shell nanocrystals with water (or oxygen) as the hole (or electron) acceptors. Kinetic rates of three photoinduced single-hole transfer reactions decrease significantly upon increasing the number of excess electrons in a nanocrystal, mainly due to efficient Auger nonradiative recombination of the charged single excitons. Conversely, the kinetic rates of three single-electron transfer reactions of an unexcited nanocrystal increase proportionally to the number of excess electrons in it. Results here reveal that charge-transfer reactions of nanocrystals, at the center of nearly all their functions, could only be deciphered at a state-resolved level on a single nanocrystal. Size-dependent studies validate the weakly confined semiconductor nanocrystals, instead of strongly confined ones (quantum dots), as optimal candidates for photochemical and optoelectronic applications.

2.
Anal Chem ; 96(32): 12991-12998, 2024 Aug 13.
Artigo em Inglês | MEDLINE | ID: mdl-39075986

RESUMO

With the increasing demand for trace sample analysis, injecting trace samples into liquid chromatography-mass spectrometry (LC-MS) systems with minimal loss has become a major challenge. Herein, we describe an in situ LC-MS analytical probe, the Falcon probe, which integrates multiple functions of high-pressure sample injection without sample loss, high-efficiency LC separation, and electrospray. The main body of the Falcon probe is made of stainless steel and fabricated by the computer numerical control (CNC) technique, which has ultrahigh mechanical strength. By coupling a nanoliter-scale droplet reactor made of polyether ether ketone (PEEK) material, the Falcon probe-based LC-MS system was capable of operating at mobile-phase pressures up to 800 bar, which is comparable to those of conventional ultraperformance liquid chromatography (UPLC) systems. Using the probe pressing microamount in situ (PPMI) injection approach, the Falcon probe-based LC-MS system showed high separation efficiency and good repeatability with relative standard deviations (RSDs) of retention time and peak area of 1.8% and 9.9%, respectively, in peptide mixture analysis (n = 6). We applied this system to the analysis of a trace amount of 200 pg of HeLa protein digest and successfully identified an average of 766 protein groups (n = 5). By combining in situ sample pretreatment at the nanoliter range, we further applied the present system in single-cell proteomic analysis, and 241 protein groups were identified in single 293 cells, which preliminarily demonstrated its potential in the analysis of trace amounts of samples with complex compositions.


Assuntos
Pressão , Humanos , Cromatografia Líquida/métodos , Espectrometria de Massas/métodos , Nanotecnologia , Polietilenoglicóis/química , Peptídeos/análise , Cromatografia Líquida de Alta Pressão , Células HeLa , Benzofenonas/análise , Benzofenonas/química , Polímeros/química , Cetonas/química , Cetonas/análise , Proteômica/métodos
3.
Anal Chem ; 96(14): 5499-5508, 2024 04 09.
Artigo em Inglês | MEDLINE | ID: mdl-38547315

RESUMO

Characterizing the profiles of proteome and metabolome at the single-cell level is of great significance in single-cell multiomic studies. Herein, we proposed a novel strategy called one-shot single-cell proteome and metabolome analysis (scPMA) to acquire the proteome and metabolome information in a single-cell individual in one injection of LC-MS/MS analysis. Based on the scPMA strategy, a total workflow was developed to achieve the single-cell capture, nanoliter-scale sample pretreatment, one-shot LC injection and separation of the enzyme-digested peptides and metabolites, and dual-zone MS/MS detection for proteome and metabolome profiling. Benefiting from the scPMA strategy, we realized dual-omic analysis of single tumor cells, including A549, HeLa, and HepG2 cells with 816, 578, and 293 protein groups and 72, 91, and 148 metabolites quantified on average. A single-cell perspective experiment for investigating the doxorubicin-induced antitumor effects in both the proteome and metabolome aspects was also performed.


Assuntos
Proteoma , Espectrometria de Massas em Tandem , Humanos , Proteoma/metabolismo , Cromatografia Líquida , Metaboloma , Células HeLa
4.
Zhongguo Yi Xue Ke Xue Yuan Xue Bao ; 46(1): 19-24, 2024 Feb.
Artigo em Zh | MEDLINE | ID: mdl-38433626

RESUMO

Objective To analyze the current situation of dietary diversity and caregiver self-efficacy for complementary feeding among infants and young children aged 6 to 23 months in rural Nanchong city,Sichuan province,and to explore the relationship between dietary diversity and caregiver self-efficacy. Methods Multi-stage randomized cluster sampling method was used to select infants and young children aged 6 to 23 months and their caregivers in rural areas of Nanchong city,Sichuan province as the subjects.A structured questionnaire was designed to collect the basic information of the subjects,dietary diversity,and caregiver self-efficacy for complementary feeding.Multivariate Logistic regression was adopted to analyze the relationship between the dietary diversity and caregiver self-efficacy for complementary feeding of infants and young children. Results A total of 770 pairs of infants and young children and their caregivers were included.The minimum pass rate of dietary diversity was 61.56%(474/770) for all the infants and young children and 45.00%(108/240),69.16%(287/415),and 68.70%(79/115) for the infants and young children aged 6 to 11,12 to 17,and 18 to 23 months,respectively.The results of regression analysis showed that the caregiver self-efficacy of complementary feeding was a contributing factor for qualified dietary diversity of infants and young children in the case of other confounders being controlled(OR=1.42,95%CI=1.17-1.73,P<0.001). Conclusion The dietary diversity for infants and young children in rural Nanchong city,Sichuan province needs to be improved,and caregivers with higher self-efficacy of complementary feeding are more likely to provide diversified complementary feeding for infants and young children.


Assuntos
Cuidadores , Autoeficácia , Criança , Lactente , Humanos , Pré-Escolar , Dieta , China
5.
Nat Commun ; 15(1): 2448, 2024 Mar 19.
Artigo em Inglês | MEDLINE | ID: mdl-38503734

RESUMO

Deep learning has achieved a notable success in mass spectrometry-based proteomics and is now emerging in glycoproteomics. While various deep learning models can predict fragment mass spectra of peptides with good accuracy, they cannot cope with the non-linear glycan structure in an intact glycopeptide. Herein, we present DeepGlyco, a deep learning-based approach for the prediction of fragment spectra of intact glycopeptides. Our model adopts tree-structured long-short term memory networks to process the glycan moiety and a graph neural network architecture to incorporate potential fragmentation pathways of a specific glycan structure. This feature is beneficial to model explainability and differentiation ability of glycan structural isomers. We further demonstrate that predicted spectral libraries can be used for data-independent acquisition glycoproteomics as a supplement for library completeness. We expect that this work will provide a valuable deep learning resource for glycoproteomics.


Assuntos
Aprendizado Profundo , Espectrometria de Massas em Tandem , Glicopeptídeos/química , Proteômica , Polissacarídeos/química
6.
Acad Radiol ; 2024 Jun 12.
Artigo em Inglês | MEDLINE | ID: mdl-38871552

RESUMO

RATIONALE AND OBJECTIVES: to develop a deep learning radiomics graph network (DLRN) that integrates deep learning features extracted from gray scale ultrasonography, radiomics features and clinical features, for distinguishing parotid pleomorphic adenoma (PA) from adenolymphoma (AL) MATERIALS AND METHODS: A total of 287 patients (162 in training cohort, 70 in internal validation cohort and 55 in external validation cohort) from two centers with histologically confirmed PA or AL were enrolled. Deep transfer learning features and radiomics features extracted from gray scale ultrasound images were input to machine learning classifiers including logistic regression (LR), support vector machines (SVM), KNN, RandomForest (RF), ExtraTrees, XGBoost, LightGBM, and MLP to construct deep transfer learning radiomics (DTL) models and Rad models respectively. Deep learning radiomics (DLR) models were constructed by integrating the two features and DLR signatures were generated. Clinical features were further combined with the signatures to develop a DLRN model. The performance of these models was evaluated using receiver operating characteristic (ROC) curve analysis, calibration, decision curve analysis (DCA), and the Hosmer-Lemeshow test. RESULTS: In the internal validation cohort and external validation cohort, comparing to Clinic (AUC=0.767 and 0.777), Rad (AUC=0.841 and 0.748), DTL (AUC=0.740 and 0.825) and DLR (AUC=0.863 and 0.859), the DLRN model showed greatest discriminatory ability (AUC=0.908 and 0.908) showed optimal discriminatory ability. CONCLUSION: The DLRN model built based on gray scale ultrasonography significantly improved the diagnostic performance for benign salivary gland tumors. It can provide clinicians with a non-invasive and accurate diagnostic approach, which holds important clinical significance and value. Ensemble of multiple models helped alleviate overfitting on the small dataset compared to using Resnet50 alone.

7.
Nat Commun ; 15(1): 1279, 2024 Feb 10.
Artigo em Inglês | MEDLINE | ID: mdl-38341466

RESUMO

The shotgun proteomic analysis is currently the most promising single-cell protein sequencing technology, however its identification level of ~1000 proteins per cell is still insufficient for practical applications. Here, we develop a pick-up single-cell proteomic analysis (PiSPA) workflow to achieve a deep identification capable of quantifying up to 3000 protein groups in a mammalian cell using the label-free quantitative method. The PiSPA workflow is specially established for single-cell samples mainly based on a nanoliter-scale microfluidic liquid handling robot, capable of achieving single-cell capture, pretreatment and injection under the pick-up operation strategy. Using this customized workflow with remarkable improvement in protein identification, 2449-3500, 2278-3257 and 1621-2904 protein groups are quantified in single A549 cells (n = 37), HeLa cells (n = 44) and U2OS cells (n = 27) under the DIA (MBR) mode, respectively. Benefiting from the flexible cell picking-up ability, we study HeLa cell migration at the single cell proteome level, demonstrating the potential in practical biological research from single-cell insight.


Assuntos
Proteoma , Proteômica , Animais , Humanos , Células HeLa , Proteômica/métodos , Proteoma/metabolismo , Análise de Célula Única , Fluxo de Trabalho , Mamíferos/metabolismo
8.
Front Oncol ; 13: 1268789, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-38273852

RESUMO

Objectives: To differentiate parotid pleomorphic adenoma (PA) from adenolymphoma (AL) using radiomics of grayscale ultrasonography in combination with clinical features. Methods: This retrospective study aimed to analyze the clinical and radiographic characteristics of 162 cases from December 2019 to March 2023. The study population consisted of a training cohort of 113 patients and a validation cohort of 49 patients. Grayscale ultrasonography was processed using ITP-Snap software and Python to delineate regions of interest (ROIs) and extract radiomic features. Univariate analysis, Spearman's correlation, greedy recursive elimination strategy, and least absolute shrinkage and selection operator (LASSO) correlation were employed to select relevant radiographic features. Subsequently, eight machine learning methods (LR, SVM, KNN, RandomForest, ExtraTrees, XGBoost, LightGBM, and MLP) were employed to build a quantitative radiomic model using the selected features. A radiomic nomogram was developed through the utilization of multivariate logistic regression analysis, integrating both clinical and radiomic data. The accuracy of the nomogram was assessed using receiver operating characteristic (ROC) curve analysis, calibration, decision curve analysis (DCA), and the Hosmer-Lemeshow test. Results: To differentiate PA from AL, the radiomic model using SVM showed optimal discriminatory ability (accuracy = 0.929 and 0.857, sensitivity = 0.946 and 0.800, specificity = 0.921 and 0.897, positive predictive value = 0.854 and 0.842, and negative predictive value = 0.972 and 0.867 in the training and validation cohorts, respectively). A nomogram incorporating rad-Signature and clinical features achieved an area under the ROC curve (AUC) of 0.983 (95% confidence interval [CI]: 0.965-1) and 0.910 (95% CI: 0.830-0.990) in the training and validation cohorts, respectively. Decision curve analysis showed that the nomogram and radiomic model outperformed the clinical-factor model in terms of clinical usefulness. Conclusion: A nomogram based on grayscale ultrasonic radiomics and clinical features served as a non-invasive tool capable of differentiating PA and AL.

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