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Data-independent acquisition (DIA)-based mass spectrometry is becoming an increasingly popular mass spectrometry acquisition strategy for carrying out quantitative proteomics experiments. Most of the popular DIA search engines make use of in silico generated spectral libraries. However, the generation of high-quality spectral libraries for DIA data analysis remains a challenge, particularly because most such libraries are generated directly from data-dependent acquisition (DDA) data or are from in silico prediction using models trained on DDA data. In this study, we developed Carafe, a tool that generates high-quality experiment-specific in silico spectral libraries by training deep learning models directly on DIA data. We demonstrate the performance of Carafe on a wide range of DIA datasets, where we observe improved fragment ion intensity prediction and peptide detection relative to existing pretrained DDA models., Competing Interests: The MacCoss Lab at the University of Washington receives funding from Agilent, Bruker, Sciex, Shimadzu, Thermo Fisher Scientific, and Waters to support the development of Skyline, a quantitative analysis software tool. MJM is a paid consultant for Thermo Fisher Scientific.

Precision mapping of site-specific glycans using mass spectrometry is vital in glycoproteomics. However, the diversity of glycan compositions across species often exceeds database capacity, hindering the identification of rare glycans. Here, we introduce pGlycoNovo, a software within the pGlyco3 software environment, which employs a glycan first-based full-range Y-ion dynamic searching strategy. pGlycoNovo enables de novo identification of intact glycopeptides with rare glycans by considering all possible monosaccharide combinations, expanding the glycan search space to 16~1000 times compared to non-open search methods, while maintaining accuracy, sensitivity and speed. Reanalysis of SARS Covid-2 spike protein glycosylation data revealed 230 additional site-specific N-glycans and 30 previously unreported O-glycans. pGlycoNovo demonstrated high complementarity to six other tools and superior search speed. It enables characterization of site-specific N-glycosylation across five evolutionarily distant species, contributing to a dataset of 32,549 site-specific glycans on 4602 proteins, including 2409 site-specific rare glycans, and uncovering unexpected glycan fragments., (© 2024. The Author(s).)

Background: The gut mycobiome is closely linked to health and disease; however, its role in the progression of type 2 diabetes mellitus (T2DM) remains obscure. Here, a multi-omics approach was employed to explore the role of intestinal fungi in the deterioration of glycemic control., Methods: 350 participants without hypoglycemic therapies were invited for a standard oral glucose tolerance test to determine their status of glycemic control. The gut mycobiome was identified through internal transcribed spacer sequencing, host genetics were determined by genotyping array, and plasma metabolites were measured with untargeted liquid chromatography mass spectrometry., Findings: The richness of fungi was higher, whereas its dissimilarity was markedly lower, in participants with T2DM. Moreover, the diversity and composition of fungi were closely associated with insulin sensitivity and pancreatic β-cell functions. With the exacerbation of glycemic control, the co-occurrence network among fungus taxa became increasingly complex, and the complexity of the interaction network was inversely associated with insulin sensitivity. Mendelian randomization analysis further demonstrated that the Archaeorhizomycetes class, Fusarium genus, and Neoascochyta genus were causally linked to impaired glucose metabolism. Furthermore, integrative analysis with metabolomics showed that increased 4-hydroxy-2-oxoglutaric acid, ketoleucine, lysophosphatidylcholine (20:3/0:0), and N-lactoyl-phenylalanine, but decreased lysophosphatidylcholine (O-18:2), functioned as key molecules linking the adverse effect of Fusarium genus on insulin sensitivity., Conclusions: Our study uncovers a strong association between disturbance in gut fungi and the progression of T2DM and highlights the potential of targeting the gut mycobiome for the management of T2DM., Fundings: This study was supported by MOST and NSFC of China., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

In common with other omics technologies, mass spectrometry (MS)-based proteomics produces ever-increasing amounts of raw data, making efficient analysis a principal challenge. A plethora of different computational tools can process the MS data to derive peptide and protein identification and quantification. However, during the last years there has been dramatic progress in computer science, including collaboration tools that have transformed research and industry. To leverage these advances, we develop AlphaPept, a Python-based open-source framework for efficient processing of large high-resolution MS data sets. Numba for just-in-time compilation on CPU and GPU achieves hundred-fold speed improvements. AlphaPept uses the Python scientific stack of highly optimized packages, reducing the code base to domain-specific tasks while accessing the latest advances. We provide an easy on-ramp for community contributions through the concept of literate programming, implemented in Jupyter Notebooks. Large datasets can rapidly be processed as shown by the analysis of hundreds of proteomes in minutes per file, many-fold faster than acquisition. AlphaPept can be used to build automated processing pipelines with web-serving functionality and compatibility with downstream analysis tools. It provides easy access via one-click installation, a modular Python library for advanced users, and via an open GitHub repository for developers., (© 2024. The Author(s).)

Optimizing data-independent acquisition methods for proteomics applications often requires balancing spectral resolution and acquisition speed. Here, we describe a real-time full mass range implementation of the phase-constrained spectrum deconvolution method (ΦSDM) for Orbitrap mass spectrometry that increases mass resolving power without increasing scan time. Comparing its performance to the standard enhanced Fourier transformation signal processing revealed that the increased resolving power of ΦSDM is beneficial in areas of high peptide density and comes with a greater ability to resolve low-abundance signals. In a standard 2 h analysis of a 200 ng HeLa digest, this resulted in an increase of 16% in the number of quantified peptides. As the acquisition speed becomes even more important when using fast chromatographic gradients, we further applied ΦSDM methods to a range of shorter gradient lengths (21, 12, and 5 min). While ΦSDM improved identification rates and spectral quality in all tested gradients, it proved particularly advantageous for the 5 min gradient. Here, the number of identified protein groups and peptides increased by >15% in comparison to enhanced Fourier transformation processing. In conclusion, ΦSDM is an alternative signal processing algorithm for processing Orbitrap data that can improve spectral quality and benefit quantitative accuracy in typical proteomics experiments, especially when using short gradients., Competing Interests: Conflict of interest K. L. F., A. K., D. M., K. A., D. G., and A. M. are employees of Thermo Fisher Scientific, the manufacturer of Orbitrap instrumentation used in this research. M. M. is an indirect investor in Evosep Biosystems. All other authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Distinction of non-self from self is the major task of the immune system. Immunopeptidomics studies the peptide repertoire presented by the human leukocyte antigen (HLA) protein, usually on tissues. However, HLA peptides are also bound to plasma soluble HLA (sHLA), but little is known about their origin and potential for biomarker discovery in this readily available biofluid. Currently, immunopeptidomics is hampered by complex workflows and limited sensitivity, typically requiring several mL of plasma. Here, we take advantage of recent improvements in the throughput and sensitivity of mass spectrometry (MS)-based proteomics to develop a highly sensitive, automated, and economical workflow for HLA peptide analysis, termed Immunopeptidomics by Biotinylated Antibodies and Streptavidin (IMBAS). IMBAS-MS quantifies more than 5000 HLA class I peptides from only 200 μl of plasma, in just 30 min. Our technology revealed that the plasma immunopeptidome of healthy donors is remarkably stable throughout the year and strongly correlated between individuals with overlapping HLA types. Immunopeptides originating from diverse tissues, including the brain, are proportionately represented. We conclude that sHLAs are a promising avenue for immunology and potentially for precision oncology., Competing Interests: Conflict of interest M. M. is an indirect investor in Evosep Biosystems. All other authors declare that they have no conflicts of interest with the contents of this article., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Single-cell proteomics aims to characterize biological function and heterogeneity at the level of proteins in an unbiased manner. It is currently limited in proteomic depth, throughput, and robustness, which we address here by a streamlined multiplexed workflow using data-independent acquisition (mDIA). We demonstrate automated and complete dimethyl labeling of bulk or single-cell samples, without losing proteomic depth. Lys-N digestion enables five-plex quantification at MS1 and MS2 level. Because the multiplexed channels are quantitatively isolated from each other, mDIA accommodates a reference channel that does not interfere with the target channels. Our algorithm RefQuant takes advantage of this and confidently quantifies twice as many proteins per single cell compared to our previous work (Brunner et al, PMID 35226415), while our workflow currently allows routine analysis of 80 single cells per day. Finally, we combined mDIA with spatial proteomics to increase the throughput of Deep Visual Proteomics seven-fold for microdissection and four-fold for MS analysis. Applying this to primary cutaneous melanoma, we discovered proteomic signatures of cells within distinct tumor microenvironments, showcasing its potential for precision oncology., (© 2023 The Authors. Published under the terms of the CC BY 4.0 license.)

SARS-CoV-2 may directly and indirectly damage lung tissue and other host organs, but there are few system-wide, untargeted studies of these effects on the human body. Here, we developed a parallelized mass spectrometry (MS) proteomics workflow enabling the rapid, quantitative analysis of hundreds of virus-infected FFPE tissues. The first layer of response to SARS-CoV-2 in all tissues was dominated by circulating inflammatory molecules. Beyond systemic inflammation, we differentiated between systemic and true tissue-specific effects to reflect distinct COVID-19-associated damage patterns. Proteomic changes in the lungs resembled those of diffuse alveolar damage (DAD) in non-COVID-19 patients. Extensive organ-specific changes were also evident in the kidneys, liver, and lymphatic and vascular systems. Secondary inflammatory effects in the brain were related to rearrangements in neurotransmitter receptors and myelin degradation. These MS-proteomics-derived results contribute substantially to our understanding of COVID-19 pathomechanisms and suggest strategies for organ-specific therapeutic interventions., (© 2023 The Authors. Published under the terms of the CC BY 4.0 license.)

Large-scale intact glycopeptide identification has been advanced by software tools. However, tools for quantitative analysis remain lagging behind, which hinders exploring the differential site-specific glycosylation. Here, we report pGlycoQuant, a generic tool for both primary and tandem mass spectrometry-based intact glycopeptide quantitation. pGlycoQuant advances in glycopeptide matching through applying a deep learning model that reduces missing values by 19-89% compared with Byologic, MSFragger-Glyco, Skyline, and Proteome Discoverer, as well as a Match In Run algorithm for more glycopeptide coverage, greatly expanding the quantitative function of several widely used search engines, including pGlyco 2.0, pGlyco3, Byonic and MSFragger-Glyco. Further application of pGlycoQuant to the N-glycoproteomic study in three different metastatic HCC cell lines quantifies 6435 intact N-glycopeptides and, together with in vitro molecular biology experiments, illustrates site 979-core fucosylation of L1CAM as a potential regulator of HCC metastasis. We expected further applications of the freely available pGlycoQuant in glycoproteomic studies., (© 2022. The Author(s).)

Machine learning and in particular deep learning (DL) are increasingly important in mass spectrometry (MS)-based proteomics. Recent DL models can predict the retention time, ion mobility and fragment intensities of a peptide just from the amino acid sequence with good accuracy. However, DL is a very rapidly developing field with new neural network architectures frequently appearing, which are challenging to incorporate for proteomics researchers. Here we introduce AlphaPeptDeep, a modular Python framework built on the PyTorch DL library that learns and predicts the properties of peptides ( https://github.com/MannLabs/alphapeptdeep ). It features a model shop that enables non-specialists to create models in just a few lines of code. AlphaPeptDeep represents post-translational modifications in a generic manner, even if only the chemical composition is known. Extensive use of transfer learning obviates the need for large data sets to refine models for particular experimental conditions. The AlphaPeptDeep models for predicting retention time, collisional cross sections and fragment intensities are at least on par with existing tools. Additional sequence-based properties can also be predicted by AlphaPeptDeep, as demonstrated with a HLA peptide prediction model to improve HLA peptide identification for data-independent acquisition ( https://github.com/MannLabs/PeptDeep-HLA )., (© 2022. The Author(s).)

The recent revolution in computational protein structure prediction provides folding models for entire proteomes, which can now be integrated with large-scale experimental data. Mass spectrometry (MS)-based proteomics has identified and quantified tens of thousands of posttranslational modifications (PTMs), most of them of uncertain functional relevance. In this study, we determine the structural context of these PTMs and investigate how this information can be leveraged to pinpoint potential regulatory sites. Our analysis uncovers global patterns of PTM occurrence across folded and intrinsically disordered regions. We found that this information can help to distinguish regulatory PTMs from those marking improperly folded proteins. Interestingly, the human proteome contains thousands of proteins that have large folded domains linked by short, disordered regions that are strongly enriched in regulatory phosphosites. These include well-known kinase activation loops that induce protein conformational changes upon phosphorylation. This regulatory mechanism appears to be widespread in kinases but also occurs in other protein families such as solute carriers. It is not limited to phosphorylation but includes ubiquitination and acetylation sites as well. Furthermore, we performed three-dimensional proximity analysis, which revealed examples of spatial coregulation of different PTM types and potential PTM crosstalk. To enable the community to build upon these first analyses, we provide tools for 3D visualization of proteomics data and PTMs as well as python libraries for data accession and processing., Competing Interests: The authors have declared that no competing interests exist.

Background: High mobility group box 1 (HMGB1) released by neurons and microglia was demonstrated to be an important mediator in depressive-like behaviors induced by chronic unpredictable mild stress (CUMS), which could lead to the imbalance of two different metabolic approaches in kynurenine pathway (KP), thus enhancing glutamate transmission and exacerbating depressive-like behaviors. Evidence showed that HMGB1 signaling might be regulated by Connexin (Cx) 36 in inflammatory diseases of central nervous system (CNS). Our study aimed to further explore the role of Cx36 in depressive-like behaviors and its relationship with HMGB1., Methods: After 4-week chronic stress, behavioral tests were conducted to evaluate depressive-like behaviors, including sucrose preference test (SPT), tail suspension test (TST), forced swimming test (FST), and open field test (OFT). Western blot analysis and immunofluorescence staining were used to observe the expression and location of Cx36. Enzyme-linked immunosorbent assay (ELISA) was adopted to detect the concentrations of inflammatory cytokines. And the excitability and inward currents of hippocampal neurons were recorded by whole-cell patch clamping., Results: The expression of Cx36 was significantly increased in hippocampal neurons of mice exposed to CUMS, while treatment with glycyrrhizinic acid (GZA) or quinine could both down-regulate Cx36 and alleviate depressive-like behaviors. The proinflammatory cytokines like HMGB1, tumor necrosis factor alpha (TNF-α), and interleukin-1β (IL-1β) were all elevated by CUMS, and application of GZA and quinine could decrease them. In addition, the enhanced excitability and inward currents of hippocampal neurons induced by lipopolysaccharide (LPS) could be reduced by either GZA or quinine., Conclusions: Inhibition of Cx36 in hippocampal neurons might attenuates HMGB1-mediated depressive-like behaviors induced by CUMS through down-regulation of the proinflammatory cytokines and reduction of the excitability and intracellular ion overload., (© 2021 The Authors. Brain and Behavior published by Wiley Periodicals LLC.)

The remarkable advancement of top-down proteomics in the past decade is driven by the technological development in separation, mass spectrometry (MS) instrumentation, novel fragmentation, and bioinformatics. However, the accurate identification and quantification of proteoforms, all clearly-defined molecular forms of protein products from a single gene, remain a challenging computational task. This is in part due to the complicated mass spectra from intact proteoforms when compared to those from the digested peptides. Herein, pTop 2.0 is developed to fill in the gap between the large-scale complex top-down MS data and the shortage of high-accuracy bioinformatic tools. Compared with pTop 1.0, the first version, pTop 2.0 concentrates mainly on the identification of the proteoforms with unexpected modifications or a terminal truncation. The quantitation based on isotopic labeling is also a new function, which can be carried out by the convenient and user-friendly "one-key operation," integrated together with the qualitative identifications. The accuracy and running speed of pTop 2.0 is significantly improved on the test data sets. This chapter will introduce the main features, step-by-step running operations, and algorithmic developments of pTop 2.0 in order to push the identification and quantitation of intact proteoforms to a higher-accuracy level in top-down proteomics., (© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Breast cancer (BRCA) is a heterogeneous malignancy closely related to the tumor microenvironment (TME) cell infiltration. N 6-methyladenosine (m6A) modification of mRNA plays a crucial regulator in regulating the immune microenvironment of BRCA. Immunotherapy represents a paradigm shift in BRCA treatment; however, lack of an appropriate approach for treatment evaluation is a significant issue in this field. In this study, we attempted to establish a prognostic signature of BRCA based on m6A-related immune genes and to investigate the potential association between prognosis and immunotherapy. We comprehensively evaluated the m6A modification patterns of BRCA tissues and non-tumor tissues from The Cancer Genome Atlas and the modification patterns with TME cell-infiltrating characteristics. Overall, 1,977 TME-related genes were identified in the literature. Based on LASSO and Cox regression analyses, the m6A-related immune score (m6A-IS) was established to characterize the TME of BRCA and predict prognosis and efficacy associated with immunotherapy. We developed an m6A-IS to effectively predict immune infiltration and the prognosis of patients with BRCA. The prognostic score model represented robust predictive performance in both the training and validation cohorts. The low-m6A-IS group was characterized by enhanced antigen presentation and improved immune checkpoint expression, further indicating sensitivity to immunotherapy. Compared with the patients in the high-score group, the overall survival rate after treatment in the low-score group was significantly higher in the testing and validation cohorts. We constructed an m6A-IS system to examine the ability of the m6A signature to predict the infiltration of immune cells of the TME in BRCA, and the m6A-IS system acted as an independent prognostic biomarker that predicts the response of patients with BRCA in immunotherapy., Competing Interests: The Handling Editor DZ declared a shared parent affiliation with the authors MMZ, YLL, YL, YY, ML, YJY, KJ, SW, SW at the time of the review. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Zhang, Lin, Zeng, Li, Yang, Liu, Ye, Jiang, Wang and Wang.)

Great advances have been made in mass spectrometric data interpretation for intact glycopeptide analysis. However, accurate identification of intact glycopeptides and modified saccharide units at the site-specific level and with fast speed remains challenging. Here, we present a glycan-first glycopeptide search engine, pGlyco3, to comprehensively analyze intact N- and O-glycopeptides, including glycopeptides with modified saccharide units. A glycan ion-indexing algorithm developed for glycan-first search makes pGlyco3 5-40 times faster than other glycoproteomic search engines without decreasing accuracy or sensitivity. By combining electron-based dissociation spectra, pGlyco3 integrates a dynamic programming-based algorithm termed pGlycoSite for site-specific glycan localization. Our evaluation shows that the site-specific glycan localization probabilities estimated by pGlycoSite are suitable to localize site-specific glycans. With pGlyco3, we confidently identified N-glycopeptides and O-mannose glycopeptides that were extensively modified by ammonia adducts in yeast samples. The freely available pGlyco3 is an accurate and flexible tool that can be used to identify glycopeptides and modified saccharide units., (© 2021. The Author(s).)

There is an avalanche of biomedical data generation and a parallel expansion in computational capabilities to analyze and make sense of these data. Starting with genome sequencing and widely employed deep sequencing technologies, these trends have now taken hold in all omics disciplines and increasingly call for multi-omics integration as well as data interpretation by artificial intelligence technologies. Here, we focus on mass spectrometry (MS)-based proteomics and describe how machine learning and, in particular, deep learning now predicts experimental peptide measurements from amino acid sequences alone. This will dramatically improve the quality and reliability of analytical workflows because experimental results should agree with predictions in a multi-dimensional data landscape. Machine learning has also become central to biomarker discovery from proteomics data, which now starts to outperform existing best-in-class assays. Finally, we discuss model transparency and explainability and data privacy that are required to deploy MS-based biomarkers in clinical settings., Competing Interests: Declaration of interests C.K. is an employee of AstraZeneca and M.S. of OmicEra., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Objective: In a substudy of a randomized controlled trial, we investigated the effects of the valsartan/amlodipine single-pill combination and nifedipine gastrointestinal therapeutic system (GITS) monotherapy on brachial pulse pressure (bPP) and radial augmentation index (rAI) in patients with previously uncontrolled hypertension., Methods: We performed measurements of clinic blood pressure (BP) and pulse rate and rAI (n = 63) and ambulatory BP monitoring (n = 42) at baseline and 12-week of follow-up. Analysis of covariance was performed to calculate the least square mean change from baseline and between-group differences [95% confidence interval (CI)]. Correlation analysis was performed to study the interrelationship between the changes in bPP and rAI and in pulse rate., Results: After 12-week treatment, clinic and ambulatory SBP/DBP and pulse rate were not differently changed between the valsartan/amlodipine (n = 29) and nifedipine GITS groups (n = 34, P ≥ 0.06) except daytime SBP (P = 0.01). The reductions in 24-h and daytime ambulatory bPP were significantly greater in the former than the latter group (P ≤ 0.04). rAI increased slightly by 3.5% (P = 0.20) and 5.2% (P = 0.06) in the valsartan/amlodipine and nifedipine groups, respectively, with a between-group difference of -1.7% (95% CI -9.6 to 6.1%, P = 0.66). In the two groups combined, the changes in clinic and ambulatory bPP were not or weakly associated with that in clinic or ambulatory pulse rate (r = -0.14 to 0.36, P = 0.02-0.95), while the changes in rAI were more strongly or significantly associated with that in clinic or ambulatory pulse rate (r = -0.39 to -0.23, P = 0.02-0.16)., Conclusions: Antihypertensive drug-induced changes in rAI but not bPP were dependent on pulse rate., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

In cross-linking mass spectrometry, the identification of cross-linked peptide pairs heavily relies on the ability of a database search engine to measure the similarities between experimental and theoretical MS/MS spectra. However, the lack of accurate ion intensities in theoretical spectra impairs the performance of search engines, in particular, on proteome scales. Here we introduce pDeepXL, a deep neural network to predict MS/MS spectra of cross-linked peptide pairs. To train pDeepXL, we used the transfer-learning technique because it facilitated the training with limited benchmark data of cross-linked peptide pairs. Test results on more than ten data sets showed that pDeepXL accurately predicted the spectra of both noncleavable DSS/BS3/Leiker cross-linked peptide pairs (>80% of predicted spectra have Pearson's r values higher than 0.9) and cleavable DSSO/DSBU cross-linked peptide pairs (>75% of predicted spectra have Pearson's r values higher than 0.9). pDeepXL also achieved the accurate prediction on unseen data sets using an online fine-tuning technique. Lastly, integrating pDeepXL into a database search engine increased the number of identified cross-link spectra by 18% on average.

Spectrum prediction using deep learning has attracted a lot of attention in recent years. Although existing deep learning methods have dramatically increased the prediction accuracy, there is still considerable space for improvement, which is presently limited by the difference of fragmentation types or instrument settings. In this work, we use the few-shot learning method to fit the data online to make up for the shortcoming. The method is evaluated using ten data sets, where the instruments includes Velos, QE, Lumos, and Sciex, with collision energies being differently set. Experimental results show that few-shot learning can achieve higher prediction accuracy with almost negligible computing resources. For example, on the data set from a untrained instrument Sciex-6600, within about 10 s, the prediction accuracy is increased from 69.7% to 86.4%; on the CID (collision-induced dissociation) data set, the prediction accuracy of the model trained by HCD (higher energy collision dissociation) spectra is increased from 48.0% to 83.9%. It is also shown that, the method is not critical to data quality and is sufficiently efficient to fill the accuracy gap. The source code of pDeep3 is available at http://pfind.ict.ac.cn/software/pdeep3.

Tandem mass spectrometry (MS/MS)-based phosphoproteomics is a powerful technology for global phosphorylation analysis. However, applying four computational pipelines to a typical mass spectrometry (MS)-based phosphoproteomic dataset from a human cancer study, we observed a large discrepancy among the reported phosphopeptide identification and phosphosite localization results, underscoring a critical need for benchmarking. While efforts have been made to compare performance of computational pipelines using data from synthetic phosphopeptides, evaluations involving real application data have been largely limited to comparing the numbers of phosphopeptide identifications due to the lack of appropriate evaluation metrics. We investigated three deep-learning-derived features as potential evaluation metrics: phosphosite probability, Delta RT, and spectral similarity. Predicted phosphosite probability is computed by MusiteDeep, which provides high accuracy as previously reported; Delta RT is defined as the absolute retention time (RT) difference between RTs observed and predicted by AutoRT; and spectral similarity is defined as the Pearson's correlation coefficient between spectra observed and predicted by pDeep2. Using a synthetic peptide dataset, we found that both Delta RT and spectral similarity provided excellent discrimination between correct and incorrect peptide-spectrum matches (PSMs) both when incorrect PSMs involved wrong peptide sequences and even when incorrect PSMs were caused by only incorrect phosphosite localization. Based on these results, we used all the three deep-learning-derived features as evaluation metrics to compare different computational pipelines on diverse set of phosphoproteomic datasets and showed their utility in benchmarking performance of the pipelines. The benchmark metrics demonstrated in this study will enable users to select computational pipelines and parameters for routine analysis of phosphoproteomics data and will offer guidance for developers to improve computational methods., Competing Interests: Conflict of interest The authors declare no competing interests., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Proteomics, the study of all the proteins in biological systems, is becoming a data-rich science. Protein sequences and structures are comprehensively catalogued in online databases. With recent advancements in tandem mass spectrometry (MS) technology, protein expression and post-translational modifications (PTMs) can be studied in a variety of biological systems at the global scale. Sophisticated computational algorithms are needed to translate the vast amount of data into novel biological insights. Deep learning automatically extracts data representations at high levels of abstraction from data, and it thrives in data-rich scientific research domains. Here, a comprehensive overview of deep learning applications in proteomics, including retention time prediction, MS/MS spectrum prediction, de novo peptide sequencing, PTM prediction, major histocompatibility complex-peptide binding prediction, and protein structure prediction, is provided. Limitations and the future directions of deep learning in proteomics are also discussed. This review will provide readers an overview of deep learning and how it can be used to analyze proteomics data., (© 2020 The Authors. Proteomics published by Wiley-VCH GmbH.)

Since the launch of Chinese Human Proteome Project (CNHPP) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), large-scale mass spectrometry (MS) based proteomic profiling of different kinds of human tumor samples have provided huge amount of valuable data for both basic and clinical researchers. Accurate prediction for tumor and non-tumor samples, as well as the tumor types has become a key step for biological and medical research, such as biomarker discovery, diagnosis, and monitoring of diseases. The traditional MS-based classification strategy mainly depends on the identification and quantification results of MS data, which has some inherent limitations, such as the low identification rate of MS data. Here, a deep learning-based tumor classifier directly using MS raw data is proposed, which is independent of the identification and quantification results of MS data. The potential precursors with intensities and retention times from MS data as input is first detected and extracted. Then, a deep learning-based classifier is trained, which can accurately distinguish between the tumor and non-tumor samples. Finally, it is demonstrated the deep learning-based classifier has a good performance compared with other machine learning methods and may help researchers find the potential biomarkers which are likely to be missed by the traditional strategy., (© 2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Liposuction is the treatment of choice for solid predominant extremity lymphedema. The classic lymphedema liposuction technique does not remove skin excess created following bulk removal. The skin excess is presumed to resolve with spontaneous skin contracture. We investigated the technique of simultaneously performing liposuction with immediate skin excision in patients with solid predominant lymphedema and compared the outcome with that from the classic technique., Methods: Modified liposuction with skin excision (mLIPO) and standard liposuction without skin excision (sLIPO) were offered to patients with solid predominant extremity lymphedema. Skin traction of 4 cm and undulating skin mobility constituted positive "flying squirrel" sign. Patients with negative "flying squirrel" sign were excluded. mLIPO patients underwent skin excision. Surgical outcomes and postoperative complications were compared., Results: The study enrolled 15 and 26 patients into the sLIPO and mLIPO groups, respectively. mLIPO patients demonstrated statistically significant decrease in seroma/hematoma, contour irregularity, and skin necrosis, while experiencing increased procedural satisfaction., Conclusions: Skin excision following liposuction for solid predominant lymphedema is safe. It decreases postoperative complication and improves surgical outcome., (Copyright © 2019 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of The American Society of Plastic Surgeons.)

With the serious aging of the population, the incidence of fragility fractures of the pelvis(FFPs) has gradually increased, which has become a public problem affecting the living quality of the elderly. When a surgical treatment is chosen, the procedure should be as minimal invasive as possible and avoid all surgical complications. In recent years, different techniques for percutaneous or less invasive fixation of the posterior pelvic ring have been developed. Their advantages and limitations are presented: sacroplasty, iliosacral screw osteosynthesis, cement augmentation, transiliac internal fixation, transsacral osteosynthesis, lumbopelvic fixation. The purpose of this paper is to review the classification and minimally invasive treatment of FFP., Competing Interests: The authors of this article and the planning committee members and staff have no relevant financial relationships with commercial interests to disclose., (Copyright© 2019 by the China Journal of Orthopaedics and Traumatology Press.)

In the past decade, tandem mass spectrometry (MS/MS)-based bottom-up proteomics has become the method of choice for analyzing post-translational modifications (PTMs) in complex mixtures. The key to the identification of the PTM-containing peptides and localization of the PTM-modified residues is to measure the similarities between the theoretical spectra and the experimental ones. An accurate prediction of the theoretical MS/MS spectra of the modified peptides will improve the similarity measurement. Here, we proposed the deep-learning-based pDeep2 model for PTMs. We used the transfer learning technique to train pDeep2, facilitating the training with a limited scale of benchmark PTM data. Using the public synthetic PTM data sets, including the synthetic phosphopeptides and 21 synthetic PTMs from ProteomeTools, we showed that the model trained by transfer learning was accurate (>80% Pearson correlation coefficients were higher than 0.9), and was significantly better than the models trained without transfer learning. We also showed that accurate prediction of the fragment ion intensities of the PTM neutral loss, for example, the phosphoric acid loss (-98 Da) of the phosphopeptide, will improve the discriminating power to distinguish the true phosphorylated residue from its adjacent candidate sites. pDeep2 is available at https://github.com/pFindStudio/pDeep/tree/master/pDeep2 .

We describe pLink 2, a search engine with higher speed and reliability for proteome-scale identification of cross-linked peptides. With a two-stage open search strategy facilitated by fragment indexing, pLink 2 is ~40 times faster than pLink 1 and 3~10 times faster than Kojak. Furthermore, using simulated datasets, synthetic datasets, 15 N metabolically labeled datasets, and entrapment databases, four analysis methods were designed to evaluate the credibility of ten state-of-the-art search engines. This systematic evaluation shows that pLink 2 outperforms these methods in precision and sensitivity, especially at proteome scales. Lastly, re-analysis of four published proteome-scale cross-linking datasets with pLink 2 required only a fraction of the time used by pLink 1, with up to 27% more cross-linked residue pairs identified. pLink 2 is therefore an efficient and reliable tool for cross-linking mass spectrometry analysis, and the systematic evaluation methods described here will be useful for future software development.

Motivation: De novo peptide sequencing based on tandem mass spectrometry data is the key technology of shotgun proteomics for identifying peptides without any database and assembling unknown proteins. However, owing to the low ion coverage in tandem mass spectra, the order of certain consecutive amino acids cannot be determined if all of their supporting fragment ions are missing, which results in the low precision of de novo sequencing., Results: In order to solve this problem, we developed pNovo 3, which used a learning-to-rank framework to distinguish similar peptide candidates for each spectrum. Three metrics for measuring the similarity between each experimental spectrum and its corresponding theoretical spectrum were used as important features, in which the theoretical spectra can be precisely predicted by the pDeep algorithm using deep learning. On seven benchmark datasets from six diverse species, pNovo 3 recalled 29-102% more correct spectra, and the precision was 11-89% higher than three other state-of-the-art de novo sequencing algorithms. Furthermore, compared with the newly developed DeepNovo, which also used the deep learning approach, pNovo 3 still identified 21-50% more spectra on the nine datasets used in the study of DeepNovo. In summary, the deep learning and learning-to-rank techniques implemented in pNovo 3 significantly improve the precision of de novo sequencing, and such machine learning framework is worth extending to other related research fields to distinguish the similar sequences., Availability and Implementation: pNovo 3 can be freely downloaded from http://pfind.ict.ac.cn/software/pNovo/index.html., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2019. Published by Oxford University Press.)

As the de facto validation method in mass spectrometry-based proteomics, the target-decoy approach determines a threshold to estimate the false discovery rate and then filters those identifications beyond the threshold. However, the incorrect identifications within the threshold are still unknown and further validation methods are needed. In this study, we characterized a framework of validation and investigated a number of common and novel validation methods. We first defined the accuracy of a validation method by its false-positive rate (FPR) and false-negative rate (FNR) and, further, proved that a validation method with lower FPR and FNR led to identifications with higher sensitivity and precision. Then we proposed a validation method named pValid that incorporated an open database search and a theoretical spectrum prediction strategy via a machine-learning technology. pValid was compared with four common validation methods as well as a synthetic peptide validation method. Tests on three benchmark data sets indicated that pValid had an FPR of 0.03% and an FNR of 1.79% on average, both superior to the other four common validation methods. Tests on a synthetic peptide data set also indicated that the FPR and FNR of pValid were better than those of the synthetic peptide validation method. Tests on a large-scale human proteome data set indicated that pValid successfully flagged the highest number of incorrect identifications among all five methods. Further considering its cost-effectiveness, pValid has the potential to be a feasible validation tool for peptide identification.

De novo peptide sequencing for large-scale proteomics remains challenging because of the lack of full coverage of ion series in tandem mass spectra. We developed a mirror protease of trypsin, acetylated LysargiNase (Ac-LysargiNase), with superior activity and stability. The mirror spectrum pairs derived from the Ac-LysargiNase and trypsin treated samples can generate full b and y ion series, which provide mutual complementarity of each other, and allow us to develop a novel algorithm, pNovoM, for de novo sequencing. Using pNovoM to sequence peptides of purified proteins, the accuracy of the sequence was close to 100%. More importantly, from a large-scale yeast proteome sample digested with trypsin and Ac-LysargiNase individually, 48% of all tandem mass spectra formed mirror spectrum pairs, 97% of which contained full coverage of ion series, resulting in precision de novo sequencing of full-length peptides by pNovoM. This enabled pNovoM to successfully sequence 21,249 peptides from 3,753 proteins and interpreted 44-152% more spectra than pNovo+ and PEAKS at a 5% FDR at the spectrum level. Moreover, the mirror protease strategy had an obvious advantage in sequencing long peptides. We believe that the combination of mirror protease strategy and pNovoM will be an effective approach for precision de novo sequencing on both single proteins and proteome samples., (© 2019 Yang et al.)

In a pre-specified subgroup analysis of a 12-week randomized multicenter study, we investigated effects of valsartan/amlodipine 80/5 mg single-pill combination (n = 75) and nifedipine GITS 30 mg (n = 75) on ambulatory blood pressure (BP) and arterial stiffness assessed by brachial-ankle pulse wave velocity (PWV) in patients with uncontrolled hypertension. At week 12, the between-treatment mean differences in systolic/diastolic BP were smaller for 24-hour and daytime (-2.1/-1.7 and -2.0/-1.5 mm Hg, respectively, P ≥ 0.22) but greater (P < 0.01) for nighttime (-4.0/-2.8 mm Hg, P ≤ 0.09), especially in sustained uncontrolled hypertension (-5.0/-4.1 mm Hg, P ≤ 0.04) and non-dippers (-6.5/-3.7 mm Hg, P ≤ 0.07), in favor of valsartan/amlodipine. At week 12, PWV was significantly reduced from baseline by valsartan/amlodipine (n = 59, P < 0.0001) but not nifedipine (n = 59, P = 0.06). The changes in PWV were significantly associated with that in ambulatory systolic BP and pulse pressure in the nifedipine (P ≤ 0.0008) but not valsartan/amlodipine group (P ≥ 0.57), with a significant interaction (P ≤ 0.045). The valsartan/amlodipine combination was more efficacious than nifedipine GITS in lowering nighttime BP in sustained uncontrolled hypertension and non-dippers, and in lowering arterial stiffness independent of BP lowering., (©2018 Wiley Periodicals, Inc.)

Background: Several previous researches had found artery stiffness associated skeletal muscle mass, but not considering muscle strength and physical performance, which also were compositions of sarcopenia. This study aims to reveal the relationship of artery stiffness and sarcopenia using the Asian Working Group for Sarcopenia criteria., Methods: Study was performed on 1002 Chinese community dwelling participants aged ≥65 years from November 2016 to March 2017. Body composition, muscle strength, physical performance, and brachial-ankle pulse wave velocity (baPWV) considering as artery stiffness index were measured., Results: In multiple regression analysis, baPWV was associated with handgrip (β=-0.13, P=0.04) and Relative skeletal muscle mass index (ASM/Ht2) (β=-0.02, P<0.01), but not with 4-meter velocity (P=0.21). Multiple logistic regression analysis showed that 1-SD (3.50m/s) increased in baPWV was still associated with a 11% (CI, 4%-20%; P<0.01) higher odds of being sarcopenia. In the gender subgroup analysis, the relationship of baPWV and sarcopenia remain significant in men (OR, 1.23; 95% CI, 1.07-1.42, P<0.01), but not in women (P=0.07)., Conclusions: High brachial-ankle pulse wave velocity is associated with sarcopenia in Chinese community-dwelling elderly, with gender differences., Competing Interests: None of the authors declares a conflict of interest

We present a sequence-tag-based search engine, Open-pFind, to identify peptides in an ultra-large search space that includes coeluting peptides, unexpected modifications and digestions. Our method detects peptides with higher precision and speed than seven other search engines. Open-pFind identified 70-85% of the tandem mass spectra in four large-scale datasets and 14,064 proteins, each supported by at least two protein-unique peptides, in a human proteome dataset.

MS-based de novo peptide sequencing has been improved remarkably with significant development of mass-spectrometry and computational approaches but still lacks quality-control methods. Here we proposed a novel algorithm pSite to evaluate the confidence of each amino acid rather than the full-length peptides obtained by de novo peptide sequencing. A semi-supervised learning approach was used to discriminate correct amino acids from random one; then, an expectation-maximization algorithm was used to adaptively control the false amino-acid rate (FAR). On three test data sets, pSite recalled 86% more amino acids on average than PEAKS at the FAR of 5%. pSite also performed superiorly on the modification site localization problem, which is essentially a special case of amino acid confidence evaluation. On three phosphopeptide data sets, at the false localization rate of 1%, the average recall of pSite was 91% while those of Ascore and phosphoRS were 64 and 63%, respectively. pSite covered 98% of Ascore and phosphoRS results and contributed 21% more phosphorylation sites. Further analyses show that the use of distinct fragmentation features in high-resolution MS/MS spectra, such as neutral loss ions, played an important role in improving the precision of pSite. In summary, the effective and universal model together with the extensive use of spectral information makes pSite an excellent quality control tool for both de novo peptide sequencing and modification site localization.

In tandem mass spectrometry (MS/MS)-based proteomics, search engines rely on comparison between an experimental MS/MS spectrum and the theoretical spectra of the candidate peptides. Hence, accurate prediction of the theoretical spectra of peptides appears to be particularly important. Here, we present pDeep, a deep neural network-based model for the spectrum prediction of peptides. Using the bidirectional long short-term memory (BiLSTM), pDeep can predict higher-energy collisional dissociation, electron-transfer dissociation, and electron-transfer and higher-energy collision dissociation MS/MS spectra of peptides with >0.9 median Pearson correlation coefficients. Further, we showed that intermediate layer of the neural network could reveal physicochemical properties of amino acids, for example the similarities of fragmentation behaviors between amino acids. We also showed the potential of pDeep to distinguish extremely similar peptides (peptides that contain isobaric amino acids, for example, GG = N, AG = Q, or even I = L), which were very difficult to distinguish using traditional search engines.

The precise and large-scale identification of intact glycopeptides is a critical step in glycoproteomics. Owing to the complexity of glycosylation, the current overall throughput, data quality and accessibility of intact glycopeptide identification lack behind those in routine proteomic analyses. Here, we propose a workflow for the precise high-throughput identification of intact N-glycopeptides at the proteome scale using stepped-energy fragmentation and a dedicated search engine. pGlyco 2.0 conducts comprehensive quality control including false discovery rate evaluation at all three levels of matches to glycans, peptides and glycopeptides, improving the current level of accuracy of intact glycopeptide identification. The N-glycoproteome of samples metabolically labeled with 15 N/ 13 C were analyzed quantitatively and utilized to validate the glycopeptide identification, which could be used as a novel benchmark pipeline to compare different search engines. Finally, we report a large-scale glycoproteome dataset consisting of 10,009 distinct site-specific N-glycans on 1988 glycosylation sites from 955 glycoproteins in five mouse tissues.Protein glycosylation is a heterogeneous post-translational modification that generates greater proteomic diversity that is difficult to analyze. Here the authors describe pGlyco 2.0, a workflow for the precise one step identification of intact N-glycopeptides at the proteome scale.

De novo peptide sequencing has improved remarkably, but sequencing full-length peptides with unexpected modifications is still a challenging problem. Here we present an open de novo sequencing tool, Open-pNovo, for de novo sequencing of peptides with arbitrary types of modifications. Although the search space increases by ∼300 times, Open-pNovo is close to or even ∼10-times faster than the other three proposed algorithms. Furthermore, considering top-1 candidates on three MS/MS data sets, Open-pNovo can recall over 90% of the results obtained by any one traditional algorithm and report 5-87% more peptides, including 14-250% more modified peptides. On a high-quality simulated data set, ∼85% peptides with arbitrary modifications can be recalled by Open-pNovo, while hardly any results can be recalled by others. In summary, Open-pNovo is an excellent tool for open de novo sequencing and has great potential for discovering unexpected modifications in the real biological applications.

N-glycosylation is one of the most prominent and abundant posttranslational modifications of proteins. It is estimated that over 50% of mammalian proteins undergo glycosylation. However, the analysis of N-glycoproteins has been limited by the available analytical technology. In this study, we comprehensively mapped the N-glycosylation sites in the mouse brain proteome by combining complementary methods, which included seven protease treatments, four enrichment techniques and two fractionation strategies. Altogether, 13492 N-glycopeptides containing 8386 N-glycosylation sites on 3982 proteins were identified. After evaluating the performance of the above methods, we proposed a simple and efficient workflow for large-scale N-glycosylation site mapping. The optimized workflow yielded 80% of the initially identified N-glycosylation sites with considerably less effort. Analysis of the identified N-glycoproteins revealed that many of the mouse brain proteins are N-glycosylated, including those proteins in critical pathways for nervous system development and neurological disease. Additionally, several important biomarkers of various diseases were found to be N-glycosylated. These data confirm that N-glycosylation is important in both physiological and pathological processes in the brain, and provide useful details about numerous N-glycosylation sites in brain proteins., Competing Interests: The authors declare no conflicts of interest.

Objective: To investigate the relationship between adiposity measures and plasma concentration of high molecular weight (HMW) adiponectin., Methods: In a Chinese sample (n = 1081), we performed measurements of anthropometry and bioelectrical impedance analysis (BIA). We defined overweight and obesity as a body mass index between 24 and 27.4 kg/m² and ≥ 27.5 kg/m², respectively, and central obesity as a waist circumference ≥ 90 cm in men and ≥ 80 cm in women. Plasma HMW adiponectin concentration was measured by the ELISA method., Results: Plasma HMW adiponectin concentration was significantly (P < 0.0001) higher in women (n = 677, 2.47 μg/mL) than men (n = 404, 1.58 μg/mL) and correlated with advancing age in men (r = 0.28) and women (r = 0.29). In adjusted analyses, it was lower in the presence of overweight (n = 159, 1.26 μg/mL in men and n = 227, 2.15μg/mL in women) and obesity (n = 60, 1.31 μg/mL and n = 82, 2.10 μg/mL, respectively) than normal weight subjects (n = 185, 2.07μg/mL and n = 368, 2.94 μg/mL, respectively) and in the presence of central obesity (n = 106, 1.28 μg/mL and n = 331, 2.12 μg/mL, respectively) than subjects with a normal waist circumference (n = 298, 1.74 μg/mL and n = 346, 2.74 μg/mL, respectively). In multiple regression analyses stratified for gender, adjusted for confounders and considered separately each of the adiposity measures, all adiposity measures were significantly (r -0.18 to -0.31, P < 0.001) associated with plasma HMW adiponectin concentration. However, in further stratified and adjusted regression analyses considered stepwise all adiposity measures, only waist-to-hip ratio was significantly (P < 0.05) associated with plasma HMW adiponectin concentration in men (r = -0.10) and women (r = -0.15)., Conclusions: Anthropometric measures of obesity, such as waist-to-hip ratio, but not BIA measures, are independently associated with plasma adiponectin concentration.

Confident characterization of the microheterogeneity of protein glycosylation through identification of intact glycopeptides remains one of the toughest analytical challenges for glycoproteomics. Recently proposed mass spectrometry (MS)-based methods still have some defects such as lack of the false discovery rate (FDR) analysis for the glycan identification and lack of sufficient fragmentation information for the peptide identification. Here we proposed pGlyco, a novel pipeline for the identification of intact glycopeptides by using complementary MS techniques: 1) HCD-MS/MS followed by product-dependent CID-MS/MS was used to provide complementary fragments to identify the glycans, and a novel target-decoy method was developed to estimate the false discovery rate of the glycan identification; 2) data-dependent acquisition of MS3 for some most intense peaks of HCD-MS/MS was used to provide fragments to identify the peptide backbones. By integrating HCD-MS/MS, CID-MS/MS and MS3, intact glycopeptides could be confidently identified. With pGlyco, a standard glycoprotein mixture was analyzed in the Orbitrap Fusion, and 309 non-redundant intact glycopeptides were identified with detailed spectral information of both glycans and peptides.

There has been tremendous progress in top-down proteomics (TDP) in the past 5 years, particularly in intact protein separation and high-resolution mass spectrometry. However, bioinformatics to deal with large-scale mass spectra has lagged behind, in both algorithmic research and software development. In this study, we developed pTop 1.0, a novel software tool to significantly improve the accuracy and efficiency of mass spectral data analysis in TDP. The precursor mass offers crucial clues to infer the potential post-translational modifications co-occurring on the protein, the reliability of which relies heavily on its mass accuracy. Concentrating on detecting the precursors more accurately, a machine-learning model incorporating a variety of spectral features was trained online in pTop via a support vector machine (SVM). pTop employs the sequence tags extracted from the MS/MS spectra and a dynamic programming algorithm to accelerate the search speed, especially for those spectra with multiple post-translational modifications. We tested pTop on three publicly available data sets and compared it with ProSight and MS-Align+ in terms of its recall, precision, running time, and so on. The results showed that pTop can, in general, outperform ProSight and MS-Align+. pTop recalled 22% more correct precursors, although it exported 30% fewer precursors than Xtract (in ProSight) from a human histone data set. The running speed of pTop was about 1 to 2 orders of magnitude faster than that of MS-Align+. This algorithmic advancement in pTop, including both accuracy and speed, will inspire the development of other similar software to analyze the mass spectra from the entire proteins.

Objective: To investigate the current status of occupational health and related countermeasures in Guangzhou, China., Methods: Related data were collected from occupational poisoning accident investigation, diagnosis and identification of occupational diseases, and the occupational disease hazard reporting system, and the statistical data of occupational health in Guangzhou were analyzed retrospectively., Results: The number of enterprises reporting for occupational disease hazards in Guangzhou was 20 890, and the total number of workers was 1 457 583. The number of workers exposed to occupational hazards was 284 233, and the cumulative number of workers with occupational diseases was 1 502. There were many risk factors for occupational diseases in enterprises, and there were a large number of workers with occupational diseases, as well as newly diagnosed cases. From 2001 to 2014, the total number of cases of occupational diseases was 958., Conclusion: The situation for the prevention and control of occupational diseases is grim in Guangzhou. Occupational health supervision and law enforcement should be enhanced, the three-level supervision system should be established and perfected, and the occupational health supervision system with a combination of "prevention, treatment, and protection" should be established and promoted, so as to gradually establish a technical service support system for occupational health.

Motivation: Proteogenomics has been well accepted as a tool to discover novel genes. In most conventional proteogenomic studies, a global false discovery rate is used to filter out false positives for identifying credible novel peptides. However, it has been found that the actual level of false positives in novel peptides is often out of control and behaves differently for different genomes., Results: To quantitatively model this problem, we theoretically analyze the subgroup false discovery rates of annotated and novel peptides. Our analysis shows that the annotation completeness ratio of a genome is the dominant factor influencing the subgroup FDR of novel peptides. Experimental results on two real datasets of Escherichia coli and Mycobacterium tuberculosis support our conjecture., Contact: yfu@amss.ac.cn or xupingghy@gmail.com or smhe@ict.ac.cn, Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2015. Published by Oxford University Press.)

Background: There is limited information on prevalent and incident atrial fibrillation in Chinese. We aimed to investigate the prevalence, incidence, management and risks of atrial fibrillation in an elderly Chinese population., Methods: In a population--based prospective study in elderly (≥ 60 years) Chinese, we performed cardiovascular health examinations including a 12-lead electrocardiogram at baseline in 3,922 participants and biennially during follow-up in 2,017 participants. We collected information on vital status during the whole follow-up period., Results: The baseline prevalence of atrial fibrillation was 2.0 % (n = 34) in 1718 men and 1.6 % (n = 36) in 2204 women. During a median 3.8 years of follow-up, the incidence rate of atrial fibrillation (n = 34) was 4.9 per 1000 person-years (95 % confidence interval [CI], 3.4-6.9). In univariate analysis, both the prevalence and incidence of atrial fibrillation were higher with age advancing (P < 0.0001) and in the presence of coronary heart disease (P ≤ 0.02). Of the 104 prevalent and incident cases of atrial fibrillation, only 1 (1.0 %) received anticoagulant therapy (warfarin). These patients with atrial fibrillation, compared with those with sinus rhythm, had significantly higher risks of all-cause (n = 261, hazard ratio [HR] 1.87, 95 % CI, 1.09-3.20, P = 0.02), cardiovascular (n = 136, HR 3.78, 95 % CI 2.17-6.58, P < 0.0001) and stroke mortality (n = 44, HR 6.31, 95 % CI 2.81-14.19, P = 0.0003)., Conclusions: Atrial fibrillation was relatively frequent in elderly Chinese, poorly managed and associated with higher risks of mortality.

Objective: The present study aimed to evaluate the accuracy of the Rossmax CF175 upper-arm blood pressure monitor for home blood pressure monitoring according to the International Protocol of the European Society of Hypertension revision 2010., Method: Systolic and diastolic blood pressures were sequentially measured in 33 adult Chinese (17 women, mean age 46 years) using a mercury sphygmomanometer (two observers) and the Rossmax CF175 device (one supervisor). A total of 99 pairs of comparisons were obtained from 33 participants for judgments in two parts with three grading phases., Results: All the blood pressure requirements were fulfilled. The Rossmax CF175 device achieved the targets in part 1 of the validation study. The number of absolute differences between the device and observers within 5, 10, and 15 mmHg was 78/99, 94/99, and 98/99, respectively, for systolic blood pressure, and 81/99, 96/99, and 97/99, respectively, for diastolic blood pressure. The device also achieved the criteria in part 2 of the validation study. Twenty-nine participants, for both of systolic and diastolic blood pressure, had at least two of the three device-observers differences within 5 mmHg (required ≥24). Only one participant for diastolic blood pressure had all three device-observers comparisons greater than 5 mmHg., Conclusion: The Rossmax automated oscillometric upper-arm blood pressure monitor CF175 fulfilled the requirements of the International Protocol revision 2010, and hence can be recommended for blood pressure measurement in adults.

Pulse wave velocity (PWV) is a measure of arterial stiffness and predicts cardiovascular events and mortality in the general population and various patient populations. In the present study, we investigated the predictive value of brachial-ankle PWV for mortality in an elderly Chinese population. Our study subjects were older (≥60 years) persons living in a suburban town of Shanghai. We measured brachial-ankle PWV using an automated cuff device at baseline and collected vital information till June 30, 2013, during follow-up. The 3876 participants (1713 [44.2%] men; mean [±SD] age, 68.1±7.3 years) included 2292 (59.1%) hypertensive patients. PWV was on average 17.8 (±4.0) m/s and was significantly (P<0.0001) associated with age (r=0.48) and in unadjusted analysis with all-cause (n=316), cardiovascular (n=148), stroke (n=46), and noncardiovascular mortality (n=168) during a median follow-up of 5.9 years. In further adjusted analysis, we studied the risk of mortality according to the decile distributions of PWV. Only the subjects in the top decile (23.3-39.3 m/s) had a significantly (P≤0.003) higher risk of all-cause mortality (hazard ratio relative to the whole study population, 1.56; 95% confidence interval, 1.16-2.08), especially in hypertensive patients (hazard ratio, 1.86; 95% confidence interval, 1.31-2.64; P=0.02 for the interaction between PWV and hypertension). Similar trends were observed for cardiovascular, stroke, and noncardiovascular mortality, although statistical significance was not reached (P≥0.08). In conclusion, brachial-ankle PWV predicts mortality in elderly Chinese on the conditions of markedly increased PWV and hypertension., (© 2014 American Heart Association, Inc.)

In relative protein abundance determination from peptide intensities recorded in full mass scans, a major complication that affects quantitation accuracy is signal interference from coeluting ions of similar m/z values. Here, we present pQuant, a quantitation software tool that solves this problem. pQuant detects interference signals, identifies for each peptide a pair of least interfered isotopic chromatograms: one for the light and one for the heavy isotope-labeled peptide. On the basis of these isotopic pairs, pQuant calculates the relative heavy/light peptide ratios along with their 99.75% confidence intervals (CIs). From the peptides ratios and their CIs, pQuant estimates the protein ratios and associated CIs by kernel density estimation. We tested pQuant, Census and MaxQuant on data sets obtained from mixtures (at varying mixing ratios from 10:1 to 1:10) of light- and heavy-SILAC labeled HeLa cells or (14)N- and (15)N-labeled Escherichia coli cells. pQuant quantitated more peptides with better accuracy than Census and MaxQuant in all 14 data sets. On the SILAC data sets, the nonquantified "NaN" (not a number) ratios generated by Census, MaxQuant, and pQuant accounted for 2.5-10.7%, 1.8-2.7%, and 0.01-0.5% of all ratios, respectively. On the (14)N/(15)N data sets, which cannot be quantified by MaxQuant, Census and pQuant produced 0.9-10.0% and 0.3-2.9% NaN ratios, respectively. Excluding these NaN results, the standard deviations of the numerical ratios calculated by Census or MaxQuant are 30-100% larger than those by pQuant. These results show that pQuant outperforms Census and MaxQuant in SILAC and (15)N-based quantitation.

Objective: The present study aimed to evaluate the accuracy of the automated oscillometric wrist blood pressure monitor AVITA BPM15S for home blood pressure monitoring according to the International Protocol revision 2010 of the European Society of Hypertension., Method: Systolic and diastolic blood pressures were sequentially measured in 33 Chinese adults (15 women, mean age 51 years) using a mercury sphygmomanometer (two observers) and the AVITA BPM15S device (one supervisor). Ninety-nine pairs of comparisons were obtained from 33 participants for judgments in two parts with three grading phases., Results: The AVITA BPM15S device achieved the targets in part 1 of the validation study. The number of absolute differences between the device and observers within 5, 10, and 15 mmHg were 85/99, 94/99, and 98/99, respectively, for systolic blood pressure, and 82/99, 96/99, and 98/99, respectively, for diastolic blood pressure. The device also achieved the criteria in part 2 of the validation study. Thirty-two and 28 participants for systolic and diastolic blood pressure, respectively, had at least two of the three device-observer differences within 5 mmHg (required ≥ 24). No participant had all of the three device-observer comparisons greater than 5 mmHg for systolic or diastolic blood pressure., Conclusion: The AVITA wrist blood pressure monitor BPM15S fulfilled the requirements of the International Protocol revision 2010 and hence can be recommended for home use in an adult population.