Top-Down Proteomics Analysis of Picogram-Level Complex Samples Using Spray-Capillary-Based Capillary Electrophoresis-Mass Spectrometry.

Proteomics analysis of mass-limited samples has become increasingly important for understanding biological systems in physiologically relevant contexts such as patient samples, multicellular organoids, spheroids, and single cells. However, relatively low sensitivity in top-down proteomics methods makes their application to mass-limited samples challenging. Capillary electrophoresis (CE) has emerged as an ideal separation method for mass-limited samples due to its high separation resolution, ultralow detection limit, and minimal sample volume requirements. Recently, we developed "spray-capillary", an electrospray ionization (ESI)-assisted device, that is capable of quantitative ultralow-volume sampling (e.g., pL-nL level). Here, we developed a spray-capillary-CE-MS platform for ultrasensitive top-down proteomics analysis of intact proteins in mass-limited complex biological samples. Specifically, to improve the sensitivity of the spray-capillary platform, we incorporated a polyethylenimine (PEI)-coated capillary and optimized the spray-capillary inner diameter. Under optimized conditions, we successfully detected over 200 proteoforms from 50 pg of E. coli lysate. To our knowledge, the spray-capillary CE-MS platform developed here represents one of the most sensitive detection methods for top-down proteomics. Furthermore, in a proof-of-principle experiment, we detected 261 ± 65 and 174 ± 45 intact proteoforms from fewer than 50 HeLa and OVCAR-8 cells, respectively, by coupling nanodroplet-based sample preparation with our optimized CE-MS platform. Overall, our results demonstrate the capability of the modified spray-capillary CE-MS platform to perform top-down proteomics analysis on picogram amounts of samples. This advancement presents the possibility of meaningful top-down proteomics analysis of mass-limited samples down to the level of single mammalian cells.

Equivalent circuit model of the traveling wave electrode for lithium niobate thin film Mach-Zehnder modulators.

We propose an equivalent circuit model of the traveling wave electrode for lithium niobate thin film (TFLN) Mach-Zehnder modulators, in which the distributed capacitance and conformal mapping techniques are applied to calculate the microwave refractive index, microwave loss, and characteristic impedance. Their accuracies are verified by comparing with the results of the finite element method, and the relative errors are less than 3.282%, 1.776%, and 5.334%, respectively. The influence of the electrode's structural parameters on the modulation performances is analyzed, and a 3 dB modulation bandwidth around 84 GHz with an 8-mm-long traveling wave electrode is obtained.

Optimization of Higher-Energy Collisional Dissociation Fragmentation Energy for Intact Protein-Level Tandem Mass Tag Labeling.

Isobaric chemical tag labeling (e.g., TMT) is a commonly used approach in quantitative proteomics, and quantification is enabled through detection of low-mass reporter ions generated after MS2 fragmentation. Recently, we have introduced and optimized an intact protein-level TMT labeling platform that demonstrated >90% labeling efficiency in complex samples with top-down proteomics. Higher-energy collisional dissociation (HCD) is commonly utilized for isobaric tag-labeled peptide fragmentation because it produces accurate reporter ion intensities and avoids loss of low mass ions. HCD energies have been optimized for isobaric tag labeled-peptides but have not been systematically evaluated for isobaric tag-labeled intact proteins. In this study, we report a systematic evaluation of normalized HCD fragmentation energies (NCEs) on TMT-labeled HeLa cell lysate using top-down proteomics. Our results suggested that reporter ions often result in higher ion intensities at higher NCEs. Optimal fragmentation of intact proteins for identification, however, required relatively lower NCE. We further demonstrated that a stepped NCE scheme with energies from 30% to 50% resulted in optimal quantification and identification of TMT-labeled HeLa proteins. These parameters resulted in an average reporter ion intensity of ∼4E4 and average proteoform spectrum matches (PrSMs) of >1000 per RPLC-MS/MS run with a 1% false discovery rate (FDR) cutoff.

Clinical significance of plasma PD-L1+ exosomes in the management of diffuse large B cell lymphoma.

PD-L1+ exosome have been reported to be a promising prognostic biomarker in various cancers. However, its clinical value in diffuse large B cell lymphoma (DLBCL) has not been defined yet. In this study, a total of 165 plasma samples from 78 patients with DLBCL undergoing standard first-line R-CHOP regimens were collected at three different time points (pretreatment, and after 3 and 6 cycles of R-CHOP) to determine the proportions of PD-L1+ exosomes by flow cytometry. We found that high pretreatment plasma PD-L1+ exosome correlated with indicators of poor clinical outcome that included high Ki-67 expression (P = 0.02), double expressor lymphoma (P = 0.005), immunohistochemical PD-L1+ tumor tissue (P = 0.006), and the baseline maximal standardized uptake values (P = 0.0003). Pretreatment plasma PD-L1+ exosome was an independent factor by multivariate analysis with logistic regression (P = 0.0301). Moreover, the pretreatment PD-L1+ exosome was a strong predictor of final treatment responses of either CR or non-CR by ROC analysis (P < 0.001). PD-L1+ exosome level declined significantly in patients who experienced CR (pretreatment vs. after 3 cycles/after 6 cycles, P < 0.05), but not in the non-CR group. Intriguingly, plasma PD-L1+ exosome after 3 cycles (AUC = 0.857; 95%CI: 0.728-0.939) might represent a more sensitive indicator than radiographic assessment after 3 cycles (AUC = 0.626; 95%CI: 0.477-0.758) for evaluating the therapeutic response of DLBCL patients (P = 0.0136). Our results suggest that plasma PD-L1+ exosomes may represent a new biomarker for the dynamic monitoring of treatment response.

An automated spray-capillary platform for the microsampling and CE-MS analysis of picoliter- and nanoliter-volume samples.

Capillary electrophoresis mass spectrometry (CE-MS) is an emerging analytical tool for microscale biological sample analysis that offers high separation resolution, low detection limit, and low sample consumption. We recently developed a novel microsampling device, "spray-capillary," for quantitative low-volume sample extraction (as low as 15 pL/s) and online CE-MS analysis. This platform can efficiently analyze picoliter samples (e.g., single cells) with minimal sample loss and no additional offline sample-handling steps. However, our original spray-capillary-based experiments required manual manipulation of the sample inlet for sample collection and separation, which is time consuming and requires proficiency in device handling. To optimize the performance of spray-capillary CE-MS analysis, we developed an automated platform for robust, high-throughput analysis of picoliter samples using a commercially available CE autosampler. Our results demonstrated high reproducibility among 50 continuous runs using the standard peptide angiotensin II (Ang II), with an RSD of 14.70% and 0.62% with respect to intensity and elution time, respectively. We also analyzed Ang II using varying injection times to evaluate the capability of the spray-capillary to perform quantitative sampling and found high linearity for peptide intensity with respect to injection time (R2 > 0.99). These results demonstrate the capability of the spray-capillary sampling platform for high-throughput quantitative analysis of low-volume, low-complexity samples using pressure elution (e.g., direct injection). To further evaluate and optimize the automated spray-capillary platform to analyze complex biological samples, we performed online CE-MS analysis on Escherichia coli lysate digest spiked with Ang II using varying injection times. We maintained high linearity of intensity with respect to injection time for Ang II and E. coli peptides (R2 > 0.97 in all cases). Furthermore, we observed good CE separation and high reproducibility between automated runs. Overall, we demonstrated that the automated spray-capillary CE-MS platform can efficiently and reproducibly sample picoliter and nanoliter biological samples for high-throughput proteomics analysis.

Overexpression of miR-17 predicts adverse prognosis and disease recurrence for acute myeloid leukemia.

BACKGROUND: The clinical significance of miR-17 in patients with acute myeloid leukemia (AML) remains unknown. METHODS: Real-time quantitative reverse transcription-polymerase chain reaction (qPCR) was performed to detect the miR-17 expression in 115 de novo AML patients, 31 patients at complete remission (CR) time, 8 patients at relapse time and 30 normal controls. RESULTS: MiR-17 was upregulated in de novo AML compared with normal controls. Patients with high expression of miR-17 had less CEBPA double mutation, less favorable ELN-risk and lower CR rate. The level of miR-17 was significantly decreased at CR phase and was returned to primary level even higher when in relapse phase. In addition, Cox regression analysis revealed that miR-17 expression retained independent prognostic significance for overall survival (OS). Moreover, the gene-expression profile analysis of miR-17 in AML obtained from TCGA database was involved in multiple biological functions and signal pathways. Among the differential expressed genes (DEGs), we identified FGL2, PLAUR, SLC2A3, GPR65, CTSS, TLR7, S1PR3, OGFRL1, LILRB1, IL17RA, SIGLEC10, SLAMF7, PLXDC2, HPSE, TCF7 and MYCL as potential direct targets of miR-17 according to in silico analysis. CONCLUSIONS: High expression of miR-17 in de novo AML patients pointed to dismal clinical outcome and disease recurrence, which could serve as novel prognostic biomarker for AML patients.

[Change in serum IgG antibody during the recovery stage of Omicron variant infection in children: an analysis of 110 cases]. / 110ä¾‹å„¿ç«¥å¥¥å¯†å ‹æˆŽå˜å¼‚æ ªæ„ŸæŸ“æ¢å¤æœŸè¡€æ¸ IgG抗体变化.

OBJECTIVES: To investigate the serum level of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific RBD IgG antibody (SARS-CoV-2 IgG antibody for short) in children with SARS-CoV-2 Omicron variant infection during the recovery stage, as well as the protective effect of SARS-CoV-2 vaccination against Omicron infection. METHODS: A retrospective analysis was performed on 110 children who were diagnosed with coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 Omicron variant infection in Tianjin of China from January 8 to February 7, 2022. According to the status of vaccination before diagnosis, they were divided into a booster vaccination (3 doses) group with 2 children, a complete vaccination (2 doses) group with 90 children, an incomplete vaccination (1 dose) group with 5 children, and a non-vaccination group with 13 children. The clinical data and IgG level were compared among the 4 groups. RESULTS: The complete vaccination group had a significantly higher age than the non-vaccination group at diagnosis (P<0.05), and there was a significant difference in the route of transmission between the two groups (P<0.05). There were no significant differences among the four groups in sex, clinical classification, and re-positive rate of SARS-CoV-2 nucleic acid detection (P>0.05). All 97 children were vaccinated with inactivated vaccine, among whom 85 children (88%) were vaccinated with BBIBP-CorV Sinopharm vaccine (Beijing Institute of Biological Products, Beijing, China). At 1 month after diagnosis, the booster vaccination group and the complete vaccination group had a significantly higher level of SARS-CoV-2 IgG antibody than the non-vaccination group (P<0.05), and at 2 months after diagnosis, the complete vaccination group had a significantly higher level of SARS-CoV-2 IgG antibody than the non-vaccination group (P<0.05). For the complete vaccination group, the level of SARS-CoV-2 IgG antibody at 2 months after diagnosis was significantly lower than that at 1 month after diagnosis (P<0.05). CONCLUSIONS: Vaccination with inactivated SARS-CoV-2 vaccine has a protective effect against Omicron infection in children. For children vaccinated with 2 doses of the vaccine who experience Omicron infection, there may be a slight reduction in the level of SARS-CoV-2 IgG antibody at 2 months after diagnosis. Citation:Chinese Journal of Contemporary Pediatrics, 2022, 24(7): 736-741.

Photonic instantaneous microwave frequency measurement with adjustable measurement range based on an electro-optical polarization modulator.

This paper presents a simple photonic-assisted instantaneous microwave frequency measurement approach with an adjustable measurement range. In our scheme, different polarization processing is performed on the upper and the lower branches, and then the powers of the two branches are compared to obtain the amplitude comparison function (ACF), which provides the frequency-amplitude mapping. The measurement system is significantly simplified since only one polarization modulator (PolM) and one single laser source are required. In addition, our scheme achieves a larger ACF slope, which significantly increases the measurement resolution. In the simulation, a frequency measurement over the range of 3-42.8 GHz with measurement errors within $\pm 0.1\;{\rm GHz}$±0.1GHz is achieved by optimizing the dc bias voltage applied to the PolM and the polarization angle. This scheme provides a reference for measuring the unknown instantaneous frequency of the received signal from a radar system.

Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning.

BACKGROUND: As a high-prevalence health condition, hypertension is clinically costly, difficult to manage, and often leads to severe and life-threatening diseases such as cardiovascular disease (CVD) and stroke. OBJECTIVE: The aim of this study was to develop and validate prospectively a risk prediction model of incident essential hypertension within the following year. METHODS: Data from individual patient electronic health records (EHRs) were extracted from the Maine Health Information Exchange network. Retrospective (N=823,627, calendar year 2013) and prospective (N=680,810, calendar year 2014) cohorts were formed. A machine learning algorithm, XGBoost, was adopted in the process of feature selection and model building. It generated an ensemble of classification trees and assigned a final predictive risk score to each individual. RESULTS: The 1-year incident hypertension risk model attained areas under the curve (AUCs) of 0.917 and 0.870 in the retrospective and prospective cohorts, respectively. Risk scores were calculated and stratified into five risk categories, with 4526 out of 381,544 patients (1.19%) in the lowest risk category (score 0-0.05) and 21,050 out of 41,329 patients (50.93%) in the highest risk category (score 0.4-1) receiving a diagnosis of incident hypertension in the following 1 year. Type 2 diabetes, lipid disorders, CVDs, mental illness, clinical utilization indicators, and socioeconomic determinants were recognized as driving or associated features of incident essential hypertension. The very high risk population mainly comprised elderly (age>50 years) individuals with multiple chronic conditions, especially those receiving medications for mental disorders. Disparities were also found in social determinants, including some community-level factors associated with higher risk and others that were protective against hypertension. CONCLUSIONS: With statewide EHR datasets, our study prospectively validated an accurate 1-year risk prediction model for incident essential hypertension. Our real-time predictive analytic model has been deployed in the state of Maine, providing implications in interventions for hypertension and related diseases and hopefully enhancing hypertension care.

Assessing Statewide All-Cause Future One-Year Mortality: Prospective Study With Implications for Quality of Life, Resource Utilization, and Medical Futility.

BACKGROUND: For many elderly patients, a disproportionate amount of health care resources and expenditures is spent during the last year of life, despite the discomfort and reduced quality of life associated with many aggressive medical approaches. However, few prognostic tools have focused on predicting all-cause 1-year mortality among elderly patients at a statewide level, an issue that has implications for improving quality of life while distributing scarce resources fairly. OBJECTIVE: Using data from a statewide elderly population (aged ≥65 years), we sought to prospectively validate an algorithm to identify patients at risk for dying in the next year for the purpose of minimizing decision uncertainty, improving quality of life, and reducing futile treatment. METHODS: Analysis was performed using electronic medical records from the Health Information Exchange in the state of Maine, which covered records of nearly 95% of the statewide population. The model was developed from 125,896 patients aged at least 65 years who were discharged from any care facility in the Health Information Exchange network from September 5, 2013, to September 4, 2015. Validation was conducted using 153,199 patients with same inclusion and exclusion criteria from September 5, 2014, to September 4, 2016. Patients were stratified into risk groups. The association between all-cause 1-year mortality and risk factors was screened by chi-squared test and manually reviewed by 2 clinicians. We calculated risk scores for individual patients using a gradient tree-based boost algorithm, which measured the probability of mortality within the next year based on the preceding 1-year clinical profile. RESULTS: The development sample included 125,896 patients (72,572 women, 57.64%; mean 74.2 [SD 7.7] years). The final validation cohort included 153,199 patients (88,177 women, 57.56%; mean 74.3 [SD 7.8] years). The c-statistic for discrimination was 0.96 (95% CI 0.93-0.98) in the development group and 0.91 (95% CI 0.90-0.94) in the validation cohort. The mortality was 0.99% in the low-risk group, 16.75% in the intermediate-risk group, and 72.12% in the high-risk group. A total of 99 independent risk factors (n=99) for mortality were identified (reported as odds ratios; 95% CI). Age was on the top of list (1.41; 1.06-1.48); congestive heart failure (20.90; 15.41-28.08) and different tumor sites were also recognized as driving risk factors, such as cancer of the ovaries (14.42; 2.24-53.04), colon (14.07; 10.08-19.08), and stomach (13.64; 3.26-86.57). Disparities were also found in patients' social determinants like respiratory hazard index (1.24; 0.92-1.40) and unemployment rate (1.18; 0.98-1.24). Among high-risk patients who expired in our dataset, cerebrovascular accident, amputation, and type 1 diabetes were the top 3 diseases in terms of average cost in the last year of life. CONCLUSIONS: Our study prospectively validated an accurate 1-year risk prediction model and stratification for the elderly population (≥65 years) at risk of mortality with statewide electronic medical record datasets. It should be a valuable adjunct for helping patients to make better quality-of-life choices and alerting care givers to target high-risk elderly for appropriate care and discussions, thus cutting back on futile treatment.

A polymer visualization system with accurate heating and cooling control and high-speed imaging.

A visualization system to observe crystal and bubble formation in polymers under high temperature and pressure has been developed. Using this system, polymer can be subjected to a programmable thermal treatment to simulate the process in high pressure differential scanning calorimetry (HPDSC). With a high-temperature/high-pressure view-cell unit, this system enables in situ observation of crystal formation in semi-crystalline polymers to complement thermal analyses with HPDSC. The high-speed recording capability of the camera not only allows detailed recording of crystal formation, it also enables in situ capture of plastic foaming processes with a high temporal resolution. To demonstrate the system's capability, crystal formation and foaming processes of polypropylene/carbon dioxide systems were examined. It was observed that crystals nucleated and grew into spherulites, and they grew at faster rates as temperature decreased. This observation agrees with the crystallinity measurement obtained with the HPDSC. Cell nucleation first occurred at crystals' boundaries due to CO2 exclusion from crystal growth fronts. Subsequently, cells were nucleated around the existing ones due to tensile stresses generated in the constrained amorphous regions between networks of crystals.

An electrochemical H2S sensor based on the screen printing Fe@Pt/C/PTFE sensing electrode.

A novel electrochemical gas sensor for sensitive detection of H2S at room temperature is constructed based on the Fe@Pt/C composite material. The core-shell structured Fe@Pt catalyst was synthesized by a two-step reduction method and physically dispersed in Vulcan XC-72 carbon powders. The core-shell structure increases the effective catalytic surface area of Pt while significantly reducing the usage of the noble metal Pt, leading to improved catalytic performance and decreased production costs. Additionally, the mature screen-printing process is used to coat the catalyst film. A waterproof and breathable PTFE film was used as the substrate and the parameters in the screen printing process were also optimized to achieve the best gas sensing performance of the electrode film. Through the detection of hydrogen sulfide (H2S) with different concentrations, it is found that the sensor strictly shows linear correlation in the range of 1-20 ppm, R2 = 0.99974. Notably, the sensor exhibits high sensitivity (658.45 nA ppm-1) and a low detection limit of 0.33 ppm. Moreover, the consistency and stability of the sensor are satisfactory. The constructed gas sensor is expected to be well applied to industrial H2S detection.

[Survival and Prognosis of Patients with Acute Myeloid Leukemia with Myelodysplasia-Related Changes Transformed from Myelodysplastic Syndrome].

OBJECTIVE: To explore the risk factors affecting the survival and efficacy of patients with acute myeloid leukemia with myelodysplasia-related changes (AML-MRC) transformed from myelodysplastic syndrome (MDS). METHODS: The clinical data of 60 patients with AML-MRC transformed from MDS who hospitalized in The Third Affiliated Hospital of Soochow University from January 2010 to December 2021 were retrospectively analyzed. The demographic data and laboratory parameters, cytogenetic karyotypes, target genes of AML detected by next generation sequence, risk stratification, treatment regimen, therapeutic efficacy and survival outcome were documented. Rank sum test and Chi-square test or Fisher exact test were used to compare the survival and efficacy. The effects of clinical parameters, risk stratification and treatment regimens on the survival and efficacy of the AML-MRC patients were analyzed by univariate and multivariate analysis. RESULTS: The median overall survival (OS) of the AML-MRC patients was 4.5 months, the 1-year OS rate was 28.3%, and the complete remission (CR) rate after treatment was 33.3%. The univariate analysis showed that age≥60 years, leukocytosis, severe thrombocytopenia, poor-risk group and only accepted hypomethylating agents(HMAs) or supportive therapy were the risk factors affecting OS. COX multivariate analysis showed that thrombocytopenia ( HR=4.46), HMAs therapy (compared to transplantation, HR=10.47), supportive therapy (compared to transplantation, HR=25.80) and poor-risk group (compared to medium-risk group, HR=13.86) were independent hazard factors for median OS of patients with AML-MRC. The univariate analysis showed that the risk factors affecting 1-year OS in patients with AML-MRC were age≥60 years, thrombocytopenia, time of transformation from MDS to AML (TTA)≥3 months, fibrinogen-albumin ratio index (FARI)≥0.07, CONUT score≥5, poor-risk group and supportive therapy. Binary logistic regression analysis showed that the independent risk factors for 1-year OS in AML-MRC patients were age≥60 years ( HR=11.23), thrombocytopenia ( HR=8.71), FARI≥0.07 ( HR=5.19) and poor-risk group ( HR=14.00). The risk factors affecting CR of AML-MRC patients in univariate analysis were age≥60 years, thrombocytopenia, FARI≥0.1, CONUT score≥5, poor-risk group and supportive therapy, while binary logistic regression analysis showed that age≥60 years( HR=7.35), CONUT score≥5 ( HR=9.60), thrombocytopenia ( HR=12.05) and poor-risk group ( HR=32.5) were independent risk factors affecting CR of the patients. CONCLUSION: The OS of AML-MRC patients is poor, old age(≥60 years old), supportive therapy, HMA therapy, poor-risk, thrombocytopenia, FARI≥0.07 and CONUT score≥5 may be associated with poor prognosis.

[Analysis of the treatment of a premature infant with critical coronavirus disease caused by the Omicron variant of SARS-CoV-2].

The patient is a female infant who was born at a gestational age of 30+4 weeks in the breech position and was delivered by assisted vaginal delivery. She was admitted to the neonatal department of Tianjin First Central Hospital for 44 days, during which she had stable respiration, oxygen saturation, and regular weight gain. The patient was discharged home by her family. The infant was readmitted to the hospital due to poor appetite for 15 hours and irregular breathing with weak response for 4 hours at the corrected gestational age of 37+2 weeks at 47 days after birth. The day before admission, the patient's mother experienced throat discomfort, and on the day of admission, the mother had a fever, with the highest temperature of 37.9 centigrade (she later tested positive for SARS-CoV-2 antigen). About 15 hours before admission, the family noticed that the patient had poor milk intake and weakened suction. About 4 hours before admission, the patient developed irregular breathing and weakened responses. After admission, the patient presented frequent apnea, and it was not relieved by adjusting the respiratory mode and parameters of non-invasive assisted ventilation, as well as caffeine citrate to stimulate the respiratory center. The patient was eventually given mechanical ventilation and other symptomatic support treatments. The pharyngeal swab was positive for COVID nucleic acid testing with a Ct value of 20.1 for the N gene. And the chest X-ray showed multiple patchy shadows in both lungs. The patient was diagnosed with critical coronavirus disease (COVID) caused by the Omicron variant in premature infants. After treatment, the child was clinically cured and discharged 8 days after hospitalization. Symptoms of COVID in premature infants may be atypical, and the condition can deteriorate rapidly. During the Omicron variant epidemic, we should pay more attention to premature infants so as to detect severe and critical cases as early as possible, and treat them actively to improve the prognosis.

Multidimensional Separations in Top-Down Proteomics.

Top-down proteomics (TDP) identifies, quantifies, and characterizes proteins at the intact proteoform level in complex biological samples to understand proteoform function and cellular mechanisms. However, analyzing complex biological samples using TDP is still challenging due to high sample complexity and wide dynamic range. High-resolution separation methods are often applied prior to mass spectrometry (MS) analysis to decrease sample complexity and increase proteomics throughput. These separation methods, however, may not be efficient enough to characterize low abundance intact proteins in complex samples. As such, multidimensional separation techniques (combination of two or more separation methods with high orthogonality) have been developed and applied that demonstrate improved separation resolution and more comprehensive identification in TDP. A suite of multidimensional separation methods that couple various types of liquid chromatography (LC), capillary electrophoresis (CE), and/or gel electrophoresis-based separation approaches have been developed and applied in TDP to analyze complex biological samples. Here, we reviewed multidimensional separation strategies employed for TDP, summarized current applications, and discussed the gaps that may be addressed in the future.

Fast and Robust Attribute Reduction Based on the Separability in Fuzzy Decision Systems.

Attribute reduction is one of the most important preprocessing steps in machine learning and data mining. As a key step of attribute reduction, attribute evaluation directly affects classification performance, search time, and stopping criterion. The existing evaluation functions are greatly dependent on the relationship between objects, which makes its computational time and space more costly. To solve this problem, we propose a novel separability-based evaluation function and reduction method by using the relationship between objects and decision categories directly. The degree of aggregation (DA) of intraclass objects and the degree of dispersion (DD) of between-class objects are first defined to measure the significance of an attribute subset. Then, the separability of attribute subsets is defined by DA and DD in fuzzy decision systems, and we design a sequentially forward selection based on the separability (SFSS) algorithm to select attributes. Furthermore, a postpruning strategy is introduced to prevent overfitting and determine a termination parameter. Finally, the SFSS algorithm is compared with some typical reduction algorithms using some public datasets from UCI and ELVIRA Biomedical repositories. The interpretability of SFSS is directly presented by the performance on MNIST handwritten digits. The experimental comparisons show that SFSS is fast and robust, which has higher classification accuracy and compression ratio, with extremely low computational time.

Optimization of protein-level tandem mass tag (TMT) labeling conditions in complex samples with top-down proteomics.

Isobaric chemical tag labels (e.g., iTRAQ and TMT) have been extensively utilized as a standard quantification approach in bottom-up proteomics, which provides high multiplexing capacity and enables MS2-level quantification while not complicating the MS1 scans. We recently demonstrated the feasibility of intact protein TMT labeling for the identification and quantification with top-down proteomics of smaller intact proteoforms (<35 kDa) in complex biological samples through the removal of large proteins prior to labeling. Still, the production of side products during TMT labeling (i.e., incomplete labeling or labeling of unintended residues) complicated the analysis of complex protein samples. In this study, we systematically evaluated the protein-level TMT labeling reaction parameters, including TMT-to-protein mass ratio, pH/concentration of quenching buffer, protein concentration, reaction time, and reaction buffer. Our results indicated that: (1) high TMT-to-protein mass ratio (e.g., 8:1, 4:1), (2) high pH/concentration of quenching buffer (pH > 9.1, final hydroxylamine concentration >0.3%), and (3) high protein concentration (e.g., > 1.0 µg/µL) resulted in optimal labeling efficiency and minimized production of over/underlabeled side products. >90% labeling efficiency was achieved for E. coli cell lysate after optimization of protein-level TMT labeling conditions. In addition, a double labeling approach was developed for efficiently labeling limited biological samples with low concentrations. This research provides practical guidance for efficient TMT labeling of complex intact protein samples, which can be readily adopted in the high-throughput quantification top-down proteomics.

A benchmarking protocol for intact protein-level Tandem Mass Tag (TMT) labeling for quantitative top-down proteomics.

Isobaric chemical tag labeling for quantification of intact proteins in complex samples is limited due to the tendency of intact proteins precipitate under labeling conditions and increased sample complexity as a result of side products (i.e., incomplete labeling or labeling of unintended residues). To reduce precipitation under labeling conditions, we developed a technique to remove large proteoforms that allowed for the labeling and characterization of small proteoforms (<35 kDa) using top-down proteomics. We also systematically optimized protein-level Tandem Mass Tag (TMT) labeling conditions to obtain optimal labeling parameters for complex samples. Here, we present a benchmarking protocol for protein-level TMT labeling for quantitative top-down proteomics, including complex intact protein sample preparation, protein-level TMT labeling, top-down LC/MS analysis, and TMT reporter ion quantification.•An optimized protocol for protein-level TMT labeling in complex sample.•Limits production of incorrectly labeled side products for minimization of spectral complexity.•A guideline for isobaric chemical tag quantification in top-down proteomics.

[The Clinical Value of Neutrophil CD64 Index in Hematological Malignancies with Pulmonary Infection].

OBJECTIVE: To investigate the clinical value of neutrophil CD64 index in hematological malignancies with pulmonary infection. METHODS: The cohort study method was used to retrospectively analyze the clinical data of 125 patients with hematological malignancies and pulmonary infections who were treated in The Third Affiliated Hospital of Soochow University. All the patients were divided into four stages according to the diagnosis and treatment process: non-infected stage (T1), the symptoms of infection had appeared before using antibiotics (T2), one week after anti-infective treatment (T3), and after stopping antibiotics (T4). CD64 index, C-reactive protein (CRP), blood cell count, and immune cell level were compared before and after infection (T1 vs T2), the correlation between CD64 index and other indicators were explored, the change trends of the significantly different indicators in the course of the disease were observed, and the diagnostic efficacy of CD64 index and CRP were compared. The surviving patients were followed up for whether reinfection occurred within 30 days after discharge, and the re-examination results of indices before discharge (in stage of T4) between reinfected and non-reinfected patients were compared to find the risk factors of reinfection. RESULTS: Before and after infection, the CD64 index, CRP, CD14+HLA-DR+, CD4+, and lymphocyte counts were significantly different (all P<0.05). There was a negative correlation of CD64 index with CD14+HLA-DR+ (r=-0.395, P<0.001), a negative correlation with CD3+ (r=-0.1.87, P=0.047), and a negative correlation with lymphocyte count (r=-0.230, P=0.006), while a positive correlation with CRP(r=0.313, P<0.001). The area under the curve of CD64 index, CRP, and CD64 index combined with CRP was 0.790 (95%CI: 0.711-0.868), 0.754(95%CI: 0.667-0.841), and 0.835(95%CI: 0.762-0.907), respectively; the sensitivity was 59.6%, 72.7%, and 74.7%, the specificity was 89.2%, 73.0%, and 78.4%, and the cut-off value was 0.488, 0.457, and 0.531, respectively. There were only two re-examination indexes showed significantly different before discharge between reinfected patients and non-reinfected patients: CD14+HLA-DR+ (F=8.524, P=0.004) and CD64 index (F=9.993, P=0.002). The increase of CD64 index was an independent risk factor for reinfection within 30 days after discharge from the hospital (HR=1.790, 95%CI: 1.343-2.386, P<0.001). CONCLUSION: CD64 index has diagnostic value in patients with hematological malignancies and pulmonary infection, and its specificity is higher than that of CRP. The combination of the two indicators can improve the diagnostic sensitivity. CD64 index has a predictive value for reinfection within 30 days after infection treatment.

[The Effects of Decitabine Combined with All-Trans Retinoic Acid on the Number of Immune Cells in Myeloid Neoplasms].

OBJECTIVE: To investigate the effects of decitabine (DEC) combined with all-trans retinoic acid (ATRA) on the number of immune cells, efficacy and adverse reactions in the treatment of myeloid neoplasms patients. METHODS: Eighty-four patients with myeloid tumors, including AML, MDS-EB-1 or MDS-EB-2 treated by the regimen containing decitabine in our hospital from January 2009 to October 2019 were enrolled and retrospectively analyzed, among the patients, 21 patients treated with DEC alone, 24 patients treated with DEC combined with ATRA (DEC/ATRA) and 39 patients treated with DEC combined with G-CSF priming regimen (DEC/priming). The changes of peripheral blood immune cell levels before and after treatment of the patients between the three groups were compared, and the differences in clinical efficacy and adverse reactions of the patients between the three groups were also compared. RESULTS: There was no statistical differences in the number of immune cells among the patients in the three groups before treatment (P>0.05). NK cell levels decreased significantly in the patients in DEC and DEC/ATRA group after treatment (P<0.05); After treatment, the levels of CD8+ and CD3+T cells in the patients treated by DEC /priming regimen significantly increased (P<0.05), while the levels of CD3-HLA-DR+ B cells significantly decreased (P<0.05). The overall response rate (ORR) of the patients in DEC/ATRA group (75%) and DEC/priming group (74.36%) was significantly higher than 42.86% in DEC monotherapy group, and the differences showed statistically significant (P<0.05), while the ORR between the patients in DEC/ATRA and DEC/priming group showed no statistic differences (P>0.05). There were no statistical differences in overall survival (OS) and incidence of bleeding between the patients in the three groups (P>0.05). The incidences of grade 3 to 4 bone marrow suppression and the infection rate of the patients in DEC monotherapy and DEC/ATRA group were significantly lower than that in DEC/priming regimen group after treatment (all P<0.05), however, there was no statistical difference between DEC monotherapy and the DEC/ATRA group. CONCLUSION: The efficacy of DEC/ATRA on myeloid neoplasms is comparable to that of DEC/priming regimen, and the anti-myeloid tumor effect of DEC/ATRA regimen may be related to the regulation of NK cells and T cells.