Multiplexed discrimination of SARS-CoV-2 variants via duplex-specific nuclease combined MALDI-TOF MS.

The frequent mutations in SARS-CoV-2 significantly increase the virus's pathogenicity and transmissibility while also diminishing the effectiveness of vaccines. Consequently, assays capable of rapidly and simultaneously identifying multiple SARS-CoV-2 variants are essential for large-scale applications that aim to monitor the evolution of the virus. In this work, we propose a method combining duplex-specific nuclease (DSN)-assisted cyclic amplification with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) detection, enabling the simultaneous identification of multiple SARS-CoV-2 variants at high-throughput. Due to the high specificity of DSN, single-base mutations can be resolved by the method. With ultra-sensitive detection by MALDI-TOF MS, a limit of detection of 100 pM viral RNA fragment was demonstrated. The assay was used for simultaneous identification and typing of SARS-CoV-2 Alpha, Beta, and Delta variants. The whole assay can be accomplished within 3 h, and the amplification is performed under constant temperature, making the technique simple in operation and efficient. It is also feasible to extend the technique to the detection of many other variants of the virus. We expect that the method can add value to the rapid screening of viral variants and can play an important role in pandemic control.

Platelet Aggregation in Apparently Healthy Elderly Populations: Reference Ranges and Influence of Hematology Parameters.

BACKGROUND: Reference intervals based on younger populations may not apply to elderly populations. The aim of the current study was to calculate reference ranges for platelet aggregation in apparently healthy Chinese elderly populations and analyze the impact of gender, age, and hematological parameters on platelet aggregation in vitro. METHODS: A total of 138 males and 161 females were enrolled based on stringent inclusion and exclusion criteria. Platelet aggregation was measured with sodium citrate anticoagulation whole blood samples using a PL12 analyzer, triggered by adenosine-5'-diphosphate (ADP) and arachidonic acid (AA) agonists. Hematological parameters were measured using a Sysmex XE2100 instrument. RESULTS: In the total sample tested in this study, the platelet aggregation induced by AA and ADP was not dependent on age (p > 0.05) but gender. Platelet aggregation triggered by ADP and AA was more enhanced in females than males (p = 0.024 for AA, p = 0.036 for ADP). The recommended reference values of AA-induced platelet aggregation were 48.42% - 85.93% for males and 43.62% - 87.80% for females. The recommended reference values of ADP-induced platelet aggregation were 38.12% - 80.21% for males and 40.12% - 83.05% for females. Furthermore, for all agonists, a positive correlation was found between platelet aggregation and platelet count. The ADP-triggered aggregation showed a significant positive correlation with white blood cell (WBC) count and a negative correlation with red blood cell (RBC) count. Moreover, platelet aggregation showed no significant correlation with mean platelet volume or hemoglobin concentration. CONCLUSIONS: Combined and gender-specific reference ranges for platelet aggregation in healthy elderly populations were established in whole blood measured by a sequential platelet counting method.

Rapid Detection of COVID-19 Using MALDI-TOF-Based Serum Peptidome Profiling.

The outbreak of coronavirus disease 2019 (COVID-19) caused by SARS CoV-2 is ongoing and a serious threat to global public health. It is essential to detect the disease quickly and immediately to isolate the infected individuals. Nevertheless, the current widely used PCR and immunoassay-based methods suffer from false negative results and delays in diagnosis. Herein, a high-throughput serum peptidome profiling method based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is developed for efficient detection of COVID-19. We analyzed the serum samples from 146 COVID-19 patients and 152 control cases (including 73 non-COVID-19 patients with similar clinical symptoms, 33 tuberculosis patients, and 46 healthy individuals). After MS data processing and feature selection, eight machine learning methods were used to build classification models. A logistic regression machine learning model with 25 feature peaks achieved the highest accuracy (99%), with sensitivity of 98% and specificity of 100%, for the detection of COVID-19. This result demonstrated a great potential of the method for screening, routine surveillance, and diagnosis of COVID-19 in large populations, which is an important part of the pandemic control.

Prediction of the VeriStrat test in first-line therapy of pemetrexed-based regimens for advanced lung adenocarcinoma patients.

BACKGROUND: Although advanced non-squamous non-small cell lung cancer (NSCLC) patients have significantly better survival outcomes after pemetrexed based treatment, a subset of patients still show intrinsic resistance and progress rapidly. Therefore we aimed to use a blood-based protein signature (VeriStrat, VS) to analyze whether VS could identify the subset of patients who had poor efficacy on pemetrexed therapy. METHODS: This study retrospectively analysed 72 advanced lung adenocarcinoma patients who received first-line pemetrexed/platinum or combined with bevacizumab treatment. RESULTS: Plasma samples from these patients were analysed using VS and classified into the Good (VS-G) or Poor (VS-P) group. The relationship between efficacy and VS status was further investigated. Of the 72 patients included in this study, 35 (48.6%) were treated with pemetrexed plus platinum and 37 (51.4%) were treated with pemetrexed/platinum combined with bevacizumab. Among all patients, 60 (83.3%) and 12 (16.7%) patients were classified as VS-G and VS-P, respectively. VS-G patients had significantly better median progression-free survival (PFS) (Unreached vs. 4.2 months; P < 0.001) than VS-P patients. In addition, the partial response (PR) rate was higher in the VS-G group than that in the VS-P group (46.7% vs. 25.0%, P = 0.212). Subgroup analysis showed that PFS was also significantly longer in the VS-G group than that in the VS-P group regardless of whether patients received chemotherapy alone or chemotherapy plus bevacizumab. CONCLUSIONS: Our study indicated that VS might be considered as a novel and valid method to predict the efficacy of pemetrexed-based therapy and identify a subset of advanced lung adenocarcinoma patients who had intrinsic resistance to pemetrexed based regimens. However, larger sample studies are still needed to further confirm this result.

Hematological factors associated with immunity, inflammation, and metabolism in patients with systemic lupus erythematosus: Data from a Zhuang cohort in Southwest China.

INTRODUCTION: Hematological parameters play important role in multiple diseases. This study was to investigate the possible association of the routine hematological parameters involved in immunity, inflammation, and metabolism with systemic lupus erythematosus (SLE) in patients of Zhuang ethnicity in Guangxi, southwest China. METHODS: The medical records of 195 Zhuang SLE patients between January 2013 and November 2018 were retrospectively reviewed. Random forest algorithm and multivariate logistic regression were used to identify the feature hematological parameters in patients with SLE. Association rules were explored between each parameter and immunity- (IgG, IgA, IgM, C3, and C4), inflammation- (ESR, hs-CRP, and CAR), and metabolism- (TG, TC, HDL-C, LDL-C, TP, PA, ALB, and UA) related indexes. RESULTS: Random forest algorithm and logistic regression analysis showed that neutrophil-to-lymphocyte ratio (NLR), red blood cell distribution width (RDW), and platelet-to-lymphocyte ratio (PLR) were the feature parameters for distinguishing SLE patients from healthy controls. According to the ROC curves, the optimal cutoff values to predict SLE were 1.98 for NLR, 13.35 for RDW, and 145.64 for PLR. Association rule analysis showed that NLR was strongly associated with C3, hs-CRP, TG, ALB, and UA; RDW was strongly associated with C3, C4, hs-CRP, TG, and ALB; PLR was strongly associated with IgG, hs-CRP, HDL-C, and UA. CONCLUSIONS: Neutrophil-to-lymphocyte ratio, RDW, and PLR may serve as effective predictors of dysregulation in immunity, inflammation, and metabolism. These three indicators may be potential for cardiovascular risk assessment in Zhuang SLE patients in southwest China.

[Determination of 20 Trace Elements in the Blood Collection Tubes with ICP-MS].

To investigate the contamination of blood collection tubes, 20 trace elements (Al, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Mo, Cd, Sn, Sb, Ba, W, Hg, Tl, Pb) in 13 different types of blood collection tube were studied with ICP-MS method. The lixivium of H(2)O and 10% HNO(3) were measured with ICP-MS, and then the contamination coming from the blood collection tube is specified. According to the concentration range of human blood, plasma and serum from recently published literature, this report presents a detailed analysis of capable trace elements for each blood collection tube. The results showed that, tube No.1 is capable to analyze 18 trace elements in the human serum; tube No.6 is capable to analyze 15 trace elements in the human plasma; tube No. 13 is capable to analyze 17 trace elements in the human blood. But we still should be aware that, the elements Sb and W in tube No.1, the elements V, Cr, Ni, and Sb in tube No.6, and the elements Al, Sb and W in tube No.13, are in the same magnitude of the normal trace element concentration range in the human serum, plasma and blood. They might affect the testing results. The serum collected from the same volunteer by tube No.1 and tube No.3 were compared here, the results show that, almost each trace element concentration of human serum from tube No.1 is lower than from tube No.3, especially for elements Al, V, Cr, Mn, As, Sn, and Sb. The results indicate that the blood collection tubes show great impact on determination of trace element.

Analysis of the reptile CD1 genes: evolutionary implications.

CD1, as the third family of antigen-presenting molecules, is previously only found in mammals and chickens, which suggests that the chicken and mammalian CD1 shared a common ancestral gene emerging at least 310 million years ago. Here, we describe CD1 genes in the green anole lizard and Crocodylia, demonstrating that CD1 is ubiquitous in mammals, birds, and reptiles. Although the reptilian CD1 protein structures are predicted to be similar to human CD1d and chicken CD1.1, CD1 isotypes are not found to be orthologous between mammals, birds, and reptiles according to phylogenetic analyses, suggesting an independent diversification of CD1 isotypes during the speciation of mammals, birds, and reptiles. In the green anole lizard, although the single CD1 locus and MHC I gene are located on the same chromosome, there is an approximately 10-Mb-long sequence in between, and interestingly, several genes flanking the CD1 locus belong to the MHC paralogous region on human chromosome 19. The CD1 genes in Crocodylia are located in two loci, respectively linked to the MHC region and MHC paralogous region (corresponding to the MHC paralogous region on chromosome 19). These results provide new insights for studying the origin and evolution of CD1.

Serum peptidome profiling analysis for the identification of potential biomarkers in cervical intraepithelial neoplasia patients.

Cervical intraepithelial neoplasia (CIN) is a precancerous disease of cervical squamous cell carcinoma. We Used Mass Spectrometry based peptidome profile study to predict the transformation of CIN1, which is the primary stage of this lesion. . Serum samples of 34 Cervical squamous cell carcinoma patients, 31 healthy controls, and 29 CIN1 samples were analyzed. Peptides were purified by WCX magnetic beads (Bioyong), and analyzed by MALDI TOF (Bruker). Raw data were analyzed by BioExplorer software (Bioyong). The results showed 14 mass peaks with significant differences. The diagnosis model is established by analyzing peptide profiles of 15 SCC patients and 20 healthy women serum, with a sensitivity of 100% and specificity of 100.00%. In validation set, the SCC diagnosis model also had good performance with a sensitivity of 80%, a specificity of 100%. In addition, this model could predict 29 CIN1 patients with accuracy of 55.17%. These results would provide a new method to predict the trend of CIN1 and take effective measures for high risk group timely.

[Identification of Streptococcus mutans in carious patients' saliva samples by MALDI-TOF mass spectrometry].

OBJECTIVE: To identify Streptococcus mutans (S. mutans) in carious patients' saliva using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), and to establish a faster and more accurate method to identify S. mutans. METHODS: In this study, a total of 90 carious patients from Department of Endodontics of Peking University School of Stomatology were recruited. All these patients' saliva was collected. After extracting the protein of the samples, they were identified by MALDI-TOF MS. The composite profile was analyzed using BioExplorer 1.0 software. The scores ≥ 25 were considered as S. mutans, whereas the scores <25 were as considered as non S. mutans. Finally, these results were compared with 16S rDNA sequencing to figure out the sensitivity and concordance rate, respectively. RESULTS: The sensitivity of MALDI-TOF MS was 96.0%, and the concordance rate compared with 16S rDNA sequencing was as high as 98.7%. CONCLUSION: MALDI-TOF MS is high throughput, rapid and easy to perform in comparison to other conventional methods. It has a high sensivity and concordance rate. Thus, MALDI-TOF MS can serve as an effective tool for identification of S. mutans.

MazF-rolling circle amplification combined MALDI-TOF MS for site-specific detection of N6-methyladenosine RNA.

N6-methyladenosine (m6A) is one of the most abundant chemical modifications in RNA and has vital significance in cellular processes and tumor development. However, the accurate analysis of site-specific m6A modification remains a challenge. In this work, a MazF endoribonuclease activated rolling circle amplification (MazF-RCA) combined MALDI-TOF MS assay is developed for the detection of site-specific m6A-RNA. MazF endoribonuclease can specifically cleave the ACA motif, leaving methylated (m6A)CA motif intact. The intact methylated RNA can then be amplified through rolling circle amplification, and the generated reporter oligonucleotides are detected by MALDI-TOF MS. The assay exhibits good quantification ability, presenting a wide linear range (100 fM to 10 nM) with the limit-of-detection lower than 100 fM. Additionally, the assay can accurately detect methylated RNA in the presence of large amount of non-methylated RNA with a relative abundance of methylated RNA down to 0.5%. The developed assay was further applied to detect m6A-RNA spiked in MCF-7 cell RNA extracts, with the recovery rates in the range of 90.64-106.93%. The present assay provides a novel platform for the analysis of site-specific m6A-RNA at high specificity and sensitivity, which can promote the study of RNA methylation in clinical and biomedical research.

Melatonin derivative 6a protects Caenorhabditis elegans from formaldehyde neurotoxicity via ADH5.

Formaldehyde (FA) is a carcinogen that is not only widespread in the environment, but is also produced endogenously by metabolic processes. In organisms, FA is converted to formic acid in a glutathione (GSH)-dependent manner by alcohol dehydrogenase 5 (ADH5). The abnormal accumulation of FA in the body can cause a variety of diseases, especially cognitive impairment leading to Alzheimer's disease (AD). In this study, melatonin derivative 6a (MD6a) markedly improved the survival and chemotactic performance of wild-type Caenorhabditis elegans exposed to high concentrations of FA. MD6a lowered FA levels in the nematodes by enhancing the release of covalently-bound GSH from S-hydroxymethyl-GSH in an adh-5-dependent manner. In addition, MD6a protected against mitochondrial dysfunction and cognitive impairment in beta-amyloid protein (Aß) transgenic nematodes by lowering endogenous FA levels and reducing Aß aggregation in an adh-5-dependent manner. Our findings suggest that MD6a detoxifies FA via ADH5 and protects against Aß toxicity by reducing endogenous FA levels in the C. elegans AD models. Thus, ADH5 might be a potential therapeutic target for FA toxicity and AD.

Melatonin derivative 6a as a PARP-1 inhibitor for the treatment of Parkinson's disease.

Both continuous oxidative stress and poly (ADP-ribose) polymerase 1 (PARP-1) activation occur in neurodegenerative diseases such as Parkinson's disease. PARP-1 inhibition can reverse mitochondrial damage and has a neuroprotective effect. In a previous study, we synthesized melatonin derivative 6a (MD6a) and reported that it has excellent antioxidant activity and significantly reduces α-synuclein aggregation in Caenorhabditis elegans; however, the underlying mechanism is largely unknown. In the present study, we revealed that MD6a is a potential PARP-1 inhibitor, leading to mammalian targe of rapamycin/heat shock factor 1 signaling downregulation and reducing heat shock protein 4 and 6 expression, thus helping to maintain protein homeostasis and improve mitochondrial function. Together, these findings suggest that MD6a might be a viable candidate for the prevention and treatment of Parkinson's disease.

The close association of micronutrients with COVID-19.

Objectives: The present study was conducted to explore the performance of micronutrients in the prediction and prevention of coronavirus disease 2019 (COVID-19). Methods: This is an observational case-control study. 149 normal controls (NCs) and 214 COVID-19 patients were included in this study. Fat-soluble and water-soluble vitamins were determined by liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis, and inorganic elements were detected by inductively coupled plasma-mass spectrometry (ICP-MS) analysis. A logistic regression model based on six micronutrients were constructed using DxAI platform. Results: Many micronutrients were dysregulated in COVID-19 compared to normal control (NC). 25-Hydroxyvitamin D3 [25(OH)D3], magnesium (Mg), copper (Cu), calcium (Ca) and vitamin B6 (pyridoxic acid, PA) were significantly independent risk factors for COVID-19. The logistic regression model consisted of 25(OH)D3, Mg, Cu, Ca, vitamin B5 (VB5) and PA was developed, and displayed a strong discriminative capability to differentiate COVID-19 patients from NC individuals [area under the receiver operating characteristic curve (AUROC) = 0.901]. In addition, the model had great predictive ability in discriminating mild/normal COVID-19 patients from NC individuals (AUROC = 0.883). Conclusions: Our study showed that micronutrients were associated with COVID-19, and our logistic regression model based on six micronutrients has potential in clinical management of COVID-19, and will be useful for prediction of COVID-19 and screening of high-risk population.

Genetic Polymorphism of Lipoprotein-Associated Phospholipase A2 Influences Susceptibility to Gestational Diabetes Mellitus in Chinese Population.

Purpose: This paper aims to study the relationship between lipoprotein-associated phospholipase A2 (Lp-PLA2) and GDM (gestational diabetes mellitus) by detecting Lp-PLA2 level and its gene polymorphism. Patients and Methods: From January to June 2022, 82 GDM patients treated in our hospital were included as an experimental group, and 89 healthy pregnant women during the same period were selected as the control group. Lp-PLA2 concentration and TG, TC, HDL-C, and LDL-C levels were tested with specialized instruments in clinical laboratories. The PLA2G7 gene polymorphisms (rs1805017, rs1805018, and rs76863441) were detected by fluorescent probe method and sequencing. Results: Lp-PLA2 concentration was significantly higher in GDM group than control group (P<0.05). Among three polymorphism loci of PLA2G7 gene (rs1805017, rs1805018, and rs76863441) the significant associations were only found in GT genotype of rs76863441 loci (P<0.05). Conclusion: Pregnant women with high levels of Lp-PLA2 concentration are more likely to develop GDM, especially those with PLA2G7 rs76863441 polymorphism. Lp-PLA2 concentration and PLA2G7 rs1805017 polymorphism may be a novel marker for GDM diagnosis and prediction.

N7-methylguanosine-related miRNAs predict hepatocellular carcinoma prognosis and immune therapy.

N7-methylguanosine (m7G) modification has been notably linked with the development of many tumors. However, no investigations have been conducted on whether m7G-related miRNA (m7G-miRNA) is a prognostic index of hepatocellular carcinoma (HCC). Therefore, this investigation aimed to establish a predictive m7G-miRNA signature for efficient HCC prognosis and elucidate the associated immune cell infiltration (ICI) and functions in the tumor microenvironment. RNA sequencing and clinical data on 375 HCC and 50 healthy tissue samples were acquired from The Cancer Genome Atlas database. The m7G-miRNA regulators methyltransferase-like 1 and WD repeat domain 4 were acquired from the TargetScan database. Univariate Cox regression analysis was conducted on the 63 differentially expressed m7G-miRNAs identified. A prognostic signature that consisted of seven miRNAs was identified. According to their risk scores, individuals with HCC were divided into high-risk (HR) and low-risk (LR) cohorts. A Kaplan-Meier test revealed that survival in the HR HCC patients was poorer than in the LR cohort (p < 0.001). The area under the receiver operating characteristic curves of 1-, 3-, and 5-year overall survival were 0.706, 0.695, and 0.715, respectively. A nomogram of sex, risk score, age, and stage indicated the HCC patients' overall survival. Furthermore, it was indicated that the HR and LR patients had different degrees of ICI and immune function. A pathway enrichment analysis revealed the association of several immunity-linked pathways with the risk model. In conclusion, the signature established has great prognostic value and could be used as a new immunotherapy target for individuals with HCC.

Isothermal gene amplification coupled MALDI-TOF MS for SARS-CoV-2 detection.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been spreading worldwide for more than a year and has undergone several mutations and evolutions. Due to the lack of effective therapeutics and long-active vaccines, accurate and large-scale screening and early diagnosis of infected individuals are crucial to control the pandemic. Nevertheless, the current widely used RT-qPCR-based methods suffer from complicated temperature control, long processing time and the risk of false-negative results. Herein, we present a three-way junction induced exponential rolling circle amplification (3WJ-eRCA) combined MALDI-TOF MS assay for SARS-CoV-2 detection. The assay can detect simultaneously the target nucleocapsid (N) and open reading frame 1 ab (orf1ab) genes of SARS-CoV-2 in a single test within 30 min, with an isothermal process (55 °C). High specificity to discriminate SARS-CoV-2 from other coronaviruses, like SARS-CoV, MERS-CoV and bat SARS-like coronavirus (bat-SL-CoVZC45), was observed. We have further used the method to detect pseudovirus of SARS-CoV-2 in various matrices, e.g. water, saliva and urine. The results demonstrated a great potential of the method for large scale screening of COVID-19, which is an important part of the pandemic control.

A MALDI-TOF mass spectrometry-based haemoglobin chain quantification method for rapid screen of thalassaemia.

BACKGROUND: Thalassaemia is one of the most common inherited monogenic diseases worldwide with a heavy global health burden. Considering its high prevalence in low and middle-income countries, a cheap, accurate and high-throughput screening test of thalassaemia prior to a more expensive confirmatory diagnostic test is urgently needed. METHODS: In this study, we constructed a machine learning model based on MALDI-TOF mass spectrometry quantification of haemoglobin chains in blood, and for the first time, evaluated its diagnostic efficacy in 674 thalassaemia (including both asymptomatic carriers and symptomatic patients) and control samples collected in three hospitals. Parameters related to haemoglobin imbalance (α-globin, ß-globin, γ-globin, α/ß and α-ß) were used for feature selection before classification model construction with 8 machine learning methods in cohort 1 and further model efficiency validation in cohort 2. RESULTS: The logistic regression model with 5 haemoglobin peak features achieved good classification performance in validation cohort 2 (AUC 0.99, 95% CI 0.98-1, sensitivity 98.7%, specificity 95.5%). Furthermore, the logistic regression model with 6 haemoglobin peak features was also constructed to specifically identify ß-thalassaemia (AUC 0.94, 95% CI 0.91-0.97, sensitivity 96.5%, specificity 87.8% in validation cohort 2). CONCLUSIONS: For the first time, we constructed an inexpensive, accurate and high-throughput classification model based on MALDI-TOF mass spectrometry quantification of haemoglobin chains and demonstrated its great potential in rapid screening of thalassaemia in large populations.Key messagesThalassaemia is one of the most common inherited monogenic diseases worldwide with a heavy global health burden.We constructed a machine learning model based on MALDI-TOF mass spectrometry quantification of haemoglobin chains to screen for thalassaemia.

Serum peptidome profiles immune response of COVID-19 Vaccine administration.

Background: Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused significant loss of life and property. In response to the serious pandemic, recently developed vaccines against SARS-CoV-2 have been administrated to the public. Nevertheless, the research on human immunization response against COVID-19 vaccines is insufficient. Although much information associated with vaccine efficacy, safety and immunogenicity has been reported by pharmaceutical companies based on laboratory studies and clinical trials, vaccine evaluation needs to be extended further to better understand the effect of COVID-19 vaccines on human beings. Methods: We performed a comparative peptidome analysis on serum samples from 95 participants collected at four time points before and after receiving CoronaVac. The collected serum samples were analyzed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to profile the serum peptides, and also subjected to humoral and cellular immune response analyses to obtain typical immunogenicity information. Results: Significant difference in serum peptidome profiles by MALDI-TOF MS was observed after vaccination. By supervised statistical analysis, a total of 13 serum MALDI-TOF MS feature peaks were obtained on day 28 and day 42 of vaccination. The feature peaks were identified as component C1q receptor, CD59 glycoprotein, mannose-binding protein C, platelet basic protein, CD99 antigen, Leucine-rich alpha-2-glycoprotein, integral membrane protein 2B, platelet factor 4 and hemoglobin subunits. Combining with immunogenicity analysis, the study provided evidence for the humoral and cellular immune responses activated by CoronaVac. Furthermore, we found that it is possible to distinguish neutralizing antibody (NAbs)-positive from NAbs-negative individuals after complete vaccination using the serum peptidome profiles by MALDI-TOF MS together with machine learning methods, including random forest (RF), partial least squares-discriminant analysis (PLS-DA), linear support vector machine (SVM) and logistic regression (LR). Conclusions: The study shows the promise of MALDI-TOF MS-based serum peptidome analysis for the assessment of immune responses activated by COVID-19 vaccination, and discovered a panel of serum peptides biomarkers for COVID-19 vaccination and for NAbs generation. The method developed in this study can help not only in the development of new vaccines, but also in the post-marketing evaluation of developed vaccines.

Change of dominant phytoplankton groups in the eutrophic coastal sea due to atmospheric deposition.

Nutrient stoichiometry and input of trace metals may profoundly affect the growth and community structure of phytoplankton. A bioassay experiment was designed to explore the key components in atmospheric deposition that affect marine phytoplankton growth by adding aerosols and analogues nutrients and Cu to the surface water of the coastal East China Sea (ECS). Our results showed that atmospheric deposition along with the input of phosphate could largely enhance the chlorophyll a (Chl a) concentrations in this eutrophic water. Phosphorus addition lifted the proportions of T. oceanica in Diatoms and B. brevisulcata in Dinoflagellates. T. oceanica replaced S. costatum and became the dominant diatom species after the Chl a peak, probably associated with the N/P ratio approaching to 16. Atmospheric aerosols containing affluent N and little P showed limited promotion to Chl a, and the positive effect was very likely due to the soluble Cu and other trace metals supplied by the aerosol. Moreover, soluble aerosol Cu was found to be conducive to the relative abundance of most dominant class Coscinodiscophyceae, and both soluble aerosol Fe and Cu seemed to be very important for increasing the proportion of S. costatum. Soluble metals could be the key components in aerosols controlling the phytoplankton composition in the eutrophic sea and such impact might exceed affluent P provided by other exogenous sources.

High-resolution estimation of ambient sulfate concentration over Taiwan Island using a novel ensemble machine-learning model.

Heavy loadings of sulfate aerosol trigger haze formation and pose great damage to human health in Taiwan Island. Nevertheless, high-resolution spatiotemporal variation of ambient sulfate across Taiwan Island still remained unknown because of the scarce monitoring sites. Thus, we developed a novel ensemble model named extreme gradient boosting coupled with geographically and temporally weighted regression (XGBoost-GTWR) to predict the high-resolution sulfate concentration (0.05°) based on satellite data, assimilated meteorology, and the output of chemical transport models (CTMs). The result suggested that XGBoost-GTWR model outperformed other five models in predicting the sulfate concentration with the highest R2 value (R2 = 0.58) and the lowest relative mean square error (RMSE = 1.96 µg/m3). Besides, the transferability of the XGBoost-GTWR model was also validated based on the ground-level sulfate data in 2019. The result suggested that the R2 value of the extrapolation equation (0.53) did not show notable decrease compared with the 10-fold cross-validation result (0.58), indicating that the model was robust to predict the sulfate concentration. The ambient sulfate concentration in Taiwan Island displayed featured spatial variation with the highest one in Southwest Taiwan and the lowest one in Northeast Taiwan, respectively. It was assumed that the higher anthropogenic emission combined with the adverse meteorological condition led to the higher sulfate level in the southwestern coastal region. The ambient sulfate concentration exhibited significantly seasonal variation with the highest value in spring (5.65 ± 0.84 µg/m3), followed by those in winter (5.45 ± 1.25 µg/m3) and autumn (4.60 ± 0.80 µg/m3), and the lowest one in summer (3.80 ± 0.65 µg/m3). The higher sulfate concentration in spring was mainly contributed by the dense biomass burning and scarce rainfall amount. The present study develops a novel model to capture the high-resolution sulfate map and provides basic data for effective regulations of air pollution and epidemiological studies.