Major Depressive Disorder and Chronic Fatigue Syndrome Show Characteristic Heart Rate Variability Profiles Reflecting Autonomic Dysregulations: Differentiation by Linear Discriminant Analysis.

Major depressive disorder (MDD) and chronic fatigue syndrome (CFS) have overlapping symptoms, and differentiation is important to administer the proper treatment. The present study aimed to assess the usefulness of heart rate variability (HRV) indices. Frequency-domain HRV indices, including high-frequency (HF) and low-frequency (LF) components, their sum (LF+HF), and their ratio (LF/HF), were measured in a three-behavioral-state paradigm composed of initial rest (Rest), task load (Task), and post-task rest (After) periods to examine autonomic regulation. It was found that HF was low at Rest in both disorders, but was lower in MDD than in CFS. LF and LF+HF at Rest were low only in MDD. Attenuated responses of LF, HF, LF+HF, and LF/HF to task load and an excessive increase in HF at After were found in both disorders. The results indicate that an overall HRV reduction at Rest may support a diagnosis of MDD. HF reduction was found in CFS, but with a lesser severity. Response disturbances of HRV to Task were observed in both disorders, and would suggest the presence of CFS when the baseline HRV has not been reduced. Linear discriminant analysis using HRV indices was able to differentiate MDD from CFS, with a sensitivity and specificity of 91.8% and 100%, respectively. HRV indices in MDD and CFS show both common and different profiles, and can be useful for the differential diagnosis.

ATR-FTIR spectrum analysis of plasma samples for rapid identification of recovered COVID-19 individuals.

The development of fast, cheap and reliable methods to determine seroconversion against infectious agents is of great practical importance. In the context of the COVID-19 pandemic, an important issue is to study the rate of formation of the immune layer in the population of different regions, as well as the study of the formation of post-vaccination immunity in individuals after vaccination. Currently, the main method for this kind of research is enzyme immunoassay (ELISA, enzyme-linked immunosorbent assay). This technique is sufficiently sensitive and specific, but it requires significant time and material costs. We investigated the applicability of attenuated total reflection (ATR) Fourier transform infrared (FTIR) spectroscopy associated with machine learning in blood plasma to detect seroconversion against SARS-CoV-2. The study included samples of 60 patients. Clear spectral differences in plasma samples from recovered COVID-19 patients and conditionally healthy donors were identified using multivariate and statistical analysis. The results showed that ATR-FTIR spectroscopy, combined with principal components analysis (PCA) and linear discriminant analysis (LDA) or artificial neural network (ANN), made it possible to efficiently identify specimens from recovered COVID-19 patients. We built classification models based on PCA associated with LDA and ANN. Our analysis led to 87% accuracy for PCA-LDA model and 91% accuracy for ANN, respectively. Based on this proof-of-concept study, we believe this method could offer a simple, label-free, cost-effective tool for detecting seroconversion against SARS-CoV-2. This approach could be used as an alternative to ELISA.

Differences between COVID-19-vaccinated and unvaccinated participants from Croatia.

AIM: To compare Croatian participants vaccinated against coronavirus disease 2019 (COVID-19) and unvaccinated participants in terms of socio-demographic, personal, social, and COVID-19-related variables. METHODS: From August till December 2021, 721 (465 vaccinated and 256 unvaccinated) participants completed an online survey about socio-demographic (age, sex income, education, marital status), personal (well-being indicators, personality measures and health), social (trust in experts, trust in government), and COVID-19-related characteristics (fear of COVID-19, history of COVID-19 infection). Differences between the groups were assessed with discriminant analysis. RESULTS: The variables that best discriminated between vaccinated and unvaccinated participants were higher trust in experts, no history of COVID-19 infection, older age, higher fear of COVID-19, and intellect. Conclusion The study points to the importance of trust in experts in the promotion of COVID-19 vaccine.

Depression detection based on linear and nonlinear speech features in I-vector/SVDA framework.

This study proposes depression detection systems based on the i-vector framework for classifying speakers as depressed or healthy and predicting depression levels according to the Beck Depression Inventory-II (BDI-II). Linear and non-linear speech features are investigated as front-end features to i-vectors. To take advantage of the complementary effects of features, i-vector systems based on linear and non-linear features are combined through the decision-level fusion. Variability compensation techniques, such as Linear Discriminant Analysis (LDA) and Within-Class Covariance Normalization (WCCN), are widely used to reduce unwanted variabilities. A more generalizable technique than the LDA is required when limited training data are available. We employ a support vector discriminant analysis (SVDA) technique that uses the boundary of classes to find discriminatory directions to address this problem. Experiments conducted on the 2014 Audio-Visual Emotion Challenge and Workshop (AVEC 2014) depression database indicate that the best accuracy improvement obtained using SVDA is about 15.15% compared to the uncompensated i-vectors. In all cases, experimental results confirm that the decision-level fusion of i-vector systems based on three feature sets, TEO-CB-Auto-Env+Δ, Glottal+Δ, and MFCC+Δ+ΔΔ, achieves the best results. This fusion significantly improves classifying results, yielding an accuracy of 90%. The combination of SVDA-transformed BDI-II score prediction systems based on these three feature sets achieved RMSE and MAE of 8.899 and 6.991, respectively, which means 29.18% and 30.34% improvements in RMSE and MAE, respectively, over the baseline system on the test partition. Furthermore, this proposed combination outperforms other audio-based studies available in the literature using the AVEC 2014 database.

ATR-FTIR spectroscopy and chemometrics as a quick and simple alternative for discrimination of SARS-CoV-2 infected food of animal origin.

Alternative routes such as virus transmission or cross-contamination by food have been suggested, due to reported cases of SARS-CoV-2 in frozen chicken wings and fish or seafood. Delay in routine testing due to the dependence on the PCR technique as the standard method leads to greater virus dissemination. Therefore, alternative detection methods such as FTIR spectroscopy emerge as an option. Here, we demonstrate a fast (3 min), simple and reagent-free methodology using attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy for discrimination of food (chicken, beef and fish) contaminated with the SARS-CoV-2 virus. From the IR spectra of the samples, the "bio-fingerprint" (800 - 1900 cm-1) was selected to investigate the distinctions caused by the virus contamination. Exploratory analysis of the spectra, using Principal Component of Analysis (PCA), indicated the differentiation in the data due to the presence of single bands, marked as contamination from nucleic acids including viral RNA. Furthermore, the partial least squares discriminant analysis (PLS-DA) classification model allowed for discrimination of each matrix in its pure form and its contaminated counterpart with sensitivity, specificity and accuracy of 100 %. Therefore, this study indicates that the use of ATR-FTIR can offer a fast and low cost and not require chemical reagents and with minimal sample preparation to detect the SARS-CoV-2 virus in food matrices, ensuring food safety and non-dissemination by consumers.

Test-trace-isolate-quarantine (TTIQ) intervention strategies after symptomatic COVID-19 case identification.

The test-trace-isolate-quarantine (TTIQ) strategy, where confirmed-positive pathogen carriers are isolated from the community and their recent close contacts are identified and pre-emptively quarantined, is used to break chains of transmission during a disease outbreak. The protocol is frequently followed after an individual presents with disease symptoms, at which point they will be tested for the pathogen. This TTIQ strategy, along with hygiene and social distancing measures, make up the non-pharmaceutical interventions that are utilised to suppress the ongoing COVID-19 pandemic. Here we develop a tractable mathematical model of disease transmission and the TTIQ intervention to quantify how the probability of detecting and isolating a case following symptom onset, the fraction of contacts that are identified and quarantined, and the delays inherent to these processes impact epidemic growth. In the model, the timing of disease transmission and symptom onset, as well as the frequency of asymptomatic cases, is based on empirical distributions of SARS-CoV-2 infection dynamics, while the isolation of confirmed cases and quarantine of their contacts is implemented by truncating their respective infectious periods. We find that a successful TTIQ strategy requires intensive testing: the majority of transmission is prevented by isolating symptomatic individuals and doing so in a short amount of time. Despite the lesser impact, additional contact tracing and quarantine increases the parameter space in which an epidemic is controllable and is necessary to control epidemics with a high reproductive number. TTIQ could remain an important intervention for the foreseeable future of the COVID-19 pandemic due to slow vaccine rollout and highly-transmissible variants with the potential for vaccine escape. Our results can be used to assess how TTIQ can be improved and optimised, and the methodology represents an improvement over previous quantification methods that is applicable to future epidemic scenarios.

COVID-19 related distress in the Swedish population: Validation of the Swedish version of the COVID Stress Scales (CSS).

BACKGROUND: The COVID Stress Scales (CSS) assess health- and contamination-related distress in the face of a medical outbreak like the ongoing COVID-19 pandemic. Though the CSS is translated into 21 languages, it has not been validated in a Swedish national sample. AIM: Our general objective is to provide a translation, replication, and validation of the CSS and test its convergent- and discriminant validity in relation to anxiety, health anxiety, depression, and stress in the general Swedish population. We also present latent psychometric properties by modelling based on item response theory. METHODS: Participants consisted of 3044 Swedish adults (> 18 years) from a pre-stratified (gender, age, and education) sample from The Swedish Citizen Panel. Mental health status was assessed by validated instruments, including the CSS, PHQ-4, SHAI-14, and PSS-10. RESULTS: Results indicate that our Swedish translation of CSS has good psychometric properties and consists of 5 correlated factors. DISCUSSION: The CSS is useful either as a unidimensional or multidimensional construct using the CSS scales to measure key facets of pandemic-related stress. CONCLUSIONS: The findings support the cross-cultural validity of the CSS and its potential utility in understanding many of the emotional challenges posed by the current and future pandemics.

Laser induced breakdown spectroscopy for the rapid detection of SARS-CoV-2 immune response in plasma.

As the SARS-CoV-2 pandemic persists, methods that can quickly and reliably confirm infection and immune status is extremely urgently and critically needed. In this contribution we show that combining laser induced breakdown spectroscopy (LIBS) with machine learning can distinguish plasma of donors who previously tested positive for SARS-CoV-2 by RT-PCR from those who did not, with up to 95% accuracy. The samples were also analyzed by LIBS-ICP-MS in tandem mode, implicating a depletion of Zn and Ba in samples of SARS-CoV-2 positive subjects that inversely correlate with CN lines in the LIBS spectra.

Noninvasive Diagnostic for COVID-19 from Saliva Biofluid via FTIR Spectroscopy and Multivariate Analysis.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the worst global health crisis in living memory. The reverse transcription polymerase chain reaction (RT-qPCR) is considered the gold standard diagnostic method, but it exhibits limitations in the face of enormous demands. We evaluated a mid-infrared (MIR) data set of 237 saliva samples obtained from symptomatic patients (138 COVID-19 infections diagnosed via RT-qPCR). MIR spectra were evaluated via unsupervised random forest (URF) and classification models. Linear discriminant analysis (LDA) was applied following the genetic algorithm (GA-LDA), successive projection algorithm (SPA-LDA), partial least squares (PLS-DA), and a combination of dimension reduction and variable selection methods by particle swarm optimization (PSO-PLS-DA). Additionally, a consensus class was used. URF models can identify structures even in highly complex data. Individual models performed well, but the consensus class improved the validation performance to 85% accuracy, 93% sensitivity, 83% specificity, and a Matthew's correlation coefficient value of 0.69, with information at different spectral regions. Therefore, through this unsupervised and supervised framework methodology, it is possible to better highlight the spectral regions associated with positive samples, including lipid (∼1700 cm-1), protein (∼1400 cm-1), and nucleic acid (∼1200-950 cm-1) regions. This methodology presents an important tool for a fast, noninvasive diagnostic technique, reducing costs and allowing for risk reduction strategies.

Diagnosing COVID-19 in human serum using Raman spectroscopy.

This study proposed the diagnosis of COVID-19 by means of Raman spectroscopy. Samples of blood serum from 10 patients positive and 10 patients negative for COVID-19 by RT-PCR RNA and ELISA tests were analyzed. Raman spectra were obtained with a dispersive Raman spectrometer (830 nm, 350 mW) in triplicate, being submitted to exploratory analysis with principal component analysis (PCA) to identify the spectral differences and discriminant analysis with PCA (PCA-DA) and partial least squares (PLS-DA) for classification of the blood serum spectra into Control and COVID-19. The spectra of both groups positive and negative for COVID-19 showed peaks referred to the basal constitution of the serum (mainly albumin). The difference spectra showed decrease in the peaks referred to proteins and amino acids for the group positive. PCA variables showed more detailed spectral differences related to the biochemical alterations due to the COVID-19 such as increase in lipids, nitrogen compounds (urea and amines/amides) and nucleic acids, and decrease of proteins and amino acids (tryptophan) in the COVID-19 group. The discriminant analysis applied to the principal component loadings (PC2, PC4, PC5, and PC6) could classify spectra with 87% sensitivity and 100% specificity compared to 95% sensitivity and 100% specificity indicated in the RT-PCR kit leaflet, demonstrating the possibilities of a rapid, label-free, and costless technique for diagnosing COVID-19 infection.

Endocannabinoid Anandamide Attenuates Acute Respiratory Distress Syndrome through Modulation of Microbiome in the Gut-Lung Axis.

Acute respiratory distress syndrome (ARDS) is a serious lung condition characterized by severe hypoxemia leading to limitations of oxygen needed for lung function. In this study, we investigated the effect of anandamide (AEA), an endogenous cannabinoid, on Staphylococcal enterotoxin B (SEB)-mediated ARDS in female mice. Single-cell RNA sequencing data showed that the lung epithelial cells from AEA-treated mice showed increased levels of antimicrobial peptides (AMPs) and tight junction proteins. MiSeq sequencing data on 16S RNA and LEfSe analysis demonstrated that SEB caused significant alterations in the microbiota, with increases in pathogenic bacteria in both the lungs and the gut, while treatment with AEA reversed this effect and induced beneficial bacteria. AEA treatment suppressed inflammation both in the lungs as well as gut-associated mesenteric lymph nodes (MLNs). AEA triggered several bacterial species that produced increased levels of short-chain fatty acids (SCFAs), including butyrate. Furthermore, administration of butyrate alone could attenuate SEB-mediated ARDS. Taken together, our data indicate that AEA treatment attenuates SEB-mediated ARDS by suppressing inflammation and preventing dysbiosis, both in the lungs and the gut, through the induction of AMPs, tight junction proteins, and SCFAs that stabilize the gut-lung microbial axis driving immune homeostasis.

Towards a predictive model for prevention of the risk of COVID-19 infection.

INTRODUCTION: The scarcity of person-centered applications aimed at developing awareness on the risk posed by the COVID-19 pandemic, stimulates the exploration and creation of preventive tools that are accessible to the population. OBJECTIVE: To develop a predictive model that allows evaluating the risk of mortality in the event of SARS-CoV-2 virus infection. METHODS: Exploration of public data from 16,000 COVID-19-positive patients to generate an efficient discriminant model, evaluated with a score function and expressed by a self-rated preventive interest questionnaire. RESULTS: A useful linear function was obtained with a discriminant capacity of 0.845; internal validation with bootstrap and external validation, with 25 % of tested patients showing marginal differences. CONCLUSION: The predictive model with statistical support, based on 15 accessible questions, can become a structured prevention tool.

INTRODUCCIÓN: La escasez de aplicaciones centradas en la persona y con vistas al desarrollo de la conciencia del riesgo que representa la pandemia de COVID-19 estimula la exploración y creación de herramientas de carácter preventivo accesibles a la población. OBJETIVO: Elaboración de un modelo predictivo que permita evaluar el riesgo de letalidad ante infección por el virus SARS-CoV-2. MÉTODOS: Exploración de datos públicos de 16 000 pacientes positivos a COVID-19, para generar un modelo discriminante eficiente, valorado con una función score y que se expresa mediante un cuestionario autocalificado de interés preventivo. RESULTADOS: Se obtuvo una función lineal útil con capacidad discriminante de 0.845; la validación interna con bootstrap y la externa, con 25 % de los pacientes de prueba, mostraron diferencias marginales. CONCLUSIÓN: El modelo predictivo, basado en 15 preguntas accesibles puede convertirse en una herramienta de prevención estructurada.

Cytokine signature and COVID-19 prediction models in the two waves of pandemics.

In Europe, multiple waves of infections with SARS-CoV-2 (COVID-19) have been observed. Here, we have investigated whether common patterns of cytokines could be detected in individuals with mild and severe forms of COVID-19 in two pandemic waves, and whether machine learning approach could be useful to identify the best predictors. An increasing trend of multiple cytokines was observed in patients with mild or severe/critical symptoms of COVID-19, compared with healthy volunteers. Linear Discriminant Analysis (LDA) clearly recognized the three groups based on cytokine patterns. Classification and Regression Tree (CART) further indicated that IL-6 discriminated controls and COVID-19 patients, whilst IL-8 defined disease severity. During the second wave of pandemics, a less intense cytokine storm was observed, as compared with the first. IL-6 was the most robust predictor of infection and discriminated moderate COVID-19 patients from healthy controls, regardless of epidemic peak curve. Thus, serum cytokine patterns provide biomarkers useful for COVID-19 diagnosis and prognosis. Further definition of individual cytokines may allow to envision novel therapeutic options and pave the way to set up innovative diagnostic tools.

Drug-resistant Staphylococcus aureus bacteria detection by combining surface-enhanced Raman spectroscopy (SERS) and deep learning techniques.

Over the past year, the world's attention has focused on combating COVID-19 disease, but the other threat waiting at the door-antimicrobial resistance should not be forgotten. Although making the diagnosis rapidly and accurately is crucial in preventing antibiotic resistance development, bacterial identification techniques include some challenging processes. To address this challenge, we proposed a deep neural network (DNN) that can discriminate antibiotic-resistant bacteria using surface-enhanced Raman spectroscopy (SERS). Stacked autoencoder (SAE)-based DNN was used for the rapid identification of methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-sensitive S. aureus (MSSA) bacteria using a label-free SERS technique. The performance of the DNN was compared with traditional classifiers. Since the SERS technique provides high signal-to-noise ratio (SNR) data, some subtle differences were found between MRSA and MSSA in relative band intensities. SAE-based DNN can learn features from raw data and classify them with an accuracy of 97.66%. Moreover, the model discriminates bacteria with an area under curve (AUC) of 0.99. Compared to traditional classifiers, SAE-based DNN was found superior in accuracy and AUC values. The obtained results are also supported by statistical analysis. These results demonstrate that deep learning has great potential to characterize and detect antibiotic-resistant bacteria by using SERS spectral data.

The Serum Metabolome of Moderate and Severe COVID-19 Patients Reflects Possible Liver Alterations Involving Carbon and Nitrogen Metabolism.

COVID-19 is a global threat that has spread since the end of 2019, causing severe clinical sequelae and deaths, in the context of a world pandemic. The infection of the highly pathogenetic and infectious SARS-CoV-2 coronavirus has been proven to exert systemic effects impacting the metabolism. Yet, the metabolic pathways involved in the pathophysiology and progression of COVID-19 are still unclear. Here, we present the results of a mass spectrometry-based targeted metabolomic analysis on a cohort of 52 hospitalized COVID-19 patients, classified according to disease severity as mild, moderate, and severe. Our analysis defines a clear signature of COVID-19 that includes increased serum levels of lactic acid in all the forms of the disease. Pathway analysis revealed dysregulation of energy production and amino acid metabolism. Globally, the variations found in the serum metabolome of COVID-19 patients may reflect a more complex systemic perturbation induced by SARS-CoV-2, possibly affecting carbon and nitrogen liver metabolism.

Perturbation of ACE2 Structural Ensembles by SARS-CoV-2 Spike Protein Binding.

The human ACE2 enzyme serves as a critical first recognition point of coronaviruses, including SARS-CoV-2. In particular, the extracellular domain of ACE2 interacts directly with the S1 tailspike protein of the SARS-CoV-2 virion through a broad protein-protein interface. Although this interaction has been characterized by X-ray crystallography, these structures do not reveal significant differences in the ACE2 structure upon S1 protein binding. In this work, using several all-atom molecular dynamics simulations, we show persistent differences in the ACE2 structure upon binding. These differences are determined with the linear discriminant analysis (LDA) machine learning method and validated using independent training and testing datasets, including long trajectories generated by D. E. Shaw Research on the Anton 2 supercomputer. In addition, long trajectories for 78 potent ACE2-binding compounds, also generated by D. E. Shaw Research, were projected onto the LDA classification vector in order to determine whether the ligand-bound ACE2 structures were compatible with S1 protein binding. This allows us to predict which compounds are "apo-like" versus "complex-like" and to pinpoint long-range ligand-induced allosteric changes in the ACE2 structure.

Motivation-based segmentation of game meat consumers: A look at the beliefs of food consumers during the COVID-19 crisis in China.

OBJECTIVES: This research aims to explore the factors motivate consumers to eat game meat during a multi-state disease outbreak. METHODS: It proposes a segmentation of consumers based on their attitudes toward and reveals the consumers' food beliefs that motivate their actions. Three segments of game meat consumers were identified: identity seekers, health seekers, and taste seekers. RESULTS: A survey of the potential impact that the COVID-19 crisis has on these three clusters' future food choices showed that the identity and health seekers are more open to a change in food choices. However, the taste seekers are less likely to be influenced by external factors. CONCLUSIONS: This research indicates that for the policymakers, the key is to take game meat consumers as an effective intervention entry point. It is crucial to facilitate healthy food choices and to promote socially- and culturally-appropriate food beliefs by improving public awareness of the risks of game meat, and invest in organic food. RESEARCH IMPLICATIONS: This research provides new insights into the food beliefs of game meat consumers via motivation-based segmentation.

Rapid Classification of COVID-19 Severity by ATR-FTIR Spectroscopy of Plasma Samples.

The coronavirus disease 2019 (COVID-19) pandemic continues to ravage the world, with many hospitals overwhelmed by the large number of patients presenting during major outbreaks. A rapid triage for COVID-19 patient requiring hospitalization and intensive care is urgently needed. Age and comorbidities have been associated with a higher risk of severe COVID-19 but are not sufficient to triage patients. Here, we investigated the potential of attenuated total reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy as a rapid blood test for classification of COVID-19 disease severity using a cohort of 160 COVID-19 patients. A simple plasma processing and ATR-FTIR data acquisition procedure was established using 75% ethanol for viral inactivation. Next, partial least-squares-discriminant analysis (PLS-DA) models were developed and tested using data from 130 and 30 patients, respectively. Addition of the ATR-FTIR spectra to the clinical parameters (age, sex, diabetes mellitus, and hypertension) increased the area under the ROC curve (C-statistics) for both the training and test data sets, from 69.3% (95% CI 59.8-78.9%) to 85.7% (78.6-92.8%) and 77.8% (61.3-94.4%) to 85.1% (71.3-98.8%), respectively. The independent test set achieved 69.2% specificity (42.4-87.3%) and 94.1% sensitivity (73.0-99.0%). Diabetes mellitus was the strongest predictor in the model, followed by FTIR regions 1020-1090 and 1588-1592 cm-1. In summary, this study demonstrates the potential of ATR-FTIR spectroscopy as a rapid, low-cost COVID-19 severity triage tool to facilitate COVID-19 patient management during an outbreak.

Infrared Based Saliva Screening Test for COVID-19.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in an unprecedented need for diagnostic testing that is critical in controlling the spread of COVID-19. We propose a portable infrared spectrometer with purpose-built transflection accessory for rapid point-of-care detection of COVID-19 markers in saliva. Initially, purified virion particles were characterized with Raman spectroscopy, synchrotron infrared (IR) and AFM-IR. A data set comprising 171 transflection infrared spectra from 29 subjects testing positive for SARS-CoV-2 by RT-qPCR and 28 testing negative, was modeled using Monte Carlo Double Cross Validation with 50 randomized test and model sets. The testing sensitivity was 93 % (27/29) with a specificity of 82 % (23/28) that included positive samples on the limit of detection for RT-qPCR. Herein, we demonstrate a proof-of-concept high throughput infrared COVID-19 test that is rapid, inexpensive, portable and utilizes sample self-collection thus minimizing the risk to healthcare workers and ideally suited to mass screening.

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.