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Objective: To predict the optimal cut-off values for screening and predicting metabolic syndrome(MetS) in a middle-aged and elderly Chinese population using 13 obesity and lipid-related indicators, and to identify the most suitable predictors. Methods: The data for this cross-sectional investigation came from the China Health and Retirement Longitudinal Study (CHARLS), including 9457 middle-aged and elderly people aged 45-98 years old. We examined 13 indicators, including waist circumference (WC), body mass index (BMI), waist-height ratio (WHtR), visceral adiposity index (VAI), a body shape index (ABSI), body roundness index (BRI), lipid accumulation product index (LAP), conicity index (CI), Chinese visceral adiposity index (CVAI), triglyceride-glucose index (TyG-index) and their combined indices (TyG-BMI, TyG-WC, TyG-WHtR). The receiver operating characteristic curve (ROC) was used to determine the usefulness of indicators for screening for MetS in the elderly and to determine their cut-off values, sensitivity, specificity, and area under the curve (AUC). Association analysis of 13 obesity-related indicators with MetS was performed using binary logistic regression analysis. Results: A total of 9457 middle-aged and elderly Chinese were included in this study, and the overall prevalence of the study population was 41.87% according to the diagnostic criteria of NCEP ATP III. According to age and gender, the percentage of males diagnosed with MetS was 30.67% (45-54 years old: 30.95%, 55-64 years old: 41.02%, 65-74 years old: 21.19%, ≥ 75 years old: 6.84%). The percentage of females diagnosed with MetS was 51.38% (45-54 years old: 31.95%, 55-64 years old: 39.52%, 65-74 years old: 20.43%, ≥ 75 years old: 8.10%). The predictive power of Tyg-related parameters was more prominent in both sexes. In addition, LAP and CVAI are also good at predicting MetS. ABSI had a poor prediction ability. Conclusions: Among the middle-aged and elderly population in China, after adjusting for confounding factors, all the indicators except ABSI had good predictive power. The predictive power of Tyg-related parameters was more prominent in both sexes. In addition, LAP and CVAI are also good at predicting MetS.

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Introduction: Finding a non-invasive and repeatable tool has been recommended to make an accurate diagnosis of Alzheimer's disease (AD) and Parkinson's disease (PD). Methods: 70 volunteers participated in three groups: 24 with mild dementia of AD, 24 in the first and second stages of PD, and 22 healthy controls. After valuing the scores of cognitive tests, the salivary levels of phosphorylated tau (p-tau), total alpha-synuclein (α-syn), and beta-amyloid 1-42 (Aß) proteins have been evaluated. Finally, the cutoff points, receiver operating characteristic (ROC), sensitivity, and specificity have been calculated to find accurate and detectable biomarkers. Results: Findings showed that the salivary level of Aß was higher in both PD (p < 0.01) and AD (p < 0.001) patients than in controls. Moreover, the level of α-syn in both PD and AD patients was similarly lower than in controls (p < 0.05). However, the level of p-tau was only higher in the AD group than in the control (p < 0.01). Salivary Aß 1-42 level at a 60.3 pg/ml cutoff point revealed an excellent performance for diagnosing AD (AUC: 0.81). Conclusion: Evaluation of p-tau, α-syn, and Aß 1-42 levels in the saliva of AD and PD patients could help the early diagnosis. The p-tau level might be valuable for differentiation between AD and PD. Therefore, these hopeful investigations could be done to reduce the usage of invasive diagnostic methods, which alone is a success in alleviating the suffering of AD and PD patients. Moreover, introducing accurate salivary biomarkers according to the pathophysiology of AD and PD should be encouraged.

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Introduction: Patients with pulmonary tuberculosis (PTB) disease and positive sputum cultures are the main source of infection. Culture conversion time is inconsistent and defining the length of respiratory isolation is challenging. The objective of this study is to develop a score to predict the length of isolation period. Methods: A retrospective study was carried out to evaluated risk factors associated with persistent positive sputum cultures after 4 weeks of treatment in 229 patients with PTB. A multivariable logistic regression model was used to determinate predictors for positive culture and a scoring system was created based on the coefficients of the final model. Results: Sputum culture was persistently positive in 40.6%. Fever at consultation (1.87, 95% CI:1.02-3.41), smoking (2.44, 95% CI:1.36-4.37), >2 affected lung lobes (1.95, 95% CI:1.08-3.54), and neutrophil-to-lymphocyte ratio > 3.5 (2.22, 95% CI:1.24-3.99), were significantly associated with delayed culture conversion. Therefore, we assembled a severity score that achieved an area under the curve of 0.71 (95% CI:0.64-0.78). Conclusions: In patients with smear positive PTB, a score with clinical, radiological and analytical parameters can be used as a supplemental tool to assist clinical decisions in isolation period.

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Cancer-associated fibroblasts (CAFs) can exert their immunosuppressive effects by secreting various effectors that are involved in the regulation of tumor-infiltrating immune cells as well as other immune components in the tumor immune microenvironment (TIME), thereby promoting tumorigenesis, progression, metastasis, and drug resistance. Although a large number of studies suggest that CAFs play a key regulatory role in the development of head and neck squamous cell carcinoma (HNSCC), there are limited studies on the relevance of CAFs to the prognosis of HNSCC. In this study, we identified a prognostic signature containing eight CAF-related genes for HNSCC by univariate Cox analysis, lasso regression, stepwise regression, and multivariate Cox analysis. Our validation in primary cultures of CAFs from human HNSCC and four human HNSCC cell lines confirmed that these eight genes are indeed characteristic markers of CAFs. Immune cell infiltration differences analysis between high-risk and low-risk groups according to the eight CAF-related genes signature hinted at CAFs regulatory roles in the TIME, further revealing its potential role on prognosis. The signature of the eight CAF-related genes was validated in different independent validation cohorts and all showed that it was a valid marker for prognosis. The significantly higher overall survival (OS) in the low-risk group compared to the high-risk group was confirmed by Kaplan-Meier (K-M) analysis, suggesting that the signature of CAF-related genes can be used as a non-invasive predictive tool for HNSCC prognosis. The low-risk group had significantly higher levels of tumor-killing immune cell infiltration, as confirmed by CIBERSORT analysis, such as CD8+ T cells, follicular helper T cells, and Dendritic cells (DCs) in the low-risk group. In contrast, the level of infiltration of pro-tumor cells such as M0 macrophages and activated Mast cells (MCs) was lower. It is crucial to delve into the complex mechanisms between CAFs and immune cells to find potential regulatory targets and may provide new evidence for subsequently targeted immunotherapy. These results suggest that the signature of the eight CAF-related genes is a powerful indicator for the assessment of the TIME of HNSCC. It may provide a new and reliable potential indicator for clinicians to predict the prognosis of HNSCC, which may be used to guide treatment and clinical decision-making in HNSCC patients. Meanwhile, CAF-related genes are expected to become tumor biomarkers and effective targets for HNSCC.

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Background & Aims: The albumin-bilirubin (ALBI) score is calculated using serum levels of total bilirubin and albumin as a simple method to assess liver function. This study investigated the ability of baseline ALBI score/grade measurements to assess histological stage and disease progression in individuals with primary biliary cholangitis (PBC) in a large Japanese nationwide cohort. Methods: A total of 8,768 Japanese patients with PBC were enrolled between 1980 and 2016 from 469 institutions, among whom 83% received ursodeoxycholic acid (UDCA) only, 9% received UDCA and bezafibrate, and 8% were given neither drug. Baseline clinical and laboratory parameters were retrospectively retrieved and reviewed from a central database. Associations of ALBI score/grade with histological stage, mortality, and need for liver transplantation (LT) were evaluated using Cox proportional hazards models. Results: During the median follow-up period of 5.3 years, 1,227 patients died (including 789 from liver-related causes) and 113 underwent LT. ALBI score and ALBI grade were significantly associated with Scheuer's classification (both p <0.0001). ALBI grade 2 or 3 had significant associations with all-cause mortality or need for LT as well as liver-related mortality or need for LT according to Cox proportional hazards regression analysis (hazard ratio 3.453, 95% CI 2.942-4.052 and hazard ratio 4.242, 95% CI 3.421-5.260, respectively; both p <0.0001). Cumulative LT-free survival rates at 5 years in the ALBI grade 1, 2, and 3 groups were 97.2%, 82.4%, and 38.8%, respectively, while respective non-liver-related survival rates were 98.1%, 86.0%, and 42.0% (both p <0.0001, log-rank test). Conclusions: This large nationwide study of patients with PBC suggested that baseline measurements of ALBI grade were a simple non-invasive predictor of prognosis in PBC. Impact and implications: Primary biliary cholangitis (PBC) is an autoimmune liver disease characterized by progressive destruction of intrahepatic bile ducts. This study examined the ability of albumin-bilirubin (ALBI) score/grade to estimate histological findings and disease progression in PBC by means of a large-scale nationwide cohort in Japan. ALBI score/grade were significantly associated with Scheuer's classification stage. Baseline ALBI grade measurements may be a simple non-invasive predictor of prognosis in PBC.

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Background & Aims: Blood biomarkers facilitating the diagnosis of covert hepatic encephalopathy (CHE) in patients with cirrhosis are lacking. Astrocyte swelling is a major component of hepatic encephalopathy. Thus, we hypothesised that glial fibrillary acidic protein (GFAP), the major intermediate filament of astrocytes, might facilitate early diagnosis and management. This study aimed to investigate the utility of serum GFAP (sGFAP) levels as a biomarker of CHE. Methods: In this bicentric study, 135 patients with cirrhosis, 21 patients with ongoing harmful alcohol use and cirrhosis, and 15 healthy controls were recruited. CHE was diagnosed using psychometric hepatic encephalopathy score. sGFAP levels were measured using a highly sensitive single-molecule array (SiMoA) immunoassay. Results: In total, 50 (37%) people presented with CHE at study inclusion. Participants with CHE displayed significantly higher sGFAP levels than those without CHE (median sGFAP, 163 pg/ml [IQR 136; 268] vs. 106 pg/ml [IQR 75; 153]; p <0.001) or healthy controls (p <0.001). sGFAP correlated with results in psychometric hepatic encephalopathy score (Spearman's ρ = -0.326, p <0.001), model for end-stage liver disease score (Spearman's ρ = 0.253, p = 0.003), ammonia (Spearman's ρ = 0.453, p = 0.002), and IL-6 serum levels (Spearman's ρ = 0.323, p = 0.006). Additionally, sGFAP levels were independently associated with the presence of CHE in multivariable logistic regression analysis (odds ratio 1.009; 95% CI 1.004-1.015; p <0.001). sGFAP levels did not differ between patients with alcohol-related cirrhosis vs. patients with non-alcohol-related cirrhosis or between patients with ongoing alcohol use vs. patients with discontinued alcohol use.Conclusions: sGFAP levels are associated with CHE in patients with cirrhosis. These results suggest that astrocyte injury may already occur in patients with cirrhosis and subclinical cognitive deficits and that sGFAP could be explored as a novel biomarker. Impact and implications: Blood biomarkers facilitating the diagnosis of covert hepatic encephalopathy (CHE) in patients with cirrhosis are lacking. In this study, we were able to demonstrate that sGFAP levels are associated with CHE in patients with cirrhosis. These results suggest that astrocyte injury may already occur in patients with cirrhosis and subclinical cognitive deficits and that sGFAP could be explored as a novel biomarker.

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Tripterygium glycosides tablets (TGT) are the commonly used preparation for rheumatoid arthritis (RA). However, the changes in TGT on RA are still unclear at the metabolic level. This study aimed to reveal the biological processes of TGT in collagen-induced arthritis (CIA) rats through integrated metabolomics and network analysis. First, the CIA model in rats was established, and the CIA rats were given three doses of TGT. Then, the endogenous metabolites in the serum from normal rats, CIA rats, and CIA rats treated with varying doses of TGT were detected by UHPLC-QTOF-MS/MS. Next, univariate and multivariate statistical analyses were performed to find the differential metabolites. Finally, differential metabolites, metabolic pathways, and hub genes were analyzed integrally to reveal the biological processes of TGT in CIA rats. The paw diameter, arthritis score, immunoglobulin G (IgG) concentration, CT image, and histological assay showed that TGT had evident therapeutic effects on CIA rats. Untargeted metabolomics revealed that TGT could ameliorate the down-regulation of lipid levels in CIA rats. Four key differential metabolites were found including LysoP(18:0), LysoPA(20:4), LysoPA(18:2), and PS(O-20:0/17:1). The glycerophospholipid metabolic pathway was perturbed in treating CIA with TGT. A total of 24 genes, including PLD1, LPCAT4, AGPAT1, and PLA2G4A, were found to be the hub genes of TGT in CIA rats. In conclusion, the integrated analysis provided a novel and holistic perspective on the biological processes of TGT in CIA rats, which could give helpful guidance for further TGT on RA. Future studies based on human samples are necessary.

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The vital cellular functions in Gram-positive bacteria are controlled by signaling molecules known as quorum sensing peptides (QSPs), considered promising therapeutic interventions for bacterial infections. In the bacterial system QSPs bind to membrane-coupled receptors, which then auto-phosphorylate and activate intracellular response regulators. These response regulators induce target gene expression in bacteria. One of the most reliable trends in drug discovery research for virulence-associated molecular targets is the use of peptide drugs or new functionalities. In this perspective, computational methods act as auxiliary aids for biologists, where methodologies based on machine learning and in silico analysis are developed as suitable tools for target peptide identification. Therefore, the development of quick and reliable computational resources to identify or predict these QSPs along with their receptors and inhibitors is receiving considerable attention. The databases such as Quorumpeps and Quorum Sensing of Human Gut Microbes (QSHGM) provide a detailed overview of the structures and functions of QSPs. The tools and algorithms such as QSPpred, QSPred-FL, iQSP, EnsembleQS and PEPred-Suite have been used for the generic prediction of QSPs and feature representation. The availability of compiled key resources for utilizing peptide features based on amino acid composition, positional preferences, and motifs as well as structural and physicochemical properties, including biofilm inhibitory peptides, can aid in elucidating the QSP and membrane receptor interactions in infectious Gram-positive pathogens. Herein, we present a comprehensive survey of diverse computational approaches that are suitable for detecting QSPs and QS interference molecules. This review highlights the utility of these methods for developing potential biomarkers against infectious Gram-positive pathogens.

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Background/Aims: Global liquid chromatography mass spectrometry (LC-MS) profiling in a Thai population has previously identified a urinary metabolic signature in Opisthorchis viverrini-induced cholangiocarcinoma (CCA), primarily characterised by disturbance in acylcarnitine, bile acid, steroid, and purine metabolism. However, the detection of thousands of analytes by LC-MS in a biological sample in a single experiment potentially introduces false discovery errors. To verify these observed metabolic perturbations, a second validation dataset from the same population was profiled in a similar fashion. Methods: Reverse-phase ultra-performance liquid-chromatography mass spectrometry was utilised to acquire the global spectral profile of 98 spot urine samples (from 46 healthy volunteers and 52 CCA patients) recruited from Khon Kaen, northeast Thailand (the highest incidence of CCA globally). Results: Metabolites were differentially expressed in the urinary profiles from CCA patients. High urinary elimination of bile acids was affected by the presence of obstructive jaundice. The urine metabolome associated with non-jaundiced CCA patients showed a distinctive pattern, similar but not identical to published studies. A panel of 10 metabolites achieved a diagnostic accuracy of 93.4% and area under the curve value of 98.8% (CI = 96.3%-100%) for the presence of CCA. Conclusions: Global characterisation of the CCA urinary metabolome identified several metabolites of biological interest in this validation study. Analyses of the diagnostic utility of the discriminant metabolites showed excellent diagnostic potential. Further larger scale studies are required to confirm these findings internationally, particularly in comparison to sporadic CCA, not associated with liver fluke infestation.

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Although immunotherapy has revolutionized cancer management, most patients do not derive benefits from it. Aiming to explore an appropriate strategy for immunotherapy efficacy prediction, we collected 6251 patients' transcriptome data from multicohort population and analyzed the data using a machine learning algorithm. In this study, we found that patients from three immune gene clusters had different overall survival when treated with immunotherapy (P < 0.001), and that these clusters had differential states of hypoxia scores and metabolism functions. The immune gene score showed good immunotherapy efficacy prediction (AUC was 0.737 at 20 months), which was well validated. The immune gene score, tumor mutation burden, and long non-coding RNA score were further combined to build a tumor immune microenvironment signature, which correlated more strongly with overall survival (AUC, 0.814 at 20 months) than when using a single variable. Thus, we recommend using the characterization of the tumor immune microenvironment associated with immunotherapy efficacy via a multi-omics analysis of cancer.

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Treatment of patients with oesophageal and gastric cancer (OeGC) is guided by disease stage, patient performance status and preferences. Lymph node (LN) status is one of the strongest prognostic factors for OeGC patients. However, survival varies between patients with the same disease stage and LN status. We recently showed that LN size from patients with OeGC might also have prognostic value, thus making delineations of LNs essential for size estimation and the extraction of other imaging biomarkers. We hypothesized that a machine learning workflow is able to: (1) find digital H&E stained slides containing LNs, (2) create a scoring system providing degrees of certainty for the results, and (3) delineate LNs in those images. To train and validate the pipeline, we used 1695 H&E slides from the OE02 trial. The dataset was divided into training (80%) and validation (20%). The model was tested on an external dataset of 826 H&E slides from the OE05 trial. U-Net architecture was used to generate prediction maps from which predefined features were extracted. These features were subsequently used to train an XGBoost model to determine if a region truly contained a LN. With our innovative method, the balanced accuracies of the LN detection were 0.93 on the validation dataset (0.83 on the test dataset) compared to 0.81 (0.81) on the validation (test) datasets when using the standard method of thresholding U-Net predictions to arrive at a binary mask. Our method allowed for the creation of an "uncertain" category, and partly limited false-positive predictions on the external dataset. The mean Dice score was 0.73 (0.60) per-image and 0.66 (0.48) per-LN for the validation (test) datasets. Our pipeline detects images with LNs more accurately than conventional methods, and high-throughput delineation of LNs can facilitate future LN content analyses of large datasets.

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Purpose: To evaluate diagnostic precision and prove equivalence of 2 devices, Advanced vision analyzer (AVA, Elisar Vision Technology) and Humphrey field analyzer (HFA, Zeiss) for the detection of glaucoma on 10-2 program. Design: Prospective, cross-sectional, observational study. Participants: Threshold estimates of 1 eye each of 66 patients with glaucoma, 36 control participants, and 10 glaucoma suspects were analyzed on 10-2 test with AVA and HFA. Methods: Mean sensitivity (MS) values of 68 points and central 16 test points were calculated and compared. Intraclass correlation (ICC), Bland-Altman (BA) plots, linear regression of MS, mean deviation (MD), and pattern standard deviation (PSD) were computed to assess the 10-2 threshold estimate of the devices. Receiver operating characteristic curves were generated for MS and MD values, and the area under the curve (AUC) was compared with assessing diagnostic precision. Main Outcome Measures: Mean sensitivity values of 68 points and central 16 points, AUC for MS and MD values, ICC values, BA plots, and linear-regression analysis. Results: Bland-Altman plot showed significant correlation for MS, MD, and PSD values for both devices. For MS, the overall ICC value was 0.96 (P < 0.001) with a mean bias of 0.0 dB and limits of agreement range of 7.59. The difference in MS values between both devices was -0.4760 ± 1.95 (P > 0.05). The AUC for MS values for AVA was 0.89 and for HFA was 0.92 (P = 0.188); whereas it was similar at 0.88 for MD values (P = 0.799). Advanced vision analyzer and HFA identically discriminated between healthy and patients with glaucoma (P < 0.001), although HFA denoted marginally greater ability (P > 0.05). Conclusions: Statistical results denote adequate equivalence between AVA and HFA because threshold estimates of AVA strongly correlate with HFA for 10-2 program. Financial Disclosures: Proprietary or commercial disclosure may be found after the references.

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ß-thalassemia is a quantitative hemoglobin (Hb) disorder resulting in reduced production of Hb A and increased levels of Hb A2. Diagnosis of ß-thalassemia can be problematic when combined with other structural Hb variants, so that the separation approaches in routine clinical centers are not sufficiently decisive to obtain accurate results. Here, we separate the intact Hb subunits by high-performance liquid chromatography, followed by top-down tandem mass spectrometry of intact subunits to distinguish Hb variants. Proton transfer reaction-parallel ion parking (PTR-PIP), in which a radical anion removes protons from multiply charged precursor ions and produces charge-reduced ions spanning a limited m/z range, was used to increase the signal-to-noise ratio of the subunits of interest. We demonstrate that the δ/ß ratio can act as a biomarker to identify ß-thalassemia in normal electrospray ionization MS1 and PTR-PIP MS1. The application of PTR-PIP significantly increases the sensitivity and specificity of the HPLC-MS method to identify δ/ß ratio as a thalassemia biomarker.

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Rationale and objectives: To evaluate the usefulness of temporal subtraction using the bone suppression method in digital chest radiography for the detection of pulmonary lesions. Materials and methods: The images of 31 patients with pulmonary lesions and 19 normal cases were included in the study. Conventional and bone suppression temporal subtraction were performed in the 50 cases selected and used for an observer performance study. Five radiologists participated in the study, and the differences between using conventional and bone suppression temporal subtraction were assessed using jackknife free-response receiver operating characteristic analysis. Results: The average figure-of-merit values for all radiologists increased significantly using the bone suppression method, from 0.619 (conventional) to 0.696 (p = 0.032). The average sensitivity for detecting pulmonary lesions improved from 67.9% to 75.4%, and the average number of false-positive per case decreased from 0.336 to 0.252 using bone suppression temporal subtraction. Conclusion: Bone suppression temporal subtraction processing can assist with the detection of subtle pulmonary lesions in digital chest radiographs.

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E3 ubiquitin ligases (E3s) and deubiquitinating enzymes (DUBs) play key roles in protein degradation. However, a large number of E3 substrate interactions (ESIs) and DUB substrate interactions (DSIs) remain elusive. Here, we present DeepUSI, a deep learning-based framework to identify ESIs and DSIs using the rich information present in protein sequences. Utilizing the collected golden standard dataset, key hyperparameters in the process of model training, including the ones relevant to data sampling and number of epochs, have been systematically assessed. The performance of DeepUSI was thoroughly evaluated by multiple metrics, based on internal and external validation. Application of DeepUSI to cancer-associated E3 and DUB genes identified a list of druggable substrates with functional implications, warranting further investigation. Together, DeepUSI presents a new framework for predicting substrates of E3 ubiquitin ligases and deubiquitinates.

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Background: Visceral adipose tissue (VAT) is involved in the pathogenesis of Crohn's disease (CD). However, data describing its effects on CD progression remain scarce. We developed and validated a VAT-radiomics model (RM) using computed tomography (CT) images to predict disease progression in patients with CD and compared it with a subcutaneous adipose tissue (SAT)-RM. Methods: This retrospective study included 256 patients with CD (training, n = 156; test, n = 100) who underwent baseline CT examinations from June 19, 2015 to June 14, 2020 at three tertiary referral centres (The First Affiliated Hospital of Sun Yat-Sen University, The First Affiliated Hospital of Shantou University Medical College, and The First People's Hospital of Foshan City) in China. Disease progression referred to the development of penetrating or stricturing diseases or the requirement for CD-related surgeries during follow-up. A total of 1130 radiomics features were extracted from VAT on CT in the training cohort, and a machine-learning-based VAT-RM was developed to predict disease progression using selected reproducible features and validated in an external test cohort. Using the same modeling methodology, a SAT-RM was developed and compared with the VAT-RM. Findings: The VAT-RM exhibited satisfactory performance for predicting disease progression in total test cohort (the area under the ROC curve [AUC] = 0.850, 95% confidence Interval [CI] 0.764-0.913, P < 0.001) and in test cohorts 1 (AUC = 0.820, 95% CI 0.687-0.914, P < 0.001) and 2 (AUC = 0.871, 95% CI 0.744-0.949, P < 0.001). No significant differences in AUC were observed between test cohorts 1 and 2 (P = 0.673), suggesting considerable efficacy and robustness of the VAT-RM. In the total test cohort, the AUC of the VAT-RM for predicting disease progression was higher than that of SAT-RM (AUC = 0.786, 95% CI 0.692-0.861, P < 0.001). On multivariate Cox regression analysis, the VAT-RM (hazard ratio [HR] = 9.285, P = 0.005) was the most important independent predictor, followed by the SAT-RM (HR = 3.280, P = 0.060). Decision curve analysis further confirmed the better net benefit of the VAT-RM than the SAT-RM. Moreover, the SAT-RM failed to significantly improve predictive efficacy after it was added to the VAT-RM (integrated discrimination improvement = 0.031, P = 0.102). Interpretation: Our results suggest that VAT is an important determinant of disease progression in patients with CD. Our VAT-RM allows the accurate identification of high-risk patients prone to disease progression and offers notable advantages over SAT-RM. Funding: This study was supported by the National Natural Science Foundation of China, Guangdong Basic and Applied Basic Research Foundation, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Nature Science Foundation of Shenzhen, and Young S&T Talent Training Program of Guangdong Provincial Association for S&T. Translation: For the Chinese translation of the abstract see Supplementary Materials section.

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Critically ill patients infected with SARS-CoV-2 display adaptive immunity, but it is unknown if they develop cross-reactivity to variants of concern (VOCs). We profiled cross-immunity against SARS-CoV-2 VOCs in naturally infected, non-vaccinated, critically ill COVID-19 patients. Wave-1 patients (wild-type infection) were similar in demographics to Wave-3 patients (wild-type/alpha infection), but Wave-3 patients had higher illness severity. Wave-1 patients developed increasing neutralizing antibodies to all variants, as did patients during Wave-3. Wave-3 patients, when compared to Wave-1, developed more robust antibody responses, particularly for wild-type, alpha, beta and delta variants. Within Wave-3, neutralizing antibodies were significantly less to beta and gamma VOCs, as compared to wild-type, alpha and delta. Patients previously diagnosed with cancer or chronic obstructive pulmonary disease had significantly fewer neutralizing antibodies. Naturally infected ICU patients developed adaptive responses to all VOCs, with greater responses in those patients more likely to be infected with the alpha variant, versus wild-type.

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Background: Misconceptions about adverse side effects are thought to influence public acceptance of the Coronavirus disease 2019 (COVID-19) vaccines negatively. To address such perceived disadvantages of vaccines, a novel machine learning (ML) approach was designed to generate personalized predictions of the most common adverse side effects following injection of six different COVID-19 vaccines based on personal and health-related characteristics. Methods: Prospective data of adverse side effects following COVID-19 vaccination in 19943 participants from Iran and Switzerland was utilized. Six vaccines were studied: The AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and the mRNA-1273 vaccine. The eight side effects were considered as the model output: fever, fatigue, headache, nausea, chills, joint pain, muscle pain, and injection site reactions. The total input parameters for the first and second dose predictions were 46 and 54 features, respectively, including age, gender, lifestyle variables, and medical history. The performances of multiple ML models were compared using Area Under the Receiver Operating Characteristic Curve (ROC-AUC). Results: The total number of people receiving the first dose of the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and mRNA-1273 were 6022, 7290, 5279, 802, 277, and 273, respectively. For the second dose, the numbers were 2851, 5587, 3841, 599, 242 and 228. The Logistic Regression model for predicting different side effects of the first dose achieved ROC-AUCs of 0.620-0.686, 0.685-0.716, 0.632-0.727, 0.527-0.598, 0.548-0.655, 0.545-0.712 for the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2 and mRNA-1273 vaccines, respectively. The second dose models yielded ROC-AUCs of 0.777-0.867, 0.795-0.848, 0.857-0.906, 0.788-0.875, 0.683-0.850, and 0.486-0.680, respectively. Conclusions: Using a large cohort of recipients vaccinated with COVID-19 vaccines, a novel and personalized strategy was established to predict the occurrence of the most common adverse side effects with high accuracy. This technique can serve as a tool to inform COVID-19 vaccine selection and generate personalized factsheets to curb concerns about adverse side effects.