Shape restricted additive hazards models: Monotone, unimodal, and U-shape hazard functions.

We consider estimation of the semiparametric additive hazards model with an unspecified baseline hazard function where the effect of a continuous covariate has a specific shape but otherwise unspecified. Such estimation is particularly useful for a unimodal hazard function, where the hazard is monotone increasing and monotone decreasing with an unknown mode. A popular approach of the proportional hazards model is limited in such setting due to the complicated structure of the partial likelihood. Our model defines a quadratic loss function, and its simple structure allows a global Hessian matrix that does not involve parameters. Thus, once the global Hessian matrix is computed, a standard quadratic programming method can be applicable by profiling all possible locations of the mode. However, the quadratic programming method may be inefficient to handle a large global Hessian matrix in the profiling algorithm due to a large dimensionality, where the dimension of the global Hessian matrix and number of hypothetical modes are the same order as the sample size. We propose the quadratic pool adjacent violators algorithm to reduce computational costs. The proposed algorithm is extended to the model with a time-dependent covariate with monotone or U-shape hazard function. In simulation studies, our proposed method improves computational speed compared to the quadratic programming method, with bias and mean square error reductions. We analyze data from a recent cardiovascular study.

Nonparametric estimation of linear personalized diagnostics rules via efficient grid algorithm.

Many diseases are heterogeneous, comprised of multiple disease subgroups. It is of great interest but highly unlikely to find a single biomarker that can accurately detect such heterogeneous diseases across different subgroups. In this article, we propose to estimate a personalized diagnostic rule (PDR) to tailor more effective biomarkers to each individual according to a linear combination of his or her profiles. A standard grid search algorithm can be used to estimate the optimal linear PDR that maximizes the area under the receiver operating characteristics curve (AUC) among all the linear PDRs, but it is time-consuming especially when the number of variables is large. Alternatively, we developed an efficient grid rotation algorithm that provides a nearly suboptimal solution and studied its variation to find the optimal solution. We implemented the cross-validated forward variable selection method to find a subset of useful variables while avoid overfitting. Extensive simulations show that our proposed method reduces bias and variance. Analysis of a gastric cancer biomarker study and censored survival outcome data illustrates the practical utility of our proposed method. The proposed method is implemented in the open-source R package persDx.

Quantitative assessment of multiple pathogen exposure and immune dynamics at scale.

IMPORTANCE: Serology reveals exposure to pathogens, as well as the state of autoimmune and other clinical conditions. It is used to evaluate individuals and their histories and as a public health tool to track epidemics. Employing a variety of formats, studies nearly always perform serology by testing response to only one or a few antigens. However, clinical outcomes of new infections also depend on which previous infections may have occurred. We developed a high-throughput serology method that evaluates responses to hundreds of antigens simultaneously. It can be used to evaluate thousands of samples at a time and provide a quantitative readout. This tool will enable doctors to monitor which pathogens an individual has been exposed to and how that changes in the future. Moreover, public health officials could track populations and look for infectious trends among large populations. Testing many potential antigens at a time may also aid in vaccine development.

J-score: a robust measure of clustering accuracy.

Background: Clustering analysis discovers hidden structures in a data set by partitioning them into disjoint clusters. Robust accuracy measures that evaluate the goodness of clustering results are critical for algorithm development and model diagnosis. Common problems of clustering accuracy measures include overlooking unmatched clusters, biases towards excessive clusters, unstable baselines, and difficulties of interpretation. In this study, we presented a novel accuracy measure, J-score, to address these issues. Methods: Given a data set with known class labels, J-score quantifies how well the hypothetical clusters produced by clustering analysis recover the true classes. It starts with bidirectional set matching to identify the correspondence between true classes and hypothetical clusters based on Jaccard index. It then computes two weighted sums of Jaccard indices measuring the reconciliation from classes to clusters and vice versa. The final J-score is the harmonic mean of the two weighted sums. Results: Through simulation studies and analyses of real data sets, we evaluated the performance of J-score and compared with existing measures. Our results show that J-score is effective in distinguishing partition structures that differ only by unmatched clusters, rewarding correct inference of class numbers, addressing biases towards excessive clusters, and having a relatively stable baseline. The simplicity of its calculation makes the interpretation straightforward. It is a valuable tool complementary to other accuracy measures. We released an R/jScore package implementing the algorithm.

Development of a rapid lateral flow assay for detection of anti-coccidioidal antibodies.

Coccidioides spp. are dimorphic fungi that are capable of infecting human and non-human mammals and can cause diverse manifestations of coccidioidomycosis or Valley fever (VF). In combination with clinical symptoms and radiographic findings, antibody-based diagnostic tests are often used to diagnose and monitor patients with VF. Chitinase 1 (CTS1) has previously been identified as the seroreactive antigen used in these diagnostic assays to detect anticoccidial IgG. Here, an indirect enzyme-linked immunosorbent assay to detect IgG to CTS1 demonstrated 165 of 178 (92.7%) patients with a positive result by immunodiffusion (ID) and/or complement fixation (CF) had antibodies to the single antigen CTS1. We then developed a rapid antibody lateral flow assay (LFA) to detect anti-CTS1 antibodies. Out of 143 samples tested, the LFA showed 92.9% positive percent agreement [95% confidence interval (CI), 84.3%-96.9%] and 97.7% negative percent agreement (95% CI, 87.9%-99.6%) with ID and CF assays. Serum or plasma from canines, macaques, and dolphins was also tested by the CTS1 LFA. Test line densities of the CTS1 LFA correlated in a linear manner with the reported CF and ID titers for human and non-human samples, respectively. This 10-min point-of-care test for the rapid detection of anti-coccidioidal antibodies could help to inform healthcare providers in real-time, potentially improving the efficiency of healthcare delivery.

Serological survey to estimate SARS-CoV-2 infection and antibody seroprevalence at a large public university: A cross-sectional study.

OBJECTIVE: This study investigated the seroprevalence of SARS-CoV-2 antibodies among adults over 18 years. DESIGN: Prospective cohort study. SETTINGS: A large public university. PARTICIPANTS: This study took volunteers over 5 days and recruited 1064 adult participants. PRIMARY OUTCOME MEASURES: Seroprevalence of SARS-CoV-2-specific antibodies due to previous exposure to SARS-CoV-2 and/or vaccination. RESULTS: The seroprevalence of the antireceptor binding domain (RBD) antibody was 90% by a lateral flow assay and 88% by a semiquantitative chemiluminescent immunoassay. The seroprevalence for antinucleocapsid was 20%. In addition, individuals with previous natural COVID-19 infection plus vaccination had higher anti-RBD antibody levels compared with those who had vaccination only or infection only. Individuals who had a breakthrough infection had the highest anti-RBD antibody levels. CONCLUSION: Accurate estimates of the cumulative incidence of SARS-CoV-2 infection can inform the development of university risk mitigation protocols such as encouraging booster shots, extending mask mandates or reverting to online classes. It could help us to have clear guidance to act at the first sign of the next surge as well, especially since there is a surge of COVID-19 subvariant infections.

Comparative Analysis of Antimicrobial Antibodies between Mild and Severe COVID-19.

Patients with 2019 coronavirus disease (COVID-19) exhibit a broad spectrum of clinical presentations. A person's antimicrobial antibody profile, as partially shaped by past infection or vaccination, can reflect the immune system health that is critical to control and resolve the infection. We performed an explorative immunoproteomics study using microbial protein arrays displaying 318 full-length antigens from 77 viruses and 3 bacteria. We compared antimicrobial antibody profiles between 135 patients with mild COVID-19 disease and 215 patients with severe disease in 3 independent cohorts from Mexico and Italy. Severe disease patients were older with higher prevalence of comorbidities. We confirmed that severe disease patients elicited a stronger anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) response. We showed that antibodies against HCoV-229E and HcoV-NL63 but not against HcoV-HKU1 and HcoV-OC43 were also higher in those who had severe disease. We revealed that for a set of IgG and IgA antibodies targeting coronaviruses, herpesviruses, and other respiratory viruses, a subgroup of patients with the highest reactivity levels had a greater incidence of severe disease compared to those with mild disease across all three cohorts. On the contrary, fewer antibodies showed consistent greater prevalence in mild disease in all 3 cohorts. IMPORTANCE The clinical presentations of COVID-19 range from asymptomatic to critical illness that may lead to intensive care or even death. The health of the immune system, as partially shaped by past infections or vaccinations, is critical to control and resolve the infection. Using an innovative protein array platform, we surveyed antibodies against hundreds of full-length microbial antigens from 80 different viruses and bacteria in COVID-19 patients from different geographic regions with mild or severe disease. We not only confirmed the association of severe COVID-19 disease with higher reactivity of antibody responses to SARS-CoV-2 but also uncovered known and novel associations with antibody responses against herpesviruses and other respiratory viruses. Our study represents a significant step forward in understanding the factors contributing to COVID-19 disease severity. We also demonstrate the power of comprehensive antimicrobial antibody profiling in deciphering risk factors for severe COVID-19. We anticipate that our approach will have broad applications in infectious diseases.

Mindfulness Meditation App Abandonment During the COVID-19 Pandemic: An Observational Study.

Objectives: Mindfulness meditation apps are used by millions of adults in the USA to improve mental health. However, many new app subscribers quickly abandon their use. The purpose of this study was to determine the behavioral, demographic, and socioeconomic factors associated with the abandonment of meditation apps during the COVID-19 pandemic. Method: A survey was distributed to subscribers of a popular meditation app, Calm, at the start of the COVID-19 pandemic in March 2020 that assessed meditation app behavior and meditation habit strength, as well as demographic and socioeconomic information. App usage data were also collected from the start of each participant's subscription until May 2021. A total of 3275 respondents were included in the analyses. Participants were divided into three cohorts according to their subscription start date: (1) long-term subscribers (> 1 year before pandemic start), (2) pre-pandemic subscribers (< 4 months before pandemic start), and (3) pandemic subscribers (joined during the pandemic). Results: Meditating after an existing routine was associated with a lower risk of app abandonment for pre-pandemic subscribers (hazard ratio = 0.607, 95% CI: 0.422, 0.874; p = 0.007) and for pandemic subscribers (hazard ratio = 0.434, 95% CI: 0.285, 0.66; p < 0.001). Additionally, meditating "whenever I can" was associated with lower risk of abandonment among pandemic subscribers (hazard ratio = 0.437, 95% CI: 0.271, 0.706; p < 0.001), and no behavioral factors were significant predictors of app abandonment among the long-term subscribers. Conclusions: These results show that combining meditation with an existing daily routine was a commonly utilized strategy for promoting persistent meditation app use during the COVID-19 pandemic for many subscribers. This finding supports existing evidence that pairing new behaviors with an existing routine is an effective method for establishing new health habits. Preregistration: This study is not pre-registered.

Antibody responses to the host microbiome in chronic rhinosinusitis.

BACKGROUND: The role of microbes in chronic rhinosinusitis (CRS) is poorly understood. We hypothesize that analyzing prior microbial exposures via assessing microbial protein serological reactivity in CRS versus controls may offer insights for CRS etiopathogenesis. METHODS: We profiled IgG and IgA antibodies to individual microbial proteins in serum samples of CRS patients and controls using a novel high-throughput microarray protein technology, Nucleic Acid Programmable Protein Array (NAPPA). The study was conducted on 118 subjects (39 CRS, 79 controls). A CRS-focused NAPPA array, with 1557 potentially sero-reactive microbial proteins elected from a pre-screening of 6500 genes of interest was constructed. It included membrane-associated proteins from 47 bacterial species and all proteins from 43 viral strains. Differences between CRS and controls were compared across individual antimicrobial antibodies and the species. RESULTS: Chronic rhinosinusitis patients had significantly elevated antimicrobial antibodies compared with controls. One bacterium (Staphylococcus aureus) and three viral strains (human metapneumovirus, human herpesvirus 5, and human herpesvirus 4) were identified as sources of the proteins that showed significantly elevated sero-reactivity in CRS patients. Within CRS, patients with polyps had elevated antibodies against S. aureus, influenza A virus (H1N1, H3N2), and rhinovirus B14. CRS patients without polyps showed more antibodies against human herpesvirus 1 and vaccinia virus WR. CONCLUSIONS: Compared with healthy controls, CRS patients' serum samples showed significantly increased sero-reactivity to both bacterial and viral proteins, reflecting recent or current infection or active colonization. Significantly higher antibodies against S. aureus, human metapneumovirus, human herpesvirus 5, and human herpesvirus 4 in CRS need further study.

Identification of anti-Helicobacter pylori antibody signatures in gastric intestinal metaplasia.

BACKGROUND: Chronic Helicobacter pylori infection may induce gastric intestinal metaplasia (IM). We compared anti-H. pylori antibody profiles between IM cases and non-atrophic gastritis (NAG) controls. METHODS: We evaluated humoral responses to 1528 H. pylori proteins among a discovery set of 50 IM and 50 NAG using H. pylori protein arrays. Antibodies with ≥ 20% sensitivity at 90% specificity for either group were selected and further validated in an independent set of 100 IM and 100 NAG using odds ratios (OR). A validated multi-signature was evaluated using the area under the receiver operating characteristics curve (AUC) and net reclassification improvement (NRI). RESULTS: Sixty-two immunoglobulin (Ig) G and 11 IgA antibodies were detected in > 10%. Among them, 22 IgG and 6 IgA antibodies were different between IM and NAG in the discovery set. Validated antibodies included 11 IgG (anti-HP1177/Omp27/HopQ [OR = 8.1, p < 0.001], anti-HP0547/CagA [4.6, p < 0.001], anti-HP0596/Tipα [4.0, p = 0.002], anti-HP0103/TlpB [3.8, p = 0.001], anti-HP1125/PalA/Omp18 [3.1, p = 0.001], anti-HP0153/RecA [0.48, p = 0.03], anti-HP0385 [0.41, p = 0.006], anti-HP0243/TlpB [0.39, p = 0.016], anti-HP0371/FabE [0.37, p = 0.017], anti-HP0900/HypB/AccB [0.35, p = 0.048], and anti-HP0709 [0.30, p = 0.003]), and 2 IgA (anti-HP1125/PalA/Omp18 [2.7, p = 0.03] and anti-HP0596/Tipα [2.5, p = 0.027]). A model including all 11 IgG antibodies (AUC = 0.81) had better discriminated IM and NAG compared with an anti-CagA only (AUC = 0.77) model (NRI = 0.44; p = 0.001). CONCLUSIONS: Our study represents the most comprehensive assessment of anti-H. pylori antibody profiles in IM. The target antigens for these novel antibodies may act together with CagA in the progression to IM. Along with other biomarkers, specific H. pylori antibodies may identify IM patients, who would benefit from surveillance.

Oxidized LDL is stable in human serum under extended thawed-state conditions ranging from -20 °C to room temperature.

Introduction: Oxidized LDL (oxLDL) is formed by the spontaneous reaction between aldehyde byproducts of lipid peroxidation and lysine residues of apolipoprotein B within LDL. Clinically, oxLDL is used as a marker of coronary artery disease and predictor of metabolic syndrome risk. Despite its popularity as a clinical marker, no systematic studies of oxLDL stability, in which serum or plasma has been pre-analytically exposed to an array of different time and temperature conditions, have been carried out. Objective: To systematically evaluate the stability of oxLDL in human serum samples exposed to thawed conditions (> -30 °C) for varying periods of time while monitoring a second protein/small molecule redox system as a positive control for non-enzymatic biomolecular activity. Methods: OxLDL was measured in serum samples, from 24 different humans, that had been pre-exposed to three different time courses at 23 °C, 4 °C and -20 °C using ELISA kits from Mercodia that employ the 4E6 mouse monoclonal antibody. A liquid chromatography/mass spectrometry-based marker of serum exposure to thawed conditions known as ΔS-Cys-Albumin was employed as a positive control. Results: OxLDL was stable in serum exposed to 23 °C for up to 48 h, 4 °C for 21 days, or -20 °C for 65 days. ΔS-Cys-Albumin changed dramatically during these time courses (p < 0.001). Conclusions: OxLDL is remarkably stable ex vivo in human serum samples exposed to thawed conditions.

Comparative Microbiomics Analysis of Antimicrobial Antibody Response between Patients with Lung Cancer and Control Subjects with Benign Pulmonary Nodules.

BACKGROUND: CT screening can detect lung cancer early but suffers a high false-positive rate. There is a need for molecular biomarkers that can distinguish malignant and benign indeterminate pulmonary nodules (IPN) detected by CT scan. METHODS: We profiled antibodies against 901 individual microbial antigens from 27 bacteria and 29 viruses in sera from 127 lung adenocarcinoma (ADC), 123 smoker controls (SMC), 170 benign nodule controls (BNC) individuals using protein microarrays to identify ADC and BNC specific antimicrobial antibodies. RESULTS: Analyzing fourth quartile ORs, we found more antibodies with higher prevalence in the three BNC subgroups than in ADC or SMC. We demonstrated that significantly more anti-Helicobacter pylori antibodies showed higher prevalence in ADC relative to SMC. We performed subgroup analysis and found that more antibodies with higher prevalence in light smokers (≤20 pack-years) compared with heavy smokers (>20 pack-years), in BNC with nodule size >1 cm than in those with ≤1 cm nodules, and in stage I ADC than in stage II and III ADC. We performed multivariate analysis and constructed antibody panels that can distinguish ADC versus SMC and ADC versus BNC with area under the ROC curve (AUC) of 0.88 and 0.80, respectively. CONCLUSIONS: Antimicrobial antibodies have the potential to reduce the false positive rate of CT screening and provide interesting insight in lung cancer development. IMPACT: Microbial infection plays an important role in lung cancer development and the formation of benign pulmonary nodules.

Tracking the Stability of Clinically Relevant Blood Plasma Proteins with Delta-S-Cys-Albumin-A Dilute-and-Shoot LC/MS-Based Marker of Specimen Exposure to Thawed Conditions.

Biomolecular integrity can be compromised when blood plasma/serum (P/S) specimens are improperly handled. Compromised analytes can subsequently produce erroneous results-without any indication of having done so. We recently introduced an LC/MS-based marker of P/S exposure to thawed conditions called ΔS-Cys-Albumin which, aided by an established rate law, quantitatively tracks exposure of P/S to temperatures greater than their freezing point of -30 °C. The purposes of this study were to (1) evaluate ΔS-Cys-Albumin baseline values in gastrointestinal cancer patients and cancer-free control donors, (2) empirically assess the kinetic profiles of ΔS-Cys-Albumin at 23 °C, 4 °C, and -20 °C, and (3) empirically link ΔS-Cys-Albumin to the stability of clinically relevant proteins. ΔS-Cys-Albumin was measured at ≥ 9 different time points per exposure temperature in serum and K2EDTA plasma samples from 24 separate donors in aliquots kept separately at 23 °C, 4 °C, and -20 °C. Twenty-one clinically relevant plasma proteins were measured at four time points per temperature via a multiplexed immunoassay on the Luminex platform. Protein stability was assessed by mixed effects models. Coordinated shifts in stability between ΔS-Cys-Albumin and the unstable proteins were documented by repeated measures and Pearson correlations. Plasma ΔS-Cys-Albumin dropped from approximately 20% to under 5% within 96 h at 23 °C, 28 days at 4 °C, and 65 days at -20 °C. On average, 22% of the 21 proteins significantly changed in apparent concentration at each exposure temperature (p < 0.0008 with >10% shift). A linear inverse relationship was found between the percentage of proteins destabilized and ΔS-Cys-Albumin (r = -0.61; p < 0.0001)-regardless of the specific time/temperature of exposure. ΔS-Cys-Albumin tracks cumulative thawed-state exposure. These results now enable ΔS-Cys-Albumin to approximate the percentage of clinically relevant proteins that have been compromised by incidental plasma exposure to thawed-state conditions.

Serological profiling of Crohn's disease and ulcerative colitis patients reveals anti-microbial antibody signatures.

BACKGROUND: The healthcare burden of inflammatory bowel disease (IBD) is rising globally and there are limited non-invasive biomarkers for accurate and early diagnosis. AIM: To understand the important role that intestinal microbiota play in IBD pathogenesis and identify anti-microbial antibody signatures that benefit clinical management of IBD patients. METHODS: We performed serological profiling of 100 Crohn's disease (CD) patients, 100 ulcerative colitis (UC) patients and 100 healthy controls against 1173 bacterial and 397 viral proteins from 50 bacteria and 33 viruses on protein microarrays. The study subjects were randomly divided into discovery (n = 150) and validation (n = 150) sets. Statistical analysis was performed using R packages. RESULTS: Anti-bacterial antibody responses showed greater differential prevalence among the three subject groups than anti-viral antibody responses. We identified novel antibodies against the antigens of Bacteroidetes vulgatus (BVU_0562) and Streptococcus pneumoniae (SP_1992) showing higher prevalence in CD patients relative to healthy controls. We also identified antibodies against the antigen of Streptococcus pyogenes (SPy_2009) showing higher prevalence in healthy controls relative to UC patients. Using these novel antibodies, we built biomarker panels with area under the curve (AUC) of 0.81, 0.87, and 0.82 distinguishing CD vs control, UC vs control, and CD vs UC, respectively. Subgroup analysis revealed that penetrating CD behavior, colonic CD location, CD patients with a history of surgery, and extensive UC exhibited highest antibody prevalence among all patients. We demonstrated that autoantibodies and anti-microbial antibodies in CD patients had minimal correlation. CONCLUSION: We have identified antibody signatures for CD and UC using a comprehensive analysis of anti-microbial antibody response in IBD. These antibodies and the source microorganisms of their target antigens improve our understanding of the role of specific microorganisms in IBD pathogenesis and, after future validation, should aid early and accurate diagnosis of IBD.

Serum autoantibodyome reveals that healthy individuals share common autoantibodies.

Autoantibodies are a hallmark of both autoimmune disease and cancer, but they also occur in healthy individuals. Here, we perform a meta-analysis of nine datasets and focus on the common autoantibodies shared by healthy individuals. We report 77 common autoantibodies based on the protein microarray data obtained from probing 182 healthy individual sera on 7,653 human proteins and an additional 90 healthy individual sera on 1,666 human proteins. There is no gender bias; however, the number of autoantibodies increase with age, plateauing around adolescence. We use a bioinformatics pipeline to determine possible molecular-mimicry peptides that can contribute to the elicitation of these common autoantibodies. There is enrichment of intrinsic properties of proteins like hydrophilicity, basicity, aromaticity, and flexibility for common autoantigens. Subcellular localization and tissue-expression analysis reveal that several common autoantigens are sequestered from the circulating autoantibodies.

Exploration of autoantibody responses in canine diabetes using protein arrays.

Canine diabetes has been considered a potential model of human type 1 diabetes (T1D), however the detection of autoantibodies common in humans with T1D in affected dogs is inconsistent. The aim of this study was to compare autoantibody responses in diabetic and healthy control dogs using a novel nucleic acid programmable protein array (NAPPA) platform. We performed a cross-sectional study of autoantibody profiles of 30 diabetic and 30 healthy control dogs of various breeds. Seventeen hundred human proteins related to the pancreas or diabetes were displayed on NAPPA arrays and interrogated with canine sera. The median normalized intensity (MNI) for each protein was calculated, and results were compared between groups to identify candidate autoantibodies. At a specificity of 90%, six autoantibodies had sensitivity greater than 10% (range 13-20%) for distinguishing diabetic and control groups. A combination of three antibodies (anti-KANK2, anti-GLI1, anti-SUMO2) resulted in a sensitivity of 37% (95% confidence interval (CI) 0.17-0.67%) at 90% specificity and an area under the receiver operating characteristics curve of 0.66 (95% CI 0.52-0.80). While this study does not provide conclusive support for autoimmunity as an underlying cause of diabetes in dogs, future studies should consider the use of canine specific proteins in larger numbers of dogs of breeds at high risk for diabetes.

Contributory Role of BLT2 in the Production of Proinflammatory Cytokines in Cecal Ligation and Puncture-Induced Sepsis.

BLT2 is a low-affinity receptor for leukotriene B4, a potent lipid mediator of inflammation generated from arachidonic acid via the 5-lipoxygenase pathway. The aim of this study was to investigate whether BLT2 plays any role in sepsis, a systemic inflammatory response syndrome caused by infection. A murine model of cecal ligation and puncture (CLP)-induced sepsis was used to evaluate the role of BLT2 in septic inflammation. In the present study, we observed that the levels of ligands for BLT2 (LTB4 [leukotriene B4] and 12(S)-HETE [12(S)-hydroxyeicosatetraenoic acid]) were significantly increased in the peritoneal lavage fluid and serum from mice with CLP-induced sepsis. We also observed that the levels of BLT2 as well as 5-LO and 12-LO, which are synthesizing enzymes for LTB4 and 12(S)-HETE, were significantly increased in lung and liver tissues in the CLP mouse model. Blockade of BLT2 markedly suppressed the production of sepsis-associated cytokines (IL-6 [interleukin-6], TNF-α [tumor necrosis factor alpha], and IL-1ß [interleukin-1ß] as well as IL-17 [interleukin-17]) and alleviated lung inflammation in the CLP group. Taken together, our results suggest that BLT2 cascade contributes to lung inflammation in CLP-induced sepsis by mediating the production of inflammatory cytokines. These findings suggest that BLT2 may be a potential therapeutic target for sepsis patients.

Identifying App-Based Meditation Habits and the Associated Mental Health Benefits: Longitudinal Observational Study.

BACKGROUND: Behavioral habits are often initiated by contextual cues that occur at approximately the same time each day; so, it may be possible to identify a reflexive habit based on the temporal similarity of repeated daily behavior. Mobile health tools provide the detailed, longitudinal data necessary for constructing such an indicator of reflexive habits, which can improve our understanding of habit formation and help design more effective mobile health interventions for promoting healthier habits. OBJECTIVE: This study aims to use behavioral data from a commercial mindfulness meditation mobile phone app to construct an indicator of reflexive meditation habits based on temporal similarity and estimate the association between temporal similarity and meditation app users' perceived health benefits. METHODS: App-use data from June 2019 to June 2020 were analyzed for 2771 paying subscribers of a meditation mobile phone app, of whom 86.06% (2359/2771) were female, 72.61% (2012/2771) were college educated, 86.29% (2391/2771) were White, and 60.71% (1664/2771) were employed full-time. Participants volunteered to complete a survey assessing their perceived changes in physical and mental health from using the app. Receiver operating characteristic curve analysis was used to evaluate the ability of the temporal similarity measure to predict future behavior, and variable importance statistics from random forest models were used to corroborate these findings. Logistic regression was used to estimate the association between temporal similarity and self-reported physical and mental health benefits. RESULTS: The temporal similarity of users' daily app use before completing the survey, as measured by the dynamic time warping (DTW) distance between app use on consecutive days, significantly predicted app use at 28 days and at 6 months after the survey, even after controlling for users' demographic and socioeconomic characteristics, total app sessions, duration of app use, and number of days with any app use. In addition, the temporal similarity measure significantly increased in the area under the receiver operating characteristic curve (AUC) for models predicting any future app use in 28 days (AUC=0.868 with DTW and 0.850 without DTW; P<.001) and for models predicting any app use in 6 months (AUC=0.821 with DTW and 0.802 without DTW; P<.001). Finally, a 1% increase in the temporal similarity of users' daily meditation practice with the app over 6 weeks before the survey was associated with increased odds of reporting mental health improvements, with an odds ratio of 2.94 (95% CI 1.832-6.369). CONCLUSIONS: The temporal similarity of the meditation app use was a significant predictor of future behavior, which suggests that this measure can identify reflexive meditation habits. In addition, temporal similarity was associated with greater perceived mental health benefits, which demonstrates that additional mental health benefits may be derived from forming reflexive meditation habits.

Statistical Methods for Analysis of Protein Microarray Data Using R.

This chapter aims to provide statistical methods for analyzing protein microarray data. It uses a publicly available protein array dataset and emphasizes practical applications in statistics using R, a statistical software. A wide range of statistical methods will be demonstrated, including descriptive statistics, hypothesis testing, false discovery rate, receiver operating characteristic curve, correlation, visualization, and power analysis. The R code used to perform the statistical analyses will be provided.

Identification of anti-Epstein-Barr virus (EBV) antibody signature in EBV-associated gastric carcinoma.

BACKGROUND: Around 10% of gastric carcinomas (GC) contain Epstein-Barr virus (EBV) DNA. We characterized the GC-specific antibody response to this common infection, which may provide a noninvasive method to detect EBV-positive GC and elucidate its contribution to carcinogenesis. METHODS: Plasma samples from EBV-positive (n = 28) and EBV-negative (n = 34) Latvian GC patients were immune-profiled against 85 EBV proteins on a multi-microbial Nucleic Acid Programmable Protein Array (EBV-NAPPA). Antibody responses were normalized for each sample as ratios to the median signal intensity (MNI) across all antigens, with seropositivity defined as MNI ≥ 2. Antibodies with ≥ 20% sensitivity at 95% specificity for tumor EBV status were verified by enzyme-linked immunosorbent assay (ELISA) and validated in independent samples from Korea and Poland (n = 24 EBV-positive, n = 65 EBV-negative). RESULTS: Forty anti-EBV IgG and eight IgA antibodies were detected by EBV-NAPPA in ≥ 10% of EBV-positive or EBV-negative GC patients, of which nine IgG antibodies were discriminative for tumor EBV status. Eight of these nine were verified and seven were validated by ELISA: anti-LF2 (odds ratio = 110.0), anti-BORF2 (54.2), anti-BALF2 (44.1), anti-BaRF1 (26.7), anti-BXLF1 (12.8), anti-BRLF1 (8.3), and anti-BLLF3 (5.4). The top three had areas under receiver operating characteristics curves of 0.81-0.85 for distinguishing tumor EBV status. CONCLUSIONS: The EBV-associated GC-specific humoral response was exclusively directed against lytic cycle immediate-early and early antigens, unlike other EBV-associated malignancies such as nasopharyngeal carcinoma and lymphoma where humoral response is primarily directed against late lytic antigens. Specific anti-EBV antibodies could have utility for clinical diagnosis, epidemiologic studies, and immune-based precision treatment of EBV-positive GC.