Identification of Marker Genes in Infectious Diseases from ScRNA-seq Data Using Interpretable Machine Learning.

A common result of infection is an abnormal immune response, which may be detrimental to the host. To control the infection, the immune system might undergo regulation, therefore producing an excess of either pro-inflammatory or anti-inflammatory pathways that can lead to widespread inflammation, tissue damage, and organ failure. A dysregulated immune response can manifest as changes in differentiated immune cell populations and concentrations of circulating biomarkers. To propose an early diagnostic system that enables differentiation and identifies the severity of immune-dysregulated syndromes, we built an artificial intelligence tool that uses input data from single-cell RNA sequencing. In our results, single-cell transcriptomics successfully distinguished between mild and severe sepsis and COVID-19 infections. Moreover, by interpreting the decision patterns of our classification system, we identified that different immune cells upregulating or downregulating the expression of the genes CD3, CD14, CD16, FOSB, S100A12, and TCRɣδ can accurately differentiate between different degrees of infection. Our research has identified genes of significance that effectively distinguish between infections, offering promising prospects as diagnostic markers and providing potential targets for therapeutic intervention.

Transcriptomics reveals shared immunosuppressive landscapes in ventilator-associated lower respiratory tract infections (VA-LRTI) patients.

BACKGROUND: Ventilator-associated pneumonia (VAP) represents a transitory status of immunoparalysis, and we hypothesized that ventilator-associated tracheobronchitis (VAT) could share also some degree of immune response to a respiratory infection. RESEARCH DESIGN AND METHODS: A prospective observational study in five medical ICUs to evaluate immunological alterations of patients with VA-LRTI. Immunological gene expression profiles in the blood using whole transcriptome microarrays in the first 24 hours following diagnosis. The area under the receiver operating characteristic curve (AUROC) was used to assess the accuracy of mRNA levels to differentiate VA-LRTI and lack of infection. A principal component analysis (PCA) was employed for analyzing the impact of each genetic expression footprint variable in explaining the variance of the cohort. RESULTS: There was overlapping between the three classes of patients encompassing gene expression levels of 8 genes (i.e. HLA, IL2RA, CD40LG, ICOS, CCR7, CD1C, CD3E). HLA-DRA was equally low among VAT and VAP patients characterizing immune depression, and significantly lower than the control group. CONCLUSIONS: Our findings suggest that VAP and VAT are not so different regarding gene expression levels suggesting a degree of immunosuppression. Our results indicate a state of immunoparalysis in respiratory infections in critically ill patients.

Multi-Omics Endotypes in ICU Sepsis-Induced Immunosuppression.

It is evident that the admission of some patients with sepsis and septic shock to hospitals is occurring late in their illness, which has contributed to the increase in poor outcomes and high fatalities worldwide across age groups. The current diagnostic and monitoring procedure relies on an inaccurate and often delayed identification by the clinician, who then decides the treatment upon interaction with the patient. Initiation of sepsis is accompanied by immune system paralysis following "cytokine storm". The unique immunological response of each patient is important to define in terms of subtyping for therapy. The immune system becomes activated in sepsis to produce interleukins, and endothelial cells express higher levels of adhesion molecules. The proportions of circulating immune cells change, reducing regulatory cells and increasing memory cells and killer cells, having long-term effects on the phenotype of CD8 T cells, HLA-DR, and dysregulation of microRNA. The current narrative review seeks to highlight the potential application of multi-omics data integration and immunological profiling at the single-cell level to define endotypes in sepsis and septic shock. The review will consider the parallels and immunoregulatory axis between cancer and immunosuppression, sepsis-induced cardiomyopathy, and endothelial damage. Second, the added value of transcriptomic-driven endotypes will be assessed through inferring regulatory interactions in recent clinical trials and studies reporting gene modular features that inform continuous metrics measuring clinical response in ICU, which can support the use of immunomodulating agents.

Parallel Dysregulated Immune Response in Severe Forms of COVID-19 and Bacterial Sepsis via Single-Cell Transcriptome Sequencing.

Critically ill COVID-19 patients start developing single respiratory organ failure that often evolves into multiorgan failure. Understanding the immune mechanisms in severe forms of an infectious disease (either critical COVID-19 or bacterial septic shock) would help to achieve a better understanding of the patient's clinical trajectories and the success of potential therapies. We hypothesized that a dysregulated immune response manifested by the abnormal activation of innate and adaptive immunity might be present depending on the severity of the clinical presentation in both COVID-19 and bacterial sepsis. We found that critically ill COVID-19 patients demonstrated a different clinical endotype that resulted in an inflammatory dysregulation in mild forms of the disease. Mild cases (COVID-19 and bacterial non severe sepsis) showed significant differences in the expression levels of CD8 naïve T cells, CD4 naïve T cells, and CD4 memory T cells. On the other hand, in the severe forms of infection (critical COVID-19 and bacterial septic shock), patients shared immune patterns with upregulated single-cell transcriptome sequencing at the following levels: B cells, monocyte classical, CD4 and CD8 naïve T cells, and natural killers. In conclusion, we identified significant gene expression differences according to the etiology of the infection (COVID-19 or bacterial sepsis) in the mild forms; however, in the severe forms (critical COVID-19 and bacterial septic shock), patients tended to share some of the same immune profiles related to adaptive and innate immune response. Severe forms of the infections were similar independent of the etiology. Our findings might promote the implementation of co-adjuvant therapies and interventions to avoid the development of severe forms of disease that are associated with high mortality rates worldwide.

Model-interpreted outcomes of artificial neural networks classifying immune biomarkers associated with severe infections in ICU.

Introduction: Millions of deaths worldwide are a result of sepsis (viral and bacterial) and septic shock syndromes which originate from microbial infections and cause a dysregulated host immune response. These diseases share both clinical and immunological patterns that involve a plethora of biomarkers that can be quantified and used to explain the severity level of the disease. Therefore, we hypothesize that the severity of sepsis and septic shock in patients is a function of the concentration of biomarkers of patients. Methods: In our work, we quantified data from 30 biomarkers with direct immune function. We used distinct Feature Selection algorithms to isolate biomarkers to be fed into machine learning algorithms, whose mapping of the decision process would allow us to propose an early diagnostic tool. Results: We isolated two biomarkers, i.e., Programmed Death Ligand-1 and Myeloperoxidase, that were flagged by the interpretation of an Artificial Neural Network. The upregulation of both biomarkers was indicated as contributing to increase the severity level in sepsis (viral and bacterial induced) and septic shock patients. Discussion: In conclusion, we built a function considering biomarker concentrations to explain severity among sepsis, sepsis COVID, and septic shock patients. The rules of this function include biomarkers with known medical, biological, and immunological activity, favoring the development of an early diagnosis system based in knowledge extracted from artificial intelligence.

Alcohol use and cognitive aging in middle-aged men: The Vietnam Era Twin Study of Aging.

OBJECTIVE: To determine associations of alcohol use with cognitive aging among middle-aged men. METHOD: 1,608 male twins (mean 57 years at baseline) participated in up to three visits over 12 years, from 2003-2007 to 2016-2019. Participants were classified into six groups based on current and past self-reported alcohol use: lifetime abstainers, former drinkers, very light (1-4 drinks in past 14 days), light (5-14 drinks), moderate (15-28 drinks), and at-risk drinkers (>28 drinks in past 14 days). Linear mixed-effects regressions modeled cognitive trajectories by alcohol group, with time-based models evaluating rate of decline as a function of baseline alcohol use, and age-based models evaluating age-related differences in performance by current alcohol use. Analyses used standardized cognitive domain factor scores and adjusted for sociodemographic and health-related factors. RESULTS: Performance decreased over time in all domains. Relative to very light drinkers, former drinkers showed worse verbal fluency performance, by -0.21 SD (95% CI -0.35, -0.07), and at-risk drinkers showed faster working memory decline, by 0.14 SD (95% CI 0.02, -0.20) per decade. There was no evidence of protective associations of light/moderate drinking on rate of decline. In age-based models, light drinkers displayed better memory performance at advanced ages than very light drinkers (+0.14 SD; 95% CI 0.02, 0.20 per 10-years older age); likely attributable to residual confounding or reverse association. CONCLUSIONS: Alcohol consumption showed minimal associations with cognitive aging among middle-aged men. Stronger associations of alcohol with cognitive aging may become apparent at older ages, when cognitive abilities decline more rapidly.

An artificial neural network classification method employing longitudinally monitored immune biomarkers to predict the clinical outcome of critically ill COVID-19 patients.

Background: The severe form of COVID-19 can cause a dysregulated host immune syndrome that might lead patients to death. To understand the underlying immune mechanisms that contribute to COVID-19 disease we have examined 28 different biomarkers in two cohorts of COVID-19 patients, aiming to systematically capture, quantify, and algorithmize how immune signals might be associated to the clinical outcome of COVID-19 patients. Methods: The longitudinal concentration of 28 biomarkers of 95 COVID-19 patients was measured. We performed a dimensionality reduction analysis to determine meaningful biomarkers for explaining the data variability. The biomarkers were used as input of artificial neural network, random forest, classification and regression trees, k-nearest neighbors and support vector machines. Two different clinical cohorts were used to grant validity to the findings. Results: We benchmarked the classification capacity of two COVID-19 clinicals studies with different models and found that artificial neural networks was the best classifier. From it, we could employ different sets of biomarkers to predict the clinical outcome of COVID-19 patients. First, all the biomarkers available yielded a satisfactory classification. Next, we assessed the prediction capacity of each protein separated. With a reduced set of biomarkers, our model presented 94% accuracy, 96.6% precision, 91.6% recall, and 95% of specificity upon the testing data. We used the same model to predict 83% and 87% (recovered and deceased) of unseen data, granting validity to the results obtained. Conclusions: In this work, using state-of-the-art computational techniques, we systematically identified an optimal set of biomarkers that are related to a prediction capacity of COVID-19 patients. The screening of such biomarkers might assist in understanding the underlying immune response towards inflammatory diseases.

Moderate Alcohol Use Is Associated with Reduced Cardiovascular Risk in Middle-Aged Men Independent of Health, Behavior, Psychosocial, and Earlier Life Factors.

We examined whether the often-reported protective association of alcohol with cardiovascular disease (CVD) risk could arise from confounding. Our sample comprised 908 men (56−67 years), free of prevalent CVD. Participants were categorized into 6 groups: never drinkers, former drinkers, and very light (1−4 drinks in past 14 days), light (5−14 drinks), moderate (15−28 drinks), and at-risk (>28 drinks) drinkers. Generalized linear mixed effect models examined the associations of alcohol use with three established CVD risk scores: The Framingham Risk Score (FRS); the atherosclerotic CVD (ASCVD) risk score; and the Metabolic Syndrome (MetS) Severity score, adjusting for group differences in demographics, body size, and health-related behaviors. In separate models we additionally adjusted for several groups of potentially explanatory factors including socioeconomic status, social support, physical and mental health status, childhood factors, and prior history of alcohol misuse. Results showed lower CVD risk among light and moderate alcohol drinkers, relative to very light drinkers, for all CVD risk scores, independent of demographics, body size, and health-related behaviors. Alcohol-CVD risk associations were robust to further adjustment for several groups of potential explanatory factors. Study limitations include the all-male sample with limited racial and ethnic diversity, and the inability to adjust for sugar consumption and for patterns of alcohol consumption. Although this observational study does not address causation, results show that middle-aged men who consume alcohol in moderation have lower CVD risk and better cardiometabolic health than men who consume little or no alcohol, independent of a variety of health, behavioral, psychosocial, and earlier life factors.

Choosing antibiotic therapy for severe community-acquired pneumonia.

PURPOSE OF REVIEW: Community-acquired pneumonia (CAP) is known as a major worldwide health concern considering it has been shown to account for 78% of infection-related deaths in the USA. It is a common cause for hospitalization with a continued incidence rise in the elderly, high mortality rate and long-term sequelae in critically ill patients. Severe CAP (sCAP) is an accepted terminology used to describe ICU admitted patients with CAP. The aim of this review is to further report on the major advances in treatment for patients with sCAP including new antibiotic treatments despite macrolide resistance as seen in the ICU, and multifaceted antibiotic stewardship interventions that may lead to the reduction broad-spectrum antibiotic use in CAP. RECENT FINDINGS: We aim to examine the most recent findings in order to determine appropriate empirical antibiotic choices, timing regimens and evidence for clinical effectiveness. This will be addressed by focusing on the use combination therapies, the usefulness of severity scores and the difficulty to treat multidrug-resistant pathogens, including gram negatives such as Pseudomonas aeruginosa and methicillin-resistant Staphylococcus aureus. Relevant reports referenced within included randomized controlled trials, meta-analyses, observational studies, systematic reviews and international guidelines where applicable. SUMMARY: New antibiotics have been recently launched with direct agent-specific properties that have been shown to avoid the overuse of previous broad-spectrum antibiotics when treating patients sCAP. Although narrow-spectrum antibiotics are now recommended and imperative in improving a patients' prognosis, there are also some considerations when prescribing antibiotics that are beyond the spectrum. There is a need to implement effective policies of de-escalation to avoid antibiotic resistance and the risk for developing subsequent infections by combining informed clinical judgement and the application of biomarkers. Reaching clinical stability and avoidance of treatment failure are the most important pillars in treatment success.

Associations of Daily Steps and Step Intensity With Incident Diabetes in a Prospective Cohort Study of Older Women: The OPACH Study.

OBJECTIVE: The primary aim was to assess associations between total steps per day and incident diabetes, whereas the secondary aim was to assess whether the intensity and/or cadence of steps is associated with incident diabetes. RESEARCH DESIGN AND METHODS: Women without physician-diagnosed diabetes (n = 4,838; mean [SD] age 78.9 [6.7] years) were followed up to 6.9 years; 395 developed diabetes. Hip-worn ActiGraph GT3X+ accelerometers worn for 1 week enabled measures of total, light-intensity, and moderate- to vigorous-intensity (MV-intensity) steps per day. Using Cox proportional hazards analysis we modeled adjusted change in the hazard rate for incident diabetes associated with total, light-intensity, and MV-intensity steps per day. We further estimated the proportion of the steps-diabetes association mediated by BMI. RESULTS: On average, participants took 3,729 (SD 2,114) steps/day, of which 1,875 (791) were light-intensity steps and 1,854 ± 1,762 were MV-intensity. More steps per day were associated with a lower hazard rate for incident diabetes. Confounder-adjusted models for a 2,000 steps/day increment yielded hazard ratio (HR) 0.88 (95% CI 0.78-1.00; P = 0.046). After further adjustment for BMI, HR was 0.90 (95% CI 0.80-1.02; P = 0.11). BMI did not significantly mediate the steps-diabetes association (proportion mediated = 17.7% [95% CI -55.0 to 142.0]; P = 0.09]). The relationship between MV-intensity steps per day (HR 0.86 [95% CI 0.74-1.00]; P = 0.04) and incident diabetes was stronger than for light-intensity steps per day (HR 0.97 [95% CI 0.73-1.29]; P = 0.84). CONCLUSIONS: These findings suggest that for older adults, more steps per day are associated with lower incident diabetes and MV-intensity steps are most strongly associated with a lower hazard of diabetes. This evidence supports that regular stepping is an important risk factor for type 2 diabetes prevention in older adults.

Novel investigational treatments for ventilator-associated pneumonia and critically ill patients in the intensive care unit.

INTRODUCTION: Ventilator-associated pneumonia (VAP) is common; its prevalence has been highlighted by the Covid-19 pandemic. Even young patients can suffer severe nosocomial infection and prolonged mechanical ventilation. Multidrug-resistant bacteria can spread alarmingly fast around the globe and new antimicrobials are struggling to keep pace; hence physicians must stay abreast of new developments in the treatment of nosocomial pneumonia and VAP. AREAS COVERED: This narrative review examines novel antimicrobial investigational drugs and their implementation in the ICU setting for VAP. This paper highlights novel approaches such as monoclonal antibody treatments for P. aeruginosa and S. aureus and phage antibiotic synthesis. This paper also examines mechanisms of resistance in Gram-negative bacteria, virulence factors, and inhaled antibiotics and questions what may be on the horizon in terms of emerging treatment strategies. EXPERT OPINION: The postantibiotic era is rapidly approaching, and the need for personalized medicine, point-of-care microbial sensitivity testing, and development of biomarkers for severe infections is clear. Results from emerging and new antibiotics are encouraging, but infection control measures and de-escalation protocols must be employed to prolong their usefulness in critical illness.

Efficacy and appropriateness of novel antibiotics in response to antimicrobial-resistant gram-negative bacteria in patients with sepsis in the ICU.

INTRODUCTION: There is an ever-increasing range of antibiotic-resistant pathogens that have led to higher community-acquired infections, and substantial mortality rates in critically ill patients. AREAS COVERED: We have critically appraised available evidence through a structured literature review, investigating effective empiric antibiotic administration and appropriateness on outcomes of critically ill patients with an increased risk of developing resistant pathogens. The use of new antibiotics should be determined based on relevant knowledge of their spectrum and properties to provide effective mode of action for critically ill patients. EXPERT OPINION: Restricting severely ill patients access to new broad-spectrum empirical drugs is not the answer. Rather there should be a focus on identifying host response to infection to differentiate between colonization or contamination and true infection, and the sensitivity to antibiotics used in the intensive care unit (ICU). Management relies on adequate antibiotic administration, the ability to monitor response, and facilitate the cessation of antibiotic treatment. The major determinant of patient success in a patient with a severe infection is the 'right' antibiotic or complementary course of treatment. As an overarching criterion, the following three appropriate "Ds" should be considered: Dosing, Duration, and De-escalation to empirically assess the right antibiotic optimal antimicrobial selection.

Tobacco Smoke and CYP1A2 Activity in a US Population with Normal Liver Enzyme Levels.

Non-alcoholic fatty liver disease (NAFLD) is common among 30% of American adults. Former and current smokers are at higher risk for NAFLD compared to never smokers. The ratio of urine caffeine metabolites to caffeine intake-namely, urine caffeine metabolite indices-has previously been used as a proxy for CYP1A2 activity, which is one of the main liver metabolizing enzymes. CYP1A2 activity is associated with NAFLD progression. No studies to our knowledge have examined the associations of liver enzymes, smoking intensity, and secondhand smoke (SES) with CYP1A2 activity (using caffeine metabolite indices) across smoking status. We analyzed national representative samples from the 2009-2010 National Health and Nutrition Examination Survey (NHANES). Interestingly, even within a normal range, several liver enzymes were associated with caffeine metabolite indices, and patterns of many of these associations varied by smoking status. For instance, within a normal range, aspartate aminotransferase (AST) in never smokers and bilirubin in current smokers were inversely associated with 1-methyluric acid and 5-acetylamino-6-amino-3-methyluracil (URXAMU). Furthermore, we observed a common pattern: across all smoking statuses, higher AST/alanine aminotransferase (AST/ALT) was associated with 1-methyluric acid and URXAMU. Moreover, in current smokers, increased lifelong smoking intensity was associated with reduced caffeine metabolite indices, but acute cigarette exposure as measured by SES levels was associated with increased caffeine metabolite indices among never smokers. In summary, commonly used liver enzyme tests can reflect the CYP1A2 activity even within a normal range, but the selection of these enzymes depends on the smoking status; the associations between smoking and the CYP1A2 activity not only depend on the intensity but also the duration of tobacco exposure.

Anti-Inflammatory and Neuroprotective Effects of PGE2 EP4 Signaling in Models of Parkinson's Disease.

Inflammation is a ubiquitous factor accompanying normal aging and neurodegeneration, and recent studies indicate a major contribution of inducible cyclooxygenase (COX-2) and its downstream prostaglandin signaling pathways in modulating neuroinflammatory responses and neuronal function. We have previously shown that the prostaglandin PGE2 receptor EP4 suppresses innate immune responses in models of systemic inflammation. Here we investigated the role of the EP4 receptor in models of Parkinson's disease (PD). Systemic co-administration of the EP4 agonist ONO-AE1-329 with the neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) prevented loss of dopaminergic (DA) neurons in the substantia nigra pars compacta (SNpc) without significant changes in glial activation, suggesting a potent neuroprotective effect of EP4 signaling in this acute model of DA neuronal loss. Cell-specific conditional ablation of EP4 in Cd11bCre;EP4lox/lox mice exacerbated MPTP-associated glial activation and T-cell infiltration in SNpc, consistent with anti-inflammatory functions of microglial EP4 signaling. In vitro, in primary microglia stimulated with oligomeric α-synuclein, EP4 receptor activation suppressed generation of pro-inflammatory and oxidative stress factors. Taken together, these findings suggest a dual neuroprotective and anti-inflammatory mechanism of action by the EP4 receptor in models of PD.