Epirubicin for the Treatment of Sepsis and Septic Shock (EPOS-1): study protocol for a randomised, placebo-controlled phase IIa dose-escalation trial.

INTRODUCTION: Sepsis remains the major cause of death among hospitalised patients in intensive care. While targeting sepsis-causing pathogens with source control or antimicrobials has had a dramatic impact on morbidity and mortality of sepsis patients, this strategy remains insufficient for about one-third of the affected individuals who succumb. Pharmacological targeting of mechanisms that reduce sepsis-defining organ dysfunction may be beneficial. When given at low doses, the anthracycline epirubicin promotes tissue damage control and lessens the severity of sepsis independently of the host-pathogen load by conferring disease tolerance to infection. Since epirubicin at higher doses can be myelotoxic, a first dose-response trial is necessary to assess the potential harm of this drug in this new indication. METHODS AND ANALYSIS: Epirubicin for the Treatment of Sepsis and Septic Shock-1 is a randomised, double-blind, placebo-controlled phase 2 dose-escalation phase IIa clinical trial to assess the safety of epirubicin as an adjunctive in patients with sepsis. The primary endpoint is the 14-day myelotoxicity. Secondary and explorative outcomes include 30-day and 90-day mortality, organ dysfunction, pharmacokinetic/pharmacodynamic (PK/PD) and cytokine release. Patients will be randomised in three consecutive phases. For each study phase, patients are randomised to one of the two study arms (epirubicin or placebo) in a 4:1 ratio. Approximately 45 patients will be recruited. Patients in the epirubicin group will receive a single dose of epirubicin (3.75, 7.5 or 15 mg/m2 depending on the study phase. After each study phase, a data and safety monitoring board will recommend continuation or premature stopping of the trial. The primary analyses for each dose level will report the proportion of myelotoxicity together with a 95% CI. A potential dose-toxicity association will be analysed using a logistic regression model with dose as a covariate. All further analyses will be descriptive. ETHICS AND DISSEMINATION: The protocol is approved by the German Federal Institute for Drugs and Medical Devices. The results will be submitted for publication in peer-reviewed journals. TRIAL REGISTRATION NUMBER: NCT05033808.

Computed tomography in patients with sepsis presenting to the emergency department: exploring its role in light of patient outcomes.

OBJECTIVES: This study aimed to explore the role of CT in septic patients presenting to the emergency department (ED). MATERIALS AND METHODS: We performed a retrospective secondary analysis of 192 septic patients from a prospective observational study, i.e., the "LIFE POC" study. Sepsis was diagnosed in accordance with the Sepsis-3 definition. Clinical and radiological data were collected from the hospital administration and radiological systems. Information on mortality and morbidity was collected. Time-to-CT between CT scan and sepsis diagnosis (ttCTsd) was calculated. Diagnostic accuracy was assessed with the final sepsis source as reference standard. The reference standard was established through the treating team of the patient based on all available clinical, imaging, and microbiological data. RESULTS: Sixty-two of 192 patients underwent a CT examination for sepsis focus detection. The final septic source was identified by CT in 69.4% (n = 43). CT detected septic foci with 81.1% sensitivity (95% CI, 68.0-90.6%) and 55.6% specificity (95% CI, 21.2-86.3%). Patients with short versus long ttCTsd did not differ in terms of mortality (16.1%, n = 5 vs 9.7, n = 3; p = 0.449), length of hospital stay (median 16 d, IQR 9 d 12 h-23 d 18 h vs median 13 d, IQR 10 d 00 h-24 d 00 h; p = 0.863), or duration of intensive care (median 3d 12 h, IQR 2 d 6 h-7 d 18 h vs median 5d, IQR 2 d-11 d; p = 0.800). CONCLUSIONS: Our findings show a high sensitivity of CT in ED patients with sepsis, confirming its relevance in guiding treatment decisions. The low specificity suggests that a negative CT requires further ancillary diagnostic tests for focus detection. The timing of CT did not affect morbidity or mortality outcomes. CLINICAL RELEVANCE STATEMENT: In patients with sepsis who present to the ED, CT can be used to identify infectious foci on the basis of clinical suspicion, but should not be used as a rule-out test. Scientific evidence for the optimal timing of CT beyond clinical decision-making is currently missing, as potential mortality benefits are clouded by differences in clinical severity at the time of ED presentation. KEY POINTS: • In patients with sepsis who present to the ED, CT for focus identification has a high sensitivity and can thereby be valuable for patient management. • As the specificity is considerably lower, a thorough microbiological assessment is important in these cases. • The timing of CT did not affect morbidity and mortality outcomes in this study, which might be due to variability in clinical severity at the time of ED presentation.

Predicting state level suicide fatalities in the united states with realtime data and machine learning.

Digital trace data and machine learning techniques are increasingly being adopted to predict suicide-related outcomes at the individual level; however, there is also considerable public health need for timely data about suicide trends at the population level. Although significant geographic variation in suicide rates exist by state within the United States, national systems for reporting state suicide trends typically lag by one or more years. We developed and validated a deep learning based approach to utilize real-time, state-level online (Mental Health America web-based depression screenings; Google and YouTube Search Trends), social media (Twitter), and health administrative data (National Syndromic Surveillance Program emergency department visits) to estimate weekly suicide counts in four participating states. Specifically, per state, we built a long short-term memory (LSTM) neural network model to combine signals from the real-time data sources and compared predicted values of suicide deaths from our model to observed values in the same state. Our LSTM model produced accurate estimates of state-specific suicide rates in all four states (percentage error in suicide rate of -2.768% for Utah, -2.823% for Louisiana, -3.449% for New York, and -5.323% for Colorado). Furthermore, our deep learning based approach outperformed current gold-standard baseline autoregressive models that use historical death data alone. We demonstrate an approach to incorporate signals from multiple proxy real-time data sources that can potentially provide more timely estimates of suicide trends at the state level. Timely suicide data at the state level has the potential to improve suicide prevention planning and response tailored to the needs of specific geographic communities.

Implications of Online Self-Diagnosis in Psychiatry.

Online self-diagnosis of psychiatric disorders by the general public is increasing. The reasons for the increase include the expansion of Internet technologies and the use of social media, the rapid growth of direct-to-consumer e-commerce in healthcare, and the increased emphasis on patient involvement in decision making. The publicity given to artificial intelligence (AI) has also contributed to the increased use of online screening tools by the general public. This paper aims to review factors contributing to the expansion of online self-diagnosis by the general public, and discuss both the risks and benefits of online self-diagnosis of psychiatric disorders. A narrative review was performed with examples obtained from the scientific literature and commercial articles written for the general public. Online self-diagnosis of psychiatric disorders is growing rapidly. Some people with a positive result on a screening tool will seek professional help. However, there are many potential risks for patients who self-diagnose, including an incorrect or dangerous diagnosis, increased patient anxiety about the diagnosis, obtaining unfiltered advice on social media, using the self-diagnosis to self-treat, including online purchase of medications without a prescription, and technical issues including the loss of privacy. Physicians need to be aware of the increase in self-diagnosis by the general public and the potential risks, both medical and technical. Psychiatrists must recognize that the general public is often unaware of the challenging medical and technical issues involved in the diagnosis of a mental disorder, and be ready to treat patients who have already obtained an online self-diagnosis.

Biomarkers Improve Diagnostics of Sepsis in Adult Patients With Suspected Organ Dysfunction Based on the Quick Sepsis-Related Organ Failure Assessment (qSOFA) Score in the Emergency Department.

OBJECTIVES: Consensus regarding biomarkers for detection of infection-related organ dysfunction in the emergency department is lacking. We aimed to identify and validate biomarkers that could improve risk prediction for overt or incipient organ dysfunction when added to quick Sepsis-related Organ Failure Assessment (qSOFA) as a screening tool. DESIGN: In a large prospective multicenter cohort of adult patients presenting to the emergency department with a qSOFA score greater than or equal to 1, admission plasma levels of C-reactive protein, procalcitonin, adrenomedullin (either bioavailable adrenomedullin or midregional fragment of proadrenomedullin), proenkephalin, and dipeptidyl peptidase 3 were assessed. Least absolute shrinkage and selection operator regression was applied to assess the impact of these biomarkers alone or in combination to detect the primary endpoint of prediction of sepsis within 96 hours of admission. SETTING: Three tertiary emergency departments at German University Hospitals (Jena University Hospital and two sites of the Charité University Hospital, Berlin). PATIENTS: One thousand four hundred seventy-seven adult patients presenting with suspected organ dysfunction based on qSOFA score greater than or equal to 1. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The cohort was of moderate severity with 81% presenting with qSOFA = 1; 29.2% of these patients developed sepsis. Procalcitonin outperformed all other biomarkers regarding the primary endpoint (area under the curve for receiver operating characteristic [AUC-ROC], 0.86 [0.79-0.93]). Adding other biomarkers failed to further improve the AUC-ROC for the primary endpoint; however, they improved the model regarding several secondary endpoints, such as mortality, need for vasopressors, or dialysis. Addition of procalcitonin with a cutoff level of 0.25 ng/mL improved net (re)classification by 35.2% compared with qSOFA alone, with positive and negative predictive values of 60.7% and 88.7%, respectively. CONCLUSIONS: Biomarkers of infection and organ dysfunction, most notably procalcitonin, substantially improve early prediction of sepsis with added value to qSOFA alone as a simple screening tool on emergency department admission.

Reframing sepsis immunobiology for translation: towards informative subtyping and targeted immunomodulatory therapies.

Sepsis is a common and deadly condition. Within the current model of sepsis immunobiology, the framing of dysregulated host immune responses into proinflammatory and immunosuppressive responses for the testing of novel treatments has not resulted in successful immunomodulatory therapies. Thus, the recent focus has been to parse observable heterogeneity into subtypes of sepsis to enable personalised immunomodulation. In this Personal View, we highlight that many fundamental immunological concepts such as resistance, disease tolerance, resilience, resolution, and repair are not incorporated into the current sepsis immunobiology model. The focus for addressing heterogeneity in sepsis should be broadened beyond subtyping to encompass the identification of deterministic molecular networks or dominant mechanisms. We explicitly reframe the dysregulated host immune responses in sepsis as altered homoeostasis with pathological disruption of immune-driven resistance, disease tolerance, resilience, and resolution mechanisms. Our proposal highlights opportunities to identify novel treatment targets and could enable successful immunomodulation in the future.

White and gray matter alterations in bipolar I and bipolar II disorder subtypes compared with healthy controls - exploring associations with disease course and polygenic risk.

Patients with bipolar disorder (BD) show alterations in both gray matter volume (GMV) and white matter (WM) integrity compared with healthy controls (HC). However, it remains unclear whether the phenotypically distinct BD subtypes (BD-I and BD-II) also exhibit brain structural differences. This study investigated GMV and WM differences between HC, BD-I, and BD-II, along with clinical and genetic associations. N = 73 BD-I, n = 63 BD-II patients and n = 136 matched HC were included. Using voxel-based morphometry and tract-based spatial statistics, main effects of group in GMV and fractional anisotropy (FA) were analyzed. Associations between clinical and genetic features and GMV or FA were calculated using regression models. For FA but not GMV, we found significant differences between groups. BD-I patients showed lower FA compared with BD-II patients (ptfce-FWE = 0.006), primarily in the anterior corpus callosum. Compared with HC, BD-I patients exhibited lower FA in widespread clusters (ptfce-FWE < 0.001), including almost all major projection, association, and commissural fiber tracts. BD-II patients also demonstrated lower FA compared with HC, although less pronounced (ptfce-FWE = 0.049). The results remained unchanged after controlling for clinical and genetic features, for which no independent associations with FA or GMV emerged. Our findings suggest that, at a neurobiological level, BD subtypes may reflect distinct degrees of disease expression, with increasing WM microstructure disruption from BD-II to BD-I. This differential magnitude of microstructural alterations was not clearly linked to clinical and genetic variables. These findings should be considered when discussing the classification of BD subtypes within the spectrum of affective disorders.

Lithium response in bipolar disorder is associated with focal adhesion and PI3K-Akt networks: a multi-omics replication study.

Lithium is the gold standard treatment for bipolar disorder (BD). However, its mechanism of action is incompletely understood, and prediction of treatment outcomes is limited. In our previous multi-omics study of the Pharmacogenomics of Bipolar Disorder (PGBD) sample combining transcriptomic and genomic data, we found that focal adhesion, the extracellular matrix (ECM), and PI3K-Akt signaling networks were associated with response to lithium. In this study, we replicated the results of our previous study using network propagation methods in a genome-wide association study of an independent sample of 2039 patients from the International Consortium on Lithium Genetics (ConLiGen) study. We identified functional enrichment in focal adhesion and PI3K-Akt pathways, but we did not find an association with the ECM pathway. Our results suggest that deficits in the neuronal growth cone and PI3K-Akt signaling, but not in ECM proteins, may influence response to lithium in BD.

Identifying genetic differences between bipolar disorder and major depression through multiple GWAS.

Background: Accurate diagnosis of bipolar disorder (BD) is difficult in clinical practice, with an average delay between symptom onset and diagnosis of about 7 years. A key reason is that the first manic episode is often preceded by a depressive one, making it difficult to distinguish BD from unipolar major depressive disorder (MDD). Aims: Here, we use genome-wide association analyses (GWAS) to identify differential genetic factors and to develop predictors based on polygenic risk scores that may aid early differential diagnosis. Methods: Based on individual genotypes from case-control cohorts of BD and MDD shared through the Psychiatric Genomics Consortium, we compile case-case-control cohorts, applying a careful merging and quality control procedure. In a resulting cohort of 51,149 individuals (15,532 BD cases, 12,920 MDD cases and 22,697 controls), we perform a variety of GWAS and polygenic risk scores (PRS) analyses. Results: While our GWAS is not well-powered to identify genome-wide significant loci, we find significant SNP-heritability and demonstrate the ability of the resulting PRS to distinguish BD from MDD, including BD cases with depressive onset. We replicate our PRS findings, but not signals of individual loci in an independent Danish cohort (iPSYCH 2015 case-cohort study, N=25,966). We observe strong genetic correlation between our case-case GWAS and that of case-control BD. Conclusions: We find that MDD and BD, including BD with a depressive onset, are genetically distinct. Further, our findings support the hypothesis that Controls - MDD - BD primarily lie on a continuum of genetic risk. Future studies with larger and richer samples will likely yield a better understanding of these findings and enable the development of better genetic predictors distinguishing BD and, importantly, BD with depressive onset from MDD.

Formation of electron traps in semiconducting polymers via a slow triple-encounter between trap precursor particles.

Already in 2012, Blom et al. reported (Nature Materials 2012, 11, 882) in semiconducting polymers on a general electron-trap density of ≈3 × 1017 cm-3, centered at an energy of ≈3.6 eV below vacuum. It was suggested that traps have an extrinsic origin, with the water-oxygen complex [2(H2O)-O2] as a possible candidate, based on its electron affinity. However, further evidence is lacking and the origin of universal electron traps remained elusive. Here, in polymer diodes, the temperature-dependence of reversible electron traps is investigated that develop under bias stress slowly over minutes to a density of 2 × 1017 cm-3, centered at an energy of 3.6 eV below vacuum. The trap build-up dynamics follows a 3rd-order kinetics, in line with that traps form via an encounter between three diffusing precursor particles. The accordance between universal and slowly evolving traps suggests that general electron traps in semiconducting polymers form via a triple-encounter process between oxygen and water molecules that form the suggested [2(H2O)-O2] complex as the trap origin.

Formation of universal electron traps in polymer light-emitting diodes is a dynamic process that occurs via a slow triple-encounter between trap precursor species, with the water-oxygen [2(H₂O)-O₂] complex as a likely candidate.

Gut microbiome in atypical depression.

BACKGROUND: Recent studies showed that immunometabolic dysregulation is related to unipolar major depressive disorder (MDD) and that it more consistently maps to MDD patients endorsing an atypical symptom profile, characterized by energy-related symptoms including increased appetite, weight gain, and hypersomnia. Despite the documented influence of the microbiome on immune regulation and energy homeostasis, studies have not yet investigated microbiome differences among clinical groups in individuals with MDD. METHODS: Fifteen MDD patients with atypical features according to the Diagnostic and Statistical Manual of Mental Disorders (DSM-5)-5, forty-four MDD patients not fulfilling the DSM-5 criteria for the atypical subtype, and nineteen healthy controls were included in the study. Participants completed detailed clinical assessment and stool samples were collected. Samples were sequenced for the prokaryotic 16S rRNA gene, in the V3-V4 variable regions. Only samples with no antibiotic exposure in the previous 12 months and a minimum of >2000 quality-filtered reads were included in the analyses. RESULTS: There were no statistically significant differences in alpha- and beta-diversity between the MDD groups and healthy controls. However, within the atypical MDD group, there was an increase in the Verrucomicrobiota phylum, with Akkermansia as the predominant bacterial genus. LIMITATIONS: Cross-sectional data, modest sample size, and significantly increased body mass index in the atypical MDD group. CONCLUSIONS: There were no overall differences among the investigated groups. However, differences were found at several taxonomic levels. Studies in larger longitudinal samples with relevant confounders are needed to advance the understanding of the microbial influences on the clinical heterogeneity of depression.

The effects of photoactivated ciprofloxacin and bile acids on biofilms on bile duct catheters.

OBJECTIVES: This study examined the potential of a novel photoactivatable ciprofloxacin to act against bacterial infections and microbiomes related to biliary diseases. It also evaluated treatment by combining the impact of bile acids and antibiotics on biofilms. Innovative strategies were evaluated to address the elusive bile duct microbiome resulting in biofilm-related infections linked to biliary catheters. The healthy biliary system is considered sterile, but bile microbiomes can occur in disease, and these correlate with hepatobiliary diseases. Causes include biofilms that form on internal-external biliary drainage catheters. These biliary catheters were used to noninvasively study the otherwise elusive bile microbiome for a pilot study. METHODS: A new photoactivatable antibiotic was tested for efficacy against human-derived pathogenic bacterial isolates - Salmonella enterica and Escherichia coli - and catheter-derived bile duct microbiomes. In addition, the effect of bile acids on the antibiotic treatment of biofilms was quantified using crystal violet staining, confocal laser scanning microscopy, and biofilm image analysis. Two novel approaches for targeting biliary biofilms were tested. RESULTS: A photoactivated antibiotic based on ciprofloxacin showed efficacy in preventing biofilm formation and reducing bacterial viability without harming eukaryotic cells. Furthermore, combination treatment of antibiotics with bile acids, such as ursodesoxycholic acid, mildly influenced biofilm biomass but reduced bacterial survival within biofilms. CONCLUSION: Bile acids, in addition to their endocrine and paracrine functions, may enhance antibiotic killing of bacterial biofilms compared with antibiotics alone. These approaches hold promise for treating biliary infections such as cholangitis.

The pathophysiology of sepsis and precision-medicine-based immunotherapy.

Sepsis remains a major cause of morbidity and mortality in both low- and high-income countries. Antibiotic therapy and supportive care have significantly improved survival following sepsis in the twentieth century, but further progress has been challenging. Immunotherapy trials for sepsis, mainly aimed at suppressing the immune response, from the 1990s and 2000s, have largely failed, in part owing to unresolved patient heterogeneity in the underlying immune disbalance. The past decade has brought the promise to break this blockade through technological developments based on omics-based technologies and systems medicine that can provide a much larger data space to describe in greater detail the immune endotypes in sepsis. Patient stratification opens new avenues towards precision medicine approaches that aim to apply immunotherapies to sepsis, on the basis of precise biomarkers and molecular mechanisms defining specific immune endotypes. This approach has the potential to lead to the establishment of immunotherapy as a successful pillar in the treatment of sepsis for future generations.

Young people at risk for developing bipolar disorder: Two-year findings from the multicenter prospective, naturalistic Early-BipoLife study.

Early identification and intervention of individuals with an increased risk for bipolar disorder (BD) may improve the course of illness and prevent long­term consequences. Early-BipoLife, a multicenter, prospective, naturalistic study, examined risk factors of BD beyond family history in participants aged 15-35 years. At baseline, positively screened help-seeking participants (screenBD at-risk) were recruited at Early Detection Centers and in- and outpatient depression and attention-deficit/hyperactivity disorder (ADHD) settings, references (Ref) drawn from a representative cohort. Participants reported sociodemographics and medical history and were repeatedly examined regarding psychopathology and the course of risk factors. N = 1,083 screenBD at-risk and n = 172 Ref were eligible for baseline assessment. Within the first two years, n = 31 screenBD at-risk (2.9 %) and none of Ref developed a manifest BD. The cumulative transition risk was 0.0028 at the end of multistep assessment, 0.0169 at 12 and 0.0317 at 24 months (p = 0.021). The transition rate with a BD family history was 6.0 %, 4.7 % in the Early Phase Inventory for bipolar disorders (EPIbipolar), 6.6 % in the Bipolar Prodrome Interview and Symptom Scale-Prospective (BPSS-FP) and 3.2 % with extended Bipolar At-Risk - BARS criteria). In comparison to help-seeking young patients from psychosis detection services, transition rates in screenBD at-risk participants were lower. The findings of Early-BipoLife underscore the importance of considering risk factors beyond family history in order to improved early detection and interventions to prevent/ameliorate related impairment in the course of BD. Large long-term cohort studies are crucial to understand the developmental pathways and long-term course of BD, especially in people at- risk.

Artificial intelligence and increasing misinformation.

With the recent advances in artificial intelligence (AI), patients are increasingly exposed to misleading medical information. Generative AI models, including large language models such as ChatGPT, create and modify text, images, audio and video information based on training data. Commercial use of generative AI is expanding rapidly and the public will routinely receive messages created by generative AI. However, generative AI models may be unreliable, routinely make errors and widely spread misinformation. Misinformation created by generative AI about mental illness may include factual errors, nonsense, fabricated sources and dangerous advice. Psychiatrists need to recognise that patients may receive misinformation online, including about medicine and psychiatry.

Prediction of estimated risk for bipolar disorder using machine learning and structural MRI features.

BACKGROUND: Individuals with bipolar disorder are commonly correctly diagnosed a decade after symptom onset. Machine learning techniques may aid in early recognition and reduce the disease burden. As both individuals at risk and those with a manifest disease display structural brain markers, structural magnetic resonance imaging may provide relevant classification features. METHODS: Following a pre-registered protocol, we trained linear support vector machine (SVM) to classify individuals according to their estimated risk for bipolar disorder using regional cortical thickness of help-seeking individuals from seven study sites (N = 276). We estimated the risk using three state-of-the-art assessment instruments (BPSS-P, BARS, EPIbipolar). RESULTS: For BPSS-P, SVM achieved a fair performance of Cohen's κ of 0.235 (95% CI 0.11-0.361) and a balanced accuracy of 63.1% (95% CI 55.9-70.3) in the 10-fold cross-validation. In the leave-one-site-out cross-validation, the model performed with a Cohen's κ of 0.128 (95% CI -0.069 to 0.325) and a balanced accuracy of 56.2% (95% CI 44.6-67.8). BARS and EPIbipolar could not be predicted. In post hoc analyses, regional surface area, subcortical volumes as well as hyperparameter optimization did not improve the performance. CONCLUSIONS: Individuals at risk for bipolar disorder, as assessed by BPSS-P, display brain structural alterations that can be detected using machine learning. The achieved performance is comparable to previous studies which attempted to classify patients with manifest disease and healthy controls. Unlike previous studies of bipolar risk, our multicenter design permitted a leave-one-site-out cross-validation. Whole-brain cortical thickness seems to be superior to other structural brain features.

Profiling the dysregulated immune response in sepsis: overcoming challenges to achieve the goal of precision medicine.

Sepsis is characterised by a dysregulated host immune response to infection. Despite recognition of its significance, immune status monitoring is not implemented in clinical practice due in part to the current absence of direct therapeutic implications. Technological advances in immunological profiling could enhance our understanding of immune dysregulation and facilitate integration into clinical practice. In this Review, we provide an overview of the current state of immune profiling in sepsis, including its use, current challenges, and opportunities for progress. We highlight the important role of immunological biomarkers in facilitating predictive enrichment in current and future treatment scenarios. We propose that multiple immune and non-immune-related parameters, including clinical and microbiological data, be integrated into diagnostic and predictive combitypes, with the aid of machine learning and artificial intelligence techniques. These combitypes could form the basis of workable algorithms to guide clinical decisions that make precision medicine in sepsis a reality and improve patient outcomes.

The non-canonical inflammasome activators Caspase-4 and Caspase-5 are differentially regulated during immunosuppression-associated organ damage.

The non-canonical inflammasome, which includes caspase-11 in mice and caspase-4 and caspase-5 in humans, is upregulated during inflammatory processes and activated in response to bacterial infections to carry out pyroptosis. Inadequate activity of the inflammasome has been associated with states of immunosuppression and immunopathological organ damage. However, the regulation of the receptors caspase-4 and caspase-5 during severe states of immunosuppression is largely not understood. We report that CASP4 and CASP5 are differentially regulated during acute-on-chronic liver failure and sepsis-associated immunosuppression, suggesting non-redundant functions in the inflammasome response to infection. While CASP5 remained upregulated and cleaved p20-GSDMD could be detected in sera from critically ill patients, CASP4 was downregulated in critically ill patients who exhibited features of immunosuppression and organ failure. Mechanistically, downregulation of CASP4 correlated with decreased gasdermin D levels and impaired interferon signaling, as reflected by decreased activity of the CASP4 transcriptional activators IRF1 and IRF2. Caspase-4 gene and protein expression inversely correlated with markers of organ dysfunction, including MELD and SOFA scores, and with GSDMD activity, illustrating the association of CASP4 levels with disease severity. Our results document the selective downregulation of the non-canonical inflammasome activator caspase-4 in the context of sepsis-associated immunosuppression and organ damage and provide new insights for the development of biomarkers or novel immunomodulatory therapies for the treatment of severe infections.

Ethnoracial Disparities in Rates of Non-Natural Causes of Death After the 2020 COVID-19 Outbreak in New York State.

Introduction: COVID-19 was associated with increases in non-natural cause mortality in the U.S., including deaths due to drug overdose, homicide, and motor vehicle crashes. Initial reports indicated higher rates of non-natural mortality among ethnoracial minority groups. This report aims to clarify these disparities by documenting trends in non-natural mortality across ethnoracial groups during the 2020 COVID-19 surge in New York State. Methods: We report monthly trends in non-natural cause mortality (overall and stratified by ethnoracial status) in New York State from January 2019 through December 2020, which included the COVID-19 onset in March 2020. Results: Total mean monthly unintentional overdose rates per 100,000 increased from 17.45 (before surge: January 2019-February 2020) to 23.19 (after surge: March 2020-December 2020) (mean difference=5.73, 95% CI=3.82, 7.65; p<0.001). Mean monthly homicide death rates increased from 2.34 before surge to 3.55 after surge (mean difference=1.20, 95% CI=0.60, 1.81; p<0.001), with the increase seen primarily in the non-Latinx Black population. Although increasing unintentional overdose death rates before surge equally affected non-Latinx White, Latinx, and non-Latinx Black persons, they remained high for non-Latinx Black persons but dropped for the other 2 groups after the pandemic onset. None of the ethnoracial subgroups showed significant increases in suicide or motor vehicle crash death rates. Conclusions: Non-Latinx Black persons showed disproportionately high and sustained increased rates of unintentional overdose and homicide death rates after the 2020 COVID-19 surge in New York State. Fatality review and death scene investigation research is needed to better understand these disparities.