Precision medicine for the treatment of sepsis: recent advances and future prospects

Interaction between post-tumor inflammation and vascular smooth muscle cell dysfunction in sepsis-induced cardiomyopathy

Background

Sepsis-induced cardiomyopathy (SIC) presents a critical complication in cancer patients, contributing notably to heart failure and elevated mortality rates. While its clinical relevance is well-documented, the intricate molecular mechanisms that link sepsis, tumor-driven inflammation, and cardiac dysfunction remain inadequately explored. This study aims to elucidate the interaction between post-tumor inflammation, intratumor heterogeneity, and the dysfunction of VSMC in SIC, as well as to evaluate the therapeutic potential of exercise training and specific pharmacological interventions.

Methods

Transcriptomic data from NCBI and GEO databases were analyzed to identify differentially expressed genes (DEGs) associated with SIC. Weighted gene co-expression network analysis (WGCNA), gene ontology (GO), and KEGG pathway enrichment analyses were utilized to elucidate the biological significance of these genes. Molecular docking and dynamics simulations were used to investigate drug-target interactions, and immune infiltration and gene mutation analyses were carried out by means of platforms like TIMER 2.0 and DepMap to comprehend the influence of DVL1 on immune responsiveness.

Results

Through the utilization of the datasets, we discovered the core gene DVL1 that exhibited remarkable up-regulated expression both in SIC and in diverse kinds of cancers, which were associated with poor prognosis and inflammatory responses. Molecular docking revealed that Digoxin could bind to DVL1 and reduce oxidative stress in SIC. The DVL1 gene module related to SIC was identified by means of WGCNA, and the immune infiltration analysis demonstrated the distinctive immune cell patterns associated with DVL1 expression and the impact of DVL1 on immunotherapeutic resistance.

Conclusions

DVL1 is a core regulator of SIC and other cancers and, therefore, can serve as a therapeutic target. The present study suggests that targeted pharmacological therapies to enhance response to exercise regimens may be a novel therapeutic tool to reduce the inflammatory response during sepsis, particularly in cancer patients. The identified drugs, Digoxin, require further in vivo and clinical studies to confirm their effects on SIC and their potential efforts to improve outcomes in immunotherapy-resistant cancer patients.

The potential of 4D's approach in curbing antimicrobial resistance among bacterial pathogens

INTRODUCTION

Antibiotics are life-saving drugs but irrational/inappropriate use leads to the emergence of antibiotic-resistant bacterial superbugs, making their treatment extremely challenging. Increasing antimicrobial resistance (AMR) among bacterial pathogens is becoming a serious public health concern globally. If ignorance persists, there would not be any antibiotics available to treat even a common bacterial infection in future.

AREA COVERED

This article intends to collate and discuss the potential of 4D's (right Drug, Dose, Duration, and De-escalation of therapy) approach to tackle the emerging problem of AMR. For this, we searched PubMed, Google Scholar, Medline, and clinicaltrials.gov databases primarily using keywords 'optimal antibiotic therapy,' 'antimicrobial resistance,' 'higher versus lower dose antibiotic treatment,' 'shorter versus longer duration antibiotic treatment,' 'de-escalation study', and 'antimicrobial stewardship measures' and based on the findings, form and expressed our opinion.

EXPERT OPINION

More efforts are needed for developing diagnostics for rapid, accurate, point-of-care, and cost-effective pathogen identification and antimicrobial susceptibility testing (AST) to facilitate rational use of antibiotics. Current dosing and duration of therapies also need to be redefined to maximize their impact. Furthermore, de-escalation approaches should be developed and encouraged in the clinic. This altogether will minimize selection pressure on the pathogens and reduce emergence of AMR.

Engineered Biomimetic Platelet Membrane-Coated Nanoparticles Block Staphylococcus aureus Cytotoxicity and Protect Against Lethal Systemic Infection

Staphylococcus aureus is a leading human pathogen capable of producing severe invasive infections such as bacteremia, sepsis and endocarditis with high morbidity and mortality, exacerbated by expanding antibiotic-resistance exemplified by methicillin-resistant strains (MRSA). S. aureus pathogenesis is fueled by secretion of secreted toxins including the membrane damaging pore-forming α-toxin that have diverse cellular targets including epithelium, endothelium, leukocytes and platelets. Here we examine human platelet membrane-coated nanoparticles (PNPs) as a biomimetic decoy strategy to neutralize S. aureus toxins and preserve host cell defense functions. PNPs blocked platelet damage induced by S. aureus secreted toxins, supporting platelet activation and bactericidal activity. Likewise, PNPs blocked macrophage damage induced by S. aureus secreted toxins, supporting macrophage oxidative burst and nitric oxide production and bactericidal activity, and diminishing MRSA-induced neutrophil extracellular trap release. In a mouse model of MRSA systemic infection, PNP administration reduced bacterial counts in the blood and protected against mortality. Taken together, the present work provides proof-of-principle of therapeutic benefit of PNPs in toxin neutralization, cytoprotection and increased host resistance to invasive S. aureus infection.

Survival prediction of patients with sepsis from age, sex, and septic episode number alone

Sepsis is a life-threatening condition caused by an exaggerated reaction of the body to an infection, that leads to organ failure or even death. Since sepsis can kill a patient even in just one hour, survival prediction is an urgent priority among the medical community: even if laboratory tests and hospital analyses can provide insightful information about the patient, in fact, they might not come in time to allow medical doctors to recognize an immediate death risk and treat it properly. In this context, machine learning can be useful to predict survival of patients within minutes, especially when applied to few medical features easily retrievable. In this study, we show that it is possible to achieve this goal by applying computational intelligence algorithms to three features of patients with sepsis, recorded at hospital admission: sex, age, and septic episode number. We applied several data mining methods to a cohort of 110,204 admissions of patients, and obtained high prediction scores both on this complete dataset (top precision-recall area under the curve PR AUC = 0.966) and on its subset related to the recent Sepsis-3 definition (top PR AUC = 0.860). Additionally, we tested our models on an external validation cohort of 137 patients, and achieved good results in this case too (top PR AUC = 0.863), confirming the generalizability of our approach. Our results can have a huge impact on clinical settings, allowing physicians to forecast the survival of patients by sex, age, and septic episode number alone.