IL-6 and IL-10 Are Associated With Gram-Negative and Gram-Positive Bacteria Infection in Lymphoma

Development and application of an early prediction model for risk of bloodstream infection based on real-world study

BACKGROUND

Bloodstream Infection (BSI) is a severe systemic infectious disease that can lead to sepsis and Multiple Organ Dysfunction Syndrome (MODS), resulting in high mortality rates and posing a major public health burden globally. Early identification of BSI is crucial for effective intervention, reducing mortality, and improving patient outcomes. However, existing diagnostic methods are flawed by low specificity, long detection times and high demands on testing platforms. The development of artificial intelligence provides a new approach for early disease identification. This study aims to explore the optimal combination of routine laboratory data and clinical monitoring indicators, and to utilize machine learning algorithms to construct an early, rapid, and universally applicable BSI risk prediction model, to assist in the early diagnosis of BSI in clinical practice.

METHODS

Clinical data of 2582 suspected BSI patients admitted to the Chongqing University Central Hospital, from January 1, 2021 to December 31, 2023 were collected for this study. The data were divided into a modeling dataset and an external validation dataset based on chronological order, while the modeling dataset was further divided into a training set and an internal validation set. The occurrence rate of BSI, distribution of pathogens, and microbial primary reporting time were analyzed within the training set. During the feature selection stage, univariate regression and ML algorithms were applied. First, Univariate logistic regression was used to screen for predictive factors of BSI. Then, the Boruta algorithm, Lasso regression, and Recursive Feature Elimination with Cross-validation (RFE-CV) were employed to determine the optimal combination of predictors for predicting BSI. Based on the optimal combination, six machine learning algorithms were used to construct an early BSI risk prediction model. The best model was selected by models' performance, and the Shapley Additive Explanations (SHAP) method was used to explain the model. The external validation set was used to evaluate the predictive performance and generalizability of the selected model, and the research findings were ultimately applied in clinical practice.

RESULTS

The incidence of BSI among inpatients at the Chongqing University Central Hospital was 12.91%. Following further feature selection, a set of 5 variables was determined, including white blood cell count, standard bicarbonate, base excess of extracellular fluid, interleukin-6, and body temperature. BSI early risk prediction models were constructed using six machine learning algorithms, with the XGBoost model demonstrating the best performance, achieving an AUC value of 0.782 in the internal validation set and an AUC value of 0.776 in the external validation set. This model is made publicly available as an online webpage tool for clinical use.

CONCLUSIONS

This study successfully identified a set of 5 features by analyzing routine laboratory data clinical monitoring indicators among hospitalized patients. Based on this set, a machine learning-based early risk prediction model for BSI was constructed. The model is capable of early and rapid differentiation between BSI and non-BSI patients. The inclusion of minimal risk prediction factors enhances its applicability in clinical settings, particularly at the primary care level. To further improve the model's real-world applicability and more convenient for clinical use, the online application of the model could greatly improve the efficiency of BSI diagnosis and reducing patients' mortality.

Cytokine-Based Insights into Bloodstream Infections and Bacterial Gram Typing in ICU COVID-19 Patients

Background: Timely and accurate identification of bloodstream infections (BSIs) in intensive care unit (ICU) patients remains a key challenge, particularly in COVID-19 settings, where immune dysregulation can obscure early clinical signs. Methods: Cytokine profiling was evaluated to discriminate between ICU patients with and without BSIs, and, among those with confirmed BSIs, to further stratify bacterial infections by Gram type. Serum samples from 45 ICU COVID-19 patients were analyzed using a 21-cytokine panel, with feature selection applied to identify candidate markers. Results: A machine learning workflow identified key features, achieving robust performance metrics with AUC values up to 0.97 for BSI classification and 0.98 for Gram typing. Conclusions: In contrast to traditional approaches that focus on individual cytokines or simple ratios, the present analysis employed programmatically generated ratios between pro-inflammatory and anti-inflammatory cytokines, refined through feature selection. Although further validation in larger and more diverse cohorts is warranted, these findings underscore the potential of advanced cytokine-based diagnostics to enhance precision medicine in infection management.

Development and validation of a model for early survival prediction following liver transplantation based on donor and recipient characteristics

BACKGROUND

Circulating cytokine levels not only correlate with the progression of liver disease but also serve as indicators for the infection status of the body. Growing evidence points to the connection between donor cytokines and graft function following transplantation. This study set out to explore the clinical significance of donor cytokines in predicting liver transplantation prognosis.

METHODS

Data from 172 deceased donor liver transplantations conducted between 2017 and 2022, with available donor serum cytokine information, were collected. The subjects were randomly divided into estimation (n = 120) and validation (n = 52) groups to establish and validate the model. The newly developed SA10 score was compared against established models EAD, MEAF, L-GrAFT7, and L-GrAFT10.

RESULTS

Donor IL-10, along with donor age and recipient AST peak value within the first 7 days post-operation, was identified as an independent factor associated with recipient survival and was incorporated into the SA10 score. SA10 exhibited robust predictive capability, particularly for 1-month survival (AUC = 0.90, 95% CI = 0.84-0.96), outperforming EAD (AUC = 0.75, 95% CI = 0.60-0.90, p = 0.04) and L-GrAFT7 (AUC = 0.65, 95% CI = 0.49-0.81, p < 0.01). Comparable performance was observed between SA10, MEAF, and L-GrAFT10.

CONCLUSION

Donor IL-10 independently influences recipient survival, with the SA10 score demonstrating comparable and even superior predictive ability compared to existing models.

Hematologic cancers and infections: how to detect infections in advance and determine the type?

Infection is one of the leading causes of death in patients with hematologic cancers. Hematologic cancer patients with compromised immune systems are already susceptible to infections, which come on even more rapidly and are difficult to control after they develop neutrophil deficiencies from high-dose chemotherapy. After patients have developed an infection, the determination of the type of infection becomes a priority for clinicians. In this review, we summarize the biomarkers currently used for the prediction of infections in patients with hematologic cancers; procalcitonin, CD64, cytokines, and CD14 et al. can be used to determine bacterial infections, and (1-3)-β-D-glucan and galactomannan et al. can be used as a determination of fungal infections. We have also focused on the use of metagenomic next-generation sequencing in infections in patients with hematologic cancers, which has excellent clinical value in infection prediction and can detect microorganisms that cannot be detected by conventional testing methods such as blood cultures. Of course, we also focused on infection biomarkers that are not yet used in blood cancer patients but could be used as a future research direction, e.g., human neutrophil lipocalin, serum amyloid A, and heparin-binding protein et al. Finally, clinicians need to combine multiple infection biomarkers, the patient's clinical condition, local susceptibility to the type of infection, and many other factors to make a determination of the type of infection.

The pros and cons of probiotic use in pediatric oncology patients following treatment for acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) treatment, involving chemotherapy, radiotherapy, and pharmacotherapy (antibiotics, antineoplastics) perturbs the gut microbiota in pediatric patients, with enduring effects post-treatment. ALL treatments diminish microbial richness and diversity, favoring pathogenic bacteria. Probiotics may offer promise in mitigating these disruptions and associated side effects. This mini-review explores the impact of ALL treatment on the gut microbiota and the potential benefits of probiotics in pediatric oncology. Probiotics have shown promise in restoring gut microbial balance, reducing treatment-associated side effects, and potentially improving quality of life. However, potential adverse effects, particularly in immunocompromised patients, warrant caution. Notably, there's emerging interest in probiotics' role in bone health and mineral bioaccessibility. Further research is needed to elucidate probiotics' mechanisms and their broader impact on pediatric health. Integration of probiotics into ALL treatment and post-treatment regimens offers significant potential for improving patient outcomes and reducing treatment-related complications and long-lasting disruptions, although careful monitoring is essential.

Microbiome-host interactions in the pathogenesis of acute exacerbation of chronic obstructive pulmonary disease

Objective

To explore the underlying mechanisms the airway microbiome contributes to Acute Exacerbation of Chronic Obstructive Pulmonary Disease(AECOPD).

Methods

We enrolled 31 AECOPD patients and 26 stable COPD patients, their sputum samples were collected for metagenomic and RNA sequencing, and then subjected to bioinformatic analyses. The expression of host genes was validated by Quantitative Real-time PCR(qPCR) using the same batch of specimens.

Results

Our results indicated a higher expression of Rothia mucilaginosa(p=0.015) in the AECOPD group and Haemophilus influenzae(p=0.005) in the COPD group. The Different expressed genes(DEGs) detected were significantly enriched in "type I interferon signaling pathway"(p<0.001, q=0.001) in gene function annotation, and "Cytosolic DNA-sensing pathway"(p=0.002, q=0.024), "Toll-like receptor signaling pathway"(p=0.006, q=0.045), and "TNF signaling pathway"(p=0.006, q=0.045) in KEGG enrichment analysis. qPCR amplification experiment verified that the expression of OASL and IL6 increased significantly in the AECOPD group.

Conclusion

Pulmonary bacteria dysbiosis may regulate the pathogenesis of AECOPD through innate immune system pathways like type I interferon signaling pathway and Toll-like receptor signaling pathway.

Development of a Dual Fluorescence Signal-Enhancement Immunosensor Based on Substrate Modification for Simultaneous Detection of Interleukin-6 and Procalcitonin

High-sensitivity detection of biomarkers is of great significance to improve the accuracy of disease diagnosis and the rate of occult disease diagnosis. Using a substrate modification and two-color quantum dot (QD) nanobeads (QBs), we have developed a dual fluorescence signal-enhancement immunosensor for sensitive, simultaneous detection of interleukin 6 (IL-6) and procalcitonin (PCT) at low volumes (∼20 μL). First, the QBs compatible with QDs with different surface ligands were prepared by optimizing surfactants based on the microemulsion method. Through the use of a fluorescence-linked immunosorbent assay (FLISA), the feasibility of a dual signal-enhancement immunosensor was verified, and a 5-fold enhancement of fluorescence intensity was achieved after the directional coating of the antibodies on sulfhydryl functionalization (-SH) substrates and the preparation of QBs by using a polymer and silica double-protection method. Next, a simple polydimethylsiloxane (HS-PDMS) immunosensor with a low volume consumption was prepared. Under optimal conditions, we achieved the simultaneous detection of IL-6 and PCT with a linear range of 0.05-50 ng/mL, and the limit of detection (LOD) was 24 and 32 pg/mL, respectively. The result is comparable to two-color QBs-FLISA with a sulfhydryl microplate, even though only 20% of its volume was used. Thus, the dual fluorescence signal-enhancement HS-PDMS immunosensor offers the capability of early microvolume diagnosis of diseases, while the detection of inflammatory factors is clinically important for assisting disease diagnosis and determining disease progression.

Diagnostic value of inflammatory indicators for surgical site infection in patients with breast cancer

Background

Breast cancer is the most commonly diagnostic cancer in women worldwide. The main treatment for these patients is surgery. However, there is a high incidence of surgical site infection (SSI) in breast cancer patients. The aim of this study was to identify effective infection-related diagnostic markers for timely diagnosis and treatment of SSI.

Methods

This retrospective study included 263 breast cancer patients who were treated between July 2018 and March 2023 at the Shandong Cancer Hospital and Institute. We analyzed differences between the SSI group and control group and differences before and during infection in the SSI group. Finally, we tested the distribution of pathogenic microorganisms and their susceptibility to antibiotics.

Results

Compared with preoperative inflammatory indicators, white blood cells (WBC), neutrophils (NEU), absolute neutrophil count to the absolute lymphocyte count (NLR), D2 polymers (D-Dimer) and fibrinogen (FIB) were significantly increased, while lymphocytes (LYM), albumin (ALB) and prealbumin (PA) were significantly decreased in the SSI group. Compared with uninfected patients, WBC, NEU, NLR and FIB were significantly increased, ALB and PA were significantly decreased in SSI patients, while LYM and D-Dimer did not differ significantly. The distribution of infection bacteria in SSI patients showed that the proportion of patients with Staphylococcus aureus infection was as high as 70.41%; of those patients, 19.33% had methicillin-resistant Staphylococcus aureus (MRSA) infection. The area under the curves (AUCs) of the receiver operating curves (ROCs) for WBC, NEU, NLR, FIB, ALB and PA were 0.807, 0.811, 0.730, 0.705, 0.663 and 0.796, respectively. The AUCs for other inflammatory indicators were not statistically significant. There was no significant difference in antibiotic resistance for Staphylococcus aureus when compared to that of gram-positive bacteria. The resistance of gram-positive bacteria to ceftriaxone (CRO), cefoxitin (FOX), chloramphenicol (CHL), minocycline (MNO) and tetracycline (TCY) was lower than that of gram-negative bacteria, while the resistance to gentamicin (GEN) was higher.

Conclusion

This study demonstrated that WBC, NEU, NLR, FIB and PA have good predictive value for identifying patients at risk of SSI. The cut-off values of inflammatory indicators can be helpful in the prevention and diagnosis of SSI.

Linking the gut microbiome to microglial activation in opioid use disorder

Substance use disorder (SUD) is a physical and psychological disorder globally prevalent today that has resulted in over 107,000 drug overdose deaths in 2021 in the United States alone. This manuscript reviews the potential relationship between opioid use disorder (OUD), a prevalent subset of SUD, and the microglia, the resident macrophages of the central nervous system (CNS), as they have been found to become significantly more activated during opioid exposure. The inflammatory response mediated by the microglia could contribute to the pathophysiology of SUDs, in particular OUD. Further understanding of the microglia and how they respond to not only signals in the CNS but also signals from other areas of the body, such as the gut microbiome, could explain how the microglia are involved in drug use. Several studies have shown extensive communication between the gut microbiome and the microglia, which may be an important factor in the initiation and development of OUD. Particularly, strategies seeking to manipulate and restore the gut microbiome have been shown to reduce microglial activation and attenuate inflammation. In this review, we discuss the evidence for a link between the microglia and OUD and how the gut microbiome might influence microglial activation to drive the disorder and its associated behaviors. Understanding this connection between microglia and the gut microbiome in the context of drug use may present additional therapeutic targets to treat the different stages of drug use.

Engineered endosymbionts that alter mammalian cell surface marker, cytokine and chemokine expression

Developing modular tools that direct mammalian cell function and activity through controlled delivery of essential regulators would improve methods of guiding tissue regeneration, enhancing cellular-based therapeutics and modulating immune responses. To address this challenge, Bacillus subtilis was developed as a chassis organism for engineered endosymbionts (EES) that escape phagosome destruction, reside in the cytoplasm of mammalian cells, and secrete proteins that are transported to the nucleus to impact host cell response and function. Two synthetic operons encoding either the mammalian transcription factors Stat-1 and Klf6 or Klf4 and Gata-3 were recombined into the genome of B. subtilis expressing listeriolysin O (LLO) from Listeria monocytogenes and expressed from regulated promoters. Controlled expression of the mammalian proteins from B. subtilis LLO in the cytoplasm of J774A.1 macrophage/monocyte cells altered surface marker, cytokine and chemokine expression. Modulation of host cell fates displayed some expected patterns towards anti- or pro-inflammatory phenotypes by each of the distinct transcription factor pairs with further demonstration of complex regulation caused by a combination of the EES interaction and transcription factors. Expressing mammalian transcription factors from engineered intracellular B. subtilis as engineered endosymbionts comprises a new tool for directing host cell gene expression for therapeutic and research purposes.

The establishment of non-pathogenic engineered endosymbionts through B. subtilis is presented, with the aim of delivering mammalian transcription factors to the host cell for therapeutics and research.