maxPIE: An innovative high throughput approach to enhance pathogen inactivation practices.

Effective pathogen inactivation is highly desired in public health but limited by existing methods each capable of assessing pathogen inactivation effectiveness (PIE) only in a specific condition. We therefore developed a novel method maxPIE designed to identify maximal PIEs across inactivation conditions by leveraging the power of massive array technologies. maxPIE implements a three-step algorithm to quickly identify maximal PIEs of inactivation treatments: (1) dilute pathogens into different initial titers each stored in an array well, (2) submit one sorted array to one treatment, (3) scan the treated array to find the maximum. maxPIE outperformed the conventional methods in (a) inactivating S. aureus using ultraviolet light of different wavelengths with different durations; (b) antibiotic treatment of S. aureus, E. coli, and multidrug-resistant E. coli; (c) inactivating S. aureus in plasma using ultraviolet light in different wavelengths with and without riboflavin. maxPIE was easy to understand and interpret and was robust in situations where conventional PIE methods would suffer. Hence, maxPIE can serve as an innovative and high throughput approach that can be widely used to enhance pathogen inactivation practices.

Elucidating prognosis in cervical squamous cell carcinoma and endocervical adenocarcinoma: a novel anoikis-related gene signature model.

Background: Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) are among the most prevalent gynecologic malignancies globally. The prognosis is abysmal once cervical cancer progresses to lymphatic metastasis. Anoikis, a specialized form of apoptosis induced by loss of cell adhesion to the extracellular matrix, plays a critical role. The prediction model based on anoikis-related genes (ARGs) expression and clinical data could greatly aid clinical decision-making. However, the relationship between ARGs and CESC remains unclear. Methods: ARGs curated from the GeneCards and Harmonizome portals were instrumental in delineating CESC subtypes and in developing a prognostic framework for patients afflicted with this condition. We further delved into the intricacies of the immune microenvironment and pathway enrichment across the identified subtypes. Finally, our efforts culminated in the creation of an innovative nomogram that integrates ARGs. The utility of this prognostic tool was underscored by Decision Curve Analysis (DCA), which illuminate its prospective benefits in guiding clinical interventions. Results: In our study, We discerned a set of 17 survival-pertinent, anoikis-related differentially expressed genes (DEGs) in CESC, from which nine were meticulously selected for the construction of prognostic models. The derived prognostic risk score was subsequently validated as an autonomous prognostic determinant. Through comprehensive functional analyses, we observed distinct immune profiles and drug response patterns among divergent prognostic stratifications. Further, we integrated the risk scores with the clinicopathological characteristics of CESC to develop a robust nomogram. DCA corroborated the utility of our model, demonstrating its potential to enhance patient outcomes through tailored clinical treatment strategies. Conclusion: The predictive signature, encompassing nine pivotal genes, alongside the meticulously constructed nomogram developed in this research, furnishes clinicians with a sophisticated tool for tailoring treatment strategies to individual patients diagnosed with CESC.

Extracorporeal photopheresis reduces inflammation and joint damage in a rheumatoid arthritis murine model.

BACKGROUND: Rheumatoid arthritis (RA) is an autoimmune disease characterized by inflammatory reactions and tissue damage in the joints. Long-term drug use in clinical practice is often accompanied by adverse reactions. Extracorporeal photopheresis (ECP) is an immunomodulatory therapy with few side effects, offering a potential and safe therapeutic alternative for RA through the induction of immune tolerance. This study aimed to investigate the therapeutic effects of ECP on RA using a collagen-induced arthritis (CIA) murine model, as well as to explore its immunomodulatory effects in vivo. Additionally, particular attention was given to the significant role of monocytes during the ECP process. METHODS: A murine model of rheumatoid arthritis was established by administering two injections of bovine type II collagen to DBA/1J mice. ECP, ECP-MD (mononuclear cells were depleted during the ECP), MTX, and PBS treatment were applied to the CIA mice. During the treatment process, clinical scores and body weight changes of CIA mice were closely monitored. After six treatment sessions, micro-CT images of the hind paws from live mice were captured. Ankle joints and paws of the mice were collected and processed for histological evaluation. Spleen samples were collected to measure the Th17/Treg cells ratio, and serum samples were collected to assess cytokine and anti-type II collagen IgG levels. Monocytes and dendritic cells populations before and after ECP in vitro were detected by flow cytometry. RESULT: ECP therapy significantly attenuated the progression of CIA, alleviated the severity of clinical symptoms in CIA mice and effectively suppressed synovial hyperplasia, inflammation, and cartilage damage. There was an expansion in the percentage of CD3 + CD4 + CD25 + FoxP3 + Tregs and a decrease in CD3 + CD4 + IL17A + Th17 cells in vivo. Furthermore, ECP reduced the serum levels of pro-inflammatory cytokines IL-6 (53.47 ± 7.074 pg/mL vs 5.142 ± 1.779 pg/mL, P < 0.05) and IL-17A (3.077 ± 0.401 pg/mL vs 0.238 ± 0.082 pg/mlL, P < 0.0001) compared with PBS. Interestingly, the depletion of monocytes during the ECP process did not lead to any improvement in clinical symptoms or histological scores in CIA mice. Moreover, the imbalance in the Th17/Treg cells ratio became even more pronounced, accompanied by an augmented secretion of pro-inflammatory cytokines IL-6 and IL-17A. In vitro, compared with cells without ECP treatment, the proportion of CD11b + cells were significantly reduced (P < 0.01), the proportion of CD11c + cells were significantly elevated (P < 0.001) 24 h after ECP treatment. Additionally, the expression of MHC II (P < 0.0001), CD80 (P < 0.01), and CD86 (P < 0.001) was downregulated in CD11c + cells 24 h after ECP treatment. CONCLUSION: Our study demonstrates that ECP exhibits a therapeutic effect comparable to conventional therapy in CIA mice, and the protective mechanisms of ECP against RA involve Th17/Treg cells ratio, which result in decreased IL-6 and IL-17A. Notably, monocytes derived from CIA mice are an indispensable part to the efficacy of ECP treatment, and the proportion of monocytes decreased and the proportion of tolerogenic dendritic cells increased after ECP treatment in vitro.

A novel NET-related gene signature for predicting DLBCL prognosis.

BACKGROUND: Diffuse large B-cell lymphoma (DLBCL) is an aggressive malignancy. Neutrophil extracellular traps (NETs) are pathogen-trapping structures in the tumor microenvironment that affect DLBCL progression. However, the predictive function of NET-related genes (NRGs) in DLBCL has received little attention. This study aimed to investigate the interaction between NRGs and the prognosis of DLBCL as well as their possible association with the immunological microenvironment. METHODS: The gene expression and clinical data of patients with DLBCL were downloaded from the Gene Expression Omnibus database. We identified 148 NRGs through the manual collection of literature. GSE10846 (n = 400, GPL570) was used as the training dataset and divided into training and testing sets in a 7:3 ratio. Univariate Cox regression analysis was used to identify overall survival (OS)-related NETs, and the least absolute shrinkage and selection operator was used to evaluate the predictive efficacy of the NRGs. Kaplan-Meier plots were used to visualize survival functions. Receiver operating characteristic (ROC) curves were used to assess the prognostic predictive ability of NRG-based features. A nomogram containing the clinical information and prognostic scores of the patients was constructed using multivariate logistic regression and Cox proportional risk regression models. RESULTS: We identified 36 NRGs that significantly affected patient overall survival (OS). Eight NRGs (PARVB, LYZ, PPARGC1A, HIF1A, SPP1, CDH1, S100A9, and CXCL2) were found to have excellent predictive potential for patient survival. For the 1-, 3-, and 5-year survival rates, the obtained areas under the receiver operating characteristic curve values were 0.8, 0.82, and 0.79, respectively. In the training set, patients in the high NRG risk group presented a poorer prognosis (p < 0.0001), which was validated using two external datasets (GSE11318 and GSE34171). The calibration curves of the nomogram showed that it had excellent predictive ability. Moreover, in vitro quantitative real-time PCR (qPCR) results showed that the mRNA expression levels of CXCL2, LYZ, and PARVB were significantly higher in the DLBCL group. CONCLUSIONS: We developed a genetic risk model based on NRGs to predict the prognosis of patients with DLBCL, which may assist in the selection of treatment drugs for these patients.