Corticosteroid in non-COVID-19 induced community-acquired pneumonia, a meta-analysis.

BACKGROUND: Corticosteroid treatment in non-COVID-19 induced Community-acquired pneumonia (CAP) remains inconclusive. OBJECTIVES: We aimed to assess the role of corticosteroid treatment in CAP. METHODS: We conducted a comprehensive search of online databases, including PubMed, Embase, and Cochrane, to identify articles published from January 1, 2000, to May 5, 2023. Double-blind RCTs were selected. Two authors screened studies and extracted data. The evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation approach. RESULTS: We analyzed data from 12 RCTs, involving 2446 patients. Corticosteroids therapy may reduce short-term mortality in patients with severe CAP (sCAP) and shorten the hospital length of stay in patients with CAP. Furthermore, corticosteroids treatment can decrease the risk of requiring mechanical ventilation, developing septic shock and multiple organ dysfunction syndrome (MODS). There were no significant differences between the corticosteroid and control groups concerning gastrointestinal bleeding and nosocomial infection. The use of corticosteroids could increase the risk of hyperglycemia. CONCLUSION: Corticosteroid treatment for sCAP has the potential to provide benefits in reducing short-term mortality, but this conclusion necessitates more evidence. Besides, we found no evidence that strongly prevents us from using corticosteroids in patients with sCAP or those at risk of progressing to sCAP.

TheMarker: a comprehensive database of therapeutic biomarkers.

Distinct from the traditional diagnostic/prognostic biomarker (adopted as the indicator of disease state/process), the therapeutic biomarker (ThMAR) has emerged to be very crucial in the clinical development and clinical practice of all therapies. There are five types of ThMAR that have been found to play indispensable roles in various stages of drug discovery, such as: Pharmacodynamic Biomarker essential for guaranteeing the pharmacological effects of a therapy, Safety Biomarker critical for assessing the extent or likelihood of therapy-induced toxicity, Monitoring Biomarker indispensable for guiding clinical management by serially measuring patients' status, Predictive Biomarker crucial for maximizing the clinical outcome of a therapy for specific individuals, and Surrogate Endpoint fundamental for accelerating the approval of a therapy. However, these data of ThMARs has not been comprehensively described by any of the existing databases. Herein, a database, named 'TheMarker', was therefore constructed to (a) systematically offer all five types of ThMAR used at different stages of drug development, (b) comprehensively describe ThMAR information for the largest number of drugs among available databases, (c) extensively cover the widest disease classes by not just focusing on anticancer therapies. These data in TheMarker are expected to have great implication and significant impact on drug discovery and clinical practice, and it is freely accessible without any login requirement at: https://idrblab.org/themarker.

TTD: Therapeutic Target Database describing target druggability information.

Target discovery is one of the essential steps in modern drug development, and the identification of promising targets is fundamental for developing first-in-class drug. A variety of methods have emerged for target assessment based on druggability analysis, which refers to the likelihood of a target being effectively modulated by drug-like agents. In the therapeutic target database (TTD), nine categories of established druggability characteristics were thus collected for 426 successful, 1014 clinical trial, 212 preclinical/patented, and 1479 literature-reported targets via systematic review. These characteristic categories were classified into three distinct perspectives: molecular interaction/regulation, human system profile and cell-based expression variation. With the rapid progression of technology and concerted effort in drug discovery, TTD and other databases were highly expected to facilitate the explorations of druggability characteristics for the discovery and validation of innovative drug target. TTD is now freely accessible at: https://idrblab.org/ttd/.

Clinical Timing-Sequence Warning Models for Serious Bacterial Infections in Adults Based on Machine Learning: Retrospective Study.

BACKGROUND: Serious bacterial infections (SBIs) are linked to unplanned hospital admissions and a high mortality rate. The early identification of SBIs is crucial in clinical practice. OBJECTIVE: This study aims to establish and validate clinically applicable models designed to identify SBIs in patients with infective fever. METHODS: Clinical data from 945 patients with infective fever, encompassing demographic and laboratory indicators, were retrospectively collected from a 2200-bed teaching hospital between January 2013 and December 2020. The data were randomly divided into training and test sets at a ratio of 7:3. Various machine learning (ML) algorithms, including Boruta, Lasso (least absolute shrinkage and selection operator), and recursive feature elimination, were utilized for feature filtering. The selected features were subsequently used to construct models predicting SBIs using logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost) with 5-fold cross-validation. Performance metrics, including the receiver operating characteristic (ROC) curve and area under the ROC curve (AUC), accuracy, sensitivity, and other relevant parameters, were used to assess model performance. Considering both model performance and clinical needs, 2 clinical timing-sequence warning models were ultimately confirmed using LR analysis. The corresponding predictive nomograms were then plotted for clinical use. Moreover, a physician, blinded to the study, collected additional data from the same center involving 164 patients during 2021. The nomograms developed in the study were then applied in clinical practice to further validate their clinical utility. RESULTS: In total, 69.9% (661/945) of the patients developed SBIs. Age, hemoglobin, neutrophil-to-lymphocyte ratio, fibrinogen, and C-reactive protein levels were identified as important features by at least two ML algorithms. Considering the collection sequence of these indicators and clinical demands, 2 timing-sequence models predicting the SBI risk were constructed accordingly: the early admission model (model 1) and the model within 24 hours of admission (model 2). LR demonstrated better stability than RF and XGBoost in both models and performed the best in model 2, with an AUC, accuracy, and sensitivity of 0.780 (95% CI 0.720-841), 0.754 (95% CI 0.698-804), and 0.776 (95% CI 0.711-832), respectively. XGBoost had an advantage over LR in AUC (0.708, 95% CI 0.641-775 vs 0.686, 95% CI 0.617-754), while RF achieved better accuracy (0.729, 95% CI 0.673-780) and sensitivity (0.790, 95% CI 0.728-844) than the other 2 approaches in model 1. Two SBI-risk prediction nomograms were developed for clinical use based on LR, and they exhibited good performance with an accuracy of 0.707 and 0.750 and a sensitivity of 0.729 and 0.927 in clinical application. CONCLUSIONS: The clinical timing-sequence warning models demonstrated efficacy in predicting SBIs in patients suspected of having infective fever and in clinical application, suggesting good potential in clinical decision-making. Nevertheless, additional prospective and multicenter studies are necessary to further confirm their clinical utility.

Effect of Food on the Pharmacokinetics of Tenofovir Amibufenamide: A Phase I, Randomized, Open-Label, Two-Period Crossover Trial in Healthy Adult Subjects.

Purpose: Tenofovir amibufenamide (TMF) is a novel nucleotide reverse transcriptase inhibitor. The aim of this study was to investigate the effect of food on the single-dose pharmacokinetic properties of TMF. Patients and Methods: In this open-label, randomized, crossover study, after an overnight fast, eligible subjects received a single 25 mg dose of TMF tablet, either under fasted conditions or following consumption of a high-fat, high-calorie meal, followed by a two-week washout period. Blood samples were collected until 144 h after administration. TMF and its metabolite, tenofovir (TFV), were analyzed using validated liquid chromatography-tandem mass spectrometry methods. The geometric mean ratio (GMR) and the corresponding 90% confidence interval (CI) values of AUC0-t, AUC0-∞, and Cmax were acquired for analysis. The absence of an effect of food was indicated if the 90% CI values were within the predefined equivalence limits of 80%-125%. Safety and tolerability were also assessed. Results: For TMF, adjusted GMR (90% CI) values for the fed versus fasted states were 150.28% (125.36%-180.16%), 158.24% (130.42%-192.00%), and 57.65% (45.68%-72.76%) for AUC0-t, AUC0-∞, and Cmax, respectively. For TFV, the GMR (90% CI) of Cmax was 82.00% (74.30%-90.49%) after administration under fed conditions, slightly outside the bioequivalence boundary of 80%-125%, while the corresponding values for AUC0-t and AUC0-∞ were within range. The absorption of TMF was delayed by food, with median Tmax values of 0.33 and 1.00 h in fasted and fed conditions, respectively. The adverse events observed in subjects were all mild. Conclusion: Our results demonstrated that TMF tablets were well-tolerated in healthy volunteers. When TMF tablets were taken with food, Tmax was delayed and exposures of TMF and TFV were higher than under fasted conditions. The modest changes observed are not considered clinically relevant, so TMF can be taken with or without food.

Molecular subtypes and scoring tools related to Foxo signaling pathway for assessing hepatocellular carcinoma prognosis and treatment responsiveness.

Background: Transcription factors in Foxo signaling pathway influence hepatocellular carcinoma metastasis through epithelial mesenchymal transition-related pathways. Prognostic factors in the Foxo signaling pathway are feasible for HCC prognosis and therapeutic management. Methods: Based on the differentially expressed genes and Foxo signaling pathway genes in HCC, the ConsensusClusterPlus package was conducted to identify Foxo signaling pathway-related molecular subtypes in HCC. Based on the DEGs in the FMSs, the optimal prognostic factors in HCC were screened by cox and least absolute shrinkage and selection operator (LASSO) cox analysis to form the Foxo prognosis score (FPS). The prognostic predictive effectiveness of FPS was assessed by Kaplan Meier (K-M) analysis and Receiver Operating Characteristic (ROC) analysis. Additionally, tumor microenvironment (TME) score, tumor mutation burden (TMB) and treatment sensitivity differences in FMSs and FPS groups were also evaluated. Results: There were low, medium and high Foxo signaling pathway activity molecular subtypes in HCC named FMS 1, FMS 2 and FMS 3, respectively. FMS 1 with lowest Foxo signaling pathway activity presented an excellent survival advantage, while FMS 3 with highest Foxo signaling pathway activity exhibited an inhibitory TME status. According to FPS grouping, low FPS exhibited favorable survival, low TMB and anti-tumor activity. Patients in the low FPS group were mostly in the early stage of cancer. Moreover, we found that patients with high and low FPS exhibited different sensitivity to chemotherapy, and patients with low FPS were more sensitive to immunotherapy. Conclusion: We revealed a novel molecular subtype and prognostic tool based on Foxo signaling pathway signature, which could potentially provide a direction for accurate and effective assessment of potential personalized treatment options and prognostic management for HCC patients.

Integrating Medical Domain Knowledge for Early Diagnosis of Fever of Unknown Origin: An Interpretable Hierarchical Multimodal Neural Network Approach.

Accurate and interpretable differential diagnostic technologies are crucial for supporting clinicians in decision-making and treatment-planning for patients with fever of unknown origin (FUO). Existing solutions commonly address the diagnosis of FUO by transforming it into a multi-classification task. However, after the emergence of COVID-19 pandemic, clinicians have recognized the heightened significance of early diagnosis in patients with FUO, particularly for practical needs such as early triage. This has resulted in increased demands for identifying a wider range of etiologies, shorter observation windows, and better model interpretability. In this article, we propose an interpretable hierarchical multimodal neural network framework (iHMNNF) to facilitate early diagnosis of FUO by incorporating medical domain knowledge and leveraging multimodal clinical data. The iHMNNF comprises a top-down hierarchical reasoning framework (Td-HRF) built on the class hierarchy of FUO etiologies, five local attention-based multimodal neural networks (La-MNNs) trained for each parent node of the class hierarchy, and an interpretable module based on layer-wise relevance propagation (LRP) and attention mechanism. Experimental datasets were collected from electronic health records (EHRs) at a large-scale tertiary grade-A hospital in China, comprising 34,051 hospital admissions of 30,794 FUO patients from January 2011 to October 2020. Our proposed La-MNNs achieved area under the receiver operating characteristic curve (AUROC) values ranging from 0.7809 to 0.9035 across all five decomposed tasks, surpassing competing machine learning (ML) and single-modality deep learning (DL) methods while also providing enhanced interpretability. Furthermore, we explored the feasibility of identifying FUO etiologies using only the first N-hour time series data obtained after admission.

RNAenrich: a web server for non-coding RNA enrichment.

MOTIVATION: With the rapid advances of RNA sequencing and microarray technologies in non-coding RNA (ncRNA) research, functional tools that perform enrichment analysis for ncRNAs are needed. On the one hand, because of the rapidly growing interest in circRNAs, snoRNAs, and piRNAs, it is essential to develop tools for enrichment analysis for these newly emerged ncRNAs. On the other hand, due to the key role of ncRNAs' interacting target in the determination of their function, the interactions between ncRNA and its corresponding target should be fully considered in functional enrichment. Based on the ncRNA-mRNA/protein-function strategy, some tools have been developed to functionally analyze a single type of ncRNA (the majority focuses on miRNA); in addition, some tools adopt predicted target data and lead to only low-confidence results. RESULTS: Herein, an online tool named RNAenrich was developed to enable the comprehensive and accurate enrichment analysis of ncRNAs. It is unique in (i) realizing the enrichment analysis for various RNA types in humans and mice, such as miRNA, lncRNA, circRNA, snoRNA, piRNA, and mRNA; (ii) extending the analysis by introducing millions of experimentally validated data of RNA-target interactions as a built-in database; and (iii) providing a comprehensive interacting network among various ncRNAs and targets to facilitate the mechanistic study of ncRNA function. Importantly, RNAenrich led to a more comprehensive and accurate enrichment analysis in a COVID-19-related miRNA case, which was largely attributed to its coverage of comprehensive ncRNA-target interactions. AVAILABILITY AND IMPLEMENTATION: RNAenrich is now freely accessible at https://idrblab.org/rnaenr/.

SISPRO: Signature Identification for Spatial Proteomics.

Spatial proteomics aims for a global description of organelle-specific protein distribution and dynamics, which is essential for understanding the molecular functions and cellular processes in health and disease. However, the application of this technique is seriously restricted by the neglect of robustness among proteomic signatures identified using standard statistical frameworks. Moreover, it is still a major bottleneck to automatically interpretate the identified proteomic signatures due to lack of integration of subcellular information. Herein, an online-tool SISPRO was constructed to (a) identify proteomic signatures with good robustness and accuracy via collectively evaluating relative weighted consistency (CWrel) & area under the curve (AUC) and (b) interpretate the identified signature based on comprehensive subcellular information from 9 organelles and 22 subcellular structures. All in all, SISPRO provides the endeavor to realize the simultaneous improvement of robustness and accuracy in signature identification and the unique capacity in biological annotation lies in its wide coverage of proteins and comprehensive spatial information. SISPRO is expected to be critical in spatial proteomic studies, which can be freely accessed without any login requirement at https://idrblab.org/sispro/.

Serum Ribonucleotide Reductase Subunit M2 in Patients with Chronic Liver Diseases and Hepatocellular Carcinoma.

BACKGROUND: Ribonucleotide reductase subunit M2 (RRM2) plays a key role in cell and hepatitis B virus (HBV) replication. Nevertheless, its clinical implications for managing liver diseases have been inadequately studied. METHODS: A total of 412 participants were enrolled, including 60 healthy control individuals, 55 patients with chronic hepatitis B (CHB), 173 patients with cirrhosis, and 124 patients with hepatocellular carcinoma (HCC). Serum RRM2 was measured via ELISA. RESULTS: The level of serum RRM2 in patients with CHB, cirrhosis, and HCC was higher than that in healthy controls (P < .05). A large difference in serum RRM2 was found between HBV-related and non-HBV-related patients in the cirrhosis group (P < .001), compared with the difference between HBV-related HCC and non-HBV-related HCC (P = .86). In the HBV-related cirrhosis group, the serum RRM2 level showed significant positive correlations with HBV DNA, hepatitis B surface antigen, hepatitis B e antigen, Child-Pugh scores, and MELD scores and played a strong role in diagnosing HBV-related cirrhosis in CHB, compared with fibrosis-4 score and aspartate aminotransferase-to-platelet ratio index. CONCLUSIONS: Serum RRM2 is a reliable biomarker for accurate HBV-related cirrhosis diagnosis and evaluation. Also, serum RRM2 could reflect the expression state of HBV replication in patients with HBV-related cirrhosis.

Enterococcus faecalis promotes the progression of colorectal cancer via its metabolite: biliverdin.

BACKGROUND: Enterococcus faecalis (Efa) has been shown to be a "driver bacteria" in the occurrence and development of colorectal cancer (CRC). This study aims to explore the effect of specific metabolites of Efa on CRC. METHODS: The pro-tumor effects of Efa were assessed in colonic epithelial cells. The tumor-stimulating molecule produced by Efa was identified using liquid chromatography mass spectrometry (LC-MS). The proliferative effect of metabolites on CRC cells in vitro was assayed as well. The concentration of vascular endothelial growth factor A (VEGFA) and interleukin-8 (IL-8) was determined using enzyme-linked immunosorbent assay (ELISA). Tubular formation assay of human umbilical vein endothelial cells (HUVEC) and cell migration assay were applied to study angiogenesis. Additionally, western blot analysis was used to investigate key regulatory proteins involved in the angiogenesis pathway. Tumor growth was assessed using mouse models with two CRC cells and human colon cancer organoid. RESULTS: Co-incubation with the conditioned medium of Efa increased the proliferation of cultured CRC cells. Biliverdin (BV) was determined as the key metabolite produced by Efa using LC-MS screening. BV promoted colony formation and cell proliferation and inhibited cell cycle arrest of cultured CRC cells. BV significantly increased the expression level of IL-8 and VEGFA by regulating the PI3K/AKT/mTOR signaling pathway, leading to the acceleration of angiogenesis in CRC. The up-regulation of proliferation and angiogenesis by BV were also confirmed in mice. CONCLUSION: In conclusion, BV, as the tumor-stimulating metabolite of Efa, generates proliferative and angiogenic effects on CRC, which is mainly mediated by the activation of PI3K/AKT/mTOR.

MecDDI: Clarified Drug-Drug Interaction Mechanism Facilitating Rational Drug Use and Potential Drug-Drug Interaction Prediction.

Drug-drug interactions (DDIs) are a major concern in clinical practice and have been recognized as one of the key threats to public health. To address such a critical threat, many studies have been conducted to clarify the mechanism underlying each DDI, based on which alternative therapeutic strategies are successfully proposed. Moreover, artificial intelligence-based models for predicting DDIs, especially multilabel classification models, are highly dependent on a reliable DDI data set with clear mechanistic information. These successes highlight the imminent necessity to have a platform providing mechanistic clarifications for a large number of existing DDIs. However, no such platform is available yet. In this study, a platform entitled "MecDDI" was therefore introduced to systematically clarify the mechanisms underlying the existing DDIs. This platform is unique in (a) clarifying the mechanisms underlying over 1,78,000 DDIs by explicit descriptions and graphic illustrations and (b) providing a systematic classification for all collected DDIs based on the clarified mechanisms. Due to the long-lasting threats of DDIs to public health, MecDDI could offer medical scientists a clear clarification of DDI mechanisms, support healthcare professionals to identify alternative therapeutics, and prepare data for algorithm scientists to predict new DDIs. MecDDI is now expected as an indispensable complement to the available pharmaceutical platforms and is freely accessible at: https://idrblab.org/mecddi/.

Longitudinal Relationship between Bullying Victimization and Non-Suicidal Self-Injury among Chinese Adolescents: The Buffering Roles of Gratitude and Parental Autonomy Support.

Drawing on the resilience-oriented socioecological framework, the current study contributes to scarce scholarship by exploring intrapersonal (i.e., gratitude) and interpersonal (i.e., parental autonomy support) factors in the longitudinal association between bullying victimization and adolescent non-suicidal self-injury (NSSI). Participants were 238 Chinese adolescents (Mage at Time 1 (T1) = 13.45 years; 106 girls and 132 boys) based on a two-wave prospective design with data spanning one year. At T1, adolescents self-rated all study variables, and at Time 2 (T2), youth again reported their NSSI. The results showed a significant main effect (b = 0.12, SE = 0.05, p = 0.04), indicating that bullying victimization was positively related to T2 NSSI one year later, even controlling for T1 NSSI. Moderation analyses further indicated that parental autonomy support buffered against the positive association between bullying victimization and T2 NSSI, but only when adolescents experienced lower levels of gratitude. Specifically, for adolescents with lower levels of gratitude, high levels of parental autonomy support, in a compensatory way, prevented adolescents from NSSI after victimization occurred (b = -0.03, SE = 0.09, p = 0.78); by contrast, for those with higher levels of gratitude, bullying victimization was not significantly related to T2 NSSI, regardless of the levels of parental autonomy support (b = 0.07, SE = 0.04, p = 0.59 for higher parental autonomy support; b = 0.01, SE = 0.07, p = 0.93 for lower parental autonomy support). These findings suggest that gratitude and parental autonomy support, manifesting in a compensatory interaction pattern, could serve as targeted agents for breaking the vicious linkage between bullying victimization and NSSI.

DRESIS: the first comprehensive landscape of drug resistance information.

Widespread drug resistance has become the key issue in global healthcare. Extensive efforts have been made to reveal not only diverse diseases experiencing drug resistance, but also the six distinct types of molecular mechanisms underlying this resistance. A database that describes a comprehensive list of diseases with drug resistance (not just cancers/infections) and all types of resistance mechanisms is now urgently needed. However, no such database has been available to date. In this study, a comprehensive database describing drug resistance information named 'DRESIS' was therefore developed. It was introduced to (i) systematically provide, for the first time, all existing types of molecular mechanisms underlying drug resistance, (ii) extensively cover the widest range of diseases among all existing databases and (iii) explicitly describe the clinically/experimentally verified resistance data for the largest number of drugs. Since drug resistance has become an ever-increasing clinical issue, DRESIS is expected to have great implications for future new drug discovery and clinical treatment optimization. It is now publicly accessible without any login requirement at: https://idrblab.org/dresis/.

CovInter: interaction data between coronavirus RNAs and host proteins.

Coronavirus has brought about three massive outbreaks in the past two decades. Each step of its life cycle invariably depends on the interactions among virus and host molecules. The interaction between virus RNA and host protein (IVRHP) is unique compared to other virus-host molecular interactions and represents not only an attempt by viruses to promote their translation/replication, but also the host's endeavor to combat viral pathogenicity. In other words, there is an urgent need to develop a database for providing such IVRHP data. In this study, a new database was therefore constructed to describe the interactions between coronavirus RNAs and host proteins (CovInter). This database is unique in (a) unambiguously characterizing the interactions between virus RNA and host protein, (b) comprehensively providing experimentally validated biological function for hundreds of host proteins key in viral infection and (c) systematically quantifying the differential expression patterns (before and after infection) of these key proteins. Given the devastating and persistent threat of coronaviruses, CovInter is highly expected to fill the gap in the whole process of the 'molecular arms race' between viruses and their hosts, which will then aid in the discovery of new antiviral therapies. It's now free and publicly accessible at: https://idrblab.org/covinter/.

M6AREG: m6A-centered regulation of disease development and drug response.

As the most prevalent internal modification in eukaryotic RNAs, N6-methyladenosine (m6A) has been discovered to play an essential role in cellular proliferation, metabolic homeostasis, embryonic development, etc. With the rapid accumulation of research interest in m6A, its crucial roles in the regulations of disease development and drug response are gaining more and more attention. Thus, a database offering such valuable data on m6A-centered regulation is greatly needed; however, no such database is as yet available. Herein, a new database named 'M6AREG' is developed to (i) systematically cover, for the first time, data on the effects of m6A-centered regulation on both disease development and drug response, (ii) explicitly describe the molecular mechanism underlying each type of regulation and (iii) fully reference the collected data by cross-linking to existing databases. Since the accumulated data are valuable for researchers in diverse disciplines (such as pathology and pathophysiology, clinical laboratory diagnostics, medicinal biochemistry and drug design), M6AREG is expected to have many implications for the future conduct of m6A-based regulation studies. It is currently accessible by all users at: https://idrblab.org/m6areg/.

Diagnostic accuracy of metagenomic next-generation sequencing in diagnosing infectious diseases: a meta-analysis.

Many common pathogens are difficult or impossible to detect using conventional microbiological tests. However, the rapid and untargeted nature of metagenomic next-generation sequencing (mNGS) appears to be a promising alternative. To perform a systematic review and meta-analysis of evidence regarding the diagnostic accuracy of mNGS in patients with infectious diseases. An electronic literature search of Embase, PubMed and Scopus databases was performed. Quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Summary receiver operating characteristics (sROC) and the area under the curve (AUC) were calculated; A random-effects model was used in cases of heterogeneity. A total of 20 papers were eligible for inclusion and synthesis. The sensitivity and specificity of diagnostic mNGS were 75% and 68%, respectively. The AUC from the SROC was 85%, corresponding to excellent performance. mNGS demonstrated satisfactory diagnostic performance for infections and yielded an overall detection rate superior to conventional methods.

Structural and biochemical characteristics of mRNA nanoparticles determine anti-SARS-CoV-2 humoral and cellular immune responses.

The coronavirus disease 2019 (COVID-19) pandemic underlines the urgent need for effective mRNA vaccines. However, current understanding of the immunological outcomes of mRNA vaccines formulated under different nanoplatforms is insufficient. Here, severe acute respiratory syndrome coronavirus 2 receptor binding domain mRNA delivered via lipid nanoparticle (LNP), cationic nanoemulsion (CNE), and cationic liposome (Lipo) was constructed. Results demonstrated that the structural and biochemical characteristics of nanoparticles shaped their tissue dissemination, cellular uptake, and intracellular trafficking, which eventually determined the activation of antiviral humoral and cellular immunity. Specifically, LNP was mainly internalized by myocyte and subsequently circumvented lysosome degradation, giving rise to humoral-biased immune responses. Meanwhile, CNE and Lipo induced cellular-preferred immunity, which was respectively attributed to the better lysosomal escape in dendritic cells and the superior biodistribution in secondary lymphoid organs. Overall, this study may guide the design and clinical use of mRNA vaccines against COVID-19.

A therapeutic DC vaccine with maintained immunological activity exhibits robust anti-tumor efficacy.

Dendritic cells (DCs) vaccines are a major focus of future anti-tumor immunotherapy for their pivotal role in eliciting reactive tumor-specific T-cell responses. Tumor cell-mediated DCs (TC-DC) activation and tumor antigen-mediated DCs (TA-DC) activation are two conventional modes of DC vaccine construction in clinical studies. The former physiologically mimicks the tumor identification and rejection, significantly contributing to DC-based immune recognition and migration towards the complexed tumor microenvironment (TME). However, as immunosuppressive molecules may exist in TME, these TC-DC are generally characterized with aberrant lipid accumulation and inositol-requiring kinase 1α (IRE1α)-X-box binding protein 1 (XBP1) hyperactivation, which is provoked by overwhelming oxidative stress and endoplasmic reticulum (ER) stress, resulting in TC-DC malfunction. Oppositely, without contacting immunosuppressive TME, TA-DC vaccines perform better in T-cell priming and lymph nodes (LNs) homing, but are relatively weak in TME infiltration and identification. Herein, we prepared a KIRA6-loaded α-Tocopherol nanoemulsion (KT-NE), which simultaneously ameliorated oxidative stress and ER stress in the dysfunctional lipid-laden TC-DC. The TC-DC treated by KT-NE could maintain immunological activity, simultaneously, exhibited satisfactory chemotaxis towards LNs and tumor sites in vivo, and effectively suppressed malignant progression by unleashing activated tumor-reactive T cells. This study generated a new DC-vaccine that owned puissant aptitude to identify complicated TME as well as robust immunological activity to boost T-cell initiation, which may provide some insights into the design and application of DC-vaccines for clinical application.

ConSIG: consistent discovery of molecular signature from OMIC data.

The discovery of proper molecular signature from OMIC data is indispensable for determining biological state, physiological condition, disease etiology, and therapeutic response. However, the identified signature is reported to be highly inconsistent, and there is little overlap among the signatures identified from different biological datasets. Such inconsistency raises doubts about the reliability of reported signatures and significantly hampers its biological and clinical applications. Herein, an online tool, ConSIG, was constructed to realize consistent discovery of gene/protein signature from any uploaded transcriptomic/proteomic data. This tool is unique in a) integrating a novel strategy capable of significantly enhancing the consistency of signature discovery, b) determining the optimal signature by collective assessment, and c) confirming the biological relevance by enriching the disease/gene ontology. With the increasingly accumulated concerns about signature consistency and biological relevance, this online tool is expected to be used as an essential complement to other existing tools for OMIC-based signature discovery. ConSIG is freely accessible to all users without login requirement at https://idrblab.org/consig/.