A circadian rhythm-related lncRNA signature correlates with prognosis and tumor immune microenvironment in head and neck squamous cell carcinoma.

OBJECTIVE: To investigate circadian rhythm-associated long non-coding RNA (lncRNA) signatures in predicting prognosis, metabolism, and immune infiltration in Head and Neck Squamous Cell Carcinoma (HNSC). METHODS: HNSC samples were collected from the TCGA database. A signature was constructed using Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) methods. The immune cell infiltration was analyzed using CIBERSORT, ssGSEA, and MCPcounter. The RT-qPCR was used to detect the expression of signature lncRNAs. RESULTS: A signature comprising 8 lncRNAs was constructed. The constructed signature demonstrated good prognostic prediction capability for HNSC. A nomogram encompassing risk score accurately predicted the long-term OS probability of HNSC. The infiltration levels of T cell, B cell and Macrophages were significantly higher in the high-risk group than in the low-risk group. Cluster analysis showed that the signature lncRNAs could classify the HNSC samples into two clusters. The RT-qPCR suggested that the expression of lncRNAs in signature was consistent with the data in TCGA. CONCLUSION: The circadian rhythm-associated lncRNA signature has potential as a prognostic indicator for HNSC. It exhibits associations with metabolism, immune microenvironment, and drug sensitivity, thereby providing valuable insights for informing the treatment of HNSC.

Machine Learning: A Potential Therapeutic Tool to Facilitate Neonatal Therapeutic Decision Making.

Bacterial infection is one of the major causes of neonatal morbidity and mortality worldwide. Finding rapid and reliable methods for early recognition and diagnosis of bacterial infections and early individualization of antibacterial drug administration are essential to eradicate these infections and prevent serious complications. However, this is often difficult to perform due to non-specific clinical presentations, low accuracy of current diagnostic methods, and limited knowledge of neonatal pharmacokinetics. Although neonatal medicine has been relatively late to embrace the benefits of machine learning (ML), there have been some initial applications of ML for the early prediction of neonatal sepsis and individualization of antibiotics. This article provides a brief introduction to ML and discusses the current state of the art in diagnosing and treating neonatal bacterial infections, gaps, potential uses of ML, and future directions to address the limitations of current studies. Neonatal bacterial infections involve a combination of physiologic development, disease expression, and treatment response outcomes. To address this complex relationship, future models could consider appropriate ML algorithms to capture time series features while integrating influences from the host, microbes, and drugs to optimize antimicrobial drug use in neonates. All models require prospective clinical trials to validate their clinical utility before clinical use.

Model-informed drug development in pediatric, pregnancy and geriatric drug development: States of the art and future.

The challenges of drug development in pediatric, pregnant and geriatric populations are a worldwide concern shared by regulatory authorities, pharmaceutical companies, and healthcare professionals. Model-informed drug development (MIDD) can integrate and quantify real-world data of physiology, pharmacology, and disease processes by using modeling and simulation techniques to facilitate decision-making in drug development. In this article, we reviewed current MIDD policy updates, reflected on the integrity of physiological data used for MIDD and the effects of physiological changes on the drug PK, as well as summarized current MIDD strategies and applications, so as to present the state of the art of MIDD in pediatric, pregnant and geriatric populations. Some considerations are put forth for the future improvements of MIDD including refining regulatory considerations, improving the integrity of physiological data, applying the emerging technologies, and exploring the application of MIDD in new therapies like gene therapies for special populations.

Population pharmacokinetics of daptomycin in critically ill patients receiving extracorporeal membrane oxygenation.

BACKGROUND: Daptomycin is widely used in critically ill patients for Gram-positive bacterial infections. Extracorporeal membrane oxygenation (ECMO) is increasingly used in this population and can potentially alter the pharmacokinetic (PK) behaviour of antibiotics. However, the effect of ECMO has not been evaluated in daptomycin. Our study aims to explore the effect of ECMO on daptomycin in critically ill patients through population pharmacokinetic (PopPK) analysis and to determine optimal dosage regimens based on both efficacy and safety considerations. METHODS: A prospective, open-label PK study was carried out in critically ill patients with or without ECMO. The total concentration of daptomycin was determined by UPLC-MS/MS. NONMEM was used for PopPK analysis and Monte Carlo simulations. RESULTS: Two hundred and ninety-three plasma samples were collected from 36 critically ill patients, 24 of whom received ECMO support. A two-compartment model with first-order elimination can best describe the PK of daptomycin. Creatinine clearance (CLCR) significantly affects the clearance of daptomycin while ECMO has no significant effect on the PK parameters. Monte Carlo simulations showed that, when the MICs for bacteria are  ≥1 mg/L, the currently recommended dosage regimen is insufficient for critically ill patients with CLCR > 30 mL/min. Our simulations suggest 10 mg/kg for patients with CLCR between 30 and 90 mL/min, and 12 mg/kg for patients with CLCR higher than 90 mL/min. CONCLUSIONS: This is the first PopPK model of daptomycin in ECMO patients. Optimal dosage regimens considering efficacy, safety, and pathogens were provided for critical patients based on pharmacokinetic-pharmacodynamic analysis.

Physiological Functions of the Cello-Oligosaccharides Binding CebE in the Pathogenic Streptomyces sp. AMCC400023.

Potato common scab, an economically important disease worldwide, is caused by pathogenic Streptomyces strains mainly through the effects of thaxtomin. The cello-oligosaccharides binding protein CebE is proposed as a gateway to the pathogenic development of Streptomyces scabiei. In this study, two functional CebE encoding genes, GEO5601 and GEO7671, were identified in pathogenic Streptomyces sp. AMCC400023. With a higher binding affinity towards signal molecules, the deletion of GEO5601 severely impaired thaxtomin-producing capacity and reduced the strain's pathogenicity. Transcriptional analysis confirmed that CebE5601 is also responsible for the import and provision of carbon sources for cell growth. With lower binding affinity, the pathogenicity island (PAI)-localized CebE7671 may assume a new function of mediating the biological process of sporulation, given the significantly impaired formation of ΔGEO7671 spores. The mechanisms of action of CebE proteins unraveled in Streptomyces sp. AMCC400023 will help pave the way for more effective prevention of the potato common scab disease.

The impact of Lactococcus lactis KUST48 on the transcription profile of Aeromonas hydrophila-infected zebrafish spleen.

Aeromonas hydrophila, an aquatic pathogenic bacterium, has been found to infect many fish species and cause huge aquaculture losses. Antibiotics are the most common drugs used to treat these infections. However, antibiotic abuse can lead to the development of antibiotic resistance. Probiotics have the potential to replace antibiotics for preventing infections. Zebrafish (Danio rerio) is a model organism used to study the innate immune system and host-pathogen interactions. Currently, there is little information on how the fish immune system responds to A. hydrophila and probiotic treatment. To increase the understanding of the molecular mechanisms behind the zebrafish defense against A. hydrophila and provide evidence that antibiotics can be replaced by probiotics, a transcriptome analysis of the zebrafish spleen was conducted 48 hours after infection by A. hydrophila, as well as after treatment using Lactococcus lactis KUST48 4 hours after infection. A total of 36,499 genes were obtained. There were 3,337 genes found to have significant differential expression between treatment and control groups. According to further annotation and enrichment analysis, differentially expressed genes (DEGs) were involved in signal transduction, endocrine system cancer, and the immune system. Insulin resistance disappeared in the zebrafish after treatment. Quantitative real-time PCR was performed to confirm the significant regulation of immune defense DEGs, the results of which were consistent with the RNA-sequencing data. These results could serve as a basis for future studies on the immune response to A. hydrophila and provide suggestions for probiotic alternatives to antibiotics, which will be of great significance to aquaculture and environmental protection.IMPORTANCEIn recent years, the unreasonable use of antibiotics has led to the emergence of drug-resistant pathogenic bacteria, antibiotic residues, cross infection, toxic side effects, and so on, which has caused a serious threat to human food safety and life health. In recent years, many studies have demonstrated the potential of probiotics as a substitute for antibiotics, but there is still a lack of understanding of the molecular mechanisms underlying probiotic therapy. We conduct a research on the impact of Lactococcus lactis KUST48 on the transcription profile of Aeromonas hydrophila-infected zebrafish spleen. Mortality of zebrafish infected with A. hydrophila was significantly reduced after treatment with L. lactis KUST48. Our results can help to strengthen our understanding of the pathogenic mechanisms of zebrafish and provide a valuable reference for the molecular mechanisms of probiotic therapy.

A systematic review of risk prediction model of venous thromboembolism for patients with lung cancer.

BACKGROUND: Venous thromboembolism (VTE) increases the risk of death or adverse outcomes in patients with lung cancer. Therefore, early identification and treatment of high-risk groups of VTE have been the research focus. In this systematic review, the risk assessment tools of VTE in patients with lung cancer were systematically analyzed and evaluated to provide a reference for VTE management. METHODS: Relevant studies were retrieved from major English databases (The Cochrane Library, Embase, Web of Science, PubMed, Scopus, Medline) and Chinese databases (China National Knowledge Infrastructure [CNKI] and WanFang Data) until July 2023 and extracted by two researchers. This systematic review was registered at PROSPERO (no. CRD42023409748). RESULTS: Finally, two prospective cohort studies and four retrospective cohort studies were included from 2019. There was a high risk of bias in all included studies according to the Prediction Model Risk of Bias Assessment tool (PROBAST). In the included studies, Cox and logistic regression were used to construct models. The area under the receiver operating characteristic curve (AUC) of the model ranged from 0.670 to 0.904, and the number of predictors ranged from 4 to 11. The D-dimer index was included in five studies, but significant differences existed in optimal cutoff values from 0.0005 mg/L to 2.06 mg/L. Then, three studies validated the model externally, two studies only validated the model internally, and only one study validated the model using a combination of internal and external validation. CONCLUSION: VTE risk prediction models for patients with lung cancer have received attention for no more than 5 years. The included model shows a good predictive effect and may help identify the risk population of VTE at an early stage. In the future, it is necessary to improve data modeling and statistical analysis methods, develop predictive models with good performance and low risk of bias, and focus on external validation and recalibration of models.

A comprehensive review of Shengdeng in Tibetan medicine: textual research, herbal and botanical distribution, traditional uses, phytochemistry, and pharmacology.

"Shengdeng", a group of Tibetan medicines with diverse biological origins, has long been utilized in Tibet for the treatment of rheumatoid arthritis. It showcases remarkable efficacy in alleviating rheumatism, reducing swelling, and relieving pain. This study aimed to clarify the plant species used as "Shengdeng" and summarize their botanical distribution, traditional uses, phytochemistry, and pharmacology to promote its utilization and development. "Shengdeng" is derived from a remarkable collection of 14 plant species belonging to six distinct families. Extensive phytochemical investigations have led to the identification of 355 chemical constituents within "Shengdeng". Pharmacological studies conducted on "Shengdeng" have revealed a wide range of beneficial properties, including antioxidant, anticancer, antimicrobial, antiviral, antiparasitic, anti-inflammatory, and anti-arthritic activities. Notably, flavonoids and triterpenoids emerge as the predominant groups among these constituents, contributing to the therapeutic potential and diverse applications of "Shengdeng". The present review provides a concise summary of the recent advancements in textual research concerning the herbal and botanical distribution, traditional uses, phytochemistry, and pharmacological activities of "Shengdeng". It is crucial to note that future research on "Shengdeng" should prioritize the analysis of its active ingredients and the establishment of rigorous quality standards. These aspects are essential for ensuring consistency, efficacy, and safety in its clinical application.