Background: The outbreak of coronavirus disease 2019 (COVID-19) that occurred in Wuhan, China, has become a global public health threat. It is necessary to identify indicators that can be used as optimal predictors for clinical outcomes of COVID-19 patients. Methods: The clinical information from 126 patients diagnosed with COVID-19 were collected from Wuhan Fourth Hospital. Specific clinical characteristics, laboratory findings, treatments and clinical outcomes were analyzed from patients hospitalized for treatment from 1 February to 15 March 2020, and subsequently died or were discharged. A random forest (RF) algorithm was used to predict the prognoses of COVID-19 patients and identify the optimal diagnostic predictors for patients’ clinical prognoses. Results: Seven of the 126 patients were excluded for losing endpoints, 103 of the remaining 119 patients were discharged (alive) and 16 died in the hospital. A synthetic minority over-sampling technique (SMOTE) was used to correct the imbalanced distribution of clinical patients. Recursive feature elimination (RFE) was used to select the optimal subset for analysis. Eleven clinical parameters, Myo, CD8, age, LDH, LMR, CD45, Th/Ts, dyspnea, NLR, D-Dimer and CK were chosen with AUC approximately 0.9905. The RF algorithm was built to predict the prognoses of COVID-19 patients based on the best subset, and the area under the ROC curve (AUC) of the test data was 100%. Moreover, two optimal clinical risk predictors, lactate dehydrogenase (LDH) and Myoglobin (Myo), were selected based on the Gini index. The univariable logistic analysis revealed a substantial increase in the risk for in-hospital mortality when Myo was higher than 80 ng/ml (OR = 7.54, 95% CI [3.42–16.63]) and LDH was higher than 500 U/L (OR = 4.90, 95% CI [2.13–11.25]). Conclusion: We applied an RF algorithm to predict the mortality of COVID-19 patients with high accuracy and identified LDH higher than 500 U/L and Myo higher than 80 ng/ml to be potential risk factors for the prognoses of COVID-19 patients in the early stage of the disease.

Metabolic reprogramming, which is considered a hallmark of cancer, can maintain the homeostasis of the tumor environment and promote the proliferation, survival, and metastasis of cancer cells. For instance, increased glucose uptake and high glucose consumption, known as the “Warburg effect,” play an essential part in tumor metabolic reprogramming. In addition, fatty acids are harnessed to satisfy the increased requirement for the phospholipid components of biological membranes and energy. Moreover, the anabolism/catabolism of amino acids, such as glutamine, cystine, and serine, provides nitrogen donors for biosynthesis processes, development of the tumor inflammatory environment, and signal transduction. The ubiquitin–proteasome system (UPS) has been widely reported to be involved in various cellular biological activities. A potential role of UPS in the metabolic regulation of tumor cells has also been reported, but the specific regulatory mechanism has not been elucidated. Here, we review the role of ubiquitination and deubiquitination modification on major metabolic enzymes and important signaling pathways in tumor metabolism to inspire new strategies for the clinical treatment of cancer.

Background: In recent decades, dramatic changes in modern environmental exposures and lifestyles have resulted in a steep rise in the prevalence of allergic diseases such as asthma, allergic rhinitis, atopic dermatitis and food allergies. Evidence is mounting that the microbiota plays a crucial role in allergic disorder development and evolution. Therefore, a better understanding of allergic diseases within the context of the microbiota is urgently needed. This work aimed to comprehensively outline general characteristics, research hotspots, evolution routes, and emerging trends in this area.Methods: Relevant publications from January 2002 to December 2021 were obtained from the Web of Science Core Collection on 5 August 2022. Bibliometric and visual analyses were performed using CiteSpace; VOSviewer; an online bibliometric platform; and Microsoft Excel 2019.Results: In total, 2535 documents met the requirements. The annual number of publications has shown rapid growth in the last two decades. The USA, University of California System, and Isolauri E of the University of Turku were the most productive and influential country, institution, and author, respectively. The Journal of Allergy and Clinical Immunology was the most prolific and most cocited journal. High-frequency keywords included “gut microbiota”, “asthma”, “atopic dermatitis”, “children”, and “probiotics”. Recent studies have focused on “atopic dermatitis”, “skin”, “asthma”, and “probiotics”, according to the cocitation analysis of references. Burst detection analysis of keywords showed that “community”, “skin microbiome”, “microbiome”, “Staphylococcus aureus”, and “chain fatty acid” were emerging research frontiers, which currently have ongoing bursts.Conclusion: In the last 20 years, studies of the microbiota in allergic diseases have been flourishing, and the themes have been increasing in depth. These findings provide valuable references on the current research hotspots and gaps and development trends in the link between the microbiota and allergic diseases.

Background. Deciphering the immune characteristics within tumors and identifying the immune signals related to the prognostic factor are helpful for the treatment and management of tumor patients. However, systematic analysis of immune signatures in head and neck squamous cell carcinoma (HNSCC) remains largely unstudied. Methods. A total of 718 immune‐related genes were extracted from RNA sequencing data from 519 HNSCC patients in the TCGA database, and survival analysis with integrated bioinformatics analyses was performed to build the final predictive prognosis model. Results. The 178 survival‐associated genes (P < 0.05) participated in important immune functions, including immune cell activation and migration. Multivariate regression analysis using 93 genes (P < 0.01), together with survival‐associated clinicopathological parameters, identified 35 independent prognostic factors. The most significant 8 independent factors were CD3E, CD40LG, TNFRSF4, CD3G, CD5, ITGA2B, ABCB1, and TNFRSF13b. The final prognostic model achieved outstanding predictive efficiency with the highest AUC of 0.963. Conclusion. Our prognostic model based on the immune signature could effectively predict the prognosis of HNSCC patients, providing novel predictive biomarkers and potential therapeutic targets for HNSCC patients.