Clinical applications of ginger with an expectation of clinical benefits are receiving significant attention. This systematic review aims to provide a comprehensive discussion in terms of the clinical effects of ginger in all reported areas. Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guideline, randomized controlled trials on the effects of ginger were investigated. Accordingly, 109 eligible papers were fully extracted in terms of study design, population characteristics, evaluation systems, adverse effects, and main outcomes. The reporting quality of the included studies was assessed based on the Cochrane Collaboration’s tool for assessing the risk of bias in randomized trials and integrated together with studies that investigated the same subjects. The included studies that examined the improvement of nausea and vomiting in pregnancy, inflammation, metabolic syndromes, digestive function, and colorectal cancer’s markers were consistently supported, whereas other expected functions were relatively controversial. Nevertheless, only 43 clinical trials (39.4%) met the criterion of having a ‘high quality of evidence.’ In addition to the quality assessment result, small populations and unstandardized evaluation systems were the observed shortcomings in ginger clinical trials. Further studies with adequate designs are warranted to validate the reported clinical functions of ginger.

The authenticity determination of white rice is crucial to prevent deceptive origin labeling and dishonest trading. However, a non-destructive and comprehensive method for rapidly discriminating the geographical origins of white rice between countries is still lacking. In the current study, we developed a volatile organic compound based geographical discrimination method using headspace solid-phase microextraction coupled to gas chromatography-mass spectrometry (HS-SPME/GC-MS) to discriminate rice samples from Korea and China. A partial least squares discriminant analysis (PLS-DA) model exhibited a good classification of white rice between Korea and China (accuracy = 0.958, goodness of fit = 0.937, goodness of prediction = 0.831, and permutation test p-value = 0.043). Combining the PLS-DA based feature selection with the differentially expressed features from the unpaired t-test and significance analysis of microarrays, 12 discriminatory biomarkers were found. Among them, hexanal and 1-hexanol have been previously known to be associated with the cultivation environment and storage conditions. Other hydrocarbon biomarkers are novel, and their impact on rice production and storage remains to be elucidated. In conclusion, our findings highlight the ability to rapidly discriminate white rice from Korea and China. The developed method maybe useful for the authenticity and quality control of white rice.

Forkhead box (FOX) proteins are multifaceted transcription factors that are significantly implicated in cancer, with various critical roles in biological processes. Herein, we provide an overview of several key members of the FOXA, FOXC, FOXM1, FOXO and FOXP subfamilies. Important pathophysiological processes of FOX transcription factors at multiple levels in a context-dependent manner are discussed. We also specifically summarize some major aspects of FOX transcription factors in association with cancer research such as drug resistance, tumor growth, genomic alterations or drivers of initiation. Finally, we suggest that targeting FOX proteins may be a potential therapeutic strategy to combat cancer.

Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional “pre-pre-” and “post-post-” analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.

Lipid mediators are crucial for the pathogenesis of rheumatoid arthritis (RA); however, global analyses have not been undertaken to systematically define the lipidome underlying the dynamics of disease evolution, activation, and resolution. Here, we performed untargeted lipidomics analysis of synovial fluid and serum from RA patients at different disease activities and clinical phases (preclinical phase to active phase to sustained remission). We found that the lipidome profile in RA joint fluid was severely perturbed and that this correlated with the extent of inflammation and severity of synovitis on ultrasonography. The serum lipidome profile of active RA, albeit less prominent than the synovial lipidome, was also distinguishable from that of RA in the sustained remission phase and from that of noninflammatory osteoarthritis. Of note, the serum lipidome profile at the preclinical phase of RA closely mimicked that of active RA. Specifically, alterations in a set of lysophosphatidylcholine, phosphatidylcholine, ether-linked phosphatidylethanolamine, and sphingomyelin subclasses correlated with RA activity, reflecting treatment responses to anti-rheumatic drugs when monitored serially. Collectively, these results suggest that analysis of lipidome profiles is useful for identifying biomarker candidates that predict the evolution of preclinical to definitive RA and could facilitate the assessment of disease activity and treatment outcomes.