Examining patterns of traditional Chinese medicine use in pediatric oncology: A systematic review, meta-analysis and data-mining study.

BACKGROUND: Traditional Chinese medicine (TCM) is becoming a popular complementary approach in pediatric oncology. However, few or no meta-analyses have focused on clinical studies of the use of TCM in pediatric oncology. OBJECTIVE: We explored the patterns of TCM use and its efficacy in children with cancer, using a systematic review, meta-analysis and data mining study. SEARCH STRATEGY: We conducted a search of five English (Allied and Complementary Medicine Database, Embase, PubMed, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov) and four Chinese databases (Wanfang Data, China National Knowledge Infrastructure, Chinese Biomedical Literature Database, and VIP Chinese Science and Technology Periodicals Database) for clinical studies published before October 2021, using keywords related to "pediatric," "cancer," and "TCM." INCLUSION CRITERIA: We included studies which were randomized controlled trials (RCTs) or observational clinical studies, focused on patients aged < 19 years old who had been diagnosed with cancer, and included at least one group of subjects receiving TCM treatment. DATA EXTRACTION AND ANALYSIS: The methodological quality of RCTs and observational studies was assessed using the six-item Jadad scale and the Effective Public Healthcare Panacea Project Quality Assessment Tool, respectively. Meta-analysis was used to evaluate the efficacy of combining TCM with chemotherapy. Study outcomes included the treatment response rate and occurrence of cancer-related symptoms. Association rule mining (ARM) was used to investigate the associations among medicinal herbs and patient symptoms. RESULTS: The 54 studies included in this analysis were comprised of RCTs (63.0%) and observational studies (37.0%). Most RCTs focused on hematological malignancies (41.2%). The study outcomes included chemotherapy-induced toxicities (76.5%), infection rate (35.3%), and response, survival or relapse rate (23.5%). The methodological quality of most of the RCTs (82.4%) and observational studies (80.0%) was rated as "moderate." In studies of leukemia patients, adding TCM to conventional treatment significantly improved the clinical response rate (odds ratio [OR] = 2.55; 95% confidence interval [CI] = 1.49-4.36), lowered infection rate (OR = 0.23; 95% CI = 0.13-0.40), and reduced nausea and vomiting (OR = 0.13; 95% CI = 0.08-0.23). ARM showed that Radix Astragali, the most commonly used medicinal herb (58.0%), was associated with treating myelosuppression, gastrointestinal complications, and infection. CONCLUSION: There is growing evidence that TCM is an effective adjuvant therapy for children with cancer. We proposed a checklist to improve the quality of TCM trials in pediatric oncology. Future work will examine the use of ARM techniques on real-world data to evaluate the efficacy of medicinal herbs and drug-herb interactions in children receiving TCM as a part of integrated cancer therapy.

Effect of heat treatments on the essential oils of kumquat (Fortunella margarita Swingle).

Kumquats (Fortunella margarita Swingle) cultivated in Taiwan are eaten raw or made into candied fruit or fruit tea. For the experiments described in this paper, essential oils were obtained from kumquat peels or whole fruit by cold pressing, steam distillation or heating in water at 90°C for 15 min followed by steam distillation. The volatile components contained in the essential oils were identified by direct injection (DI) or headspace-solid phase microextraction (HS-SPME) coupled with gas chromatography (GC). A total of 43 compounds were identified, of which 37 were verified by DI/GC and 31 by HS-SPME/GC. Hot water heating increased the yields of essential oils from both peels and whole fruit. The principal constituents of the oils were similar except for the minor compounds, including linalool, terpinen-4-ol and α-terpineol, the levels of which increased after steam distillation. The whole fruit also contained higher levels of terpene alcohols.

Identification of di-isononyl phthalate metabolites for exposure marker discovery using in vitro/in vivo metabolism and signal mining strategy with LC-MS data.

Di-isononyl phthalate esters (DINPs) are endocrine-disrupting chemicals and have replaced di(2-ethylhexyl) phthalate (DEHP) as the major plasticizer for poly(vinyl chloride) (PVC) products in recent years. Exposure marker discovery of DINPs is crucial, because of their high potential for human exposure and toxicity. Here, we propose an alternative approach for tracing signals derived from stable isotope-labeled precursors with varied labeling ratios to efficiently filter probable metabolite signals. The statistical process, which involves a signal mining algorithm with isotope tracing (SMAIT), has effectively filtered 13 probable DINP metabolite signals out of the 8867 peaks in the LC-MS data obtained from incubated stable isotope-labeled precursors with liver enzymes. Seven of the 13 probable metabolite signals were confirmed as DINP structure-related metabolites by preliminary MS/MS analyses. These 7 structure-related metabolite signals were validated as effective DINP exposure markers, using urine samples collected from DINP-administered rats without time-consuming comprehensive structure identification. We propose that the 7 identified possible DINP metabolite signals of m/z 279.1, 293.1, 305.1, 307.1, 321.1, 365.1, and 375.1 are potential markers for DINP exposure and should be investigated further. The integrated approach described here can efficiently, and systematically, filter probable metabolite signals from a complex LC-MS dataset for toxic exposure marker discovery. It is a relatively low-cost/rapid workflow for exposure marker discovery.