Mobile dietary record apps have been increasingly validated by studies with various study designs. This review aims to evaluate the overall accuracy of dietary record apps in measuring the intake of energy, macro- and micronutrients, and food groups in real-life settings and the designs of validation studies. We systematically searched mobile dietary record validation studies published during the period from 2013 to 2019. We identified 14 studies for the systematic review, of which 11 studies were suitable for meta-analyses on energy intake and 8 studies on macronutrient intake. Mean differences and SDs of nutrient estimations between the app and the reference method from studies were pooled using a random-effects model. All apps underestimated energy intake when compared with their reference methods, with a pooled effect of −202 kcal/d (95% CI: −319, −85 kcal/d); the heterogeneity of studies was 72%. After stratification, studies that used the same food-composition table for both the app and the reference method had a lower level of heterogeneity (0%) and a pooled effect of −57 kcal/d (95% CI: −116, 2 kcal/d). The heterogeneity of studies in the differences in carbohydrate, fat, and protein intake was 54%, 73%, and 80%, with the pooled effect of −18.8 g/d, −12.7 g/d, and −12.2 g/d, respectively, after excluding outliers. The intakes of micronutrients and food groups were statistically nonsignificantly underestimated by the apps in most cases. In conclusion, dietary record apps underestimated food consumption compared with traditional dietary assessment methods. Moreover, varying study designs have been found across studies. Recommended practices for conducting validation studies were formulated including considering biomarkers as the reference, testing in a larger and more representative study population for a longer period, avoiding the learning effect of each method, and comparing food group or food item consumption in addition to comparing energy and nutrient intakes.

Cisplatin has been used effectively in the treatment of hepatocellular carcinoma (HCC). Long noncoding RNAs (lncRNAs) were recently reported to contribute to the pathogenesis and progression of HCC. Their molecular mechanism related to cisplatin treatment remains unclear. The purpose of this study is to identify specific lncRNAs and to clarify their functions in HCC after cisplatin exposure. Reannotation and identification of differentially expressed lncRNAs were performed using the microarray data set GSE38122 in the Gene Expression Omnibus database. Four significantly differentially expressed lncRNAs (RP11-134G8.8, RP11-612B6.2, RP11-363E7.4 and RP1-193H18.2) were identified in HepG2 cells exposed to cisplatin by bioinformatics methods. The upregulated RP11-134G8.8 and RP11-363E7.4 and the downregulated RP1-193H18.2 were confirmed by reverse transcription-quantitative polymerase chain reaction. Furthermore, 57 significant co-expressing genes and their corresponding pathways were annotated and identified. The p53 signaling pathway showed the most significant difference among all pathways. Based on these results, the cell cycle and three key genes, cyclin-dependent kinase inhibitor 1A (CDKN1A, also known as p21), tumor protein p53 inducible protein 3 (TP53I3) and wild-type p53-induced phosphatase 1 (Wip1, also known as PPM1D), were examined. CDKN1A, TP53I3 and PPM1D were all downregulated by RP1-193H18.2 but upregulated by RP11-134G8.8 and RP11-363E7.4. And obvious S phase arrest was induced by cisplatin treatment for 24 h in HepG2 cells. Finally, the immunofluorescence results showed upregulation of TP53I3 and Wip1 and downregulation of p21 at the protein level. The results suggested that the lncRNAs RP11-134G8.8, RP11-363E7.4 and RP1-193H18.2, and their co-expression genes, which annotated into the p53 signaling pathway, could be potential targets for cisplatin treatment.

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