Background Malnutrition is a common and severe problem in patients with cancer that directly increases the incidence of complications and significantly deteriorates quality of life. Nutritional risk screening and dietary assessment are critical because they are the basis for providing personalized nutritional support. No digital smartphone-based self-administered tool for nutritional risk screening and dietary assessment among hospitalized patients with cancer has been developed and evaluated. Objective This study aims to develop a digital smartphone-based self-administered mini program for nutritional risk screening and dietary assessment for hospitalized patients with cancer and to evaluate the validity of the mini program. Methods We have developed the R+ Dietitian mini program, which consists of 3 parts: (1) collection of basic information of patients, (2) nutritional risk screening, and (3) dietary energy and protein assessment. The face-to-face paper-based Nutritional Risk Screening (NRS-2002), the Patient-Generated Subjective Global Assessment Short Form (PG-SGA-SF), and 3 days of 24-hour dietary recall (3d-24HRs) questionnaires were administered according to standard procedure by 2 trained dietitians as the reference methods. Sensitivity, specificity, positive predictive value, negative predictive value, κ value, and correlation coefficients (CCs) of nutritional risk screened in R+ Dietitian against the reference methods, as well as the difference and CCs of estimated dietary energy and protein intakes between R+ Dietitian and 3d-24HRs were calculated to evaluate the validity of R+ Dietitian. Results A total of 244 hospitalized patients with cancer were recruited to evaluate the validity of R+ Dietitian. The NRS-2002 and PG-SGA-SF tools in R+ Dietitian showed high accuracy, sensitivity, and specificity (77.5%, 81.0%, and 76.7% and 69.3%, 84.5%, and 64.5%, respectively), and fair agreement (κ=0.42 and 0.37, respectively; CC 0.62 and 0.56, respectively) with the NRS-2002 and PG-SGA-SF tools administered by dietitians. The estimated intakes of dietary energy and protein were significantly higher (P<.001 for both) in R+ Dietitian (mean difference of energy intake: 144.2 kcal, SD 454.8; median difference of protein intake: 10.7 g, IQR 9.5-39.8), and showed fair agreement (CC 0.59 and 0.47, respectively), compared with 3d-24HRs performed by dietitians. Conclusions The identified nutritional risk and assessment of dietary intakes of energy and protein in R+ Dietitian displayed a fair agreement with the screening and assessment conducted by dietitians. R+ Dietitian has the potential to be a tool for nutritional risk screening and dietary intake assessment among hospitalized patients with cancer. Trial Registration Chinese Clinical Trial Registry ChiCTR1900026324; https://www.chictr.org.cn/showprojen.aspx?proj=41528

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Food recognition and weight estimation based on image methods have always been hotspots in the field of computer vision and medical nutrition, and have good application prospects in digital nutrition therapy and health detection. With the development of deep learning technology, image-based recognition technology has also rapidly extended to various fields, such as agricultural pests, disease identification, tumor marker recognition, wound severity judgment, road wear recognition, and food safety detection. This article proposes a non-wearable food recognition and weight estimation system (nWFWS) to identify the food type and food weight in the target recognition area via smartphones, so to assist clinical patients and physicians in monitoring diet-related health conditions. In addition, the system is mainly designed for mobile terminals; it can be installed on a mobile phone with an Android system or an iOS system. This can lower the cost and burden of additional wearable health monitoring equipment while also greatly simplifying the automatic estimation of food intake via mobile phone photography and image collection. Based on the system’s ability to accurately identify 1,455 food pictures with an accuracy rate of 89.60%, we used a deep convolutional neural network and visual-inertial system to collect image pixels, and 612 high-resolution food images with different traits after systematic training, to obtain a preliminary relationship model between the area of food pixels and the measured weight was obtained, and the weight of untested food images was successfully determined. There was a high correlation between the predicted and actual values. In a word, this system is feasible and relatively accurate for one automated dietary monitoring and nutritional assessment.

With the increasing demand and quality requirement for the natural nutritious food in modern society, okra has attracted much attention because of its high nutritional value and remarkable functionality. However, the occurrence of postharvest diseases of fresh okra severely limited the application and the value of okra. Therefore, in this study, the dominant pathogens causing postharvest diseases such as soft rot were isolated from naturally decaying okra. It was identified as Mucor circinelloides by its morphological characteristics and standard internal transcribed spacer ribosomal DNA sequence. Furthermore, the biological characteristics of M. circinelloides were studied, and the inhibitory effect of thymol/KGM/LG (TKL) edible coating solution on M. circinelloides and its possible mechanism was discussed. In addition, TKL edible coating solution had a dose-dependent inhibitory effect on M. circinelloides, with a 50% inhibitory concentration (EC50) of 113.55 mg/L. The TKL edible coating solution at 960 mg/L of thymol completely inhibited mycelial growth and spore germination of M. circinelloides. The results showed that the best carbon source of M. circinelloides was maltose, the best nitrogen source was beef extract and potassium nitrate, the best pH was 6, the best temperature was 28°C, the best NaCl concentration was 0.5%, and the light was conducive to the growth of M. circinelloides. It was also observed by scanning electron microscope (SEM) that TKL was more likely to destroy the cell wall integrity of M. circinelloides, inhibit spore morphology and change mycelium structure. Meanwhile, the activity of chitinase (CHI), an enzyme related to cell wall synthesis of M. circinelloides, was significantly decreased after being treated by TKL with thymol at 100 mg/L (TKL100). The content of Malondialdehyde (MDA) in M. circinelloides decreased significantly from 12 h to 48 h, which may cause oxidative damage to the cell membrane. The activity polygalacturonase (PG), pectin methylgalacturonase (PMG), and cellulase (Cx) of M. circinelloides decreased significantly. Therefore, the results showed that TKL had a good bacteriostatic effect on okra soft rot pathogen, and the main bacteriostatic mechanism might be the damage of cell membrane, degradation of the cell wall, inhibition of metabolic activities, and reduction of metabolites, which is helpful to further understand the inhibitory effect of TKL on okra soft rot pathogen and its mechanism.

Gefitinib, an epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI),is the currently recommended first-line therapy for advanced EGFR-mutant lung cancer, and understanding the mechanism of resistance is the key to formulating therapeutic strategies for EGFR-TKIs. In this study, we evaluate the expression patterns and potential biological functions of the pseudogene DUXAP10 in gefitinib resistance. We find that pseudogene DUXAP10 expression is significantly upregulated in NSCLC gefitinib-resistant cells and tissues. Gain and loss of function assays reveal that knockdown of DUXAP10 by siRNA reverses gefitinib resistance both in vitro and in vivo . Furthermore, DUXAP10 interacts with the histone methyltransferase enhancer of zeste homolog 2 (EZH2) to repress the expression of 2′,5′-oligoadenylate synthetase (OAS2). Overall, our study highlights the pivotal role of DUXAP10 in gefitinib resistance, and the DUXAP10/EZH2/OAS2 axis might be a promising therapeutic target to overcome acquired gefitinib resistance in NSCLC.