Association between nutritional status assessed by a digital self-administered tool (R+ dietitian) and clinicopathologic factors in cancer patients: A comprehensive analysis.

Objective: Malnutrition is prevalent among cancer patients, smartphone-based self-administered nutritional assessment tools offer a promising solution for effective nutritional screening. This study aims to retrospectively analyze the relationships between nutritional status evaluated by the digital tool (R+ Dietitian) and clinicopathologic factors of cancer patients. Methods: Cancer patients who met the inclusion criteria were divided into two subgroups based on age, Nutritional Risk Screening-2002, Patient-Generated Subjective Global Assessment Short Form, body mass index, and hospital stays for comparison. Correlation and regression analysis were used to comprehensively assess the relationship between nutritional status and clinicopathologic factors. Findings: A total of 535 hospitalized cancer patients (58.32 ± 11.24 years old) were recruited. Patients identified with nutritional risk assessed by R+ Dietitian were significantly older, had lower body weight, lower body mass index, greater weight loss, and longer hospital stays (all of above, P < 0.01). Multiple logistic regression analysis indicated that serum prealbumin concentration (odds ratio: 0.992, 95% confidence interval: 0.987-0.997, P = 0.001), weight loss (odds ratio: 7.309, 95% confidence interval: 4.026-13.270, P < 0.001), and body mass index < 18.5 (odds ratio: 5.882, 95% confidence interval: 2.695-12.821, P < 0.001) predicted nutritional risk indicated by Nutritional Risk Screening-2002 score ≥3. Hemoglobin concentration (odds ratio: 0.983, 95% confidence interval: 0.970-0.996, P = 0.011), weight (odds ratio: 1.111, 95% confidence interval: 1.056-1.169, P < 0.001), weight loss (odds ratio: 7.502, 95% confidence interval: 4.394-12.810, P < 0.001), body mass index (odds ratio: 0.661, 95% confidence interval: 0.564-0.775, P < 0.001), and energy intake (odds ratio: 0.996, 95% confidence interval: 0.995-0.997, P < 0.001) predicted nutritional risk indicated by Patient-Generated Subjective Global Assessment Short Form score ≥4. Multiple linear regression analysis revealed that Patient-Generated Subjective Global Assessment Short Form scores ≥3 (b = 2.032, P = 0.008) were significantly associated with longer hospital stays. Conclusions: The nutritional risks assessed by R+ Dietitian accurately reflected the characteristics of malnutrition in cancer patients and predicted hospital stay and cost, indicating the applicability of R+ Dietitian to improving the efficiency of nutritional management for cancer patients.

Development and validation of a point-of-care nursing mobile tool to guide the diagnosis of malnutrition in hospitalized adult patients: a multicenter, prospective cohort study.

Malnutrition is a prevalent and severe issue in hospitalized patients with chronic diseases. However, malnutrition screening is often overlooked or inaccurate due to lack of awareness and experience among health care providers. This study aimed to develop and validate a novel digital smartphone-based self-administered tool that uses facial features, especially the ocular area, as indicators of malnutrition in inpatient patients with chronic diseases. Facial photographs and malnutrition screening scales were collected from 619 patients in four different hospitals. A machine learning model based on back propagation neural network was trained, validated, and tested using these data. The model showed a significant correlation (p < 0.05) and a high accuracy (area under the curve 0.834-0.927) in different patient groups. The point-of-care mobile tool can be used to screen malnutrition with good accuracy and accessibility, showing its potential for screening malnutrition in patients with chronic diseases.

Protein Supplements with Short Peptides Are Better than Complex Protein-Based Supplements on Improving Early Fat-Free Mass Loss Following Bariatric Surgery: A Retrospective Cohort Study.

INTRODUCTION: Bariatric surgery (BS) patients are advised to consume protein supplements to prevent fat-free mass (FFM) loss. However, limited research has explored the efficacy of diverse protein presentations on FFM preservation. This study assesses if short peptide-based (SPB) supplements surpass complex protein-based (CPB) supplements in reducing early FFM loss post-surgery. METHODS: In this retrospective cohort study, 138 patients who underwent BS other than Roux-en-Y-gastric bypass (RYGB) between January 2021 and March 2021 at the Department of Bariatric Surgery of the Third People's Hospital of Chengdu were included for analysis. Patients were divided into two groups based on their consumption of protein supplements after surgery: SPB group and CPB group. Multiple linear regressions separated by sex were employed to examine the associations between SPB supplements and FFM loss and percentage of FFM (%FFM) loss, respectively. RESULTS: Among participants, 69.6% were female, with a mean age of 33.3 years. In multiple linear regression analyses, SPB supplements were significantly and positively associated with a lower FFM loss in both female (ꞵ = - 1.14, P = 0.047) and male (ꞵ = - 2.36, P = 0.024), and were positively associated with a lower %FFM loss in both female (ꞵ = - 1.83) and male (ꞵ = - 2.26) but only significant in male (P = 0.049). CONCLUSION: SPB supplements may be more effective in preventing early FFM loss after BS, compared to CPB supplements, particularly among male patients. Therefore, SPB supplements may be recommended to patients undergoing BS. Further research is needed to validate these findings.

Novel Biomimetic "Spider Web" Robust, Super-Contractile Liquid Crystal Elastomer Active Yarn Soft Actuator.

In nature, spider web is an interwoven network with high stability and elasticity from silk threads secreted by spider. Inspired by the structure of spider webs, light-driven liquid crystal elastomer (LCE) active yarn is designed with super-contractile and robust weavability. Herein, a novel biomimetic gold nanorods (AuNRs) @LCE yarn soft actuator with hierarchical structure is fabricated by a facile electrospinning and subsequent photocrosslinking strategies. Meanwhile, the inherent mechanism and actuation performances of the as-prepared yarn actuator with interleaving network are systematically analyzed. Results demonstrate that thanks to the unique "like-spider webs" structure between fibers, high molecular orientation within the LCE microfibers and good flexibility, they can generate super actuation strain (≈81%) and stable actuation performances. Importantly, benefit from the robust covalent bonding at the organic-inorganic interface, photopolymerizable AuNRs molecules are uniformly introduced into the polymer backbone of electrospun LCE yarn to achieve tailorable shape-morphing under different light intensity stimulation. As a proof-of-concept illustration, light-driven artificial muscles, micro swimmers, and hemostatic bandages are successfully constructed. The research disclosed herein can offer new insights into continuous production and development of LCE-derived yarn actuator that are of paramount significance for many applications from smart fabrics to flexible wearable devices.

Flexible Self-Supporting MOF-Based Bean Pod Cube Hollow Nanofibers for Ultralong Cycling and High Rate Na Storage.

Sodium-ion batteries (SIBs) have garnered significant attention due to their potential as an emerging energy storage solution. Tin sulfide (SnS) has emerged as a promising anode material for SIBs due to its impressive theoretical specific capacity of 1022 mA h g-1 and excellent electrical conductivity. However, its practical application has been hindered by issues such as large volume expansion, which adversely affects cycling stability and rate performance during the charge/discharge processes. In this study, a novel approach to address these issues by synthesizing the bean pod cube hollow metal-organic framework (MOF)-SnSx/NC@N-doped carbon nanofibers through a process involving electrospinning, PDA coating, and calcination. The Sn-MOF serves as a self-sacrificing template, facilitating the simultaneous dissociation of MOF and polymerization of dopamine, leading to the creation of hollow intermediates that retain tin components. Subsequent sulfidation results in the integration of the hollow MOF-SnSx/NC nanoparticles within 3D nitrogen-doped carbon nanofibers, forming the distinctive bean pod cube composite structure. This unique configuration effectively shortens the diffusion path and mitigates volume expansion for sodium ions, ultimately yielding an exceptional high rate performance of 130 mA h g-1 (10 A g-1) and an ultralong cycling performance of 328 mA h g-1 even after 3500 cycles (2 A g-1) as the anode for SIBs.

Establishing a machine learning model for predicting nutritional risk through facial feature recognition.

Background: Malnutrition affects many worldwide, necessitating accurate and timely nutritional risk assessment. This study aims to develop and validate a machine learning model using facial feature recognition for predicting nutritional risk. This innovative approach seeks to offer a non-invasive, efficient method for early identification and intervention, ultimately improving health outcomes. Methods: We gathered medical examination data and facial images from 949 patients across multiple hospitals to predict nutritional status. In this multicenter investigation, facial images underwent preprocessing via face alignment and cropping. Orbital fat pads were isolated using the U-net model, with the histogram of oriented gradient (HOG) method employed for feature extraction. Standardized HOG features were subjected to principal component analysis (PCA) for dimensionality reduction. A support vector machine (SVM) classification model was utilized for NRS-2002 detection. Our approach established a non-linear mapping between facial features and NRS-2002 nutritional risk scores, providing an innovative method for evaluating patient nutritional status. Results: In context of orbital fat pad area segmentation with U-net model, the averaged dice coefficient is 88.3%. Our experimental results show that the proposed method to predict NRS-2002 scores achieves an accuracy of 73.1%. We also grouped the samples by gender, age, and the location of the hospital where the data were collected to evaluate the classification accuracy in different subsets. The classification accuracy rate for the elderly group was 85%, while the non-elderly group exhibited a classification accuracy rate of 71.1%; Furthermore, the classification accuracy rate for males and females were 69.2 and 78.6%, respectively. Hospitals located in remote areas, such as Tibet and Yunnan, yielded a classification accuracy rate of 76.5% for collected patient samples, whereas hospitals in non-remote areas achieved a classification accuracy rate of 71.1%. Conclusion: The attained accuracy rate of 73.1% holds significant implications for the feasibility of the method. While not impeccable, this level of accuracy highlights the potential for further improvements. The development of this algorithm has the potential to revolutionize nutritional risk assessment by providing healthcare professionals and individuals with a non-invasive, cost-effective, and easily accessible tool.

Multi-Pleated Alkalized Ti3 C2 Tx MXene-Based Sandwich-Like Structure Composite Nanofibers for High-Performance Sodium/Lithium Storage.

The volume expansion of CoFe2 O4 anode poses a significant challenge in the commercial application of lithium/sodium-ion batteries (LIBs/SIBs). However, metal-organic-frameworks (MOF) offer superior construction of heterostructures with refined interfacial interactions and lower ion diffusion barriers in Li/Na storage. In this study, the CoFe2 O4 @carbon nanofibers derived from MOF are produced through electrospinning, in situ growth followed by calcination, which are then confined within an MXene-confined MOF-derived porous CoFe2 O4 @carbon composite architecture under alkali treatment. The CoFe2 O4 nanofibers anchor on the alkalized MXene that is decorated with the NaOH solution to form a multi-pleated structure. The sandwich-like structure of the composite effectively alleviates the volume expansion and shortens the Li/Na-ion diffusion path, which displays high capacity and outstanding rate performance as anode materials for LIBs/SIBs. As a consequence, the obtained CoFe2 O4 @carbon@alkalized MXene composite anode shows satisfied rate performance at current density of 10 A g-1 for LIBs (318 mAh·g-1 ) and 5 A g-1 for SIBs (149 mAh g-1 ). The excellent cycling performance is further demonstrated at a high current density, where it maintains a discharge capacity of 807 mAh g-1 at 2 A g-1 after 400 cycles for LIBs and 130 mAh g-1 at 1 A g-1 even after 1000 cycles for SIBs.

Eliminate the hardware: Mobile terminals-oriented food recognition and weight estimation system.

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.

A Digital Smartphone-Based Self-administered Tool (R+ Dietitian) for Nutritional Risk Screening and Dietary Assessment in Hospitalized Patients With Cancer: Evaluation and Diagnostic Accuracy Study.

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.

Heterostructure Fe2O3 nanorods@imine-based covalent organic framework for long cycling and high-rate lithium storage.

Fe2O3 as an anode for lithium-ion batteries has attracted intense attention because of its high theoretical capacity, natural abundance, and good safety. However, the inferior cycling stability, low-rate performance, and limited composite varieties hinder the application of Fe2O3-based materials. In this work, an Fe2O3@COF-LZU1 (FO@LZU1) anode was prepared via an imine-based covalent organic framework (COF-LZU1) covering on the exterior surface of Fe2O3 after rational optimization. With its unique heterostructure, the COF-LZU1 layer not only effectively alleviated the volume expansion during cycling but also improved the charge-transfer capability because of the π-conjugated system. Moreover, the organic functional group (CN, benzene ring) for COF-LZU1 provided more redox-active sites for Li+ storage. Under the contributions of both Fe2O3 nanorods and COF-LZU1, the FO@LZU150% exhibited an ultrahigh initial capacity and long-term cycling performance with initial discharge capacities of 2143 and 2171 mA h g-1 after 300 cycles under 0.1 A g-1, and rate performance of 1310 and 501 mA h g-1 at 0.3 and 3 A g-1, respectively. In addition, a high retention capacity of 1185 mA h g-1 was achieved at 1 A g-1 after 500 cycles. Furthermore, a full-cell with the FO@LZU150% anode and LiCoO2 cathode exhibited superior cycling and rate performance, which still maintained a reversible capacity of 260 mA h g-1 after 200 cycles even at a current density of 1 A g-1. The proposed strategy offers a new perspective for exploring the high-rate capability and designability of Fe2O3-based electrode materials.

Nature-Inspired Hierarchical Protrusion Structure Construction for Washable and Wear-Resistant Superhydrophobic Textiles with Self-Cleaning Ability.

The use of toxic components and short longevity greatly restricted the commercial application of superhydrophobic surfaces in oil-water separation, antifouling, and self-cleaning. To address these concerns, a durable, robust, and fluorine-free superhydrophobic fabric is prepared on account of inspiration of nature. In this work, submicrometer-sized silica particles with different particle sizes are deposited onto cotton fabrics, followed by hydrophobic modification of poly(dimethylsiloxane) (PDMS), and consequently bonded the substrate and coating via powerful covalent bonds through a simple dip-coating technique. The rough surface with an imitated lotus-leaf-like hierarchical protrusion structure is constructed by deposited submicrometer-sized particles with different particle sizes, while the fabric with a low surface energy is achieved by the hydrophobic modification of PDMS. Ultimately, the fabricated fabric exhibits extraordinary superhydrophobicity with a high water contact angle (WCA) of 161° and a small sliding hysteresis angle (SHA) of 2.4°. Besides, considerable mechanical stability to withstand 130 sandpaper abrasion cycles and 40 washing cycles, and chemical resistance with sustained superhydrophobic property in various harsh environments (e.g., boiling water, strong acid/base solutions, and various organic solvents), are presented. Moreover, higher than 90% separation efficiency with a contact angle >150 ° is produced even after 50 cycles when the fabricated fabric serves as a filter during the oil-water separation besides its outstanding staining resistance and self-cleaning property.