The genus Rhodiola L., an integral part of traditional Chinese medicine and Tibetan medicine in China, exhibits a broad spectrum of applications. This genus contains key compounds such as ginsenosides, polysaccharides, and flavonoids, which possess anti-inflammatory, antioxidant, hypoglycaemic, immune-enhancing, and anti-hypoxic properties. As a vital raw material, Rhodiola L. contributes to twenty-four kinds of Chinese patent medicines and 481 health food products in China, finding extensive application in the health food sector. Recently, polysaccharides have emerged as a focal point in natural product research, with applications spanning the medicine, food, and materials sectors. Despite this, a comprehensive and systematic review of polysaccharides from the genus Rhodiola L. polysaccharides (TGRPs) is warranted. This study undertakes a systematic review of both domestic and international literature, assessing the research advancements and chemical functional values of polysaccharides derived from Rhodiola rosea. It involves the isolation, purification, and identification of a variety of homogeneous polysaccharides, followed by a detailed analysis of their chemical structures, pharmacological activities, and molecular mechanisms, structure-activity relationship (SAR) of TGRPs. The discussion includes the influence of molecular weight, monosaccharide composition, and glycosidic bonds on their biological activities, such as sulfation and carboxymethylation et al. Such analyses are crucial for deepening the understanding of Rhodiola rosea and for fostering the development and exploitation of TGRPs, offering a reference point for further investigations into TGRPs and their resource utilization.

BACKGROUND: Older adults are at greater risk of eating rotten fruits and of getting food poisoning because cognitive function declines as they age, making it difficult to distinguish rotten fruits. To address this problem, researchers have developed and evaluated various tools to detect rotten food items in various ways. Nevertheless, little is known about how to create an app to detect rotten food items to support older adults at a risk of health problems from eating rotten food items.
OBJECTIVE: This study aimed to (1) create a smartphone app that enables older adults to take a picture of food items with a camera and classifies the fruit as rotten or not rotten for older adults and (2) evaluate the usability of the app and the perceptions of older adults about the app.
METHODS: We developed a smartphone app that supports older adults in determining whether the 3 fruits selected for this study (apple, banana, and orange) were fresh enough to eat. We used several residual deep networks to check whether the fruit photos collected were of fresh fruit. We recruited healthy older adults aged over 65 years (n=15, 57.7%, males and n=11, 42.3%, females) as participants. We evaluated the usability of the app and the participants\' perceptions about the app through surveys and interviews. We analyzed the survey responses, including an after-scenario questionnaire, as evaluation indicators of the usability of the app and collected qualitative data from the interviewees for in-depth analysis of the survey responses.
RESULTS: The participants were satisfied with using an app to determine whether a fruit is fresh by taking a picture of the fruit but are reluctant to use the paid version of the app. The survey results revealed that the participants tended to use the app efficiently to take pictures of fruits and determine their freshness. The qualitative data analysis on app usability and participants\' perceptions about the app revealed that they found the app simple and easy to use, they had no difficulty taking pictures, and they found the app interface visually satisfactory.
CONCLUSIONS: This study suggests the possibility of developing an app that supports older adults in identifying rotten food items effectively and efficiently. Future work to make the app distinguish the freshness of various food items other than the 3 fruits selected still remains.

BACKGROUND: Interventions are required that address delays in treatment-seeking and low treatment coverage among people consuming methamphetamine.
OBJECTIVE: We aim to determine whether a self-administered smartphone-based intervention, the \"S-Check app\" can increase help-seeking and motivation to change methamphetamine use, and determine factors associated with app engagement.
METHODS: This study is a randomized, 28-day waitlist-controlled trial. Consenting adults residing in Australia who reported using methamphetamine at least once in the last month were eligible to download the app for free from Android or iOS app stores. Those randomized to the intervention group had immediate access to the S-Check app, the control group was wait-listed for 28 days before gaining access, and then all had access until day 56. Actual help-seeking and intention to seek help were assessed by the modified Actual Help Seeking Questionnaire (mAHSQ), modified General Help Seeking Questionnaire, and motivation to change methamphetamine use by the modified readiness ruler. χ2 comparisons of the proportion of positive responses to the mAHSQ, modified General Help Seeking Questionnaire, and modified readiness ruler were conducted between the 2 groups. Logistic regression models compared the odds of actual help-seeking, intention to seek help, and motivation to change at day 28 between the 2 groups. Secondary outcomes were the most commonly accessed features of the app, methamphetamine use, feasibility and acceptability of the app, and associations between S-Check app engagement and participant demographic and methamphetamine use characteristics.
RESULTS: In total, 560 participants downloaded the app; 259 (46.3%) completed eConsent and baseline; and 84 (32.4%) provided data on day 28. Participants in the immediate access group were more likely to seek professional help (mAHSQ) at day 28 than those in the control group (n=15, 45.5% vs n=12, 23.5%; χ21=4.42, P=.04). There was no significant difference in the odds of actual help-seeking, intention to seek help, or motivation to change methamphetamine use between the 2 groups on the primary logistic regression analyses, while in the ancillary analyses, the imputed data set showed a significant difference in the odds of seeking professional help between participants in the immediate access group compared to the waitlist control group (adjusted odds ratio 2.64, 95% CI 1.19-5.83, P=.02). For participants not seeking help at baseline, each minute in the app increased the likelihood of seeking professional help by day 28 by 8% (ratio 1.08, 95% CI 1.02-1.22, P=.04). Among the intervention group, a 10-minute increase in app engagement time was associated with a decrease in days of methamphetamine use by 0.4 days (regression coefficient [β] -0.04, P=.02).
CONCLUSIONS: The S-Check app is a feasible low-resource self-administered intervention for adults in Australia who consume methamphetamine. Study attrition was high and, while common in mobile health interventions, warrants larger studies of the S-Check app.
BACKGROUND: Australian New Zealand Clinical Trials Registry ACTRN12619000534189; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377288&isReview=true.

BACKGROUND: Artificial intelligence (AI) has the potential to enhance physical activity (PA) interventions. However, human factors (HFs) play a pivotal role in the successful integration of AI into mobile health (mHealth) solutions for promoting PA. Understanding and optimizing the interaction between individuals and AI-driven mHealth apps is essential for achieving the desired outcomes.
OBJECTIVE: This study aims to review and describe the current evidence on the HFs in AI-driven digital solutions for increasing PA.
METHODS: We conducted a scoping review by searching for publications containing terms related to PA, HFs, and AI in the titles and abstracts across 3 databases-PubMed, Embase, and IEEE Xplore-and Google Scholar. Studies were included if they were primary studies describing an AI-based solution aimed at increasing PA, and results from testing the solution were reported. Studies that did not meet these criteria were excluded. Additionally, we searched the references in the included articles for relevant research. The following data were extracted from included studies and incorporated into a qualitative synthesis: bibliographic information, study characteristics, population, intervention, comparison, outcomes, and AI-related information. The certainty of the evidence in the included studies was evaluated using GRADE (Grading of Recommendations Assessment, Development, and Evaluation).
RESULTS: A total of 15 studies published between 2015 and 2023 involving 899 participants aged approximately between 19 and 84 years, 60.7% (546/899) of whom were female participants, were included in this review. The interventions lasted between 2 and 26 weeks in the included studies. Recommender systems were the most commonly used AI technology in digital solutions for PA (10/15 studies), followed by conversational agents (4/15 studies). User acceptability and satisfaction were the HFs most frequently evaluated (5/15 studies each), followed by usability (4/15 studies). Regarding automated data collection for personalization and recommendation, most systems involved fitness trackers (5/15 studies). The certainty of the evidence analysis indicates moderate certainty of the effectiveness of AI-driven digital technologies in increasing PA (eg, number of steps, distance walked, or time spent on PA). Furthermore, AI-driven technology, particularly recommender systems, seems to positively influence changes in PA behavior, although with very low certainty evidence.
CONCLUSIONS: Current research highlights the potential of AI-driven technologies to enhance PA, though the evidence remains limited. Longer-term studies are necessary to assess the sustained impact of AI-driven technologies on behavior change and habit formation. While AI-driven digital solutions for PA hold significant promise, further exploration into optimizing AI\'s impact on PA and effectively integrating AI and HFs is crucial for broader benefits. Thus, the implications for innovation management involve conducting long-term studies, prioritizing diversity, ensuring research quality, focusing on user experience, and understanding the evolving role of AI in PA promotion.

BACKGROUND: Dermatology is an ideal specialty for artificial intelligence (AI)-driven image recognition to improve diagnostic accuracy and patient care. Lack of dermatologists in many parts of the world and the high frequency of cutaneous disorders and malignancies highlight the increasing need for AI-aided diagnosis. Although AI-based applications for the identification of dermatological conditions are widely available, research assessing their reliability and accuracy is lacking.
OBJECTIVE: The aim of this study was to analyze the efficacy of the Aysa AI app as a preliminary diagnostic tool for various dermatological conditions in a semiurban town in India.
METHODS: This observational cross-sectional study included patients over the age of 2 years who visited the dermatology clinic. Images of lesions from individuals with various skin disorders were uploaded to the app after obtaining informed consent. The app was used to make a patient profile, identify lesion morphology, plot the location on a human model, and answer questions regarding duration and symptoms. The app presented eight differential diagnoses, which were compared with the clinical diagnosis. The model\'s performance was evaluated using sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and F1-score. Comparison of categorical variables was performed with the χ2 test and statistical significance was considered at P<.05.
RESULTS: A total of 700 patients were part of the study. A wide variety of skin conditions were grouped into 12 categories. The AI model had a mean top-1 sensitivity of 71% (95% CI 61.5%-74.3%), top-3 sensitivity of 86.1% (95% CI 83.4%-88.6%), and all-8 sensitivity of 95.1% (95% CI 93.3%-96.6%). The top-1 sensitivities for diagnosis of skin infestations, disorders of keratinization, other inflammatory conditions, and bacterial infections were 85.7%, 85.7%, 82.7%, and 81.8%, respectively. In the case of photodermatoses and malignant tumors, the top-1 sensitivities were 33.3% and 10%, respectively. Each category had a strong correlation between the clinical diagnosis and the probable diagnoses (P<.001).
CONCLUSIONS: The Aysa app showed promising results in identifying most dermatoses.

Salmonella, a prominent foodborne pathogen, has posed enduring challenges to the advancement of food safety and global public health. The escalating concern over antibiotic misuse, resulting in the excessive presence of drug residues in animal-derived food products, necessitates urgent exploration of alternative strategies for Salmonella control. Bacteriophages emerge as promising green biocontrol agents against pathogenic bacteria. This study delineates the identification of two novel virulent Salmonella phages, namely phage vB_SalS_ABTNLsp11241 (referred to as sp11241) and phage 8-19 (referred to as 8-19). Both phages exhibited efficient infectivity against Salmonella enterica serotype Enteritidis (SE). Furthermore, this study evaluated the effectiveness of two phages to control SE in three different foods (whole chicken eggs, raw chicken meat, and lettuce) at different MOIs (1, 100, and 10000) at 4°C. It\'s worth noting that sp11241 and 8-19 achieved complete elimination of SE on eggs after 3 h and 6 h at MOI = 100, and after 2 h and 5 h at MOI = 10000, respectively. After 12 h of treatment with sp11241, a maximum reduction of 3.17 log10 CFU/mL in SE was achieved on raw chicken meat, and a maximum reduction of 3.00 log10 CFU/mL was achieved on lettuce. Phage 8-19 has the same effect on lettuce as sp11241, but is slightly less effective than sp11241 on chicken meat (a maximum 2.69 log10 CFU/mL reduction). In conclusion, sp11241 and 8-19 exhibit considerable potential for controlling Salmonella contamination in food at a low temperature and represent viable candidates as green antibacterial agents for food applications.

BACKGROUND: Emergency department crowding continues to threaten patient safety and cause poor patient outcomes. Prior models designed to predict hospital admission have had biases. Predictive models that successfully estimate the probability of patient hospital admission would be useful in reducing or preventing emergency department \"boarding\" and hospital \"exit block\" and would reduce emergency department crowding by initiating earlier hospital admission and avoiding protracted bed procurement processes.
OBJECTIVE: To develop a model to predict imminent adult patient hospital admission from the emergency department early in the patient visit by utilizing existing clinical descriptors (ie, patient biomarkers) that are routinely collected at triage and captured in the hospital\'s electronic medical records. Biomarkers are advantageous for modeling due to their early and routine collection at triage; instantaneous availability; standardized definition, measurement, and interpretation; and their freedom from the confines of patient histories (ie, they are not affected by inaccurate patient reports on medical history, unavailable reports, or delayed report retrieval).
METHODS: This retrospective cohort study evaluated 1 year of consecutive data events among adult patients admitted to the emergency department and developed an algorithm that predicted which patients would require imminent hospital admission. Eight predictor variables were evaluated for their roles in the outcome of the patient emergency department visit. Logistic regression was used to model the study data.
RESULTS: The 8-predictor model included the following biomarkers: age, systolic blood pressure, diastolic blood pressure, heart rate, respiration rate, temperature, gender, and acuity level. The model used these biomarkers to identify emergency department patients who required hospital admission. Our model performed well, with good agreement between observed and predicted admissions, indicating a well-fitting and well-calibrated model that showed good ability to discriminate between patients who would and would not be admitted.
CONCLUSIONS: This prediction model based on primary data identified emergency department patients with an increased risk of hospital admission. This actionable information can be used to improve patient care and hospital operations, especially by reducing emergency department crowding by looking ahead to predict which patients are likely to be admitted following triage, thereby providing needed information to initiate the complex admission and bed assignment processes much earlier in the care continuum.

Even with intensive treatment of high-risk neuroblastoma (NB) patients, half of high-risk NB patients still relapse. New therapies targeting the biological characteristics of NB have important clinical value for the personalized treatment of NB. However, the current biological markers for NB are mainly analyzed by tissue biopsy. In recent years, circulating biomarkers of NB based on liquid biopsy have attracted more and more attention. This review summarizes the analytes and methods for liquid biopsy of NB. We focus on the application of liquid biopsy in the diagnosis, prognosis assessment, and monitoring of NB. Finally, we discuss the prospects and challenges of liquid biopsy in NB.

Breathable membranes with micropores enable the transfer of gas molecules while blocking liquids and solids, and have a wide range of applications in medical, industrial, environmental, and energy fields. Breathability is highly influenced by the nature of a material, pore size, and pore structure. Preparation methods and the incorporation of functional materials are responsible for the variety of physical properties and applications of breathable membranes. In this review, the preparation methods of breathable membranes, including blown film extrusion, cast film extrusion, phase separation, and electrospinning, are discussed. According to the antibacterial, hydrophobic, thermal insulation, conductive, and adsorption properties, the application of breathable membranes in the fields of electronics, medicine, textiles, packaging, energy, and the environment are summarized. Perspectives on the development trends and challenges of breathable membranes are discussed.

The integration of two-dimensional Ti3C2Tx nanosheets and other materials offers broader application options in the antibacterial field. Ti3C2Tx-based composites demonstrate synergistic physical, chemical, and photodynamic antibacterial activity. In this review, we aim to explore the potential of Ti3C2Tx-based composites in the fabrication of an antibiotic-free antibacterial agent with a focus on their systematic classification, manufacturing technology, and application potential. We investigate various components of Ti3C2Tx-based composites, such as metals, metal oxides, metal sulfides, organic frameworks, photosensitizers, etc. We also summarize the fabrication techniques used for preparing Ti3C2Tx-based composites, including solution mixing, chemical synthesis, layer-by-layer self-assembly, electrostatic assembly, and three-dimensional (3D) printing. The most recent developments in antibacterial application are also thoroughly discussed, with special attention to the medical, water treatment, food preservation, flexible textile, and industrial sectors. Ultimately, the future directions and opportunities are delineated, underscoring the focus of further research, such as elucidating microscopic mechanisms, achieving a balance between biocompatibility and antibacterial efficiency, and investigating effective, eco-friendly synthesis techniques combined with intelligent technology. A survey of the literature provides a comprehensive overview of the state-of-the-art developments in Ti3C2Tx-based composites and their potential applications in various fields. This comprehensive review covers the variety, preparation methods, and applications of Ti3C2Tx-based composites, drawing upon a total of 171 English-language references. Notably, 155 of these references are from the past five years, indicating significant recent progress and interest in this research area.