Almost 1 million people in the UK are living with heart failure, which is a progressive and life-limiting condition. Managing symptoms in the community is widely acknowledged as a means of reducing deterioration and hospital admissions. Recent literature suggests that smartphone applications may support this agenda by facilitating symptom monitoring and promoting self-care. However, the evidence base for their long-term clinical effectiveness remains limited, with many studies focusing on feasibility or user engagement rather than outcomes, such as mortality or quality of life. Furthermore, reliance on technology risks exacerbating existing inequalities, as those with poor digital literacy or limited access to smartphones may be excluded. With pressures on the NHS intensifying, district nurses are increasingly positioned as leaders in community-based chronic disease management. However, the expectation that they integrate digital health solutions into practice requires critical consideration of feasibility, ethical implications and the potential unintended consequence of shifting responsibility for care onto service users.

Biopsy remains gold standard for definitive diagnosis of numerous diseases, yet conventional approaches confront inherent limitations encompassing procedural invasiveness, limited spatial resolution, poor deep-tissue accessibility, and complicated post-operative histopathology examinations. Herein, a multifunctional magnetically actuated microrobotic platform for targeted biopsy of deep-seated micro-lesion in lung for potential use of early lung cancer diagnosis is reported. This conical helix-structured microrobot features controllable gold nanospike decoration at its tip, integrating dual functionality in drilling-enabled tissue sampling and surface-enhanced Raman scattering (SERS) biosensing. Utilizing real-time tracheoscopy and fluoroscopy guidance, this platform achieves magnetic navigation through saline-filled bronchi to target lesions, followed by on-demand sampling and retrieval, whose feasibility is systematically verified through ex vivo porcine lung models and in vivo rabbit trials. The collected biomarker and tissue samples can be rapidly recognized and quantified based on SERS spectroscopy. Moreover, through establishing a convolutional neural network-based deep learning model, it is succeed to distinguish between the healthy and various cancerous lung tissues from clinical patients, achieving a recognition accuracy of over 94.3% within just 3min. Therefore, this work represents a microrobot-based biopsy techniques for noninvasive and precise sampling in lung with diagnosis capability of early-stage lung cancer, offering substantial improvements over conventional biopsy methods.

Nanoassemblies for oral protein delivery - The case of monoclonals for inflammatory bowel disease.

This study analyzed the factors influencing the technical efficiency of maize production among large-scale farms in Tanzania, using data from the 2019/2020 National Sample Census of Agriculture. Analytical methods included correlation analysis, multiple linear regression, and the stochastic frontier production function. Correlation results showed strong positive relationships between maize output and the costs of fertilizers (r=0.92), improved seeds (r=0.93), and modern machinery (r=0.89), while weaker correlations were observed with yield, agrochemical costs, and labor inputs. Regression analysis indicated that increased investment in improved seeds (1% increase leading to 63.1% rise in output), fertilizers (43.6%), and modern machinery (30.6%) significantly boosts maize production. Conversely, agrochemical costs and labor inputs negatively affected output, suggesting inefficiencies and possible misuse. Stochastic frontier results revealed that 48.69% of farmers operate with low technical efficiency (scores between 0.1 and 0.3), largely due to inadequate farm management, poor input access, and limited technical support. Around 43.46% fell into a moderate efficiency range (0.4–0.6), while only 7.85% were highly efficient (0.7–0.9). The study concluded that most large-scale maize farmers are not reaching optimal efficiency levels. It recommended enhancing access to fertilizers, improved seeds, and modern machinery through supportive policies and capacity building.

ABSTRACT Background Asthma and chronic obstructive pulmonary disease (COPD) are common noncommunicable diseases, exacerbated in conflict settings by the heightened environmental exposure to triggers, weakened health systems, and poor access to medicines and healthcare. However, accurate data on medicines dispensing in this context are generally scarce. Objective We examined the patterns and rates of medicines dispensing for asthma and COPD among the beneficiaries of the Syrian governmental health insurance scheme between June 2018 and March 2021. Methods We retrospectively analyzed the outpatient dispensing records for 125,371 adults. Medicines for asthma and COPD were identified using the Anatomical Therapeutic Chemical (ATC) classification system. Dispensing rates were calculated as the number of packages per 1,000 beneficiaries per month, stratified by age, sex, and route of administration. Results Out of our sample, 15.02% received at least one package of a medicine for asthma or COPD. Oral formulations were the most frequently dispensed (92.67% of patients), particularly oral salbutamol and xanthines. Inhaled medicines, including inhaled corticosteroids (ICS) and ICS long-acting β2-agonists (ICS–LABA) combinations, were markedly under-dispensed (17.08% of patients). Dispensing rates were higher in females and older adults. Seasonal variation showed peaks in autumn and winter, with a notable decline in April 2020, coinciding with the early COVID-19 period. Conclusions The study highlights substantial gaps in dispensing of medicines for asthma and COPD, with particularly low rates for inhalers, likely reflecting barriers driven by the conflict, economic instability, and sanctions. Robust coordinated action is needed to improve their availability in Syria.

The persistent accumulation of polyolefin plastic waste poses a significant environmental challenge, while its sustainable conversion into valuable olefins is limited by poor accessibility and severe coking of conventional microporous zeolite catalysts. Here, we report a rationally designed amorphous-crystalline composite (ACC) catalyst that integrates short-range ordered SOD-type domains into an amorphous aluminosilicate matrix to decouple acidity from micropore confinement. This hybrid architecture provides both open diffusion channels and accessible acid sites, enabling efficient C-C bond scission while suppressing secondary reactions. In low-density polyethylene cracking at 440 °C and a catalyst-to-feed weight ratio of 10/1, the optimized ACC catalyst achieved an exceptional C3-C5 olefin selectivity of 94.92% and a yield of 88.08 wt %, outperforming commercial ZSM-5 and USY by factors of more than 13 and 37, respectively. The catalyst exhibited outstanding stability over 20 consecutive cycles with minimal coke formation (1.13 wt %) and broad applicability across various polyolefins and postconsumer plastic wastes. This work establishes a generalizable design strategy for decoupling acidity and mass transport in solid acid catalysts, offering a robust platform for the sustainable valorization of polyolefin waste.

The objective of this paper was to investigate how middlemen’s economic exploitation of soapstone carvers shaped the social fabric of their community. It examined the ripple effects of this economic injustice—poverty, resentment, and social division—among the carvers. Ultimately, the study aimed to reveal how unequal trade relations eroded both livelihoods and social harmony. The analysis was grounded in Marxian theory, which provided a framework for understanding the class struggles between the middlemen—who controlled and profited from the soapstone trade—and the working-class carvers, who remained underpaid and marginalized. Ultimately, the study established that such exploitative dynamics resulted in economic deprivation, thereby restricting access to quality education and healthcare among the affected communities. To investigate these concerns, the study utilized both primary and secondary sources. It involved interviews with a randomly selected group of 30 respondents drawn from soapstone carvers and miners. These sources provided firsthand insights into the social impact of economic exploitation. The findings revealed that most of these intermediaries were privately owned companies and wholesalers who purchased carvings from local carvers at ridiculously low prices, only to resell them at exorbitant rates in international markets. This systematic exploitation trapped the carvers in cycles of poverty, giving rise to a host of social problems. Equally, lack of fair compensation bred animosity between the haves and the have-nots, deepening social divisions among the carvers. Similarly, widespread poverty followed, driving many into desperate survival strategies. Consequently, crime, poor access to healthcare, and prostitution surged—intensifying the spread of HIV and AIDS within the soapstone carving communities. Moreover, the study found out that poverty curtailed access to education, as many parents could neither afford school fees nor meet basic medical needs.

Pediatric obesity remains a major public health concern in the United States and is associated with early development of cardiometabolic and psychosocial comorbidities. Although early recognition and escalation to evidence-based interventions are recommended, the extent to which primary care providers are prepared to manage pediatric obesity varies. This study assessed the knowledge, attitudes, and practices (KAP) of pediatric and family medicine providers regarding pediatric obesity management, including the use of pharmacologic and surgical treatments.A cross-sectional, anonymous electronic survey was distributed to pediatricians and family medicine providers across Oklahoma through professional networks. The survey included multiple-choice, Likert-scale, and open-ended questions evaluating knowledge of obesity definitions, comfort with treatment options, practice patterns, and access to supportive resources. Quantitative responses were analyzed descriptively, and qualitative responses underwent thematic analysis.Forty providers met inclusion criteria. While most respondents correctly identified obesity thresholds, only 60% accurately defined severe obesity and 65% reported minimal familiarity with pediatric metabolic and bariatric surgery. Limited comfort with obesity management (55%) and poor access to exercise counseling (83%), behavioral health (44%), and bariatric surgical consultation (61%) were common. Pharmacotherapy and surgical referrals remained substantially underutilized.Primary care providers demonstrated knowledge gaps, limited confidence, and restricted access to resources for managing pediatric obesity. Enhanced provider education and improved system-level support are needed to facilitate timely, evidence-based care.

Background: Cancer poses a growing public health challenge in India, yet regional data remain scarce. This study evaluates the prevalence and distribution of cancer types in Ahilyanagar district, Maharashtra. Methods: A retrospective observational study was conducted using patient data collected from healthcare facilities, cancer registries, and pathology laboratories in Ahilyanagar district. Data from a total of 528 patients diagnosed with cancer over a six-month period were analyzed. The study focused on identifying the frequency of different cancer types and associated trends. Results: The most common cancers were buccal mucosa (17.8%), breast (14.2%), and cervical (11.3%). High oral cancer rates were linked to tobacco and betel nut use, while cervical and breast cancer prevalence reflected poor screening access and lifestyle changes. Conclusion: Ahilyanagar district faces a significant burden of preventable cancers. Region-specific strategies—including early detection, HPV vaccination, and anti-tobacco campaigns—are essential to reduce cancer morbidity and mortality.

Rural women remain the backbone of agricultural and household economies in Assam. Their contribution is visible in farming, small businesses, and community-based activities. However, their work is often unpaid, unrecognized, and unsupported. This paper examines the livelihood activities, health conditions, and development challenges faced by women in selected villages of the Palengi area in Sivasagar district of Assam. The study focuses on Palengi Dhyan Pathar, Palengi Dhundar Mukh, and Palengi Kachari Pathar. Using a descriptive and qualitative approach, the paper highlights women’s engagement in agriculture, Self-Help Group activities, and small income-generating practices. It also discusses problems such as lack of land ownership, low wages, poor access to credit, domestic workload, health issues, and environmental challenges. The role of government welfare schemes and community participation is examined to understand their impact on women’s lives. The study finds that while rural women play a critical role in sustaining livelihoods, meaningful development requires better health services, financial access, infrastructure, and gender-sensitive policy interventions.

Stunting remains a critical public health issue in Indonesia, particularly in East Nusa Tenggara (NTT), where prevalence rates are among the highest nationally. This study aims to classify districts and municipalities in East Nusa Tenggara Province based on socioeconomic and health-related indicators associated with stunting vulnerability. Using the Gath–Geva (Fuzzy K-Means Entropy) clustering algorithm, four key variables were analyzed, including poverty rate, access to proper housing, open unemployment rate, and number of health facilities. The results identified three distinct clusters with different regional characteristics. Cluster 1 consists of areas with low poverty and well-developed health infrastructure but relatively high unemployment rates. Cluster 2 represents the most vulnerable regions characterized by high poverty, poor housing access, and limited health facilities, while Cluster 3 comprises more stable areas with better housing, low unemployment, and adequate healthcare services. The silhouette coefficient value of 0.41 indicates that the three-cluster structure provides a reasonably good level of separation and internal consistency. These findings highlight that stunting vulnerability is strongly influenced by socioeconomic disparities and the distribution of health infrastructure. Therefore, intervention strategies should be tailored to the characteristics of each cluster, emphasizing integrated actions in high-risk regions and preventive measures in more stable areas to accelerate stunting reduction across East Nusa Tenggara Province.

The National Food Assistance Program (Non-Cash Food Assistance/BPNT) is one of the social policy instruments designed by the Indonesian government to enhance food security and improve the welfare of poor households. This research aims to analyze the effectiveness of this program by assessing its accessibility, targeting accuracy, commodity quality, and its impact on household expenditure and stability of food consumption.The research methodology uses a descriptive-analytical approach, combining secondary data from official government reports and academic literature with primary data in the form of a limited survey of beneficiaries.The results of the analysis indicate that the program is quite effective in increasing poor households' access to staple foods, primarily through the more transparent and flexible non-cash distribution mechanism. However, several constraints are still found, including inaccurate targeting of beneficiaries, variation in food quality at the e-warung level, and the limited digital literacy of some households.Nevertheless, the program is proven to be able to reduce the burden of food expenditure and improve consumption quality, thereby contributing positively to the welfare of poor households. This research suggests improving the validation of beneficiary data, supervising distribution, and expanding food and digital education for the community to further optimize the program's effectiveness.

Aotearoa New Zealand has wide disparities in health outcomes between indigenous and non-indigenous groups, which are strongly associated with inequitable access to determinants of health by ethnicity. Experiences of health determinants culminate in higher rates of diabetes, asthma, kidney disease and other chronic diseases and worse outcomes for Māori. This paper explores the relationships between health determinants and chronic disease from the perspectives of Māori patients and healthcare providers. Using a Kaupapa Māori methodology and qualitative methods, six semi-structured focus groups with 21 Māori living with chronic disease and semi-structured consultations with 130 healthcare providers across the Waikato region were undertaken between 2021 and 2023. Participants identified five key health determinants shaping the everyday experience and management of chronic disease, including employment, welfare support, income, access to information and racism. Access to economic resources had significant impacts. Those in situations of economic insecurity experienced significant complications to everyday disease management and had fewer resources to mediate their effects. Chronic disease also reduced access to economic resources through increased health costs, reduced earning power and the impacts of dealing with the welfare and health systems. Comparatively, stable access to economic resources, such as health insurance, enabled disease management. Everyday experiences of health determinants are shaped considerably by economic circumstances. Claiming welfare support continues to be difficult for eligible participants, and welfare payment inadequate. The health system needs to re-design healthcare delivery so people with poor access to economic resources are actively supported to access health determinants.

This study examined resource use efficiency among small-scale yam farmers in Ikom Agricultural Zone of Cross River State, Nigeria during the 2023 cropping season. Specifically, the sub-objectives were to estimate the level of technical and allocative efficiency in yam production and identify the constraints encountered by farmers in yam production. Two hundred and forty small scale yam farmers were selected using multistage sampling. A well-structured questionnaire was used for data collection. The collected data were analyzed using an efficiency model and stochastic frontier function. The study revealed that inputs such as fertilizer, labour, seedlings, and credit were underutilized, while farm size was overutilized. The marginal value product (MVP) adjustment revealed a 95.14%, 91.42%, 99.89%, and 99.86% increase for fertilizer, labour, seedlings, and credit, respectively, from the current level to attain the highest levels of allocative efficiency and profit. The technical efficiency of yam farmers ranged from 6% to 99%, with a mean technical efficiency of 0.63 (63%). The farmers operate in the first stage of production (Ep > 1), implying increasing returns to scale (2.70). High farm input costs (71), land tenure (70), inadequate access to credit (67), poor access to extension services (66), and pest and disease incidence (65) were identified as problems militating against yam production. This study highlighted the importance of targeted interventions to address constraints and improve input use. The study recommends that farmers should be provided with targeted training programs on efficient input utilization, including best fertilizer practices, seed spacing, and crop rotation to ensure efficient resource use and higher productivity. Low-interest credit and subsidized inputs should be provided to yam farmers. Government should employ more trained extension personnel and increase participation in extension activities to ensure the proper implementation of new farming practices. Farmers should be educated on proper herbicide application using knapsack sprayers.

BackgroundPoor access to antenatal care (ANC), skilled delivery, and postnatal checks within 48 h of delivery are linked to adverse pregnancy outcomes. In Kenya, unequal use of these services has caused significant regional disparities, with 15 out of 47 counties being high priority.ObjectivesTo evaluate the effectiveness of a digital health solution to improve maternal and newborn health (MNH) uptake.MethodsFrom July 2017 to March 2019, we implemented an integrated community and hospital digital health information system, in ten health facilities and four community health units in Turkana County, Northern Kenya. We used a non-randomized before-after household survey. We assessed the proportion of mothers attending at least four antenatal visits, receiving skilled delivery, and receiving postnatal checks within 48 h at baseline and 12 months post-intervention. Statistical comparisons included p-values and 95% confidence intervals, accounting for clustering at the CHU level. These findings were compared with data from the Kenya Health Information System for the study subcounty and Turkana County.ResultsAmong a catchment population of 4,300 women of reproductive age (WRA), 692 and 608 women were interviewed at baseline and endline, respectively. STONE-HMIS® led to 5%, 23%, and 16% improvements in 4th antenatal care visits, skilled delivery, and postnatal checks within 48 h, respectively. For the same period, subcounty and county data showed that 57.7% and 65.8% of WRA attended at least 4 ANC visits, 39.5% and 67.8% delivered with skilled assistance, and 23.5% and 24% had postnatal checks.ConclusionsIntegrating digital health systems at provider and community levels, aligned with health system priorities, showed marked improvements MNH indicators in an underserved, remote area.

Early detection of breast cancer significantly improves survival rates; however, existing screening modalities such as mammography are limited by radiation exposure, discomfort, reduced sensitivity in dense breasts, and poor accessibility in low‑resource settings. This paper proposes a novel, non‑invasive, radiation‑free IoT‑enabled wearable patch integrated with thermal sensors and complementary biosensors to enable early breast cancer screening through continuous physiological monitoring. The patch wirelessly connects to a smartphone and cloud platform, where artificial intelligence (AI) models analyze longitudinal data to identify abnormal thermal asymmetry, vascular heat dispersion, and biosignal patterns indicative of tumor‑associated angiogenesis and metabolic activity. The proposed system acts as a first‑line screening and triage tool, enabling frequent, home‑based monitoring and timely referral for diagnostic imaging. System architecture, sensing methodology, AI pipeline, and potential clinical impact are presented, demonstrating the feasibility of scalable, personalized breast cancer screening using wearable technology.

Background: Equitable access to healthcare is a core principle of national health systems globally. However, individuals living in rural communities continue to face substantial informational, geographical, and financial barriers to primary health care (PHC) services. These barriers contribute to poorer health outcomes and widen rural–urban health disparities. Aim: This study assessed factors associated with access to PHC services and examined the influence of PHC access on tuberculosis (TB) awareness among adult women residing in rural communities in Orlu, Imo State. Methods: A community-based descriptive cross-sectional study was conducted using a convenience sampling approach to select the study area based on proximity, security, and accessibility. A simple random sampling technique was then used to recruit 430 women. Data were collected using a semi-structured questionnaire administered through self-report and interviewer assistance, encoded using Open Data Kit (ODK), and analysed with SPSS version 28. Descriptive statistics (frequencies and percentages) and chi-square tests were used, with significance set at p < 0.05. Results: Most respondents (60%) demonstrated poor knowledge or awareness of pulmonary TB. More than half (51%) reported poor to moderate health status, and 47% had poor access to PHC services. TB awareness/knowledge was significantly associated with educational level (p = 0.002), occupation (p = 0.020), and household room density (p = 0.002). However, TB awareness was not significantly associated with access to PHC services (p = 0.119). Conclusion: Despite limited TB awareness and restricted access to PHC services, women in these rural communities reported generally good perceived health status, suggesting notable resilience. The findings highlight the need for holistic, strengthened PHC systems that ensure equitable, accessible, and high-quality care for rural women, alongside targeted interventions to improve TB knowledge.

In the six weeks between the announcement of the 2024 UK General Election (22 May) and election day (4 July), national newspapers published numerous articles depicting a "crisis" in general practice. These portrayals varied across publications and may have influenced public discourse and political pledges. To explore how UK national newspapers depicted access to general practice during the six weeks preceding the 2024 General Election. Thematic analysis of UK national newspaper articles published in the pre-election period. Articles were retrieved from the LexisNexis® Academic database using predefined search terms and inclusion/exclusion criteria. After screening, 194 articles were included and analysed thematically. Key themes identified were the crisis itself, regional disparities, access challenges for the elderly, consequences of poor access, and political promises. All newspapers articulated a "GP crisis", though with distinct emphases. Inequalities featured prominently, with coverage highlighting rural GP shortages and difficulties for elderly patients. Articles frequently linked poor access with adverse outcomes, such as delayed or missed diagnoses, as well as wider system pressures, including strain on emergency services. Coverage was overwhelmingly negative, and political promises mirrored the crisis narrative presented in the press. In the run-up to the 2024 General Election, UK print media consistently portrayed general practice as being in crisis, often through sensationalised accounts of waiting times and limited access. These framings likely shaped public perception and influenced election pledges. Editorial choices played a key role in constructing dominant narratives around general practice and health policy discourse.

The therapeutic potential of chimeric antigen receptor (CAR) T-cell therapy in treating solid tumors is highly recognized, yet the complex and immunosuppressive nature of the tumor microenvironment, poor accessibility, and the instability of target antigens pose substantial challenges. Here, we present an mRNA-LNP-based therapeutic strategy that delivers mRNA encoding a fibroblast activation protein (FAP)-specific CAR to reprogram host immune cells in vivo and target cancer-associated fibroblasts within the tumor stroma. In multiple solid tumor mouse models, this approach, combined with chemotherapeutic agents and immune checkpoint inhibitors, achieved significant tumor regression and induced durable, antigen-specific immune memory. Incorporation of m6A-modified CAR mRNA accelerated and amplified antitumor responses, while blockade of the macrophage migration inhibitory factor (MIF)-CD74 axis further improved tumor control by alleviating immune suppression. In patient-derived xenograft models, HOX family transcription factors were implicated in treatment resistance, highlighting a potential biomarker and therapeutic target. The evidence from this study demonstrates that targeting the tumor microenvironment with a controllable mRNA-modulated strategy achieves substantial antitumor efficacy and holds significant potential to enhance the applicability and acceptance of CAR-T cell therapy across a variety of cancers.

Aims: This study aims to construct an AI-based emotion-color mapping model with high precision, develop a lightweight and user-friendly mobile application (ArtThera) for college students' emotion regulation, and systematically verify its effectiveness in campus mental health intervention through rigorous randomized controlled trials. Study Design: A randomized controlled trial (RCT) integrating multi-methodological approaches, including systematic literature review, data-driven analysis with machine learning algorithms, iterative prototype development based on user-centered design, and comprehensive user testing combining physiological indicators and psychological scales. Place and Duration of Study: Conducted at the School of Digital Media and University Psychological Counseling Center of Chongqing Polytechnic University of Electronic Technology, spanning from June 2024 to June 2025 (12 months), including 6 months of model construction and APP development, and 6 months of user recruitment, intervention implementation, and data collection. Methodology: 1) Literature research: Systematically review classic and cutting-edge theories of color psychology (e.g., PAD emotional state model, HSV color space theory) and evidence-based research on art therapy, summarizing the limitations of current research and identifying research gaps; 2) Data-driven analysis: Use Python programming language to collect 1000+ emotion-labeled artworks from authoritative open databases (WikiArt, COSMOS), where WikiArt covers works by 500+ international artists and COSMOS provides standardized emotion annotation data. Extract color features (RGB values, brightness, saturation) using Pandas, NumPy, and OpenCV libraries, convert RGB values to HSV color space for standardized calculation (H=arctan2(√3(G-B), 2R-G-B), S=(max(R,G,B)-min(R,G,B))/max(R,G,B), V=max(R,G,B)/255), exclude abnormal data (e.g., color distortion) using the IQR method (calculate quartiles Q1 and Q3, define outliers as values less than Q1-1.5IQR or greater than Q3+1.5IQR), and apply K-means clustering (Scikit-learn library, k=8, init='k-means++', n_init=10, max_iter=500, convergence threshold=0.001) to group color features by 8 core emotion labels, establishing a scientific and quantifiable emotion-color rule library; 3) Prototype iteration: Develop the ArtThera APP using Flutter (frontend) and Firebase (backend), integrating multiple functions such as detailed emotion input, personalized color scheme generation, dynamic painting templates, white noise synthesis, vibration feedback, and "emotion memory capsules". Referring to the user-centered development approach of the LiveWell app, conduct semi-structured interviews with 30 college students first, then complete three rounds of prototype iteration and user testing; 4) User testing: Recruit 200 college students (100 males, 100 females; age 18-22 years) via the University Psychological Association, including 50 students with mild anxiety (SAS score 50-59). Divide participants into experimental group (using ArtThera) and control group (engaging in traditional static coloring) through random assignment. Evaluate intervention effects at three time points (T0: pre-intervention 1 day, T1: mid-intervention 2 weeks, T2: post-intervention 4 weeks) using Heart Rate Variability (HRV, including SDNN and RMSSD indicators), Self-Rating Anxiety Scale (SAS), and PANAS scale, combined with semi-structured interviews to collect user feedback. Results: 1) Emotion-color mapping model: Clear and significant correlations between emotions and color features were identified (e.g., anxiety→low brightness [35±5], low saturation [28±4], hue range 210-240°; excitement→high brightness [82±6], high saturation [75±7], hue range 30-60°), which are highly consistent with findings from classic mandala coloring studies; 2) APP performance: Fully meets preset technical indicators (iOS average launch time 1.2s, Android average 1.4s, both ≤1.5s; color scheme generation average response time 1.7s, dynamic template loading average 1.9s, both ≤2s), supporting iOS 12.0+ and Android 8.0+ systems. In preliminary testing (n=20), 85% of participants rated "ease of use" ≥4/5, and 90% reported that dynamic templates are "more engaging" than static coloring books, exceeding the average acceptance rate of adolescent mental health apps (78%); 3) Intervention effects: Expected to achieve ≥10% increase in HRV (SDNN indicator) in the experimental group, ≥12% decrease in SAS scores, and ≥15% increase in PANAS positive affect scores, which is comparable to or even better than mindfulness-based interventions. Conclusion: The AI-driven ArtThera system effectively addresses the limitations of traditional art therapy (lack of personalization, poor accessibility) and existing mental health apps (single function, insufficient integration of art therapy and data-driven technology), providing a scientific, convenient, and scalable tool for campus mental health services. It enriches the theoretical framework of digital art therapy and contributes to the development of data-driven psychological intervention strategies. Fund Project: 2025 Student Science and Technology Innovation "Mayue" Project of Chongqing Polytechnic University of Electronic Technology.