ethicalabs/Research-Intent-Collab huggingface dataset:
Improving Health-Related Quality of Life in Hepatocellular Carcinoma Patients: Key Methodologies for Assessing Patient Reported Outcomes and Intervention Targets

Andrew M Moon,1,2 Michael D Kappelman,3 A Sidney Barritt IV,1 Donna M Evon,1 Hanna K Sanoff,2,4 Lynne I Wagner2,5 1Division of Gastroenterology and Hepatology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA; 2Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA; 3Department of Pediatrics, Division of Pediatric Gastroenterology, University of North Carolina, Chapel Hill, NC, USA; 4Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA; 5Department of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USACorrespondence: Andrew M Moon, Division of Gastroenterology and Hepatology, University of North Carolina at Chapel Hill School of Medicine, 8009 Burnett Womack Bldg, CB&num;7584, Chapel Hill, NC, 27599-7584, USA, Tel +1-919-445-4742, Email Andrew.Moon@unchealth.unc.eduAbstract: Hepatocellular carcinoma (HCC) is a complex cancer that generally arises in the context of cirrhosis. Patients with HCC have symptom burden and impact on health-related quality of life (HRQOL) resulting from underlying liver disease, HCC, and cancer treatments. Patient-reported outcome (PRO) measures may improve the management of patients with HCC by accurately capturing the patient perspective, informing prognosis, guiding treatment decisions, and supporting symptom based and palliative care. Furthermore, PRO use in HCC research could enhance patient-focused therapy development. This review focuses on the clinical and research assessment of PROs among patients with HCC.Keywords: liver cancer, cirrhosis, PROs, HRQOL
Analysis for spatial distribution of Oncomelania snail in mainland China by geographic information system (GIS) database

To analyze espatial distribution of Oncomelania snail populations in mainland China by geographic information sy ( GIS ) database.stem
Tracing the Continued Existence of Ulayat Land: Granting Management Rights in the Context of Protecting the Rights of Indigenous Peoples

AbstractThe issue of granting management rights over ulayat land to third parties is still being debated. Management rights originating from communal land are contrary to the concept of the management right itself. Management rights, which are part of the authority of State control rights, are contradicted by management rights that can originate from ulayat land because indigenous peoples have ulayat rights on the ulayat land. For ulayat land that still has the status of ulayat rights of indigenous peoples, if given management rights, the ulayat rights must be released by the indigenous peoples. Thus, the communal land becomes free state land. The granting of management rights on ulayat land is becoming increasingly relevant and complex. Several changes related regulations have raised significant questions regarding the existence of ulayat land and its impact on the sustainability of the lives of indigenous peoples. To accelerate the economy, the government needs to pay attention to the ulayat rights of indigenous peoples over their ulayat land and ensure that these rights are protected and respected. The government also needs to ensure that effective national law enforcement mechanisms are in place to resolve ulayat land disputes. As a result of the granting of HPL on ulayat land, legal uncertainty arises. The existence of ulayat land for indigenous peoples is very important in maintaining the identity, culture, and sustainability of indigenous peoples. Ulayat rights protect the rights of ownership and management of communal land by indigenous peoples so that they can continue cultural practices, traditions, and customs that have existed since the time of their ancestors.Keywords: Management Rights; Ulayat Land; Indigenous People.
Missed nursing care in Latin American Hospitals and its repercussions on patient safety and quality of care

According to the patient safety literature, missed nursing care is considered an error of omission. It is defined as any aspect of required patient care that is omitted—either partially or entirely—or delayed (Kalisch et al. 2009). The MISSCARE Survey is an instrument developed to measure missed nursing care and to analyze its underlying causes. This instrument was translated and validated in a Brazilian Portuguese version in 2017 and, more recently - 2024, was validated for use in the Chilean context in Spanish.
Factors affecting the adoption of human resource information system (HRIS) in Bangladesh: A study on private banking industry

Adoption of Human Resource Information System is the significant issue in banking sector from last decade though it is the challenge for developing countries like Bangladesh to adopt HRIS successfully. The purpose of this study is to find out different driving and restraining factors that affect the adoption of HRIS in Bangladesh from private banking perspective. Data were collected from 52 respondents of 18 private banks operating in Bangladesh through a structured questionnaire including 12 questions. Different statistical tools such as correlation, regression analysis , anova, co-efficients, hypothesis tests have been used that has showed that there is a positive relationship between driving factors and HRIS adoption in private banking sector in Bangladesh that refers these driving factors support the organization to adopt the HRIS in its function and explore the benefits of its usage successfully while there exists negative relationship between restraining factors and adoption that becomes the challenge for the banking sector to adopt HRIS specially by developing countries like Bangladesh. The study suggests that as HRIS is both the beneficial and challenging issue for Bangladesh, the organization should concern about the adoption and proper usage of HRIS so that it can lead to the better performance of the organization.
A Circular Restricted n-body Problem

This paper introduces the Circular Restricted n-Body Problem (CRNBP), an extension of the bicircular restricted four-body problem (BCR4BP) designed to describe the dynamics of an n-body system. In the CRNBP, each massive body in the system is constrained to follow a Keplerian motion, similar to the BCR4BP's artificial constraint. The CRNBP is an efficient alternative for trajectory design in multiple-body systems, particularly for outer planetary systems, as it requires integrating only six first-order ordinary differential equations compared to the 6N equations in an ephemerides model. By reproducing complex dynamical behaviors observed in ephemerides n-body problems, we demonstrate the structural stability of the CRNBP. Additionally, we propose a straightforward approach to relate the CRNBP with ephemerides, enabling the exploration of trajectory design possibilities before committing to a dedicated ephemerides analysis. This allows for the identification of general dynamical behaviors and provides valuable insights into the dynamics of multiple body systems. Finally, illustrative examples highlight the richness of trajectories and potential advantages of using the CRNBP for designing complex trajectories in outer planetary systems. The CRNBP proves to be a valuable tool for preliminary trajectory design, facilitating the identification of low-energy trajectories and providing a foundation for further exploration in future dedicated studies.
A Scoping Review of AI-Driven Digital Interventions in Mental Health Care: Mapping Applications Across Screening, Support, Monitoring, Prevention, and Clinical Education

Artificial intelligence (AI)-enabled digital interventions, including Generative AI (GenAI) and Human-Centered AI (HCAI), are increasingly used to expand access to digital psychiatry and mental health care. This PRISMA-ScR scoping review maps the landscape of AI-driven mental health (mHealth) technologies across five critical phases: pre-treatment (screening/triage), treatment (therapeutic support), post-treatment (remote patient monitoring), clinical education, and population-level prevention. We synthesized 36 empirical studies implemented through early 2024, focusing on Large Language Models (LLMs), machine learning (ML) models, and autonomous conversational agents. Key use cases involve referral triage, empathic communication enhancement, and AI-assisted psychotherapy delivered via chatbots and voice agents. While benefits include reduced wait times and increased patient engagement, we address recurring challenges like algorithmic bias, data privacy, and human-AI collaboration barriers. By introducing a novel four-pillar framework, this review provides a comprehensive roadmap for AI-augmented mental health care, offering actionable insights for researchers, clinicians, and policymakers to develop safe, effective, and equitable digital health interventions.
Artificial intelligence in the diagnosis and prognosis of multimorbidity in the elderly

Aim.                     To evaluate the effectiveness of artificial intelligence in diagnosing and predicting multimorbidity in people over 65 years based on current literature data.                                                           Materials                                          and                                          methods.                                          A systematic review of 153 studies from January 1, 2020 to March 1, 2025 was conducted following PRISMA 2020 guidelines. The PICO model was applied: population – elderly people with multimorbidity (two or more chronic conditions), intervention – artificial intelligence tools (machine learning, deep learning), outcomes – diagnostic accuracy and prognostic performance. Keyword searches were performed in PubMed, Scopus, Web of Science, and Google Scholar databases. Data were synthesized narratively and quantitatively via meta-analysis using the R software version 4.3.2. The method excels in detecting hidden patterns compared to clinical scales.                                                           Results.                     Artificial intelligence demonstrated high diagnostic accuracy for dementia (AUC = 0.833), stroke (AUC = 0.91), cardiovascular diseases (AUC = 0.986–0.991), and osteoporosis (AUC= 0.972). Prognostic performance reached AUC ≈ 0.87 (95% confidence interval: 0.83–0.91) for mortality and hospitalizations. However, for multimorbidity, accuracy was lower (AUC = 0.787– 0.93) due to data heterogeneity and the complexity of disease interactions.                                                           Conclusion.                     Artificial intelligence enhances diagnostic and prognostic capabilities in geriatrics, particularly for individual conditions, but requires data standardization and dynamic models for multimorbidity. Challenges, such as digital ageism and data quality, still hinder its implementation.
EVALUATION OF CONCEPT OF ANTIMICROBIAL STEWARDSHIP AMONG UG MEDICAL STUDENTS FOR BETTER UNDERSTANDING OF USE OF ANTIMICROBIAL AGENTS AND MINIMIZING ANTIMICROBIAL RESISTANCE

Objectives: The objective of this study is to assess interns perception and knowledge about antibiotic stewardship and their preparedness for appropriate use of antimicrobials and to prescribe antimicrobials. Methods: The 164 medical interns of BLDE (DU)’s Shri B.M. Patil Medical College who provided consent to participate in the study were included in this cross-sectional web-based online survey. The study was carried out using a validated questionnaire. The data were analyzed using the statistical tool for the social sciences (Version 20). Results: Out of the 164 interns, 133 (83.1%) completed the entire survey. The term “antimicrobial stewardship” was familiar to 51% (n=68). 50% (n=66) agreed that antibiotics are inappropriately used at the hospital. Approximately 88% (n=104) and 85% (n=113) of the interns thought that insufficient infection control practices and the use of wide spectrum antibiotics were the main causes of resistance development, respectively. About 90% (n=119) thought that lecture series and grand round discussions on the responsible use of antibiotics were good educational tools. About 37% (n=49), 41% (n=54), and 24% (n=32) of the interns understood the proper antibiotic selection for treating simple cases of urinary tract infections, could de-escalate antibiotic therapy, understand an anti-biogram, and could match a different combination of antimicrobials and microorganisms, respectively. Conclusion: The major findings of our study were, despite students having good perception, the knowledge component was inadequate, that is, they lacked their preparedness and confidence to prescribe antibiotics which emphasizes that the students are aware of the antimicrobial stewardship but their knowledge domain needs to be improved for better application in their clinical practice.
Emergence of Complex Structures by Spontaneous Cessation of Diffusion in Self-Organizing Systems

Generating and sustaining complex structures and patterns in diffusion-driven self-organizing systems is challenging in the face of indefinite continuance of diffusion. Carefully tailored heterogen...
SpikeProphecy: A Large-Scale Benchmark for Autoregressive Neural Population Forecasting

Neural population models, which predict the joint firing of many simultaneously recorded neurons forward in time, are typically evaluated by a single aggregate Pearson correlation $r$ between predicted and actual spike counts, a number that masks critical structure. We argue that how we evaluate spike forecasting matters as much as what we build, and introduce SpikeProphecy, the first large-scale benchmark for causal, autoregressive spike-count forecasting on real electrophysiology recordings. Our core contribution is a population metric decomposition that separates aggregate performance into temporal fidelity, spatial pattern accuracy, and magnitude-invariant alignment. The decomposition surfaces aspects of the underlying data that an aggregate scalar collapses together. We apply the protocol to 105 Neuropixels sessions (Steinmetz 2019 + IBL Repeated Site; ~89,800 neurons) with seven architecture baselines spanning four structural families: four SSMs (three diagonal and one non-diagonal), a Transformer, an LSTM, and a spiking network. The decomposition surfaces a brain-region predictability ranking that reproduces across all seven baselines and survives ANCOVA correction for firing-statistics constraints (region $ΔR^2 = 0.018$ above the firing-statistics covariates). It also exposes a sub-Poisson evaluation floor where rigorous metrics combine with genuine biophysical constraints on regular spike trains, and yields a negative result on KL-on-output-rates distillation for ANN-to-SNN transfer in this Poisson count domain.
MMRL: Multi-Modal Representation Learning for Vision-Language Models

Accepted by CVPR 2025
Improving Patient Communication by Simplifying AI-Generated Dental Radiology Reports With ChatGPT: Comparative Study (Preprint)

BACKGROUND                         Medical reports, particularly radiology findings, are often written for professional communication, making them difficult for patients to understand. This communication barrier can reduce patient engagement and lead to misinterpretation. Artificial intelligence (AI), especially large language models such as ChatGPT, offers new opportunities for simplifying medical documentation to improve patient comprehension.                                                                       OBJECTIVE                         We aimed to evaluate whether AI-generated radiology reports simplified by ChatGPT improve patient understanding, readability, and communication quality compared to original AI-generated reports.                                                                       METHODS                         In total, 3 versions of radiology reports were created using ChatGPT: an original AI-generated version (text 1), a patient-friendly, simplified version (text 2), and a further simplified and accessibility-optimized version (text 3). A total of 300 patients (n=100, 33.3% per group), excluding patients with medical education, were randomly assigned to review one text version and complete a standardized questionnaire. Readability was assessed using the Flesch Reading Ease (FRE) score and LIX indices.                                                                       RESULTS                         Both simplified texts showed significantly higher readability scores (text 1: FRE score=51.1; text 2: FRE score=55.0; and text 3: FRE score=56.4; &lt;i&gt;P&lt;/i&gt;&amp;lt;.001) and lower LIX scores, indicating enhanced clarity. Text 3 had the shortest sentences, had the fewest long words, and scored best on all patient-rated dimensions. Questionnaire results revealed significantly higher ratings for texts 2 and 3 across clarity (&lt;i&gt;P&lt;/i&gt;&amp;lt;.001), tone (&lt;i&gt;P&lt;/i&gt;&amp;lt;.001), structure, and patient engagement. For example, patients rated the ability to understand findings without help highest for text 3 (mean 1.5, SD 0.7) and lowest for text 1 (mean 3.1, SD 1.4). Both simplified texts significantly improved patients’ ability to prepare for clinical conversations and promoted shared decision-making.                                                                       CONCLUSIONS                         AI-generated simplification of radiology reports significantly enhances patient comprehension and engagement. These findings highlight the potential of ChatGPT as a tool to improve patient-centered communication. While promising, future research should focus on ensuring clinical accuracy and exploring applications across diverse patient populations to support equitable and effective integration of AI in health care communication.
Layered P2-NaxMn3/4Ni1/4O2 Cathode Materials For Sodium-Ion Batteries: Synthesis, Electrochemistry and Influence of Ambient Storage

Sodium-ion batteries promise efficient, affordable and sustainable electrical energy storage that avoids critical raw materials such as lithium, cobalt and copper. In this work, a manganese-based, cobalt-free, layered NaxMn3/4Ni1/4O2 cathode active material for sodium-ion batteries is developed. A synthesis phase diagram was developed by varying the sodium content x and the calcination temperature. The calcination process towards a phase pure P2-Na2/3Mn3/4Ni1/4O2 material was investigated in detail using in-situ XRD and TGA-DSC-MS. The resulting material was characterized with ICP-OES, XRD and SEM. A stacking fault model to account for anisotropic broadening of (10l) reflexes in XRD is presented and discussed with respect to the synthesis process. In electrochemical half-cells, P2-Na2/3Mn3/4Ni1/4O2 delivers an attractive initial specific discharge capacity beyond 200 mAh g−1, when cycled between 4.3 and 1.5 V. The structural transformation during cycling was studied using operando XRD to gain deeper insights into the reaction mechanism. The influence of storage under humid conditions on the crystal structure, particle surface and electrochemistry was investigated using model experiments. Due to the broad scope of this work, raw material questions, fundamental investigations and industrially relevant production processes are addressed.
Multimodal AI and Large Language Models for Orthopantomography Radiology Report Generation and Q&amp;A

Access to high-quality dental healthcare remains a challenge in many countries due to limited resources, lack of trained professionals, and time-consuming report generation tasks. An intelligent clinical decision support system (ICDSS), which can make informed decisions based on past data, is an innovative solution to address these shortcomings while improving continuous patient support in dental healthcare. This study proposes a viable solution with the aid of multimodal artificial intelligence (AI) and large language models (LLMs), focusing on their application for generating orthopantomography radiology reports and answering questions in the dental domain. This work also discusses efficient adaptation methods of LLMs for specific language and application domains. The proposed system primarily consists of a Blip-2-based caption generator tuned on DPT images followed by a Llama 3 8B based LLM for radiology report generation. The performance of the entire system is evaluated in two ways. The diagnostic performance of the system achieved an overall accuracy of 81.3%, with specific detection rates of 87.9% for dental caries, 89.7% for impacted teeth, 88% for bone loss, and 81.8% for periapical lesions. Subjective evaluation of AI-generated radiology reports by certified dental professionals demonstrates an overall accuracy score of 7.5 out of 10. In addition, the proposed solution includes a question-answering platform in the native Sinhala language, alongside the English language, designed to function as a chatbot for dental-related queries. We hope that this platform will eventually bridge the gap between dental services and patients, created due to a lack of human resources. Overall, our proposed solution creates new opportunities for LLMs in healthcare by introducing a robust end-to-end system for the automated generation of dental radiology reports and enhancing patient interaction and awareness.
Lean Systems Approaches to Health Technology Assessment

Many countries now use health technology assessment (HTA) to review new and emerging technologies, especially with regard to reimbursement, pricing and/or clinical guidelines. One of the common, but not universal, features of these systems is the use of economic evaluation, normally cost-effectiveness analysis (CEA), to confirm that new technologies offer value for money. Many have criticised these systems as primarily being concerned with cost containment, rather than advancing the interests of patients or innovators. This paper calls into question the underlying principles of CEA by arguing that value in the healthcare system may in fact be unconstrained. It is suggested that 'lean management principles' can be used not only to trim waste from the health system, but as a method of creating real incentives for innovation and value creation. Following the lean paradigm, this value must be defined purely from the patients' perspective, and the entire health system needs to work towards the creation of such value. This paper offers as a practical example a lean approach to HTA, arguing that such an approach would lead to better incentives for innovation in health, as well as more patient-friendly outcomes in the long run.