ethicalabs/Research-Intent-Collab huggingface dataset:
Biophoton Therapy Enhances Endogenous Stem-Cell Production: A Non-Invasive Approach to Regenerative Medicine

BackgroundEndogenous stem-cell mobilization represents a promising alternative to transplanted stem-cell therapies, which are limited by poor survival, immune incompatibility, and procedural risks. Biophotons, ultra-weak, non-thermal photons generated by biological systems have been increasingly implicated in mitochondrial regulation, redox signaling, and cellular repair. Whether controlled biophoton exposure can safely stimulate endogenous stem-cell production in humans has not been previously tested in a randomized clinical setting.ObjectiveTo evaluate whether a non-invasive, non-thermal biophoton generator can increase circulating stem/progenitor cell counts and improve functional health outcomes in adults.MethodsIn a randomized, double-blinded, placebo-controlled clinical trial (FIAM-SC255; NCT06855459), 71 adults underwent 14 days of nighttime exposure to either an active Tesla BioHealing® biophoton  generator  or  a  visually  identical  placebo.  Circulating  CD34⁺,  CD133⁺,  and  CD34⁺CD133⁺ stem/progenitor  cells  were  quantified  by  flow  cytometry  at  baseline  and  follow-up.  Quality-of-life (SF-36) and Pain Disability Index (PDI) scores were assessed concurrently. After the blinded phase, placebo participants crossed over to active treatment. Safety monitoring occurred throughout.ResultsActive biophoton exposure produced significant increases in circulating stem/progenitor cells compared with baseline and placebo, including a 2.7-fold rise in CD34⁺, 3.5-fold rise in CD133⁺ (leukocyte population), and 3.1-fold rise in CD34⁺CD133⁺ cells (all p &lt; 0.01). No significant changes were observed during placebo exposure. SF-36 scores improved by 16–30% following active treatment (p &lt; 0.05), and PDI scores decreased by an average of 7.35 points (p &lt; 0.00001). Improvements replicated in the crossover cohort. No adverse events occurred.ConclusionNon-thermal biophoton therapy safely and robustly enhances endogenous stem-cell mobilization while simultaneously improving pain-related function and overall quality of life. The magnitude of stem-cell elevation is comparable to levels typically achieved through exogenous stem-cell infusion—yet without invasive procedures or safety risks. These findings support biophoton therapy as a clinically meaningful, non-invasive regenerative modality with potential relevance to neurological, metabolic, and age-related conditions.
sj-docx-7-css-10.1177_24705470231169106 - Supplemental material for Mental Health Among People Presenting for Care of Physical Symptoms: The Factors Associated with Suicidality and Symptoms of Depression and Anxiety are Similar Across Specialties

Supplemental material, sj-docx-7-css-10.1177_24705470231169106 for Mental Health Among People Presenting for Care of Physical Symptoms: The Factors Associated with Suicidality and Symptoms of Depression and Anxiety are Similar Across Specialties by Sina Ramtin, Kathleen Carberry, Maria Correa, David Ring, Carol Alter and Donna Shanor in Chronic Stress
Towards Open-Source and Collaborative Project-Based Learning in Engineering Education: Situation, Resources and Challenges

Several strategies have been proposed for the promotion of successful and sustainable project-based learning (PBL) activities, whose benefits for higher education institutions include: improvements in student learning outcome, given the more holistic learning environments that provide the complex implications of the engineering practice; the engagement of the community of professors, which get involved in a shared dreams environment and increase their mutual communication; the promotion of technological and research vocations and the enhanced interconnections between academia and industry derived from PBL experiences devoted to solving real societal and entrepreneurial challenges. The incorporation of collaborative creation or collaborative design methods and the use of open-source resources may provide additional benefits to this teaching-learning methodology, by helping to make these experiences more formative, more easily replicable, sustainable and cost-effective. In this study we concentrate on the promotion of active learning experiences linked to collaborative engineering and supported by open-source tools and resources. To this end, we analyze available resources for supporting educators inplanning and students in living open-source collaborative PBL (OSC-PBL) experiences. Then, a case of study linked to OSC-PBL in the biomedical engineering field is presented and examined. Finally, lessons learned and current challenges for the future success of these teaching-learning experiences are discussed.
THE EFFECTS OF ARTIFICIAL INTELLIGENCE GENERATIVE CONTENT ASSISTED PRODUCT DESIGN ON COLLEGE STUDENTS CREATIVE ABILITY AND MOTIVATION

With the rapid advancement of artificial intelligence (AI) technology, the integration of AI-generated content (AIGC) in creative processes has sparked significant sociological interest. This study investigated gender differences in creative ability and motivation when using AI-assisted tools. Through a quasi-experimental design, this research examined 70 college participants to compare AI-assisted versus traditional approaches. Using standardized assessment metrics, the study measured these three key variables across gender groups. The results revealed three key findings. First, participants using AI-assisted tools demonstrated significantly higher creative ability compared to the control group, with male participants showing particularly strong performance improvements. Second, the AI-assisted group showed elevated levels of motivation across both gender groups. The findings contribute to understanding gender-based differences in human-AI interaction and creative processes in technological environments. This study advances the theoretical discourse on gender differences in AI-augmented creative processes and provides insights into the evolving relationship between gender, creativity, and technological advancement in contemporary society.
Linking dissemination and implementation science to Learning Health Systems: Opportunities for Clinical and Translational Science Award institutions

AbstractLearning Health Systems (LHS) iteratively implement and evaluate health improvement projects. Dissemination and implementation (D&amp;I) science is the study of evidence-based practices in real-world settings, a critical tool for LHS. This paper explores intersections between LHS and D&amp;I science in Clinical and Translational Science Awards (CTSAs) institutions and identifies critical components of collaboration. We conducted website scans of 34 CTSAs and their home institutions that had Dissemination, Implementation, and Knowledge Translation (DIKT) Workgroup members. We identified linkages between CTSAs and their institutions’ LHS. We interviewed six CTSA leaders experienced in LHS and D&amp;I sciences. Nearly half of CTSAs identified an LHS structure on their websites, but only one-third indicates CTSA involvement in these efforts. Interviewees identified key components for successful integration of LHS and D&amp;I sciences: leadership, infrastructure, balance between rigor and efficiency, and aligned incentives. The need for research integration in LHS, to improve evaluation and increase knowledge, is an emerging opportunity for D&amp;I scientists and CTSAs. CTSAs that are engaged in D&amp;I science can introduce and/or expand the role of D&amp;I science in LHS. Collaboration between CTSAs and clinical leaders could result in strengthened relationships between clinical and research enterprises, effective and efficient health care delivery, and improved health.
To Resist or Not to Resist? That is the Dangerous Situation: A Look at Antimicrobial Stewardship in Pediatric Care in North America

This article will explore the impact of antibiotic resistance on pediatric care in North America, specifically focusing on how it negatively affects patient health outcomes. The mitigation of this issue in everyday health practice will be outlined. Antibiotics are one of the most commonly prescribed medications in children, with a significant number of them being used inappropriately. Considering the growing global threat of antibiotic resistant superbugs, it is vital to develop strategies and programs for decreasing antibiotic misuse and combating antibiotic resistance. Antimicrobial stewardship is one such method of reducing antibiotic resistance and has already shown evidence of improving patient outcomes, such as decreasing risk of future invasive infections, decreasing hospitalization and decreasing mortality. With more awareness to this dangerous issue, we are beginning to see the development and implementation of a variety of practices aimed at using antibiotics judiciously in pediatric patients across North America. This article will address the severity of the issue of antibiotic resistance in pediatric care in North America and highlight how this can be managed using antimicrobial stewardship principles that are practical, evidence-based and easily implementable in healthcare practices. Although there is still much work to be done, small improvements in resistance rates show that we are moving in the right direction.
Abstract PO-004: A deep convolutional neural network for segmentation of whole-slide pathology images in glioblastoma

Abstract                Glioblastoma is the most aggressive type of brain cancer with high-levels of intra- and inter-tumour heterogeneity that contribute to its rapid growth and invasion within the brain. Here, we have used a deep convolutional neural network (DCNN) as a semantic segmentation model to segment seven different tumour regions including leading-edge (LE), infiltrating tumour (IT), cellular tumour (CT), cellular tumour microvascular proliferation (CTmvp), cellular tumour pseudopalisading region around necrosis (CTpan), cellular tumour perinecrotic zones (CTpnz) and cellular tumour necrosis (CTne) in digitised glioblastoma histopathological slides from The Cancer Genome Atlas (TCGA). Analysis of segmentation results from tumour images together with matched RNA expression data identified genetic signatures that are specific to these different tumour regions. We found that spatially resolved gene signatures were strongly correlated with survival in patients with defined genetic mutations. Moreover, in silico cell ontology analysis and single-cell RNA sequencing data from resected glioblastoma tissue samples, showed that these tumour regions had different gene signatures, suggesting they are driven by different cell types in the tumour microenvironment. This points to a key role for interactions between microglia/pericytes/monocytes and tumour cells that occur in the IT and CTmvp regions, which may contribute to poor patient survival. Overall, this work identifies key histopathological features that are indicative of patient survival and detected spatially associated genetic signatures that mediate tumour-stroma interactions that should be investigated as new targets in glioblastoma.                Citation Format: Amin Zadeh Shirazi, Mark D. McDonnell, Eric Fornaciari, Narjes Sadat Bagherian, Kaitlin G. Scheer, Michael S. Samuel, Mahdi Yaghoobi, Rebecca J. Ormsby, Santosh Poonnoose, Damon Tumes, Guillermo A. Gomez. A deep convolutional neural network for segmentation of whole-slide pathology images in glioblastoma [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-004.
Time-dependent density functional theory of high excitations: To infinity, and beyond

We review the theoretical background for obtaining both quantum defects and scattering phase shifts from time-dependent density functional theory. The quantum defect on the negative energy side of the spectrum and the phase shift on the positive energy side merge continuously at E=0, allowing both to be found by the same method. We illustrate with simple one-dimensional examples: the spherical well and the delta well potential. As an example of a real system, we study in detail elastic electron scattering from the He${}^{+}$ ion. We show how the results are influenced by different approximations to the unknown components in (time-dependent) density functional theory: the ground state exchange-correlation potential and time-dependent kernel. We also revisit our previously obtained results for $e$-H scattering. Our results are remarkably accurate in may cases, but fail qualitatively in others.
The AG Opinion in the Celmer case: why lack of judicial independence should have been framed as a rule of law issue

Verfassungsblog: On Matters Constitutional
PROBING X-RAY ABSORPTION AND OPTICAL EXTINCTION IN THE INTERSTELLAR MEDIUM USING CHANDRA OBSERVATIONS OF SUPERNOVA REMNANTS

ABSTRACT                We present a comprehensive study of interstellar X-ray extinction using the extensive Chandra supernova remnant (SNR) archive and use our results to refine the empirical relation between the hydrogen column density and optical extinction. In our analysis, we make use of the large, uniform data sample to assess various systematic uncertainties in the measurement of the interstellar X-ray absorption. Specifically, we address systematic uncertainties that originate from (i) the emission models used to fit SNR spectra; (ii) the spatial variations within individual remnants; (iii) the physical conditions of the remnant such as composition, temperature, and non-equilibrium regions; and (iv) the model used for the absorption of X-rays in the interstellar medium. Using a Bayesian framework to quantify these systematic uncertainties, and combining the resulting hydrogen column density measurements with the measurements of optical extinction toward the same remnants, we find the empirical relation N                   H = (2.87 ± 0.12) × 1021                   A                   V cm−2, which is significantly higher than the previous measurements.
The Prime Minister’s Clarion Call on Antimicrobial Resistance: From Public Concern to System Action through Diagnostics, Stewardship and One Health

Antimicrobial resistance (AMR) is a major and growing threat to health security. It weakens the treatment of common infections and undermines the safety of routine medical care. In his Mann Ki Baat message of 28 December 2025, the Prime Minister warned that indiscriminate antibiotic use is making common infections such as pneumonia and urinary tract infections harder to treat, and urged citizens not to use antibiotics as a ‘quick fix’. This commentary takes that clarion call as a starting point. It proposes three practical system shifts for India: (i) diagnostic-led stewardship at the point of care, (ii) routine monitoring of antibiotic use and resistance with feedback to facilities, and (iii) operational One Health action at priority human–animal–environment interfaces. The goal is to make the right antibiotic decision the easy decision
Modelling of flow during fracture growth driven by multiple fluid stages using high-performance parallel computing algorithms

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1992. ; Includes bibliographical references (leaves 243-265). ; by Amaury Fonseca Junior. ; Ph.D.
AIn't Nothing But a Survey? Using Large Language Models for Coding German Open-Ended Survey Responses on Survey Motivation

The recent development and wider accessibility of LLMs have spurred discussions about how they can be used in survey research, including classifying open-ended survey responses. Due to their linguistic capacities, it is possible that LLMs are an efficient alternative to time-consuming manual coding and the pre-training of supervised machine learning models. As most existing research on this topic has focused on English-language responses relating to non-complex topics or on single LLMs, it is unclear whether its findings generalize and how the quality of these classifications compares to established methods. In this study, we investigate to what extent different LLMs can be used to code open-ended survey responses in other contexts, using German data on reasons for survey participation as an example. We compare several state-of-the-art LLMs and several prompting approaches, and evaluate the LLMs' performance by using human expert codings. Overall performance differs greatly between LLMs, and only a fine-tuned LLM achieves satisfactory levels of predictive performance. Performance differences between prompting approaches are conditional on the LLM used. Finally, LLMs' unequal classification performance across different categories of reasons for survey participation results in different categorical distributions when not using fine-tuning. We discuss the implications of these findings, both for methodological research on coding open-ended responses and for their substantive analysis, and for practitioners processing or substantively analyzing such data. Finally, we highlight the many trade-offs researchers need to consider when choosing automated methods for open-ended response classification in the age of LLMs. In doing so, our study contributes to the growing body of research about the conditions under which LLMs can be efficiently, accurately, and reliably leveraged in survey research.
Methodology for tunnel risk assessment using fault and event tree analysis

The intense demand and construction of tunnels is accompanied by uncertainties. The reason for appearance of uncertainties are the complex solutions and conditions for these structures. Location and dimensions are becoming more challenging, and the construction is predicted in complexed geological conditions, leading to application of new approaches, methodologies and technologies by the engineers. Most of the uncertainties and unwanted events in tunnelling occur in the construction phase, which generally leads to economic consequences and time losses. For easier handling of the uncertainties, they should be anticipated and studied within a separate part of each project. One of the newer approaches to dealing with uncertainties is hazard and risk assessment and defining ways to deal with them i.e. management. Hazards and risks can be analysed qualitatively and quantitatively. The quantitative analysis, examines the causes and consequences in more detail way and gives explanation of the dependencies. With the quantitative approach, a more valuable information for decision-making can be provided. There are various models and methods used for the quantification of hazards and risks. This paper presents a methodology in which the fault tree analysis and event tree analysis are used in combination to obtain quantitative results. The fault tree analysis is used for assessment of various hazards and the different ways and reasons that cause them. The event tree analysis is a method for assessing the possible scenarios, which follow after a certain hazard i.e. the consequences that may occur in the project. These trees represent graphic models combined with a mathematical (probabilistic) model, which give the probability of occurrence of the risks.
Dark Energy and Cosmic Sound: w(z) Surveys with the Gemini/Subaru Wide-Field Multi-Object Spectrograph

This white paper gives an overview of the proposed Gemini/Subaru Wide-Field Multi-Object Spectrograph (WFMOS) and the proposed redshift surveys of 2.6 million galaxies with 0.5<z<3.3 over 2000 deg^2 of sky. These surveys will probe the baryonic acoustic oscillations in the galaxy power spectrum with unprecedented precision and over a range of redshifts and deliver dark energy w(z) constraints an order of magnitude better than current limits. We discuss the requirements on precursor observations and on calibrations, the systematics in the method and the quantitative precision obtainaible in distance-redshift and expansion-rate-redshift measurements which feed in to the w(z) precision. We also outline the technological and scientific strengths and risks which might be associated with the project and the relationship of WFMOS to other baryon oscillation experiments.
Prognostic value, clinical effectiveness, and cost-effectiveness of high-sensitivity C-reactive protein as a marker for major cardiac events in asymptomatic individuals: A health technology assessment report

Objectives: The aim of this study was to compare the predictive value, clinical effectiveness, and cost-effectiveness of high-sensitivity C-reactive protein (hs-CRP)-screening in addition to traditional risk factor screening in apparently healthy persons as a means of preventing coronary artery disease.Methods and Results: The systematic review was performed according to internationally recognized methods. Seven studies on risk prediction, one clinical decision-analytic modeling study, and three decision-analytic cost-effectiveness studies were included. The adjusted relative risk of high hs-CRP-level ranged from 0.7 to 2.47 (p &lt; .05 in four of seven studies). Adding hs-CRP to the prediction models increased the areas under the curve by 0.00 to 0.027. Based on the clinical decision analysis, both individuals with elevated hs-CRP-levels and those with hyperlipidemia have a similar gain in life expectancy following statin therapy. One high-quality economic modeling study suggests favorable incremental cost-effectiveness ratios for persons with elevated hs-CRP and higher risk. However, many model parameters were based on limited evidence.Conclusions: Adding hs-CRP to traditional risk factors improves risk prediction, but the clinical relevance and cost-effectiveness of this improvement remain unclear.
Risk Management Framework-Based Failure Mode and Effect Analysis for AI Risk Assessment

As artificial intelligence (AI) technologies continue to spread into human life, developers must ensure benefits while minimizing the risk of adverse impacts. This study aims to evaluate risks in real-world AI applications using the AI Incident Database. It employs Failure Mode and Effect Analysis and the National Institute of Standards and Technology AI Risk Management Framework to identify failures, their causes and effects, and assess how current systems address them. A total of 100 incident reports were analyzed. The findings indicate frequent failures in autonomous systems and biased predictions. Seven cases were classified in the highest risk categories, including those involving physical harm and loss of life. Over 80% failures originated from algorithmic flaws or poor data quality. The method employed successfully evaluates the risks in current AI applications, revealing critical gaps in risk management and emphasizing the urgent need for targeted safeguards and proactive mitigation strategies.