BACKGROUND For pediatric patients with osteosarcoma, nutritional status is associated with treatment response and survival outcome. AIM To explore computed tomography (CT)-derived body composition using artificial intelligence (AI)-based tissue segmentation in pediatric patients with osteosarcoma in order to identify possible predictors for response to neoadjuvant chemotherapy and overall survival (OS). METHODS In this study, the body composition of 133 patients aged ≤ 18 years with young osteosarcoma at the third lumbar vertebra level was analyzed on a retrospective dataset using an AI-based software tool (Visage Imaging). All patients received neoadjuvant chemotherapy and surgical resection who underwent abdominal CT scanning before and after chemotherapy. The primary outcome was OS, and the secondary outcome was response to neoadjuvant chemotherapy. The CT-derived factors, including skeletal muscle index, subcutaneous and visceral adipose tissue index, and skeletal muscle density (SMD), and other nutrition factor, such as systemic immune-inflammation index and prognostic nutritional index were collected before and after chemotherapy. Changes (Δ) in body composition parameters between pre-chemotherapy and post-chemotherapy CT were assessed. Logistic and Cox regression models were used to identify predictors of therapeutic response and OS, respectively. Statistical significance was defined as a two-sided P < 0.05. RESULTS On our results age at diagnosis, post-chemotherapy systemic immune-inflammation index, and ΔSMD had prognostic value for classification of responders and non-responders to neoadjuvant chemotherapy at multivariable logistic analysis. ΔSMD was found as an independent predictor for OS with a hazard ratio of 1.113 (95% confidence interval: 1.035-1.197, P = 0.004). At an optimal cut-off value of 6.27 Hounsfield unit per 30 days, the median OS with low ΔSMD vs high ΔSMD was 94.657 months vs 43.356 months (P = 0.001). CONCLUSION AI-based analysis of third lumbar vertebra body composition on CT images is feasible in pediatric patients receiving neoadjuvant chemotherapy. ΔSMD is a prognostic indicator that a great value significantly predicts the poor treatment response and short OS. Using CT-derived and other nutritional predictors may conduct optimized nutrition support and appropriate selection of individualized therapeutic regimen.
This repository contains the file Jurisverso_Dataset_Trabajo_v1 (2).xlsx, a relational and fully synthetic dataset designed for development, quality assurance (QA), analytics, and academic validation within the educational and legaltech ecosystem. The dataset emulates the "Jurisverso Legal" environment, a simulation and assessment system focused on the Colombian jurisdiction. It has been conceived as a secure tool for research in legal argumentation, competency assessment, and the application of artificial intelligence in law. Being composed exclusively of fictional data, it guarantees total privacy and eliminates the ethical risks associated with the use of personal information or real judicial records. Dataset Structure: The file consists of 20 spreadsheets that make up a comprehensive data ecosystem: Metadata and Data Dictionaries: The 00_Guia and 00_Diccionario sheets provide versioning, usage warnings, and the relationship map (primary and foreign keys) for the entire dataset. Main Entities: 01_Usuarios: Synthetic profiles of students/users, institutional roles, and analytics consents. 02_Materias and 03_Competencias: Academic catalog and skills map with their respective assessment thresholds. 04_Normativa: Metadata of the official normative sources applicable to the cases. Practical Components and Case Studies: 05_Casos: Detailed hypothetical legal situations including facts, legal issues, roles, and available evidence. 06_Preguntas and 07_Intentos_Diag: Assessment bank and diagnostic attempt records for the synthetic users. Interactions, Argumentation, and Analytics: Sheets oriented towards continuous learning tracking, such as 08_JurisAnalisis, 10_PlanEstudio, 11_Tutorias, 13_Argumentos (structuring of legal arguments), and 19_Dashboard for metrics visualization. Methodology and Scope: With a cutoff date of September 2026, this resource allows researchers to test legal analysis algorithms, simulate assisted tutoring workflows, and evaluate algorithmic transparency models in decision-making or competency assessment within Colombian law. Technical Details: File name: Jurisverso_Dataset_Trabajo_v1 (2).xlsx Version: 1.0.0 Format: Microsoft Excel (.xlsx) Jurisdiction: Colombia Language: Spanish
Beliña Annery Herrera Tapias, Renzo Rodrigo Gandolfi Diaz, Diego Hernández Guzmán· Zenodo (CERN European Organi...· 0 citations
This deposit contains the prospectively frozen ARS Cross-Domain Evaluation Protocol v1.8 and its reproducibility artifacts. The protocol evaluates whether the Adaptive Research Scoping (ARS) four-dimensional corpus qualification architecture generalizes across deliberately different evidence domains while allowing only the operationalization of A (Alignment), C (Coverage), K (Contamination), and R (Contextual Representation) to vary by domain. Target domains: 1. Biomedical evidence 2. Education 3. Psychology 4. Computer science / artificial intelligence 5. Social science The package contains: - ARS v1.8 protocol and preregistration-ready statistical analysis plan - reproducible analysis scaffold - frozen configuration template - README and execution instructions - SHA-256 freeze manifest - cryptographic freeze-proof JSON The SHA-256 manifest is intended to provide content-integrity evidence. The local cryptographic freeze should not be interpreted as an independent trusted timestamp. This Zenodo deposit provides an independent archival record and publication timestamp. The protocol explicitly prohibits silently substituting missing domains. A five-domain generalization claim is permitted only if all five target domains meet the predefined eligibility criteria.
MOHAMMAD PASHA, Mohammed Ali Shaikh· Zenodo (CERN European Organi...· 0 citations
This article presents a narrative review of Machine Learning for Scientific Discovery in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpretive synthesis of representative contributions, the review reconstructs the historical development of the area, examines the conceptual foundations and definitional disputes that organize its debates, and maps the contemporary landscape of research, including the methodological shift toward data-intensive approaches and the institutional pressures that shape publication practice. Particular attention is given to the role of AI for science and discovery as organizing themes, and to the conditions under which findings from different research traditions can be brought into productive comparison. The review identifies three synthetic conclusions: the literature is cumulatively strong but organizationally weak; methodological pluralism is better understood as a resource than as a defect; and the growing practical salience of the topic raises the stakes of its unresolved conceptual questions. An agenda for future work is proposed, emphasizing integrative research designs, transparent synthesis practices, and the protection of definitional and infrastructural work on which cumulative progress depends. The article is intended as both a reference map for newcomers and a provocation for specialists in Artificial Intelligence.
Zen Revista, 10 IA· Zenodo (CERN European Organi...· 0 citations
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This article presents a narrative review of Transfer Learning and the Pretraining Paradigm in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpretive synthesis of representative contributions, the review reconstructs the historical development of the area, examines the conceptual foundations and definitional disputes that organize its debates, and maps the contemporary landscape of research, including the methodological shift toward data-intensive approaches and the institutional pressures that shape publication practice. Particular attention is given to the role of transfer learning and pretraining as organizing themes, and to the conditions under which findings from different research traditions can be brought into productive comparison. The review identifies three synthetic conclusions: the literature is cumulatively strong but organizationally weak; methodological pluralism is better understood as a resource than as a defect; and the growing practical salience of the topic raises the stakes of its unresolved conceptual questions. An agenda for future work is proposed, emphasizing integrative research designs, transparent synthesis practices, and the protection of definitional and infrastructural work on which cumulative progress depends. The article is intended as both a reference map for newcomers and a provocation for specialists in Artificial Intelligence.
Zen Revista, 10 IA· Zenodo (CERN European Organi...· 0 citations
This study examines the impact of Artificial Intelligence Capability (AIC) on Corporate Financial Performance (CFP) through the mediating role of Financial Decision-Making Quality (FDMQ). A simulation-based quantitative explanatory design was applied to 200 computer-generated Likert-scale observations calibrated to represent finance and accounting decision contexts in AI-enabled organizations. The analysis uses instrument validity and reliability testing, multiple linear regression, and mediation analysis following PROCESS Model 4 logic with 5,000 bootstrap resamples in IBM SPSS Statistics. The results show that AIC positively affects FDMQ (β = 0.617, p < 0.001) and retains a significant direct effect on CFP after the mediator is included (β = 0.316, p < 0.001). FDMQ also has a positive effect on CFP (β = 0.473, p < 0.001), while the outcome model explains 50.8% of the variance in CFP. The indirect effect is significant (B = 0.319; 95% bootstrap CI [0.231, 0.412]), indicating partial mediation. The findings support the proposed mechanism that AI capability creates financial value when technological resources are translated into timely, evidence-based, and economically sound financial decisions.
Fera Lufhidarani Pranita, Mohammad Sigit adi Nugraha, Maria Evy Purwitasari· Jurnal Publikasi Ilmu Manaje...· 0 citations
The use of artificial intelligence (AI) models for gastric cancer prevention shows promise beyond traditional diagnostic approaches. Modern digital technologies can be applied to global healthcare. The implementation of deep machine learning has significantly increased the efficiency of computer vision. The introduction of AI is very effective in the field of diagnostic recognition of pathology in endoscopic, radiological and histological images. Robotic surgery has good development prospects. Also, many methods of treating diseases are implemented using AI technologies. Comparative studies of the effectiveness of a human doctor and AI have a high level of evidence. Comparative studies report high levels of performance for AI vs human clinicians in selected tasks, although evidence varies by task and dataset. AI demonstrates higher efficiency than 5-10 highly qualified experts in many areas of medicine. The main areas of medicine actively use AI: Diagnostic recognition of X-ray computed tomographic and magnetic resonance images, endoscopic and histological patterns. The use of AI has begun in the field of targeted treatment. The development of robot-associated surgery continues. There is a good prospect for using AI not only for recognizing X-ray computer images, in endoscopy, histology and targeted treatment, but also for subjective methods of examining patients. For example, the development of AI models for questioning patients by correspondence or conversation between the interlocutor - a doctor and the interlocutor-a patient has begun. The number of assistants (interlocutors, digital agents) can be more than two. The language of communication can also be any.
Sergey M Kotelevets· WArtificial Intelligence in...· 0 citations
This qualitative multiple case study follows two EAP teachers: one novice and one experienced over 24 months to explore how they enacted professional agency during significant educational reforms at a Sino-British university in China. The study is situated within a context of “flux and transformation” , a period marked by the cancellation of Year 2 EAP courses and the rapid integration of Generative Artificial Intelligence (GenAI). The study traced the co-evolution of emotions, identity negotiations, and agentive actions over 24 months (2023–2025) using critical reflective narratives, semi-structured interviews, and informal communication logs. The findings reveal that agency is jointly mediated by emotion and identity. The experienced teacher demonstrated adaptive agency, reframing anxiety as a catalyst for professional growth and constructively integrating the teacherresearcher identity. In contrast, the novice teacher exhibited strategic compliance coupled with internal resistance, as negative emotional turbulence and identity fragmentation blocked meaningful identity negotiation. The study proposes the “Emotion-Identity-Agency Nexus in Flux,” a non-linear model illustrating how macro-political forces (e.g. neoliberal Key Performance Indexes (KPIs), curriculum cuts, GenAI disruption) are filtered through teachers’ emotional and identity resources, shaping whether agency manifests as adaptation, compliance, or resistance. Our findings point to the need for emotional scaffolding for novice teachers, differentiated performance evaluation policies, and a reconceptualization of the teacher-researcher role in EMI contexts.
Can Chen· International Journal of TES...· 0 citations
This deposit contains the prospectively frozen ARS Cross-Domain Evaluation Protocol v1.8 and its reproducibility artifacts. The protocol evaluates whether the Adaptive Research Scoping (ARS) four-dimensional corpus qualification architecture generalizes across deliberately different evidence domains while allowing only the operationalization of A (Alignment), C (Coverage), K (Contamination), and R (Contextual Representation) to vary by domain. Target domains: 1. Biomedical evidence 2. Education 3. Psychology 4. Computer science / artificial intelligence 5. Social science The package contains: - ARS v1.8 protocol and preregistration-ready statistical analysis plan - reproducible analysis scaffold - frozen configuration template - README and execution instructions - SHA-256 freeze manifest - cryptographic freeze-proof JSON The SHA-256 manifest is intended to provide content-integrity evidence. The local cryptographic freeze should not be interpreted as an independent trusted timestamp. This Zenodo deposit provides an independent archival record and publication timestamp. The protocol explicitly prohibits silently substituting missing domains. A five-domain generalization claim is permitted only if all five target domains meet the predefined eligibility criteria.
MOHAMMAD PASHA, Mohammed Ali Shaikh· Zenodo (CERN European Organi...· 0 citations
This article presents a narrative review of Machine Learning for Scientific Discovery in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpretive synthesis of representative contributions, the review reconstructs the historical development of the area, examines the conceptual foundations and definitional disputes that organize its debates, and maps the contemporary landscape of research, including the methodological shift toward data-intensive approaches and the institutional pressures that shape publication practice. Particular attention is given to the role of AI for science and discovery as organizing themes, and to the conditions under which findings from different research traditions can be brought into productive comparison. The review identifies three synthetic conclusions: the literature is cumulatively strong but organizationally weak; methodological pluralism is better understood as a resource than as a defect; and the growing practical salience of the topic raises the stakes of its unresolved conceptual questions. An agenda for future work is proposed, emphasizing integrative research designs, transparent synthesis practices, and the protection of definitional and infrastructural work on which cumulative progress depends. The article is intended as both a reference map for newcomers and a provocation for specialists in Artificial Intelligence.
Zen Revista, 10 IA· Zenodo (CERN European Organi...· 0 citations
This repository contains the file Jurisverso_Dataset_Trabajo_v1 (2).xlsx, a relational and fully synthetic dataset designed for development, quality assurance (QA), analytics, and academic validation within the educational and legaltech ecosystem. The dataset emulates the "Jurisverso Legal" environment, a simulation and assessment system focused on the Colombian jurisdiction. It has been conceived as a secure tool for research in legal argumentation, competency assessment, and the application of artificial intelligence in law. Being composed exclusively of fictional data, it guarantees total privacy and eliminates the ethical risks associated with the use of personal information or real judicial records. Dataset Structure: The file consists of 20 spreadsheets that make up a comprehensive data ecosystem: Metadata and Data Dictionaries: The 00_Guia and 00_Diccionario sheets provide versioning, usage warnings, and the relationship map (primary and foreign keys) for the entire dataset. Main Entities: 01_Usuarios: Synthetic profiles of students/users, institutional roles, and analytics consents. 02_Materias and 03_Competencias: Academic catalog and skills map with their respective assessment thresholds. 04_Normativa: Metadata of the official normative sources applicable to the cases. Practical Components and Case Studies: 05_Casos: Detailed hypothetical legal situations including facts, legal issues, roles, and available evidence. 06_Preguntas and 07_Intentos_Diag: Assessment bank and diagnostic attempt records for the synthetic users. Interactions, Argumentation, and Analytics: Sheets oriented towards continuous learning tracking, such as 08_JurisAnalisis, 10_PlanEstudio, 11_Tutorias, 13_Argumentos (structuring of legal arguments), and 19_Dashboard for metrics visualization. Methodology and Scope: With a cutoff date of September 2026, this resource allows researchers to test legal analysis algorithms, simulate assisted tutoring workflows, and evaluate algorithmic transparency models in decision-making or competency assessment within Colombian law. Technical Details: File name: Jurisverso_Dataset_Trabajo_v1 (2).xlsx Version: 1.0.0 Format: Microsoft Excel (.xlsx) Jurisdiction: Colombia Language: Spanish
Beliña Annery Herrera Tapias, Renzo Rodrigo Gandolfi Diaz, Diego Hernández Guzmán· Zenodo (CERN European Organi...· 0 citations
Abstract Despite decades of intensive research and substantial clinical gains, malaria remains a major global health challenge exacerbated by the continued emergence of drug-resistant strains of the causative agent, Plasmodium parasites. Encouragingly, recent advances in functional genomics, chemical biology and computational science are reshaping antimalarial drug discovery. In this review, we examine the discovery and development of next-generation antimalarials, including advances in phenotypic and target-based screening, omics-enabled target discovery and emerging therapeutic modalities such as long-acting agents, targeted covalent inhibitors and host-directed therapies. We further evaluate the opportunities and limitations of drug repurposing and discuss how artificial intelligence and data-driven approaches are reshaping target identification, compound optimisation and clinical development. Finally, we argue that future success will depend not only on scientific innovation but also on interdisciplinary collaboration, equitable partnerships, open science and the development of accessible therapies tailored to malaria-endemic populations.
Hannah Asiki, Jason Hlozek, Kathryn J. Wicht et al.· EMBO Molecular Medicine· 0 citations
