Pelvic inflammatory disease is a common but often uncertain diagnosis, crossing the specialty boundaries of general practice, family planning, accident and emergency medicine, genitourinary medicine and gynaecology. As well as the immediate morbidity it causes, it can commonly lead to potentially serious complications (subfertility, ectopic pregnancy, chronic pelvic pain and psychological morbidity). Despite a number of published guidelines, uncertainty and variation still surround its diagnosis, clinical management and treatment. In this article, we review current guidelines and literature and highlight recent developments and controversies.
This paper develops a broad concept of systemic risk, the basic economic concept for the understanding of financial crises. It is claimed that any such concept must integrate systemic events in banking and financial markets as well as in the related payment and settlement systems. At the heart of systemic risk are contagion effects, various forms of external effects. The concept also includes simultaneous financial instabilities following aggregate shocks. The quantitative literature on systemic risk, which was evolving swiftly in the last couple of years, is surveyed in the light of this concept. Various rigorous models of bank and payment system contagion have now been developed, although a general theoretical paradigm is still missing. Direct econometric tests of bank contagion effects seem to be mainly limited to the United States. Empirical studies of systemic risk in foreign exchange and security settlement systems appear to be non-existent. Moreover, the literature surveyed reflects the general difficulty to develop empirical tests that can make a clear distinction between contagion in the proper sense and joint crises caused by common shocks, rational revisions of depositor or investor expectations when information is asymmetric ('information-based' contagion) and 'pure' contagion as well as between 'efficient' and 'inefficient' systemic events. JEL Classification: G21, G29, G12, E49
Hita Kambhamettu, Bhavana Dalvi Mishra, Andrew Head
et al.
Developing a novel research idea is hard. It must be distinct enough from prior work to claim a contribution while also building on it. This requires iteratively reviewing literature and refining an idea based on what a researcher reads; yet when an idea changes, the literature that matters often changes with it. Most tools offer limited support for this interplay: literature tools help researchers understand a fixed body of work, while ideation tools evaluate ideas against a static, pre-curated set of papers. We introduce literature-initiated pivots, a mechanism where engagement with literature prompts revision to a developing idea, and where that revision changes which literature is relevant. We operationalize this in LitPivot, where researchers concurrently draft and vet an idea. LitPivot dynamically retrieves clusters of papers relevant to a selected part of the idea and proposes literature-informed critiques for how to revise it. A lab study ($n{=}17$) shows researchers produced higher-rated ideas with stronger self-reported understanding of the literature space; an open-ended study ($n{=}5$) reveals how researchers use LitPivot to iteratively evolve their own ideas.
AbstractArtificial intelligence (AI)-based solutions offer potential remedies to the issues encountered in conventional reference identification methods. However, the effectiveness of these AI models in assisting orthodontic experts in discovering relevant material is unknown. The purpose of this study was to assess the validity of ChatGPT and Google Gemini in delivering references for orthodontic literature studies.This study utilized ChatGPT models (3.5 and 4) and Gemini to search for topics in orthodontics and specific subdomains. To verify the existence and precision of the cited references, several reputable sources were employed, including PubMed, Google Scholar, and Web of Science.Descriptive statistics were employed to present the data numerically and as percentages, focusing on three aspects: completeness, accuracy, and fabrication. Reliability analysis was conducted using Cronbach's α and the results were visually presented in the form of the correlation heat map.Out of all references, only 15.76% were correct, whereas 71.92% were fake or fabricated references and 12.32% were inaccurate references. Gemini had the significantly highest proportion of correct references (36.36%), followed by GPT 3.5 (15.76%) and GPT 4 (0.95%) (p-value < 0.01). The reliability score of 0.418 indicate low-to-moderate consistency in the accuracy of the references.While Gemini showed better performance than GPT models, significant limitation remains in all three models in reference generations. These findings advocate for balanced and cautious use of AI tools in academic research related to orthodontics, emphasizing human validation of the references and training of dental professionals and researchers in efficient use of AI tools.
Eleonora Cappuccio, Andrea Esposito, Francesco Greco
et al.
Artificial Intelligence (AI) is one of the major technological advancements of this century, bearing incredible potential for users through AI-powered applications and tools in numerous domains. Being often black-box (i.e., its decision-making process is unintelligible), developers typically resort to eXplainable Artificial Intelligence (XAI) techniques to interpret the behaviour of AI models to produce systems that are transparent, fair, reliable, and trustworthy. However, presenting explanations to the user is not trivial and is often left as a secondary aspect of the system's design process, leading to AI systems that are not useful to end-users. This paper presents a Systematic Literature Review on Explanation User Interfaces (XUIs) to gain a deeper understanding of the solutions and design guidelines employed in the academic literature to effectively present explanations to users. To improve the contribution and real-world impact of this survey, we also present a platform to support Human-cEnteRed developMent of Explainable user interfaceS (HERMES) and guide practitioners and scholars in the design and evaluation of XUIs.
Stefan Julian Kooy, Jean Paul Sebastian Piest, Rob Henk Bemthuis
Generative AI (GenAI) is reshaping enterprise architecture work in agile software organizations, yet evidence on its effects remains scattered. We report a systematic literature review (SLR), following established SLR protocols of Kitchenham and PRISMA, of 1,697 records, yielding 33 studies across enterprise, solution, domain, business, and IT architect roles. GenAI most consistently supports (i) design ideation and trade-off exploration; (ii) rapid creation and refinement of artifacts (e.g., code, models, documentation); and (iii) architectural decision support and knowledge retrieval. Reported risks include opacity and bias, contextually incorrect outputs leading to rework, privacy and compliance concerns, and social loafing. We also identify emerging skills and competencies, including prompt engineering, model evaluation, and professional oversight, and organizational enablers around readiness and adaptive governance. The review contributes with (1) a mapping of GenAI use cases and risks in agile architecting, (2) implications for capability building and governance, and (3) an initial research agenda on human-AI collaboration in architecture. Overall, the findings inform responsible adoption of GenAI that accelerates digital transformation while safeguarding architectural integrity.
Code Review consists in assessing the code written by teammates with the goal of increasing code quality. Empirical studies documented the benefits brought by such a practice that, however, has its cost to pay in terms of developers' time. For this reason, researchers have proposed techniques and tools to automate code review tasks such as the reviewers selection (i.e., identifying suitable reviewers for a given code change) or the actual review of a given change (i.e., recommending improvements to the contributor as a human reviewer would do). Given the substantial amount of papers recently published on the topic, it may be challenging for researchers and practitioners to get a complete overview of the state-of-the-art. We present a systematic literature review (SLR) featuring 119 papers concerning the automation of code review tasks. We provide: (i) a categorization of the code review tasks automated in the literature; (ii) an overview of the under-the-hood techniques used for the automation, including the datasets used for training data-driven techniques; (iii) publicly available techniques and datasets used for their evaluation, with a description of the evaluation metrics usually adopted for each task. The SLR is concluded by a discussion of the current limitations of the state-of-the-art, with insights for future research directions.
Background/purpose. The persistent risk of semantic anachronism challenges both literary interpretation and pedagogy, as modern readers frequently impose contemporary meanings onto historically charged vocabulary. This study introduces the Contextual Diachronic Semantic Framework (CDSF), a five-layered analytical model designed to trace the evolution of word meaning over time. The primary aim is to demonstrate how CDSF uncovers semantic complexity and prevents misreading in canonical literature, while offering practical applications in literature instruction and critical reading.
Materials/methods. The study employs a qualitative, text-centered methodology, applying the CDSF to five lexical items in Shirley Jackson’s The Lottery: “lottery,” “village,” “tradition,” “black box,” and “stones.” The five analytic layers—Etymological Trajectory Analysis, Diachronic Semantic Mapping, Contextual Literary Function, Cultural-Hermeneutic Embedding, and Interpretive Reconstruction—draw from historical dictionaries, linguistic corpora, literary criticism, and classroom pedagogy. Educational implications were derived by aligning findings with strategies for teaching vocabulary and symbolic language.
Results. Findings reveal that each term operates as a site of historical memory, cultural critique, and thematic irony. The CDSF allows for context-sensitive interpretation, helping both scholars and students decode deeper meanings. In pedagogical terms, the framework provides a replicable tool for guiding learners beyond surface-level readings toward historically grounded literary analysis.
Conclusion. The CDSF is a rigorous, interdisciplinary model that enhances scholarly interpretation and supports literature instruction. It promotes critical reading, prevents semantic misinterpretation, and equips teachers with a research-informed strategy for fostering historical empathy and interpretive depth in the classroom.
BackgroundMetaplastic carcinoma of the breast with mesenchymal differentiation (MCMD) is a type of metaplastic breast carcinoma (MpBC) that is very rare and aggressive. The present case provides valuable information for clinicians on this MpBC.Case presentationA 41-year-old woman visited our hospital for a palpable painless mass in the left breast. Core needle biopsy (CNB) was performed, and the pathological result was infiltrating ductal carcinoma. Epirubicin (100 mg/m2) + cyclophosphamide (600 mg/m2) for four cycles was given. Color Doppler ultrasound examination indicated no obvious change in the size of the left breast mass. We changed to paclitaxel (175 mg/m2) for two cycles. Re-examination on April 26, 2018 with color Doppler ultrasound indicated that the tumor diameter increased to 8.39 cm × 8.07 cm × 6.19 cm. Radical resection of the left breast carcinoma was performed on June 04, 2018. The postoperative pathological results showed that the left breast tumor was composed of carcinoma and sarcoma components, without nerves and vascular invasion. The immunohistochemistry results were as follows: ER: (−), PR: (−), HER2: (−), CK5/6 (+), CK7: (+), E-cadherin (+), Ki67: 40% (+), P120: (+), P53 diffuse +, P63: (+), and S100 partially positive, GATA-3: (+). Four cycles of vinorelbine (25 mg/m2) + cisplatin (40 mg/m2) were performed after the operation. Enhanced CT indicated a 6.0 cm × 4.6 cm mass in the liver on January 1, 2019 through regular review, and liver lobectomy confirmed that metastasis originated from sarcoma components, together with bone and cartilage differentiation. The immunohistochemistry results indicated the following: ER (−), PR (−), GATA-3 (−), CD34 (+), P63 (−), CK8 (−), P40: (−), and vimentin: (+). The patient received oral anlotinib 12 mg once a day, with 2 weeks on/1 week off for eight cycles. The patient survived and showed no signs of recurrence at the follow-up visit.ConclusionThis case indicated that CNB may not always give an accurate diagnosis for MCMD. Neoadjuvant chemotherapy with epirubicin, cyclophosphamide, or paclitaxel for MCMD may not be effective for patients showing no sensitivity to these drugs. In addition, regular postoperative follow-up plays an important role in the early detection of remote metastasis, and timely surgical excision of a single metastatic lesion in the liver can lead to long-term progression-free survival (PFS).
Neoplasms. Tumors. Oncology. Including cancer and carcinogens
Yathwin Kanagavel, D. Rajiv Raj, Pavitra Vittalraj
et al.
Para-testicular fibrous pseudotumors (PFP) are rare benign reactive lesions comprising of 6 % of para-testicular masses. Often misdiagnosed as malignant due to clinical and radiological overlap, they are frequently treated with aggressive surgery. We report a case of a 70-year-old male with a left inguinal swelling diagnosed post-orchidectomy as PFP. Histopathology revealed collagen-rich fibrotic tissue with lymphoplasmacytic infiltrates and IgG4-positive plasma cells. While PFP treatment requires surgical resection, testicle-sparing procedures with intraoperative frozen section assessment may prevent unnecessary orchidectomy. Further studies are needed to establish diagnostic protocols and explore the association between PFP and IgG4-related diseases.
Problem statement. The integration of artificial intelligence (AI) into the field of education has become one of the key factors transforming pedagogical activities worldwide. The proliferation of generative AI tools (ChatGPT, DeepSeek, GigaChat) is accompanied by numerous discussions about their impact on the learning process and teachers’ professional activities. Among the main challenges highlighted in the global academic literature are: 1) the lack of unified attitudes towards AI use; 2) insufficient digital literacy among participants in the educational process; and 3) ethical and long-term risks of applying AI in education. The aim of this study is to explore future teachers’ attitudes towards the use of generative AI in solving professional tasks and to determine the impact of additional training on their perception of AI tools. Methodology. The empirical study involved 32 students pursuing a pedagogical profile. Surveys were conducted before and after completing an elective course on the use of AI in teachers’ professional activities. Methods included self-assessment (attitude survey), analysis of survey data, and statistical processing of results using the Student’s t-test to assess the significance of changes in future teachers’ attitudes towards AI. Results. The significance of additional training for improving future teachers’ attitudes towards AI has been confirmed. It was found that generative AI is perceived most positively in text generation tasks, while tasks involving assignment grading and generating video and audio materials inspire the least trust. The training helped reduce negative perceptions and improved the attitude towards using AI in solving professional tasks. Conclusion. The findings confirm the need for targeted training for future teachers in the fundamentals of AI to minimize negative aspects and ensure effective use of the technology. The developed principles could form the basis for creating educational disciplines and professional development courses, enabling more rational and safe applications of AI in education.
In the dynamic landscape of contemporary business, the wave in data and technological advancements has directed companies toward embracing data-driven decision-making processes. Despite the vast potential that data holds for strategic insights and operational efficiencies, substantial challenges arise in the form of data issues. Recognizing these obstacles, the imperative for effective data governance (DG) becomes increasingly apparent. This research endeavors to bridge the gap in DG research within the Operations and Supply Chain Management (OSCM) domain through a comprehensive literature review. Initially, we redefine DG through a synthesis of existing definitions, complemented by insights gained from DG practices. Subsequently, we delineate the constituent elements of DG. Building upon this foundation, we develop an analytical framework to scrutinize the collected literature from the perspectives of both OSCM and DG. Beyond a retrospective analysis, this study provides insights for future research directions. Moreover, this study also makes a valuable contribution to the industry, as the insights gained from the literature are directly applicable to real-world scenarios.
Efficiently extracting data from tables in the scientific literature is pivotal for building large-scale databases. However, the tables reported in materials science papers exist in highly diverse forms; thus, rule-based extractions are an ineffective approach. To overcome this challenge, we present MaTableGPT, which is a GPT-based table data extractor from the materials science literature. MaTableGPT features key strategies of table data representation and table splitting for better GPT comprehension and filtering hallucinated information through follow-up questions. When applied to a vast volume of water splitting catalysis literature, MaTableGPT achieved an extraction accuracy (total F1 score) of up to 96.8%. Through comprehensive evaluations of the GPT usage cost, labeling cost, and extraction accuracy for the learning methods of zero-shot, few-shot and fine-tuning, we present a Pareto-front mapping where the few-shot learning method was found to be the most balanced solution owing to both its high extraction accuracy (total F1 score>95%) and low cost (GPT usage cost of 5.97 US dollars and labeling cost of 10 I/O paired examples). The statistical analyses conducted on the database generated by MaTableGPT revealed valuable insights into the distribution of the overpotential and elemental utilization across the reported catalysts in the water splitting literature.