Inflammatory bowel disease (IBD) is a chronic, relapsing-remitting autoimmune disorder of the gastrointestinal tract. The management of IBD is complex and requires accurate assessment of disease extent and severity which guide therapeutic decisions. Endoscopic evaluation with biopsy remains the standard for diagnosing and assessing disease activity. Additionally, other modalities such as computed tomography enterography are used for suspected small bowel involvement. However, these processes are costly, time consuming, and often rely on subjective interpretation which is influenced by clinician experience. Artificial intelligence (AI) has been used to standardize and improve efficiency in many facets of healthcare. Similarly, in the past decade, there has been growing interest in the applications of AI in the management of IBD. The applications of AI in IBD to date include automated endoscopic and histologic assessment, analysis of non-invasive imaging, discovery of novel biomarkers for the development of disease prediction models and the use of chatbots. In this article, we will discuss recent advancements in the use of AI in IBD as well as some of the practical and ethical concerns with large scale implementation of AI into clinical practice.

Abstract This paper explores the typological diversity and markedness of evidential systems in the languages in China, emphasizing their unique characteristics and cross-linguistic variations. The study reveals that evidential systems in the languages in China are shaped by the interplay of perceptual directness, perception neutrality, and grammatical economy. Direct sensory evidence, such as visual perception, typically employs unmarked zero-forms, aligning with the principle of linguistic economy, while indirect evidence, like inference or hearsay, often requires explicit morphological or syntactic markers. The analysis highlights binary oppositions in evidential systems, such as direct versus indirect evidence, personal versus mediated knowledge, and factual versus subjective interpretations. Direct evidence is generally less marked, whereas indirect evidence and subjective interpretations reflect the speaker’s evaluative stance and control over information. The degree of neutrality and markedness forms a continuum, influenced by language types and cognitive strategies. These findings underscore the intricate relationship between information sources, perception, and linguistic encoding in the evidential systems in the languages in China.

Sensitive, point-of-care detection of salivary miRNA-31 holds considerable potential for the early, noninvasive screening and diagnosis of oral squamous cell carcinoma (OSCC). This study reports the first development of a sensitive, portable biosensor that integrates an autocycling primer extension reaction (ACPER) with a bioinspired photonic-crystal (PC) microchip for quantitative analysis of miRNA-31 in saliva. The high amplification efficiency of ACPER enables sensitive detection of miRNA-31 in saliva. Meanwhile, due to the fluorescence-enhancing properties of the PC microchip, the fluorescence signal generated by the ACPER on the PC microchip can be directly visualized and captured through a smartphone imaging system under ultraviolet-light illumination. The fluorescence intensity values are subsequently extracted via image processing software for quantification analysis. This approach obviates the need for bulky instrumentation and mitigates errors arising from subjective interpretation of color depth by the naked eyes, thereby significantly enhancing detection accuracy. Preliminary clinical feasibility assessment demonstrated that this newly developed biosensor can differentiate between cancer patients and healthy individuals in clinical samples with good accuracy (area under the curve = 1), providing a novel paradigm for the early, noninvasive, and sensitive diagnosis of OSCC.

Development of a hybrid deep learning-based framework for liver fibrosis classification using ultrasound images.

Purpose This study explores how school-based supervisors in Israel's religious schools exercised leadership during the 2023–2025 Swords of Iron War. It investigates how supervision functioned as a stabilizing, trauma-sensitive, and innovation-driven practice that integrated faith-based values with pedagogical and emotional support. The research examines how the frameworks of trauma-informed leadership (TIL) and teacher entrepreneurial behaviour (TEB) intersect in supervisory roles to sustain resilience, continuity, and moral purpose under crisis conditions. Design/methodology/approach Using a qualitative design, the study draws on 24 interviews and 38 open-ended questionnaires collected from religious school supervisors between December 2023 and June 2025. Data were analyzed through thematic analysis, applying purposive intensity sampling and negotiated consensus validation. Thematic interpretation focused on supervisors' reflections regarding emotional resilience, pedagogical innovation, and faith-based leadership during wartime. Findings Four interrelated themes emerged: (1) bearing the weight of faith and leadership amid trauma, (2) encouraging teacher innovation in national emergency contexts, (3) fostering spiritually anchored leadership, and (4) leading integrative pedagogy that merges healing with instruction. Supervisors acted simultaneously as trauma-sensitive mentors and entrepreneurial innovators, transforming distress into adaptive practices that preserved community trust, moral meaning, and educational continuity. Research limitations/implications The study focused on a specific context—religious schools in Israel during the Swords of Iron War—which may limit generalizability to secular or non-conflict settings. Data were self-reported and gathered during a time of ongoing disruption, which may affect recall. Nevertheless, the study offers theoretical implications for applying TIL and TEB frameworks in future crisis research. Further longitudinal and comparative studies are needed to assess the long-term impact of supervisory practices on teacher retention, well-being, and student outcomes in diverse cultural and geopolitical contexts. Practical implications The findings suggest that leadership development programs should integrate trauma-informed and entrepreneurial principles to prepare supervisors for crises. Supervisors require tools to lead emotionally, spiritually, and pedagogically, particularly in culturally sensitive and faith-based environments. Institutions should provide mental health support, flexible professional development frameworks, and mechanisms for teacher collaboration and innovation. The study offers actionable models for building supervisor capacity in contexts of instability—emphasizing relational trust, moral clarity, and creative problem-solving as essential leadership traits during emergencies. Social implications Supervisory leadership during wartime extended beyond schools, reinforcing community bonds, civic responsibility, and spiritual resilience. Supervisors mobilized teachers as community leaders, promoting inclusive educational practices and emotional care. These findings underscore the potential of educational leadership to address broader social trauma, cultivate civic agency, and reinforce cultural identity in crisis. In faith-based settings, supervisory innovation became a form of social healing. The study highlights the school as a critical site of societal resilience, particularly for marginalized or religious communities coping with national disruption. Originality/value The study introduces the concept of trauma-sensitive entrepreneurship (TSE), a hybrid model merging the stabilizing functions of TIL with the adaptive functions of TEB. By situating this model in a faith-based wartime context, the study expands international scholarship on educational leadership in crisis, demonstrating that resilience in education is an ongoing ethical and communal process bridging healing and innovation.

The historical evolution of the natural sciences, from natural history to specialized scientific disciplines, has fundamentally altered how we study the natural world. While scientific specialization has undoubtedly brought both depth and rigor to specific scientific fields, including primatology, it has also created methodological limitations that fragment our understanding of complex primate ecosystems. This paper outlines four core principles, once central to natural history research, that are less commonly applied in contemporary primatology. These principles include: multidisciplinarity, democratized participation, integration of subjective interpretation, and creative communication. I argue for the strategic integration and/or reintegration of these principles within primatology and provide concrete recommendations for institutional enactment. However, while the incorporation of these selected principles may make the field more inclusive, ethical, and diverse, it is critical to also acknowledge that many other aspects of natural history have been historically rooted in a colonialism, racism, and exploitation. This paper, therefore, does not call for a blind return to natural history approaches as they were once practiced, but rather for the development of a new, holistic, next-generation primatology. Rather than abandoning scientific rigor, this synthesis approach would expand primatology's methodological toolkit to better reflect the interconnected nature of primate worlds while bridging the gap between academic and local knowledge systems. Such integration offers pathways toward more inclusive, comprehensive, and ultimately more effective primate research and conservation.

Abstract Background: Accurate HER2 status assessment is crucial for guiding treatment decisions in breast cancer. MammaTyper® is a CE-marked in-vitro diagnostic device utilizing real-time quantitative PCR (qRT-PCR) to quantify mRNA transcripts of ERBB2, ESR1, PGR, and MKI67. It provides an objective and standardized classification of breast cancer into intrinsic subtypes (Luminal A, Luminal B, HER2-enriched, and Triple-Negative), offering prognostic and predictive information that complements traditional immunohistochemistry (IHC). MammaTyper®'s high reproducibility and sensitivity are particularly valuable when subjective interpretation of IHC presents challenges. Objective: This study aimed to analyses whether the MammaTyper® platform improves the classification of HER2-equivocal breast cancer cases and to assess its concordance with FISH-based methods. Methods: We analysed 93 breast cancer patients. Estrogen receptor (ER) and progesterone receptor (PR) status, along with Ki67 index, were determined by IHC. HER2 status was initially assessed by IHC and FISH. MammaTyper® was then performed to classify intrinsic subtypes and determine HER2 status based on ERBB2 mRNA levels. Concordance between MammaTyper®, IHC, and FISH was evaluated. Results: Of the 93 patients, 83 (89%) were ER-positive, 42 (45%) PR-positive, and 51 (55%) had a high Ki67 index by IHC. Forty-two cases (45%) were classified as HER2-positive by IHC and FISH. MammaTyper® confirmed HER2-positivity in 25 (60%) of these cases. Among the 51 HER2-negative cases by IHC and FISH, MammaTyper® was positive in only 8 (15%). The overall concordance between MammaTyper® and the IHC/FISH combined assessment was 73%. Specifically, concordance was 60% for IHC/FISH-positive cases and 84% for IHC/FISH-negative cases. Of the 32 cases classified as HER2-positive by MammaTyper®, 23 (71%) had a HER2 FISH ratio of ≥2. Conversely, among the 55 cases classified as HER2-negative by MammaTyper®, 17 (31%) had a HER2 FISH ratio of ≥2. MammaTyper® showed a 72% concordance with IHC for HER2 phenotype determination, identifying a lower proportion of HER2-positive tumors (34%) compared to IHC (45%). This concordance was notably higher (84%) in HER2-negative cases. Conclusion: MammaTyper® demonstrates substantial concordance with IHC and FISH for HER2 status determination, particularly in HER2-negative cases. It identifies a smaller subset of HER2-positive tumors compared to IHC, suggesting its potential to refine the selection of patients for HER2-targeted therapies. Further clinical utility should be assessed by correlating MammaTyper® results with treatment response and survival outcomes. Citation Format: A. VELASCO, I. URDANIBIA, G. RODRIGUEZ, N. TUSET, G. ARIADNA, S. MORALES. Improving HER2 Status Assessment in Breast Cancer with MammaTyper®: Focus on Equivocal Cases [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS4-04-02.

The phenomenon of Satan as an absolute supernatural entity that can force humans to sin often reduces the moral responsibility of individuals. This hinders the understanding of human free will and autonomy in religious narratives. This research aims to investigate how the Qur'an inherently demythologizes satanism by shifting the focus from mythological entities to concepts oriented towards ethics and functionality. This qualitative research, based on literature studies, conducts an in-depth analysis of key verses about the devil and Satan by applying the method of thematic interpretation (mauḍū'iy), enriched by humanistic hermeneutic approaches, especially those of Fazlur Rahman and Nasr Hamid Abu Zayd. The results of the analysis crystallized in three main findings: first, the characterization of Satan as the personification of internal arrogance and Satan as a representation of temptation that is internal and external; second, the Qur'an's affirmation of the absence of satanic coercion and the placing of absolute responsibility for choice on man; and third, the pragmatic function of the phrase "real enemy" as an ethical warning rather than an ontological description. The study's main contribution lies in the detailed textual evidence of how the Qur'an constructs a human-centered moral narrative. This enriches the understanding of the Qur'anic hermeneutics, particularly in interpreting potentially mythological elements functionally and ethically. This study has implications for the realm of da'wah and Islamic education, namely emphasizing rationality and personal accountability to eliminate superstitious and fatalistic tendencies in some Muslim societies. This study is limited to textual analysis and lacks empirical data on communal perceptions. Future research should pursue field studies on social behavior or explore Sufi and philosophical interpretations in order to provide broader cross-disciplinary insights.

ABSTRACT Akata is a nocturnal masquerade in Efik communities that serves as a form of community policing. The performance is expressed through songs, often infused with satire directed at individuals and established authorities. However, contemporary influences from Western and Arabic religions, including education, have gradually eroded interest in this indigenous practice. This study aims to examine Akata songs from an ethnopragmatic perspective, exploring their culturally embedded meanings and subjective interpretations. To gather data ethnographically, the study employed semi-structured interviews and informal conversations with 20 participants, including both initiates and non-initiates of the group in Ikot Oyom, Akpabuyo Local Government Area, Cross River State, Nigeria. The findings indicate that Akata songs are socially constructed to reflect the values and ideologies of the Efik indigenous community. These songs function as social commentary on communal life, addressing themes related to maintaining law, order, and morality. They are specifically used to critique social and moral failings. The study concludes that Akata songs serve as a cultural medium through which the group expresses its sentiments, asserts agency, and challenges unacceptable social and moral behaviours. In this way, the songs act as a powerful instrument for reinforcing cultural integrity, social cohesion, and ethical values.

ABSTRACT Background Subject aversion – a persistent emotional or cognitive resistance towards specific academic subjects – is a barrier to effective learning in sub-Saharan Africa, rooted in emotional, pedagogical, and contextual dynamics. It often results in lowered engagement, performance and self-efficacy, affecting students’ motivation and long-term learning trajectories. While traditional surveys provide limited insights into students’ views and experiences of schooling, social media offers a rich, naturalistic window into public sentiment. Purpose This study investigates patterns of subject aversion among Ghanaian learners, identifying the subjects which elicited the strongest negative emotional responses, and examining the underlying instructional and socio-emotional factors that sustained these. Method Drawing on 2900 Facebook comments from 955 users responding to a post on TV3 Ghana in April 2025 – a widely followed media platform with large national reach – the study employed a mixed-method design integrating quantitative frequency analysis and qualitative thematic interpretation. Comments, primarily in English, were cleaned, coded and analysed sequentially. Findings Technical Studies, Mathematics and French emerged as the most frequently disliked subjects. Aversion was driven by fear-based discipline, cognitive abstraction, perceived irrelevance, rote instruction and peer ridicule. Five core themes were identified: teacher-driven fear and punishment; cognitive complexity and abstraction; perceived irrelevance; an overloaded curriculum and rote learning; and peer and social influence. Conclusions Subject aversion in Ghana arises less from the inherent difficulty of subjects than from pedagogical and emotional factors embedded in classroom culture. Promoting learner-centred pedagogy, empathetic teacher training and emotionally supportive classrooms is essential for reducing aversion and fostering long-term engagement.

Safety science has developed extensive taxonomies for categorizing human performance failures but lacks equivalent vocabulary for describing successful work performance, leaving practitioners without adequate language to discuss adaptive practices that enable successful work under varying conditions. This study developed a worker-centered framework for categorizing procedural adaptations through empirical research at a petrochemical facility. The research employed three-phase convergent validation: Phase 1 captured behavioral data through video observation of 1422 procedural steps; Phase 2 documented differences between Work-As-Imagined and Work-As-Done using the Skip-Order-Action Framework with subject matter expert interpretation; Phase 3 evaluated emerging patterns through worker interviews. Analysis revealed that 32.9% of procedural steps showed adaptations, yet all tasks were completed successfully. Three distinct categories emerged from convergent evidence: routine adaptations represent normalized workplace practices; efficiency adaptations optimize workflow while maintaining safety standards; and safety adaptations exceed prescribed requirements through additional verification. The resulting Routine-Efficiency-Safety (RES) framework provides practical vocabulary for Safety-II implementation, enabling organizations to distinguish between different types of procedural adaptations and their functions, moving beyond binary compliance assessments toward learning-focused conversations about successful work practices.

The phenomenon of divorce in the digital age shows a rapid increase, especially due to the involvement of social media in the family life period which is the main trigger. Divorce that used to only occur through formal legal mechanisms now often appears in the form of a digital divorce or through a social media liaison procedure such as talaq statements through text messages, uploads on social media, or online or online communication. In this context, the Qur'an remains the main source in leading Muslims to understand the nature of a divorce, not just as a breakdown of a relationship, but as a spiritual process based on the values of ihsan and piousness. This study aims to examine the law of divorce according to the Qur'an, focusing on the values of ihsan and piety as a moral solution to a major phenomenon of divorce in the digital era. This study uses a normative approach with a thematic interpretation analysis method (maudhu'i) on divorce verses in the Qur'an, such as QS. Al-Baqarah: 229–232 and QS. At-Talaq: 1-7.

Differentiating full thickness from partial thickness rotator cuff tears is crucial for optimal surgical planning and clinical decision making. Conventional MRI assessment relies largely on subjective interpretation and may lead to diagnostic variability. This study aimed to develop and validate an MRI-based radiomics model capable of accurately distinguishing full thickness from partial thickness rotator cuff tears and to evaluate its diagnostic performance and potential clinical utility. Atotal of 120 patients were included (full thickness, n = 60; partial thickness, n = 60). Shoulder MRI, with proton density-weighted fat-suppressed imaging as the core acquisition, was used for region of interest segmentation by two radiologists. The IBSI-compliant radiomic features were extracted, preselected by mRMR, and modelled using LASSO regularization. Patients were stratified by class into atraining set (n = 84) and an independent testing set (n = 36). Using fivefold cross-validation and afixed threshold, we evaluated the AUC, accuracy, sensitivity, and specificity in both cohorts. The final radiomics model comprising five features achieved an AUC of 0.86 (95% confidence interval, CI 0.78-0.94) in the training set and 0.82 (95% CI 0.68-0.96) in the testing set. On the testing set, sensitivity was 0.83 (95% CI 0.61-0.94), specificity 0.61 (95% CI 0.39-0.80), and accuracy 0.72 (95% CI 0.56-0.84). An interpretable radiomics model derived from routine shoulder MRI reliably distinguishes full from partial thickness rotator cuff tears and shows promise as aquantitative tool for preoperative stratification and decision support.

Background/Objectives: Type 2 diabetes (T2D) affects more than 38 million Americans and remains a leading public health challenge. Behavioral self-management is central to glycemic control but is often undermined by dysregulated and addictive-like eating behaviors. Continuous glucose monitoring (CGM) offers immediate feedback that may strengthen self-regulation, yet the psychological processes linking CGM use, food addiction (FA), and behavior change are poorly understood. This secondary mixed-methods study examined how CGM-supported group medical visits (GMVs) influence glycemic outcomes and FA symptoms in adults with diabetes. Methods: Adults with T2D participated in a 14-week GMV program integrating CGM review with education on nutrition, physical activity, sleep, stress, and intermittent fasting. Thirteen participants had paired CGM summaries and psychosocial data. Quantitative outcomes included mean glucose, glycemic variability, time-in-range (TIR), and symptoms of food addiction using the modified Yale Food Addiction Scale 2.0 (mYFAS 2.0). Qualitative data came from open-ended surveys analyzed using reflexive thematic analysis. Integration followed a convergent design, merging individual change trajectories with thematic interpretations and case vignettes. Results: Mean glucose decreased by 21 mg/dL and TIR improved by 9 percentage points. Among six participants with baseline FA symptoms, all showed reductions in self-reported mYFAS 2.0 symptom counts. Four moved from mild to no symptoms, one from moderate to no symptoms, and one from severe to no symptoms. Across the full sample, the mean change was a reduction of 1.2 in the mYFAS 2.0 symptom counts per participant. Thematic analysis identified four interrelated psychological mechanisms: enhanced awareness of food-glucose relationships, increased accountability through shared tracking, motivation via gamified self-monitoring, and relief from cognitive burden associated with dietary uncertainty. Conclusions: Integrating CGM feedback into GMVs was associated with improvements in glycemic metrics and reductions in addictive-like eating symptoms in this pilot sample. These findings position CGM as a behavioral intervention tool that complements its traditional monitoring role and highlight the value of combining real-time biofeedback with group-based support in diabetes care.

Background Sensitive detection of protein biomarkers in neural tissue is important for both research and diagnostic applications. Enzyme-linked immunosorbent assay (ELISA) is a gold-standard immunoassay known for high sensitivity and quantification, whereas colloidal gold lateral flow assays offer rapid, instrument-free testing but are generally qualitative. This study compared ELISA and colloidal gold strip tests for detecting acetylcholinesterase (AChE) in formalin-fixed cadaveric nerve tissues. Five cadavers (3 males, 2 females) without neurological disease or limb trauma provided proximal and distal nerve segments for analysis. Tissues were formalin-fixed, heat-treated to reverse cross-links, and extracted for protein. A bicinchoninic acid (BCA) assay measured total protein yields, and an ELISA quantified AChE concentration. In parallel, a colloidal gold immunochromatographic strip test was applied to diluted extracts to visually detect AChE. Methods We evaluated detection sensitivity (limit of detection and positive detection rate), reproducibility (intra-assay variability), and quantitative agreement between methods. Results ELISA detected AChE in 12/12 extracted nerve samples, with concentrations ranging from ~0.2 to 66 ng/mL in tissue extract (mean ~15 ng/mL). The colloidal gold strips, by contrast, returned visible positive lines only for samples above ~10–20 ng/mL AChE; low-level samples yielded no signal. ELISA showed a lower limit of detection around 0.5 ng/mL, approximately one order of magnitude lower than the strip test. ELISA measurements were highly reproducible (the duplicate-well coefficient of variation ~10–15%), whereas the lateral flow results were more variable near the cutoff and required subjective interpretation of faint test lines. A strong rank correlation ( ρ ≈ 0.9) was found between ELISA concentrations and strip test positivity thresholds. However, Bland–Altman analysis revealed the strip method systematically under-reported AChE levels, highlighting poor quantitative agreement. Conclusion The ELISA demonstrated superior sensitivity and accuracy for AChE in formalin-fixed neural tissue extracts, detecting low concentrations that the colloidal gold lateral flow assay missed. While the rapid strip test may be useful for quick yes/no identification of high-abundance biomarkers in nerve samples, it lacks the sensitivity and quantitative precision necessary for reliable neural protein diagnostics. Integration of more sensitive detection labels or reader devices would be required to bridge the performance gap between lateral flow assays and ELISA in this context.

Methods for accurate, rapid blood typing and antibody titration at the point of care are constrained by cold-chain dependent red blood cell (RBC) reagents, time-consuming serial dilutions, and subjective visual interpretation. To overcome these limitations, here we develop a method based on bioengineered red-blood cells. We engineer hemoglobin-depleted RBC membranes that preserve ABO surface antigens, label them with fluorescent nanoparticles, and lyophilize them, demonstrating at least 2 years of stability at room-temperature. We capture hemagglutination on a dual-layer paper pad and quantify it by fluorescence in approximately 8 minutes, eliminating the need for serial dilution. We conduct a registered clinical trial (ChiCTR2300078864) and benchmark performance against the gel microcolumn assay. In a clinical cohort (n = 641), ABO typing achieves 100% concordance. In antibody-titration samples, quantitative titers agree with the gel microcolumn assay (coincidence 80.75%, κ = 0.8623). In summary, we develop a cold-chain-independent, handheld paper-based assay that enables simultaneous ABO grouping and quantitative antibody titration with clinical-grade performance, supporting scalable manufacturing, cross-site standardization, and transfusion decision-making in emergency and resource-limited settings.

Thyroid nodules are common clinical findings, increasingly detected due to the widespread use of high-resolution ultrasound imaging. While the majority of these nodules are benign, a minority may be malignant, necessitating accurate and efficient risk stratification. Traditional ultrasonographic evaluation relies heavily on the operator’s expertise and subjective interpretation, which introduces diagnostic variability. This narrative review explores the evolving role of artificial intelligence in the ultrasonographic diagnosis of thyroid nodules. The principal objective of this review is to critically evaluate the diagnostic performance, clinical utility, and integration potential of artificial intelligence (AI)-based methodologies—including machine learning (ML) and deep learning (DL)—in the ultrasonographic assessment of thyroid nodules. Particular attention is devoted to the enhancement of existing risk stratification frameworks, and to identifying barriers to implementation in routine clinical. The review evaluates AI-integrated diagnostic systems in relation to existing classification frameworks, such as the thyroid imaging reporting and data system, and highlights innovations in elastography, 3D imaging, and automated segmentation. Evidence suggests that AI can enhance diagnostic accuracy, reduce interobserver variability, and improve the standardization of thyroid nodule assessment. Some algorithms demonstrate performance comparable to that of experienced clinicians, particularly in differentiating benign from suspicious nodules. Despite promising results, limitations such as model generalizability, the need for large annotated datasets, and clinical validation remain challenges. The findings support the integration of artificial intelligence as a complementary tool to assist healthcare professionals in making more objective, consistent, and timely decisions regarding the evaluation and management of thyroid nodules Methodology: A comprehensive narrative review of peer-reviewed literature was undertaken, encompassing both classical and AI-augmented ultrasonographic techniques, with a specific focus on diagnostic criteria, algorithmic accuracy, and classification consistency across TI-RADS variants (ACR-TIRADS, EU-TIRADS, K-TIRADS). Additionally, the role of emerging modalities such as ultrasound elastography was examined in the context of evaluating cytologically indeterminate nodules. Literature published between 2009 and 2025 was examined to assess how machine learning and deep learning algorithms contribute to image interpretation, classification, and malignancy prediction. Abbreviated Description of The State Of Knowledge: Thyroid nodules are detected in up to 60% of the general adult population via ultrasonography. Although the malignancy rate remains relatively low (~5%), the clinical imperative is the accurate differentiation of malignant from benign lesions. Risk stratification relies on the assessment of sonographic features, including echogenicity, shape, margins, calcifications, and vascularity. Several standardized scoring systems—most notably TI-RADS—are employed to systematize malignancy risk and guide indications for fine-needle aspiration biopsy (FNAB). Despite its utility, ultrasonography remains inherently operator-dependent and subject to interpretive variability. AI-powered diagnostic systems have demonstrated promising potential in mitigating interobserver discrepancies, augmenting risk classification fidelity, and improving diagnostic throughput. Adjunctive techniques such as elastography provide additional biomechanical data, although limitations in methodological standardization currently preclude widespread adoption.

The anterolateral thigh (ALT) flap is widely used for head and neck reconstruction because of its versatility and reliable vascular supply. However, anatomical variability of ALT perforators complicates their consistent identification, which is critical for successful flap harvest. Conventional methods such as Doppler ultrasound often produce false-positive results, making perforator localization challenging. Indocyanine green (ICG) angiography enables real-time intraoperative visualization of vascular flow, but interpretation remains largely subjective. This study integrates artificial intelligence (AI) with ICG angiography to enhance perforator detection, hypothesizing that AI-assisted analysis improves mapping precision and sensitivity. This prospective cohort study included 51 patients undergoing ALT flap surgery between February and October 2024. Intraoperative indocyanine green angiography (ICG-A) was performed to identify perforators, followed by grayscale analysis of angiography videos to quantify pixel intensity over time. Perforators were classified as septocutaneous or musculocutaneous and annotated using the Roboflow platform for AI model training. The YOLOv11 object detection algorithm was applied. Model performance was compared with Doppler ultrasound and subjective ICG interpretation in terms of sensitivity and positive predictive value (PPV), with corresponding 95% confidence intervals (CIs). Statistical analysis used the independent t test, with significance set at P < 0.05. A prototype AI model for ALT perforator detection was developed using ICG-A data. Sensitivity was highest with subjective ICG interpretation (78%; 95% CI, 68%-85%), followed by Doppler ultrasound (53%; 95% CI, 43%-62%) and AI-assisted ICG-A (45%; 95% CI, 26%-65%); PPVs were 28%, 29%, and 21%, respectively. Quantitative pixel analysis showed a mean inflow time of 36 seconds, maximal slope time of 45 seconds, and maximal intensity time of 64 seconds, with a mean maximal intensity of 110 grayscale units. No significant differences were found between musculocutaneous and septocutaneous perforators. AI-assisted ICG angiography is an emerging tool with potential to support perforator mapping. Although the current AI model demonstrated limited sensitivity, its accuracy can be enhanced by expanding training datasets, integrating temporal fluorescence dynamics, and refining fluorescence-time curve analysis. Future advancements in AI-driven image processing may further optimize intraoperative perforator identification, ultimately improving surgical precision and patient outcomes.

Purpose – This study examines the phenomena of rationalization and bureaucratization in the Indonesian education system, focusing on teachers’ administrative burdens and complaints related to educational digitalization. The study is grounded in the growing implementation of efficiency-oriented, technology-driven education policies that aim to simplify bureaucracy but are often perceived by teachers as increasing administrative burdens. Methodology – A qualitative research design with a document analysis approach was employed. Data were collected from online media reports published by KOMPAS and Kompas.id using purposive sampling to select articles addressing teachers’ administrative workloads, bureaucratic simplification, digital education applications, and policy responses. Data were analyzed using thematic content analysis, including data reduction, categorization, and thematic interpretation, guided by Max Weber’s concept of the iron cage of bureaucracy. Findings – Policies emphasizing efficiency, accountability, and administrative modernization paradoxically expanded teachers’ administrative workloads. Digitalization has generated new forms of bureaucracy that demand continuous adaptation, increase work pressure, and shift teachers’ focus from pedagogical practices to documentation and reporting tasks. Consequently, teachers’ professional autonomy is diminished, revealing a misalignment between policy objectives and teachers’ lived experiences. Novelty – This study contributes original insights by combining media discourse analysis with Weberian bureaucratic theory to reveal the unintended consequences of digitalization on teacher professionalism and identity. Significance – The study has significant implications for policymakers, school administrators, and education researchers by highlighting the need to reorient policy to prioritize pedagogical quality and teacher autonomy.

An intelligent molecularly imprinted sensing platform augmented by interval partial least squares for specific detection of mycophenolic acid in agricultural silage.