Search Results (1,568)

Background/Objectives: The Parenting Sense of Competence (PSOC) scale is one of the most widely used instruments to assess perceived parental competence, understood as the degree to which parents feel capable of adequately fulfilling their parental role. Despite its widespread use, studies seeking to determine PSOC normative scores are scarce, especially in specific populations such as mothers with infants younger than 9 months, which limits the interpretation of its scores in applied contexts. This study establishes PSOC normative scores in a nonclinical sample of 522 Spanish mothers with infants aged between 3 and 37 weeks who attended a public early intervention program. Methods: Regression and ANOVA analyses were performed to examine the effect of infant and maternal age, as well as educational level and occupation, on the dimensions of Efficacy, Satisfaction, and Total score of the PSOC. Results: The results show a significant decline in parental competence starting when their infants reach 9 months of age, and lower levels of self-efficacy in mothers over 35 years of age. No significant differences were found according to the educational level or occupation of the mothers. Normative scores are presented by percentiles, offering specific criteria for this stage of child development. Z- and T-scores are included, useful for standardized comparisons between subscale and studies. Conclusions: These findings provide useful information for early detection and psychoeducational interventions within the framework of early intervention. Full article

Experience-based co-design emphasizes understanding service-users’ experiences to inform service improvement, yet little research has explored domestic abuse survivors’ perspectives within this framework. This study examined survivors’ accounts of their interactions with the police and organizations that support domestic abuse survivors. We aimed to identify aspects of practice experienced as either helpful or in need of improvement. Semi-structured interviews with six survivors in one area of England were analyzed using reflexive thematic analysis. Survivors described obstructive and supportive responses from formal services. Four interrelated themes were developed: The Importance of Being Understood, Believed, and Cared For; It Is Important That There Is Good Communication Between the Survivor and Formal Services; Survivors Want a Victim-Centered, Rapid, and Meaningful Response; and Specific Circumstances Sometimes Influence Opportunities for Help-Seeking. Survivors described being dismissed and disbelieved, which contributed to negative help-seeking experiences and heightened feelings of vulnerability. In contrast, empathic and timely responses validated survivors’ experiences and supported their sense of safety. The findings highlighted the importance of practice that recognizes the different forms abuse can take, provides timely, victim-centered support, and responds equitably to survivors in diverse circumstances. This study demonstrates the valuable insights gained through applying an experience-based co-design approach in this setting. Full article

The rise of large language models (LLMs), such as GPT-4, Codex, Code Llama, Claude 3, CodeGemma and DeepSeek, etc., is changing the way software development is approached. These models provide strong support for tasks like writing codes, analyzing bugs, and automation. At the same time, their use in software development creates both opportunities and new risks. This survey reviews how LLMs are being used to improve security practices in software development, including vulnerability detection, secure code generation, threat analysis, and patch development. It also discusses how attackers may exploit LLMs for malicious purposes, such as writing malware, carrying out phishing campaigns, or bypassing defenses. We draw on case studies that show LLMs can help uncover zero-day vulnerabilities and speed up secure coding but also highlight cases where they have been misused to generate harmful code, sometimes unintentionally. The paper examines technical challenges like bias in training data, the difficulty of interpreting model outputs, and the risks of adversarial attacks. It also considers ethical and regulatory issues related to accountability, compliance, and responsible use. By bringing together findings from recent research and industry practice, the survey outlines future directions for building safer models, developing stronger defensive frameworks, and shaping policies that balance innovation with security. Overall, the paper argues for a careful approach where LLMs are used to strengthen software security while addressing the risks they introduce through collaboration, oversight, and ongoing improvements. Full article

Background/Objectives. Abdominal computed tomography (CT) radiology reports contain critical information for abdominal aortic aneurysm (AAA) management, including aneurysm presence, size, rupture status, and prior repair. However, this information is often embedded within lengthy, heterogeneous reports, making manual extraction inefficient. We evaluated the performance of multiple large language models (LLMs) for automated extraction of AAA-related findings from radiology reports. Methods. We retrospectively analyzed 500 abdominal CT reports mentioning AAA from an urban academic health system (2020–2024). Ground truth labels were established by manual review. Four open-source LLMs (Qwen2.5-7B-Instruct, Llama3-Med42-8B, GPT-OSS-20B, and MedGemma-27B-text-it) were evaluated for extraction of aneurysm presence, size, morphology, rupture status, impending rupture, and prior aortic repair. Model outputs were compared with ground truth using exact-match accuracy, and inter-model agreement was assessed using Fleiss’ kappa. Reasoning traces were examined to characterize correct and incorrect model behavior. Results. Accuracy for identifying AAA presence ranged from 0.90 to 0.95 (κ = 0.851), and prior aortic repair from 0.90 to 0.97 (κ = 0.793). Accuracy for aneurysm size ranged from 0.67 to 0.88 (κ = 0.340), with low κ’s due to class imbalance or dimension misselection. Rupture and impending rupture were identified with accuracies exceeding 0.90 across models, though agreement was lower (κ = 0.485 and 0.589), reflecting low event prevalence. Larger models (GPT-OSS-20B, MedGemma-27B) generally outperformed smaller models. Reasoning analysis revealed strengths in measurement prioritization but recurrent errors, including dimension misselection, over-inference of prior repair, and conservative classification of rupture-related findings. Conclusions. LLMs can accurately extract clinically relevant AAA information from radiology reports with interpretable reasoning, with larger and medically trained models outperforming smaller or general-purpose models. Performance varies by task and model, underscoring the need for careful validation and human-in-the-loop deployment in clinical settings. Full article

Actinomycetes are among the richest sources of bioactive secondary metabolites in biotechnology, owing to their remarkable metabolic diversity. Although the genus Streptomyces has been extensively explored and has yielded many clinically important antibiotics, rare actinomycetes remain comparatively underinvestigated. In this study, Kitasatospora sp. SeTe27, isolated from uncontaminated soil in Sicily (Italy), was investigated for its antibacterial activity and fermentation-driven enhancement of secondary metabolite production. The strain inhibited Staphylococcus aureus ATCC 25923, prompting physiological and genomic analyses. Spore conditioning was evaluated in four media (R5A, GYM, TSB, and YEME) to enhance antibiotic production. Conditioned cultures exhibited markedly increased antibacterial activity in TSB and YEME, moderate production in R5A, and no detectable activity in GYM. Whole-genome sequencing revealed an 8.5 Mb genome (73.5% GC) containing 48 biosynthetic gene clusters (BGCs), including NRPS, PKS, terpene, and hybrid pathways. Several clusters showed high similarity to known antibiotic-associated BGCs, such as clifednamide- and phenazine-related pathways, while numerous orphan clusters indicated significant unexplored biosynthetic potential. These findings identify Kitasatospora sp. SeTe27 as a promising antimicrobial producer and demonstrate that spore conditioning in complex media is an effective strategy to enhance antibiotic production in rare actinomycetes. Full article

Biomarkers that predict disease progression and treatment-free remission (TFR) would be of significant clinical value in chronic myeloid leukaemia (CML). We have previously shown that CIP2A levels at diagnosis can identify patients at increased risk of progression. One mechanism by which CIP2A acts is through upregulation of the anti-apoptotic gene BCL-XL. In this study, we evaluated BCL-XL mRNA expression as a diagnostic biomarker using samples from the SPIRIT2 and DESTINY clinical trials. In SPIRIT2, which compared imatinib and dasatinib as first-line therapies, high BCL-XL expression was associated with treatment failure, poor early molecular response, and lower rates of MR2 and MR3 achievement in patients treated with imatinib. In the DESTINY trial, which assessed treatment de-escalation and discontinuation, BCL-XL expression was significantly higher in patients who experienced molecular relapse compared to those achieving sustained TFR. Notably, increases in BCL-XL were detectable 6 to 8 months prior to molecular relapse, suggesting it may serve as an early biomarker of unsuccessful TFR. We now propose a clinical diagnostic toolkit combining CIP2A and BCL-XL biomarkers to stratify CML patients by the risk of disease progression and likelihood of achieving successful TFR. Full article

In 2024, the United States (US) and Mexico signed Minute 330, to address water scarcity in the Colorado River. Under Minute 330, Mexico committed to creating additional water savings through 2026, complementing conservation efforts by the US Lower Basin states during this period. In this paper, we examine the motivations behind Minute 330, its negotiations, and the state of its implementation to understand how it reflects the US–Mexico cooperative relationship amidst scarcity challenges in the basin. Our research takes a multi-method, qualitative approach that draws on semi-structured interviews with members of the Minute Negotiating Group from both countries and other interviewees with expertise on the post-2000 Colorado River Minute process from federal water agencies, NGOs, and universities, as well as members of US-state water agencies and Mexican water user leaders. We conclude that Minute 330 responded to water scarcity challenges in the basin that could not be addressed through prior minutes while setting an important precedent of cooperation and cross-border collaboration between the two countries amid unprecedented circumstances. These features take relevance in light of the post-2026 process and the need to develop additional regulations to manage the Colorado River both at the binational and the US national scale. Full article

The safe integration of Large Language Models in clinical environments requires strict adherence to verified medical evidence. As part of the PARROT AI project, this study provides a systematic evaluation of how prompting strategies affect the reliability of Retrieval-Augmented Generation (RAG) pipelines using the MedQA USMLE benchmark ($N=500$). Four prompting strategies were examined: Baseline (zero-shot), Neutral, Expert Chain-of-Thought (Expert-CoT) with structured clinical reasoning, and StrictCitations with mandatory evidence grounding. The experiments covered six modern model architectures: Command R (35B), Gemma 2 (9B and 27B), Llama 3.1 (8B), Mistral Nemo (12B), and Qwen 2.5 (14B). Evaluation was conducted using the Deterministic RAG Evaluator, providing an objective assessment of grounding through the Unsupported Sentence Ratio (USR) based on TF-IDF and cosine similarity. The results indicate that structured reasoning in the Expert-CoT strategy significantly increases USR values (reaching 95–100%), as models prioritize internal diagnostic logic over verbatim context. In contrast, the StrictCitations strategy, while maintaining high USR due to the conservative evaluation threshold, achieves the highest level of verifiable grounding and source adherence. The analysis identifies a statistically significant Verbosity Signal ($r=0.81,p<0.001$), where increased response length serves as a proxy for model uncertainty and parametric leakage, a pattern particularly prominent in Llama 3.1 and Gemma 2. Overall, the findings demonstrate that prompting strategy selection is as critical for clinical reliability as model architecture. This work delivers a reproducible framework for the development of trustworthy medical AI assistants and highlights citation-enforced prompting as a vital mechanism for improving clinical safety. Full article

In the practice of enterprise data security governance, document AI has emerged as a mission-critical component that seeks to underpin the prevention of document leakage via automatic accurate classification and identification of sensitive content. Arising from this, a need to bring document classification benchmark closer to real-world engineering applications is highlighted. This paper identifies the lack of public datasets for native multi-modal hybrid document classification and, accordingly, proposes the dataset DocCLS_NMMH (Native Multi-Modal Hybrid Document Classification) along with its out-of-distribution (OOD) test subset. An experimental study on the proposed dataset demonstrates that current benchmarks have become irrelevant and need to be updated to evaluate native multi-modal hybrid documents. Meanwhile, accuracy degradation in heterogeneous documents and few-shot scenarios is assessed, as all of these are prevalent in the practice. The experimental results demonstrate that LayoutLM achieves a state-of-the-art (SOTA) performance with 98.66% accuracy on DocCLS_NMMH, with only approximately 7% accuracy degradation on its OOD test subset, while training-free models (Qwen2.5-VL-32B and Gemma3-27B) consistently achieve over 95% accuracy across the full dataset. The SOTA performance of these models on our benchmark provides an effective guidance for model selection in real engineering applications. Full article

Managed colonies of the Western honey bee, Apis mellifera, are essential to global food security by ensuring the pollination of a wide array of crops that are crucial for human consumption. However, substantial declines in managed honey bee populations have been reported worldwide, including in Australia, the United States and Europe. These losses have been attributed to the multifaceted interplay of stressors encompassing agrochemical impact, climate fluctuations, pathogens, suboptimal forage conditions, and habitat reduction. In particular, Paenibacillus larvae, the causative agent of American foulbrood (AFB), is one of the most destructive bacterial pathogens for honey bees due to its high transmissibility, environmental persistence, and capacity to cause complete colony collapse. Recurrent and widespread AFB outbreaks impose significant economic and biosecurity burdens on apiarists, exacerbating declines in pollination services and agricultural productivity. This review synthesises the current landscape of therapeutic strategies targeting AFB, including bacteriophage-based approaches, vaccine development, probiotics, and essential oils, and evaluate their reported field applications, efficacy, and practical limitations. Bacteriophages and immune-priming approaches show the greatest potential to reduce larval mortality and pathogen load, although their application is constrained by formulation stability, delivery challenges, and limited large-scale field validation. Probiotics and essential oils produce highly variable and inconsistent effectiveness under field conditions. Overall, these alternatives currently represent promising complementary tools rather than standalone treatments, underscoring the need for further investigation. Full article

Chagas disease, caused by Trypanosoma cruzi, is maintained in nature by complex interactions among wild vertebrates and triatomine insect vectors, yet the role of many introduced hosts remains poorly resolved. Here, we assessed natural T. cruzi infection in wild European rabbits (Oryctolagus cuniculus) from central Chile, where introduced rabbits overlap ecologically with the sylvatic vector Mepraia spinolai. Eight free-ranging rabbits captured in Las Chinchillas National Reserve were evaluated using an integrative diagnostic approach combining xenodiagnosis with laboratory-reared, parasite-free M. spinolai nymphs, real-time polymerase chain reaction targeting T. cruzi satellite DNA in blood and 12–14 organs per animal, and histopathology with immunohistochemistry (anti-cruzipain) to identify tissue parasite forms. Blood molecular detection was positive in seven out of eight rabbits, while xenodiagnosis detected viable parasites in two out of seven evaluated individuals. Organ molecular screening detected T. cruzi DNA in at least one organ in all rabbits, with frequent positivity in the diaphragm, reproductive tissues, spleen, and kidney. Histopathology identified parasite forms in four out of eight animals, and immunohistochemistry confirmed hepatic amastigotes in one case. These findings provide multi-method evidence of natural infection in the sampled individuals, including evidence of parasite viability in some individuals, suggesting potential epidemiological relevance within this ecological context and possible utility for surveillance in Chilean sylvatic transmission settings. Full article

Takotsubo syndrome (TTS) is a temporary and reversible form of cardiomyopathy that clinically mimics acute coronary syndrome, typically triggered by intense physical or emotional stress. It mainly affects postmenopausal women and exhibits significant variation among individuals regarding its onset, progression, and outcomes. Although significant advances have been made since its initial description in 1990, the underlying pathophysiological mechanisms remain incompletely understood, limiting the development of effective prevention and targeted treatment strategies. A potential genetic predisposition has been suggested, supported by reports of familial clustering; however, a systematic and updated characterization of genetic and epigenetic factors associated with TTS is still lacking. This systematic and critical review aims to offer a comprehensive overview of current evidence on genetic susceptibility and epigenetic biomarkers potentially involved in the pathogenesis of TTS. Due to the heterogeneity and inconsistency of available findings, particular attention is also given to the methodological limitations of existing genetic studies. Finally, the review examines emerging multimodal approaches that may offer new perspectives for understanding the complex biological foundations of this syndrome. Full article

We developed an intelligent conversational system that enhances document interaction using advanced language models and embedding techniques. The system integrates retrieval-augmented generation, Facebook AI similarity search-based retrieval, and cosine similarity for efficient information extraction from Portable Document Format documents. It employs three embedding models, namely All-MiniLM L6 v2, All-MPNet Base v2, and Instructor Large, with three large language models including LLaMA 3.3 70B, Gemma 2-9B IT, and Mixtral 8x7B-32768. System performance is evaluated using ROUGE-1, BERTScore, and a novel human-like performance (HLP) metric, showing improved retrieval accuracy, response coherence, and efficiency for academic and enterprise applications. Full article

Steering large language models (LLMs) toward desired behaviors while preserving privacy is a critical challenge in AI alignment. Existing differentially private (DP) steering methods, such as PSA, add high-dimensional noise that can severely degrade steering accuracy. We propose DP-JL, a novel approach that combines Johnson–Lindenstrauss (JL) random projection with differential privacy to reduce noise while maintaining formal privacy guarantees. DP-JL projects steering vectors into a lower-dimensional space (dimension k) before adding DP noise, reducing total noise magnitude from $O\left(\sqrt{d}\right)$ to $O\left(\sqrt{k}\right)$ where $k\ll d$, while the privacy budget $\epsilon$ remains unchanged. We evaluate DP-JL on seven behavioral datasets with LLaMA-2-7B, Mistral-7B, Qwen2.5-7B, and Gemma-2-9B, alongside general capability benchmarks (MMLU, TruthfulQA). All accuracy values are measured on held-out test sets. Results show that DP-JL achieves: (1) up to 22.76 percentage points higher steering accuracy than PSA on the myopic-reward dataset (at fixed privacy budget $\epsilon \approx 0.22$, $\delta ={10}^{-5}$); (2) 91.7% win rate on sycophancy with an average accuracy improvement of 3.01 percentage points; (3) systematic advantages in high-privacy regimes ($\epsilon <0.2$); and (4) superior capability preservation on related tasks (TruthfulQA), achieving 6.6 percentage points better accuracy than PSA. Furthermore, visualizations and layer-sensitivity analyses reveal that DP-JL faithfully preserves the geometric structure of activation spaces, explaining its robustness. Our findings demonstrate that DP-JL offers superior privacy–utility trade-offs while better preserving model capabilities. Full article

Long-Acting Buprenorphine Formulations (LAIB) have emerged as an alternative pharmacological approach for opioid use disorder, offering potential benefits extending beyond clinical stabilisation. Narrative medicine provides a unique approach to understand patients’ perspectives and experiences with sublingual buprenorphine and LAIB dispensed to fourteen patients across different Italian Addiction Services, examining how they impact the emotional, social, and motivational dimensions of recovery. Narratives were analysed by thematic content across eight domains: dependence on daily treatment regimen, emotional impact, self-perception, determination to change, quality of life, craving and withdrawal symptoms, treatment adherence, social burden, and therapeutic relationship. Statements were categorised by valence; experiential patterns were qualitatively analysed. Sublingual buprenorphine, although effective, was associated with reduced autonomy, symptom control, and difficulties in balancing treatment, work and life. These aspects were correlated with worse adherence. The stigma and burden of daily intake can reduce motivation and hinder identity reconstruction. In this setting, transitioning to LAIB resulted in improved self-autonomy, emotional balance, symptom control, self-esteem, and reduced daily and psychological burden, craving and stigma, facilitating social reintegration, and strengthening the therapeutic relationship. The results emphasise the importance of including both experiential and narrative elements in clinical care, as this helps create more tailored, recovery-focused treatment pathways. Full article