Search Results (699)

Background: Cervical cancer ranks among the most prevalent tumors in low-income countries, with the Pap test as one of the primary screening tools. The Pap smear detects abnormal cells, the CLART test identifies specific HPV genotypes, and HPV self-sampling allows for self-collected HPV testing. This study aimed to evaluate the feasibility of the first smartphone-based health device for home-collection HPV testing. Methods: Enrolled patients during the gynecological examination underwent three different samplings: Pap smear, HPV DNA genotyping test CLART, and vaginal HPV-Selfy swab. Each patient received a kit including an activation code, vaginal swab, and instructions. After performing the self-sample, patients returned the kit to our laboratory. Both the samples collected by the gynecologist and those collected by the patients themselves were analyzed. Results: A total of 277 patients were enrolled, with 226 self-collected swabs received for analysis. The assay yielded valid results for both self-collected and clinician-collected swabs in 190 patients. When comparing these results with paired clinician-taken vaginal swabs, we observed an agreement of 95.2% (Cohen’s Kappa: 0.845). We report an agreement of 93.7% (Cohen’s Kappa: 0.798). Conclusions: The study demonstrated the feasibility of HPV-Selfy as a complementary tool in cervical cancer screening, especially where adherence to traditional surveillance is low. Full article

Background/Objectives: Evidence-based nursing practice (EBNP) is essential in high-acuity settings such as intensive care units (ICUs) and emergency departments (EDs), where nurses are frequently required to make time-critical, high-stakes clinical decisions that directly influence patient safety and outcomes. Despite its recognized importance, the implementation of EBNP remains inconsistent, with frontline nurses often facing barriers to accessing and applying current evidence. Methods: This descriptive, cross-sectional study systematically mapped and prioritized clinical questions generated by ICU and ED nurses at a tertiary hospital in South Korea. Using open-ended questionnaires, 204 clinical questions were collected from 112 nurses. Each question was coded and classified according to the Nursing Interventions Classification (NIC) taxonomy (8th edition) through a structured cross-mapping methodology. Inter-rater reliability was assessed using Cohen’s kappa coefficient. Results: The majority of clinical questions (56.9%) were mapped to the Physiological: Complex domain, with infection control, ventilator management, and tissue perfusion management identified as the most frequent areas of inquiry. Patient safety was the second most common domain (21.6%). Notably, no clinical questions were mapped to the Family or Community domains, highlighting a gap in holistic and transitional care considerations. The mapping process demonstrated high inter-rater reliability (κ = 0.85, 95% CI: 0.80–0.89). Conclusions: Frontline nurses in high-acuity environments predominantly seek evidence related to complex physiological interventions and patient safety, while holistic and community-oriented care remain underrepresented in clinical inquiry. Utilizing the NIC taxonomy for systematic mapping establishes a reliable framework to identify evidence gaps and support targeted interventions in nursing practice. Regular protocol evaluation, alignment of continuing education with empirically identified priorities, and the integration of concise evidence summaries into clinical workflows are recommended to enhance EBNP implementation. Future research should expand to multicenter and interdisciplinary settings, incorporate advanced technologies such as artificial intelligence for automated mapping, and assess the long-term impact of evidence-based interventions on patient outcomes. Full article

Objective: To develop and validate a ResNet-50-based deep learning model for automatic detection of osteomyelitis (DFO) in plain radiographs of patients with diabetic foot ulcers (DFUs). Research Design and Methods: This retrospective study included 168 patients with type one or type two diabetes and clinical suspicion of DFO confirmed via a surgical bone biopsy. An experienced clinician and a pretrained ResNet-50 model independently interpreted the radiographs. The model was developed using Python-based frameworks with ChatGPT assistance for coding. The diagnostic performance was assessed against the histopathological findings, calculating sensitivity, specificity, the positive predictive value (PPV), the negative predictive value (NPV), and the likelihood ratios. Agreement between the AI model and the clinician was evaluated using Cohen’s kappa coefficient. Results: The AI model demonstrated high sensitivity (92.8%) and PPV (0.97), but low-level specificity (4.4%). The clinician showed 90.2% sensitivity and 37.8% specificity. The Cohen’s kappa coefficient between the AI model and the clinician was −0.105 (p = 0.117), indicating weak agreement. Both the methods tended to classify many cases as DFO-positive, with 81.5% agreement in the positive cases. Conclusions: This study demonstrates the potential of IA to support the radiographic diagnosis of DFO using a ResNet-50-based deep learning model. AI-assisted radiographic interpretation could enhance early DFO detection, particularly in high-prevalence settings. However, further validation is necessary to improve its specificity and assess its utility in primary care. Full article

Background/Objectives: To evaluate the importance of image processing in a previously validated model for detecting pancreatic neuroendocrine tumors (PanNETs) and to introduce Image2Radiomics, a new framework that ensures reproducibility of the image processing pipeline and facilitates the deployment of radiomics models. Methods: A previously validated model for identifying PanNETs from CT images served as the reference. Radiomics features were re-extracted using Image2Radiomics and compared to those from the original model using performance metrics. The impact of nine alterations to the image processing pipeline was evaluated. Prediction discrepancies were quantified using the mean ± SD of absolute differences in PanNET probability and the percentage of classification disagreement. Results: The reference model was successfully replicated with Image2Radiomics, achieving a Cohen’s kappa coefficient of 1. Alterations to the image processing pipeline led to reductions in model performance, with AUC dropping from 0.87 to 0.71 when image windowing was removed. Prediction disagreements were observed in up to 45% of patients. Even minor changes, such as switching the library used for spatial resampling, resulted in up to 21% disagreement. Conclusions: Reproducing image processing pipelines remains challenging and limits the clinical deployment of radiomics models. While this study is limited to one model and imaging modality, the findings underscore a common risk in radiomics reproducibility. The Image2Radiomics framework addresses this issue by allowing researchers to define and share complete processing pipelines in a standardized way, improving reproducibility and facilitating model deployment in clinical and multicenter settings. Full article

Introduction: Breast density is a well-recognized factor in breast cancer risk assessment, with higher density linked to increased malignancy risk and reduced sensitivity of conventional mammography. Background parenchymal enhancement (BPE), observed in contrast-enhanced imaging, reflects physiological contrast uptake in non-pathologic breast tissue. While extensively characterized in breast MRI, the role of BPE in contrast-enhanced mammography (CEM) remains uncertain due to inconsistent findings regarding its correlation with breast density and cancer risk. Unlike breast density—standardized through the ACR BI-RADS lexicon—BPE lacks a uniform classification system in CEM, leading to variability in clinical interpretation and research outcomes. To address this gap, we introduce the BPE-CEM Standard Scale (BCSS), a structured four-tiered classification system specifically tailored to the two-dimensional characteristics of CEM, aiming to improve consistency and diagnostic alignment in BPE evaluation. Materials and Methods: In this retrospective single-center study, 213 patients who underwent mammography (MG), ultrasound (US), and contrast-enhanced mammography (CEM) between May 2022 and June 2023 at the “A. Perrino” Hospital in Brindisi were included. Breast density was classified according to ACR BI-RADS (categories A–D). BPE was categorized into four levels: Minimal (< 10% enhancement), Light (10–25%), Moderate (25–50%), and Marked (> 50%). Three radiologists independently assessed BPE in a subset of 50 randomly selected cases to evaluate inter-observer agreement using Cohen’s kappa. Correlations between BPE, breast density, and age were examined through regression analysis. Results: BPE was Minimal in 57% of patients, Light in 31%, Moderate in 10%, and Marked in 2%. A significant positive association was found between higher breast density (BI-RADS C–D) and increased BPE (p < 0.05), whereas lower-density breasts (A–B) were predominantly associated with minimal or light BPE. Regression analysis confirmed a modest but statistically significant association between breast density and BPE (R² = 0.144), while age showed no significant effect. Inter-observer agreement for BPE categorization using the BCSS was excellent (κ = 0.85; 95% CI: 0.78–0.92), supporting its reproducibility. Conclusions: Our findings indicate that breast density is a key determinant of BPE in CEM. The proposed BCSS offers a reproducible, four-level framework for standardized BPE assessment tailored to the imaging characteristics of CEM. By reducing variability in interpretation, the BCSS has the potential to improve diagnostic consistency and facilitate integration of BPE into personalized breast cancer risk models. Further prospective multicenter studies are needed to validate this classification and assess its clinical impact. Full article

Accurate delineation of within-field management zones (MZs) is essential for implementing precision agriculture, particularly in spatially heterogeneous environments. This study evaluates the spatiotemporal consistency and practical value of MZs derived from three complementary data sources: electromagnetic conductivity (EM38-MK2), basic soil chemical properties (pH, humus, P₂O₅, K₂O, nitrogen), and vegetation/surface indices (NDVI, SAVI, LCI, BSI) derived from Sentinel-2 imagery. Using kriging, fuzzy k-means clustering, percentile-based classification, and Weighted Overlay Analysis (WOA), MZs were generated for a five-year period (2018–2022), with 2–8 zone classes. Stability and agreement were assessed using the Cohen Kappa, Jaccard, and Dice coefficients on systematic grid samples. Results showed that EM38-MK2 and humus-weighted BSP data produced the most consistent zones (Kappa > 0.90). Sentinel-2 indices demonstrated strong alignment with subsurface data (r > 0.85), offering a low-cost alternative in data-scarce settings. Optimal zoning was achieved with 3–4 classes, balancing spatial coherence and interpretability. These findings underscore the importance of multi-source data integration for robust and scalable MZ delineation and offer actionable guidelines for both data-rich and resource-limited farming systems. This approach promotes sustainable agriculture by improving input efficiency and allowing for targeted, site-specific field management. Full article

Background and objectives: Adducins are cytoskeletal proteins essential for membrane stability, actin–spectrin network organization, and cell signaling. Mutations in the genes ADD1, ADD2, and ADD3 have been linked to hypertension, neurodevelopmental disorders, and cancer. However, no comprehensive in silico saturation mutagenesis study has systematically evaluated the pathogenic potential and structural consequences of all possible missense mutations in adducins. This study aimed to identify high-risk variants and their potential impact on protein stability and function. Methods: We performed computational saturation mutagenesis for all possible single amino acid substitutions across the adducin proteins family. Pathogenicity predictions were conducted using four independent tools: AlphaMissense, Rhapsody, PolyPhen-2, and PMut. Predictions were validated against UniProt-annotated pathogenic variants. Predictive performance was assessed using Cohen’s Kappa, sensitivity, and precision. Mutations with a prediction probability ≥ 0.8 were further analyzed for structural stability using mCSM, DynaMut2, MutPred2, and Missense3D, with particular focus on functionally relevant domains such as phosphorylation and calmodulin-binding sites. Results: PMut identified the highest number of pathogenic mutations, while PolyPhen-2 yielded more conservative predictions. Several high-risk mutations clustered in known regulatory and binding regions. Substitutions involving glycine were consistently among the most destabilizing due to increased backbone flexibility. Validated variants showed strong agreement across multiple tools, supporting the robustness of the analysis. Conclusions: This study highlights the utility of multi-tool bioinformatic strategies for comprehensive mutation profiling. The results provide a prioritized list of high-impact adducin variants for future experimental validation and offer insights into potential therapeutic targets for disorders involving ADD1, ADD2, and ADD3 mutations. Full article

Background/Objectives: The Lowenstein Communication Scale (LCS) is a tool for the evaluation of communicative performance in patients with disorders of consciousness (DOC). This study investigated the reliability and validity of the LCS. Methods: We evaluated 23 inpatients with unresponsive wakefulness syndrome (UWS) and 18 in a minimally conscious state (MCS), at admission to a Consciousness Rehabilitation Department and one month later. The evaluations included assessments of LCS by two raters, and of the Coma Recovery Scale–Revised (CRS-R) by one rater. Results: Total inter-rater agreement in LCS task scoring was found in 58–100% of the patients. Cohen’s kappa values were >0.6 for most tasks. High correlations were found between the two raters on total scores and most subscales (r = 0.599–1.000, p < 0.001), and the differences between them were small. LCS subscales and total score intraclass correlations (ICC) were high. Internal consistency was acceptable (Cronbach’s α > 0.7) for most LCS subscales and total scores. Moderate to strong correlations were found between LCS and CRS-R scores (r = 0.554–0.949, p < 0.05), and the difference in responsiveness between LCS and CRS-R was non-significant. Conclusions: The findings indicate that the LCS is reliable and valid, making it a valuable clinical and research assessment tool for patients with DOC. Full article

Background/Objectives: The extensive use of computed tomography (CT) has led to a significant increase in the detection of small and non-palpable pulmonary nodules, necessitating the use of invasive methods for definitive diagnosis. Video-assisted thoracoscopic surgery (VATS) has become the preferred procedure for nodule resections; however, intraoperative localization remains challenging, especially for deep or subsolid lesions. This study explores whether SPECT/CT improves the technical and clinical outcomes of radio-guided occult lesion localization (ROLL) before uniportal video-assisted thoracoscopic surgery (u-VATS). Methods: This is a retrospective study involving consecutive patients referred for the resection of pulmonary nodules who underwent CT-guided ROLL followed by u-VATS between September 2017 and December 2024. From January 2023, SPECT/CT was systematically added after planar imaging. The cohort was divided into a planar group and a planar + SPECT/CT group. The inclusion criteria involved nodules sized ≤ 2 cm, with ground glass or solid characteristics, located at a depth of <6 cm from the pleural surface. ^99mTc-MAA injected activity, timing, the classification of planar and SPECT/CT image findings (focal uptake, multisite with focal uptake, multisite without focal uptake), spillage, and post-procedure complications were evaluated. Statistical analysis was performed, with continuous data expressed as the median and categorical data as the number. Comparisons were made using chi-square tests for categorical variables and the Mann–Whitney U test for procedural duration. Cohen’s kappa coefficient was calculated to assess agreement between imaging modalities. Results: In total, 125 patients were selected for CT-guided radiotracer injection followed by uniportal-VATS. The planar group and planar + SPECT/CT group comprised 60 and 65 patients, respectively. Focal uptake was detected in 68 (54%), multisite with focal uptake in 46 (36.8%), and multisite without focal uptake in 11 patients (8.8%). In comparative analyses between planar and SPECT/CT imaging in 65 patients, 91% exhibited focal uptake, revealing significant differences in classification for 40% of the patients. SPECT/CT corrected the classification of 23 patients initially categorized as multisite with focal uptake to focal uptake, improving localization accuracy. The mean procedure duration was 39 min with SPECT/CT. Pneumothorax was more frequently detected with SPECT/CT (43% vs. 1.6%). The intraoperative localization success rate was 96%. Conclusions: SPECT/CT imaging in the ROLL procedure for detecting pulmonary nodules before u-VATs demonstrates a significant advantage in reclassifying radiotracer positioning compared to planar imaging. Considering its limited impact on surgical success rates and additional procedural time, SPECT/CT should be reserved for technically challenging cases. Larger sample sizes, multicentric and prospective randomized studies, and formal cost–utility analyses are warranted. Full article

The rise in online communication platforms has significantly increased exposure to harmful discourse, presenting ongoing challenges for digital moderation and user well-being. This paper introduces the EsCorpiusBias corpus, designed to enhance the automated detection of sexism and racism within Spanish-language online dialogue, specifically sourced from the Mediavida forum. By means of a systematic, context-sensitive annotation protocol, approximately 1000 three-turn dialogue units per bias category are annotated, ensuring the nuanced recognition of pragmatic and conversational subtleties. Here, annotation guidelines are meticulously developed, covering explicit and implicit manifestations of sexism and racism. Annotations are performed using the Prodigy tool (v1. 16.0) resulting in moderate to substantial inter-annotator agreement (Cohen’s Kappa: 0.55 for sexism and 0.79 for racism). Models including logistic regression, SpaCy’s baseline n-gram bag-of-words model, and transformer-based BETO are trained and evaluated, demonstrating that contextualized transformer-based approaches significantly outperform baseline and general-purpose models. Notably, the single-turn BETO model achieves an ROC-AUC of 0.94 for racism detection, while the contextual BETO model reaches an ROC-AUC of 0.87 for sexism detection, highlighting BETO’s superior effectiveness in capturing nuanced bias in online dialogues. Additionally, lexical overlap analyses indicate a strong reliance on explicit lexical indicators, highlighting limitations in handling implicit biases. This research underscores the importance of contextually grounded, domain-specific fine-tuning for effective automated detection of toxicity, providing robust resources and methodologies to foster socially responsible NLP systems within Spanish-speaking online communities. Full article

Background/Objectives: This study shows a lightweight hybrid framework based on a feedforward neural network using a single frontopolar electroencephalography channel, which is a practical configuration for wearable systems combining memory and a sleep stage transition probability matrix. Methods: Motivated by autocorrelation analysis, revealing strong temporal dependencies across sleep stages, we incorporate prior epoch information as additional features. To capture temporal context without requiring long input sequences, we introduce a transition-aware feature derived from the softmax output of the previous epoch, weighted by a learned stage transition matrix. The model combines predictions from memory-based and no-memory networks using a confidence-driven fallback strategy. Results: The proposed model achieves up to 85.4% accuracy and 0.79 Cohen’s kappa, despite using only a single 30 s epoch per prediction. Compared to other models that use a single frontopolar channel, our method outperforms convolutional neural networks, recurrent neural networks, and decision tree approaches. Additionally, confidence-based rejection of low-certainty predictions enhances reliability, since most of the epochs with low confidence in the sleep stage classification contain transitions between sleep stages. Conclusions: These results demonstrate that the proposed method balances performance, interpretability, and computational efficiency, making it well-suited for real-time clinical and wearable sleep staging applications using battery-powered computing devices. Full article

Background: Salmonella enterica and Escherichia coli are common foodborne pathogens of global concern, particularly due to their antimicrobial resistance, notably to cephalosporins. Objective: This study aimed to evaluate a polymerase chain reaction-based lateral flow strip (PCR-LFS) assay for the detection of Salmonella spp. and E. coli harboring the bla_CTX-M gene, which confers resistance to third-generation cephalosporins. Methods: Two duplex PCRs (dPCR) were established to detect E. coli-harboring bla_CTX-M (set 1) and Salmonella-harboring bla_CTX-M (set 2). 600 bacterial isolates and raw pork mince spiked with bla_CTX-M-harboring E. coli and Salmonella were used to evaluated. Results: Both dPCR assays successfully detected bla_CTX-M-positive E. coli or Salmonella strains, while strains lacking the gene showed no amplification. Non-E. coli and non-Salmonella strains were PCR-negative unless they carried bla_CTX-M. The dPCR-LFS showed 100% validity including accuracy, sensitivity, specificity, positive predictive value, and negative predictive value for both E. coli or Salmonella spp. harboring or lacking bla_CTX-M. The assay accurately detected target strains without cross-reactivity with other bacteria or antimicrobial resistance genes. Cohen’s Kappa coefficient indicated perfect agreement (κ = 1), reflecting the high reliability of the dPCR-LFS. The assay could detect as low as 25 CFU/mL for bla_CTX-M-positive E. coli and 40 CFU/mL for bla_CTX-M-positive Salmonella in spiked raw pork mince. Conclusions: This assay is rapid, easy to interpret, and suitable for large-scale screening in surveillance programs. Full article

Background/Objectives: In non-muscle-invasive bladder cancer (NMIBC), the decision for immediate postoperative single-dose intravesical chemotherapy (SI) is based on clinical and presumed pathological features, as a definitive pathology is unknown at the time of surgery. This study aims to assess how accurately urologists can predict the pathological features of bladder tumors based solely on cystoscopic appearance and evaluate their ability to identify patients eligible for SI. Methods: A total of 104 patients with bladder masses were included. Seven senior urologists and four residents participated. Before transurethral resection, both groups predicted tumor stage, grade, and the presence of carcinoma in situ (CIS). Resident predictions were collected for all 104 patients, while senior predictions were collected for 72 patients. Based on these predictions, patient eligibility for SI was determined according to the EAU NMIBC guidelines. After final pathology reports, risk scores were recalculated and compared with the surgeons’ predictions. Cohen’s Kappa (κ) coefficient was used to assess agreement between predictions and pathology. Positive and negative predictive values were also calculated for both groups. Results: Strong agreement with final pathology could not be demonstrated for stage, grade, or CIS for either group. Urology residents’ predictions were slightly more accurate than those of senior urologists. Overall, 19.4% (14/72) (based on senior urologists’ predictions) and 18.2% (19/104) (based on resident predictions) of patients were misclassified and either overtreated or undertreated. Conclusions: Cystoscopic visual prediction alone is insufficient for determining eligibility for immediate postoperative intravesical chemotherapy, regardless of the urologist’s experience. More objective criteria are needed to improve the selection of appropriate patients for SI. Full article

The integration of extended reality (XR) technologies, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), is transforming forensic investigation by empowering processes such as crime scene reconstruction, evidence analysis, and professional training. This manuscript presents a systematic review of technological advances in XR technologies developed and employed for forensic investigation, their impacts, challenges, and prospects for the future. A systematic review was carried out based on the PRISMA^® methodology and considering articles published in repositories and scientific databases such as SCOPUS, Science Direct, PubMed, Web of Science, Taylor and Francis, and IEEE Xplore. Two observers carried out the selection of articles and a Cohen’s Kappa coefficient of 0.7226 (substantial agreement) was evaluated. The results show that XR technologies contribute to improving accuracy, efficiency, and collaboration in forensic investigation processes. In addition, they facilitate the preservation of crime scene data and reduce training costs. Technological limitations, implementation costs, ethical aspects, and challenges persist in the acceptability of these devices. XR technologies have significant transformative potential in forensic investigations, although additional research is required to overcome current barriers and establish standardized protocols that enable their effective integration. Full article

Barcodes are ubiquitous in manufacturing and logistics, but defects can reduce decoding efficiency and disrupt the supply chain. Existing studies primarily focus on a single barcode type or rely on small-scale datasets, limiting generalizability. We propose Y8-LiBAR Net, a lightweight two-stage framework for multi-type barcode defect detection. In stage 1, a YOLOv8n backbone localizes 1D and 2D barcodes in real time. In stage 2, a dual-branch network integrating ResNet50 and ViT-B/16 via hierarchical attention performs three-class classification on cropped regions of interest (ROIs): intact, defective, and non-barcode. Experiments conducted on the public BarBeR dataset, covering planar/non-planar surfaces, varying illumination, and sensor noise, show that Y8-LiBARNet achieves a detection-stage mAP@0.5 = 0.984 (1D: 0.992; 2D: 0.977) with a peak F1 score of 0.970. Subsequent defect classification attains 0.925 accuracy, 0.925 recall, and a 0.919 F1 score. Compared with single-branch baselines, our framework improves overall accuracy by 1.8–3.4% and enhances defective barcode recall by 8.9%. A Cohen’s kappa of 0.920 indicates strong label consistency and model robustness. These results demonstrate that Y8-LiBARNet delivers high-precision real-time performance, providing a practical solution for industrial barcode quality inspection. Full article