Search Results (21,767)

Electronic noses (E-Noses) equipped with metal-oxide semiconductor (MOS) sensors are promising tools for non-invasive medical diagnostics. Their adoption in clinical practice, however, is limited—among others—by sensor variability across devices, which makes individual calibration necessary. This study presents an approach for the development of a calibration transfer (CT) methodology for urine headspace analysis, involving the design and realization of a dedicated experimental setup and protocol. Partial least squares-discriminant analysis (PLS-DA) models were trained on human urine samples enriched with selected biomarkers to simulate pathological states. Models from a reference (“master”) device were transferred to other (“slave”) units in multiple master–slave configurations using Direct Standardization (DS). To overcome the variability of human urine, synthetic urine recipes were formulated to mimic sensor responses and serve as reproducible transfer samples. Several strategies for selecting transfer samples were evaluated, including the Kennard–Stone algorithm, a DBSCAN-based approach, and random selection. Without CT, classification accuracy on slave devices decreased markedly (37–55%) compared to the master’s performance (79%), whereas applying DS with synthetic standards restored accuracy to 75–80%. These results demonstrate that combining reproducible synthetic standards with DS enables effective model transfer across E-Noses, reducing calibration requirements and supporting their broader applicability in medical diagnostics. Full article

Background/Objectives: Toolmark analysis on cartridge cases offers critical insights in forensic ballistics, as the impressions left on cartridge cases by firearm components—such as the firing pin, breech face, and annulus—carry distinctive patterns and act as unique identifiers that can be used for firearm linkage. This study aims to develop a systematic and interpretable feature extraction pipeline for these regions to support future automation and comparison studies in forensic cartridge case analysis. Methods: A dataset of 20 high-resolution cartridge case images was prepared, and each region of interest (firing pin impression, breech face, and annulus) was manually annotated using the LabelMe tool. ImageJ and Python-based scripts were employed for feature extraction, capturing geometric descriptors (area, perimeter, circularity, and eccentricity) and texture-based features (Local Binary Patterns and Haralick statistics). In total, 61 quantitative features were derived from the annotated regions. Similarity between cartridge cases was evaluated using Euclidean distance metrics after normalization. Results: The extracted and calibrated region-wise geometric and texture features demonstrated distinct variation patterns across firing pin, breech face, and annulus regions. Pairwise similarity analysis revealed measurable intra-class differences, indicating the discriminative potential of the extracted features even within cartridges likely fired from the same firearm. Conclusions: This study provides a foundational, region-wise quantitative framework for analysing cartridge case impressions. The extracted dataset and similarity outcomes establish a baseline for subsequent research on firearm identification and model-based classification in forensic ballistics. Full article

Background/Objectives: Allele-specific genome editing using the CRISPR–Cas9 system is crucial for achieving precise therapeutic interventions in dominant inherited diseases that are otherwise difficult to treat with conventional approaches. However, Cas9–guide RNA (gRNA) complexes often tolerate single-base mismatches in target sequences, making it challenging to discriminate between wild-type and mutant alleles differing by only one nucleotide. Although previous studies have attempted to improve specificity by introducing mismatches into gRNAs, none has systematically investigated the impact of different mismatch types and positions on editing outcomes. In this study, we developed an effective strategy to enhance specificity and minimize off-target effects by deliberately introducing mismatches into gRNAs and comprehensively evaluating their editing performance. Results: We established an efficient strategy for the selective editing of mutant alleles that reduces Cas9 sequence tolerance and enhances specificity through the intentional introduction of mismatches into gRNAs. The efficacy of this approach was demonstrated by successful allele-specific editing of cancer-associated heterozygous point mutations in EGFR L858R and KRAS G12V, while minimizing editing of the corresponding wild-type alleles. Conclusion: Compared with perfectly matched gRNAs, the strategic incorporation of mismatches into gRNAs enhanced editing specificity for single-base mutant alleles. Our findings substantially improve the precision and safety of CRISPR-based genome editing for cancer therapy, particularly in cases involving mutant alleles. Full article

The scarcity of labeled hyperspectral video samples has motivated existing methods to leverage RGB-pretrained networks; however, many existing methods of hyperspectral object tracking (HOT) select only three representative spectral bands from hyperspectral images, leading to spectral information loss and weakened target discrimination. To address this issue, we propose the Band and Context Refinement Network (BCR-Net) for HOT. Firstly, we design a band importance learning module to partition hyperspectral images into multiple false-color images for pre-trained backbone network. Specifically, each hyperspectral band is expressed as a non-negative linear combination of other bands to form a correlation matrix. This correlation matrix is used to guide an importance ranking of the bands, enabling the grouping of bands into false-color images that supply informative spectral features for the multi-branch tracking framework. Furthermore, to exploit spectral–spatial relationships and contextual information, we design a Contextual Feature Refinement Module, which integrates multi-scale fusion and context-aware optimization to improve feature discrimination. Finally, to adaptively fuse multi-branch features according to band importance, we employ a correlation matrix-guided fusion strategy. Extensive experiments on two public hyperspectral video datasets show that BCR-Net achieves competitive performance compared with existing classical tracking methods. Full article

Background: Performance status is often cited as an independent predictor of survival after metastatic spine tumor surgery (MSTS), but its standalone predictive value for short-term outcomes remains unclear. We aimed to evaluate how well Karnofsky (KPS) and Eastern Cooperative Oncology Group performance status (ECOG-PS) predict 90-day survival, a common surgical candidacy threshold, in patients managed with MSTS. Methods: We conducted a retrospective study of 175 adult patients who underwent MSTS at a single institution (2012–2025). All patients had documented preoperative KPS and ECOG-PS scores. Univariable logistic regression was used to assess associations with 90-day survival. Predictive performance was assessed by discrimination (AUC), diagnostic accuracy, calibration (Brier score), and clinical utility (decision curve analysis). Results: The crude 90-day survival rate was 73%. Both KPS (OR 1.02 [95% CI 1.01 to 1.05]; p = 0.001) and ECOG-PS (OR 0.51 [95% CI 0.36 to 0.73]; p < 0.001) were statistically associated with survival. However, discrimination was modest (AUC 0.65 for KPS, 0.68 for ECOG-PS), with the most balanced diagnostic accuracy achieved at KPS ≥ 70 (sensitivity 0.66, specificity 0.62) and ECOG-PS ≤ 2 (sensitivity 0.76, specificity 0.5). Calibration was fair (Brier scores 0.185 and 0.182, respectively). Decision curve analysis showed minimal net benefit across most threshold probabilities, with ECOG-PS performing slightly better at intermediate thresholds (30–60%), the zone of greatest clinical uncertainty. Conclusions: Despite being widely cited as an independent predictor of postoperative survival in patients with metastatic spine disease, performance status assessed via the KPS and ECOG-PS demonstrated only modest overall discriminatory ability, diagnostic accuracy, calibration, and clinical utility when used alone to predict 90-day survival after MSTS. While both scores retained meaningful value at the extremes (i.e., patients with very poor or very good performance status had more predictable outcomes), caution is warranted in intermediate cases, where performance status alone may be insufficient to guide treatment decisions. These findings highlight the critical difference between statistical association and the real-world clinical utility of a single metric to predict outcome in this patient population. Full article

Apocynum venetum honey, a characteristic Chinese herbal honey, is a key agricultural product in Xinjiang. To better understand its unique flavor and geographical authenticity, this study analyzed the volatile components of honey samples from three production regions via headspace solid-phase microextraction combined with gas chromatography–mass spectrometry. Overall, 160 volatile compounds were identified, with 34 exhibiting aroma activity values of >1. Notably, chemometric analysis revealed 24 key differential compounds, including phenylethyl alcohol, benzyl alcohol, 2-furanmethanol, 5-ethenyltetrahydro-α,α,5-trimethyl-, cis-, cedrol, 2,4-di-tert-butylphenol, decanal, and nonanal, which significantly contributed to both geographical discrimination and unique flavor profiles. Cluster heatmap analysis demonstrated that these markers could be used to effectively differentiate the samples by origin. The research results provide a theoretical basis for the further development and utilization of this honey as well as support for expanding honey resources for use in traditional Chinese medicine. Full article

This paper presents an automated threshold-based multi-channel epileptic seizure detection algorithm designed for low-complexity hardware implementations. The algorithm relies on two discriminative, computationally simple time-domain features, based on power and amplitude variations, that enable accurate and timely detections due to their rapid adaptiveness to fluctuations in neural activity. To ensure long-term functionality and high sensitivity, system thresholds are optimized through an offline calibration process that exploits the statistical analysis of patient-specific inter-ictal and ictal periods. The novelty of the approach lies in its multi-channel decision-making strategy, which enhances reliability against false alarms. The proposed algorithm is tested on multiple datasets to assess its adaptability to different recording conditions, achieving roughly 98% accuracy and over 98% sensitivity on both the EEG CHB-MIT dataset and the iEEG SWEC-ETHZ dataset, with average latencies of 3.37 s and 7.84 s, respectively. These results are comparable to, and in some cases outperform, several published machine-learning-based approaches. On the hardware side, FPGA synthesis highlights the minimal and scalable resource requirements of the proposed architecture, achieved through Time-Division Multiplexing (TDM) of both filtering and feature extraction. When compared to state-of-the-art proposals, the system emerges as an ideal candidate for real-time, resource-constrained hardware implementations. Full article

The root of Zanthoxylum nitidum is used in traditional Chinese medicine, whereas its leaves remain an under-exploited resource rich in essential oil (EO). By integrating cytological, analytical–chemical, and chemometric approaches, we have dissected the ontogeny of secretory cavities and the temporal accumulation of EO in Z. nitidum leaves for the first time. Cytological analyses revealed marginal-tooth-slit secretory cavities consisting solely of a spherical domain formed via a schizogenous mechanism. The EO yield followed a unimodal trajectory, peaking at growth stages ZN-2 and ZN-3. Gas chromatography–mass spectrometry (GC-MS) profiling identified 60 constituents; sesquiterpenoids reached maximal abundance at ZN-3, whereas monoterpenoids predominated at ZN-2. Second-derivative Fourier transform infrared spectroscopy (FTIR) spectra exhibited pronounced stage-specific differences, and hierarchical cluster analysis coupled with principal component analysis reliably discriminated developmental stages based on their chemical fingerprints. These findings provide a robust cytological and analytical framework for quality control and rational utilization of Z. nitidum leaves, laying the groundwork for their full medicinal exploitation. Full article

Nowadays, the explosive growth of knowledge in the epigenetics field has highlighted DNA methyltransferase 1 (DNMT1) as a key regulator of genomic methylation patterns and a promising therapeutic target in several diseases. In light of the increasing clinical interest in epigenetic enzymes, the present study aimed to develop a robust computational framework for the discovery of novel DNMT1 inhibitors, merging both structure and data-driven strategies. Particularly, the study compiled a dataset of established DNMT1 inhibitors and calculated a series of molecular properties, thus enabling the training of a machine learning model to capture critical structure–activity relationships (SARs). When benchmarked against known active compounds, the model effectively discriminated between putative inhibitors and non-inhibitors with high accuracy. In parallel, molecular docking was conducted to screen additional uncharacterized compounds, estimating their binding affinity to human DNMT1. Their respective properties were then extracted and fed into the aforementioned model to predict their inhibitory potential. Our comparative evaluation against known human DNMT1 inhibitors demonstrated high predictive accuracy, confirming the reliability of the proposed integrated approach. By uniting molecular docking with data-driven SAR modelling, this workflow offers an expedited fast-track avenue for identifying promising human DNMT1 inhibitors while reducing experimental overhead. The results highlight the effectiveness of combining cheminformatics, machine learning, and in silico techniques to guide rational drug design, and accelerate the discovery of novel epigenetic inhibitors. Full article

Background/Objectives: Acute coronary syndrome (ACS) remains a leading cause of short-term mortality, particularly in elderly patients with multimorbidity and frailty. Conventional models such as the GRACE score provide robust prognostication but do not incorporate comorbidity or frailty burden. This study investigated the prognostic value of combining the Cumulative Illness Rating Scale for Geriatrics (CIRS-G) and Clinical Frailty Score (CFS) with GRACE in predicting one-month mortality in older ACS patients. Methods: A single-center, retrospective cohort study was conducted including 90 patients aged ≥65 years admitted with ACS. Demographic, clinical, echocardiographic, and laboratory data were collected. CIRS-G, CFS, and GRACE scores were calculated at admission. The primary endpoint was one-month all-cause mortality. Statistical analyses included group comparisons, correlation tests, logistic regression, and ROC curve analysis. Results: The mean age was 74.8 ± 6.6 years, and 73.3% were male. At one month, mortality was 8.9% (n = 8). Non-survivors had significantly higher CIRS-G (median 18.5 vs. 14.0, p = 0.006), CFS (6.0 vs. 4.0, p = 0.008), and GRACE scores (183 vs. 122, p < 0.001), and lower ejection fraction (32.5 vs. 50.0, p < 0.001) compared with survivors. Logistic regression identified GRACE as the only independent predictor of mortality (OR = 1.081 per 10-point increase, p = 0.044). ROC analysis showed GRACE had the highest discriminative performance (AUC = 0.919), while CIRS-G (AUC = 0.796) and CFS (AUC = 0.777) also demonstrated significant predictive value. The combined CIRS-G + CFS model provided comparable discrimination (AUC = 0.785; sensitivity 75%, specificity 87%). Conclusions: GRACE remains the strongest independent predictor of one-month mortality in elderly ACS patients; however, comorbidity and frailty scores also contribute meaningful prognostic information. Integrating these geriatric assessments with traditional risk models may improve individualized risk stratification and management. Full article

The You Only Look Once (YOLO) series of models, particularly the recently introduced YOLOv12 model, have demonstrated significant potential in achieving accurate and rapid recognition of electric power operation violations, due to their comprehensive advantages in detection accuracy and real-time inference. However, the current YOLO models still have three limitations: (1) the absence of a dedicated feature extraction for multi-scale objects, resulting in suboptimal detection capabilities for objects with varying sizes; (2) naive integration of spatial and channel attentions, which restricts the enhancement of feature discriminability and consequently impairs the detection performance for challenging objects in complex backgrounds; and (3) weak representation capability in low-level features, leading to insufficient accuracy for small-sized objects. To address these limitations, a novel YOLO model named DFA-YOLO is proposed, a real-time object detection model with YOLOv12n as its baseline, which makes three key contributions. Firstly, a dynamic weighted multi-scale convolution (DWMConv) module is proposed to address the first limitation, which employs lightweight multi-scale convolution followed by learnable weighted fusion to enhance feature representation for multi-scale objects. Secondly, a full-dimensional attention (FDA) module is proposed to address the second limitation, which gives a unified attention computation scheme that effectively integrates attention across height, width, and channel dimensions, thereby improving feature discriminability. Thirdly, a set of auxiliary detection heads (Aux-Heads) are introduced to address the third limitation and inserted into the backbone network to strengthen the training effect of labels on the low-level feature extraction module. The ablation studies on the EPOVR-v1.0 dataset demonstrate the validity of the proposed DWMConv module, FDA module, Aux-Heads, and their synergistic integration. Relative to the baseline model, DFA-YOLO achieves significant improvements in mAP@0.5 and mAP@0.5–0.95, by 3.15% and 4.13%, respectively, meanwhile reducing parameters and GFLOPS by 0.06M and 0.06, respectively, and increasing FPS by 3.52. Comprehensive quantitative comparisons with nine official YOLO models, including YOLOv13n, confirm that DFA-YOLO achieves superior performance in both detection precision and real-time inference, further validating the effectiveness of the DFA-YOLO model. Full article

The simultaneous detection of multiple cancer biomarkers using microfluidic multiplexed immunosensors is gaining significant interest in the field of Point-of-Care diagnostics. This study highlights integrating surface-enhanced infrared Fourier transform (SE-FTIR) with a plasmonic-active nanostructure thin film (PANTF) on a printed circuit board (PCB), housed within a microfluidic device for rapid, non-destructive detection of breast cancer (BC). Detection uses monoclonal antibody (mAb)-functionalized gold nanourchins (GNUs) on dual sensing regions. A total of 12 serum samples (24 data points) were tested for HER-II and CA 15-3. The system demonstrated a SE-FTIR enhancement factor (EF) of ~0.18 × 10⁵ using Rhodamine 6G (R6G). Calibration with HER-II (1–100 ng/mL) and CA 15-3 (10–100 U/mL) showed linear responses (R² = 0.8 and 0.76, respectively). Measurements of unknowns were performed at 1 µL/min over 68 min, with 43 min for biomarker interaction. SE-FTIR spectra were recorded at active zones and analyzed using SpectraView (SV), a custom Python 3.12-based tool. Data preprocessing included filtering (SciPy’s filtfilt) and baseline correction using the Improved Asymmetric Least Squares (IASLS) algorithm (pybaselines.Whittaker). Fourier cross-correlation (FCC) showed stronger signal consistency for HER-II. Partial Least Squares (PLS) regression, a dimensionality reduction technique, enabled clear discrimination between the samples and types, with classification accuracy reaching 1.0. Cancer staging based on these biomarkers yielded an overall accuracy of 0.54, indicating that classification regardless of biomarker type. Further studies involving larger and more diverse sample sets are critical before any definitive conclusions can be drawn. Full article

UAV object detection is still difficult to achieve due to large-scale variation, dense small objects, a complicated background, and resource constraints from onboard computing. To solve these problems, we develop a diffusion-enhanced detection network, DL-DEIM, tailored for aerial images. The proposed scheme generalizes the DEIM baseline across three orthogonal axes. First, we propose a lightweight backbone network called DCFNet, which utilizes a DRFD module and a FasterC3k2 module to maintain spatial information and reduce computational complexity. Second, we propose a LFDPN module, which can conduct bidirectional multi-scale fusion via frequency-spatial self-attention and deep feature refinement and largely enhance cross-scale contextual propagation for small objects. Third, we propose LAWDown, an adaptive-content-aware downsampling to preserve the discriminative representation with higher accuracy at lower resolutions, which can effectively capture the spatially-variant weights and group channel interactions. On the VisDrone2019 dataset, DL-DEIM achieves a mAP@0.5 of 34.9% and a mAP@0.5:0.95 of 20.0%, outperforming the DEIM baseline by +4.6% and +2.9%, respectively. The model maintains real-time inference speed (356 FPS) with only 4.64 M parameters and 11.73 GFLOPs. Ablation studies validate the fact that DCFNet, LFDPN, and LAWDown collaboratively contribute to the accuracy and efficiency. Visualizations also display clustered and better localized activation in crowded scenes. These results show that DL-DEIM achieves a good tradeoff between detection probability and computation burden and it can be used in practice on resource-limited UAV systems. Full article

Artificial intelligence (AI) is generating a profound and quick transformation in several areas of knowledge, as well as in industry and society on a global scale, and is considered one of the most significant technological advances of the present era. Understanding citizens’ attitudes toward AI is essential forguiding its development and implementation. To achieve this, valid and reliable instruments are needed to assess attitudesin different sociocultural contexts. With this objective, the General Attitudes towards Artificial Intelligence Scale (GAAIS) was adapted to Spanish. The sample comprised 644 participants: 327 men and 316 women, aged between 18 and 78 years (M = 33.06, SD = 14.91). The original two-factor structure (Positive GAAIS and Negative GAAIS) was validated using Confirmatory Factor Analysis (CFA). Both the fit indices and the internal consistency of the scale were adequate. Furthermore, the validity of the measure (i.e., convergent and discriminant) and the invariance of the model were confirmed. The analyses performed support the adequacy of the model and, therefore, the usefulness of the instrument, considering the ambivalence that people often experience regarding AI. The limitations of the study and the implications for the design of public policies and intervention strategies that promote the ethical, equitable, and socially responsible use of AI are discussed in this study. Full article

In the US, research suggests that racial disparities exist in the prevalence of postpartum depression (PPD) and postnatal anxiety (PNA), with Black mothers experiencing PPD and PNA at a higher rate than their white counterparts. As a result, research that attempts to understand the antecedents of PPD and PNA in Black mothers may have value to the development of better interventions to reduce both in this subpopulation. Theory suggests that race-related stress (RRS) may be a contributing factor to PPD and PNA symptoms among Black mothers. RRS is defined as the stress associated with racism and discrimination encountered by Black women in their daily lives. In the current study, to test the relationship of RRS to PPD and PNA, we surveyed (N = 79) Black mothers who recently gave birth. The survey consisted of the Index of Race-Related Stress (IRRS), the Edinburgh Postnatal Depression Scale (EPDS), and the Postpartum Specific Anxiety Scale (PSAS-RSF), along with items capturing income, education, mental health status, and the number of children per mother. Income and mental health status, education, and the number of children per mother were used as covariates in a multivariate regression model with IRRS scores as the independent variable and EPDS and PSAS-RSF scores as twin dependent variables. These covariates were selected because of their established relationship with PPD and PNA. The data was analyzed using structural equation modeling. The results indicated that the model provided good fit to the data, (X² = 6.32, df = 9; p = 0.707; RMSEA = 0.00 [90% CI: 0.000, 0.097]; CFI: 1.0). Moreover, IRRS scores were significantly correlated with both PPD symptoms (β = 0.45; p < 0.001) and PNA symptoms (β = 0.3837, p < 0.001), respectively. Such results suggest that future research into the role race-related stress plays in the development of PPD symptoms and PNA symptoms may have value in the reduction in both among Black mothers. Full article