Search Results (21,940)

Background/Objectives: Acute myeloid leukemia (AML) is a heterogeneous hematologic malignancy with historically poor outcomes, particularly among older adults and patients harboring high-risk molecular features. Advances in genomic profiling have enabled the development of targeted therapies, reshaping treatment algorithms beyond conventional cytarabine-anthracycline induction and hypomethylating agent-based regimens. This review summarizes current evidence and emerging therapeutic strategies across four evolving areas: menin inhibition, FLT3 inhibition, IDH inhibition and treatment approaches for TP53-mutated AML. Methods: We reviewed published clinical trials, preclinical studies, and ongoing clinical trials evaluating targeted therapies in AML. Emphasis was placed on agents with regulatory approval or substantial clinical development, including menin inhibitors, FLT3 inhibitors, IDH inhibitors and novel therapies directed at TP53-mutated disease. Mechanistic data, response rates, survival outcomes, and resistance patterns were analyzed to provide an updated overview of therapeutic progress. Results: Menin inhibitors have demonstrated significant activity in NPM1-mutated and KMT2A-rearranged AML, with agents such as revumenib and ziftomenib producing meaningful remission rates and ongoing studies exploring combination strategies to mitigate resistance. FLT3 inhibitors, including midostaurin, gilteritinib, and quizartinib, have improved survival in FLT3-mutated AML, while emerging evidence supports potential benefit in selected FLT3–wild-type disease based on FLT3-like gene expression signatures. IDH inhibitors, namely ivosidenib and enasidenib, have provided increased efficacy in AML patients carrying these mutations. Questions still remain regarding their efficacy in contrast to venetoclax which has been shown to be particularly effective against this population. In contrast, TP53-mutated AML remains a therapeutic challenge: although hypomethylating-agent/venetoclax-based regimens yield improved initial responses, remissions are generally short-lived and overall survival remains poor. Early-phase therapies, including p53 reactivators and multi-kinase inhibitors, show preclinical promise but lack definitive clinical efficacy to date. Conclusions: Targeted therapies have improved outcomes in molecularly defined subsets of AML, with menin, IDH and FLT3 inhibitors representing major advances. However, TP53-mutated AML continues to carry a dismal prognosis, underscoring the need for more effective therapeutic strategies. Continued biomarker-driven research, novel drug combinations, and mechanistic insights will be essential to further refine AML treatment and improve long-term survival across disease subsets. Full article

Background: The aim of this study was to develop an artificial intelligence (AI)-based decision support model for evaluating the extraction indication of permanent first molars in pediatric patients using panoramic radiographs, and to investigate the potential contribution of this model to the clinical decision-making process. Methods: This retrospective observational study analyzed 1000 panoramic radiographs obtained from children aged 8–10 years who attended the Clinics of Batman University Faculty of Dentistry for routine dental examination. Among the radiographs meeting the inclusion criteria, a total of 176 panoramic images were selected based on dental maturation assessment using the Demirjian tooth development staging system. Cases in which the permanent second molar was classified as Demirjian stages E–F were labeled as “extraction indication present”, while the remaining stages were labeled as “extraction indication absent”. A balanced dataset was created, consisting of 88 cases in each group. Image features were extracted using Gabor filters and Histogram of Oriented Gradients (HOG). The selected features were analyzed using a Support Vector Machine (SVM) classifier with a radial basis function (RBF) kernel. Model performance was evaluated using accuracy, sensitivity, specificity, F1-score, and area under the receiver operating characteristic curve (ROC–AUC). Results: The proposed Gabor–HOG–SVM-based AI model achieved an overall classification accuracy of 77.78% with an AUC value of 0.77 in distinguishing between “extraction indication present” and “extraction indication absent” cases. For the extraction-indicated group, the sensitivity was 0.81 and the F1-score was 0.79, whereas for the non-indicated group, the sensitivity and F1-score were 0.74 and 0.77, respectively. No statistically significant differences were observed between the groups in terms of age or sex distribution (p > 0.05). Conclusions: This study demonstrates that artificial intelligence-based analysis of panoramic radiographic images can provide an objective and reproducible decision support approach for evaluating extraction indications of permanent first molars in pediatric patients. The proposed model should be considered as an adjunctive tool to reduce observer-dependent variability rather than a replacement for clinical judgment, and its clinical applicability should be further validated through multicenter and multi-parametric studies. Full article

Assessing genetic diversity in various native poultry breeds, including bantam/dwarf ones, is instrumental for their conservation as genetic resources, identifying their specific genetic features, and exploring the history of their genetic divergence. Rare chicken breeds are usually carriers of peculiar phenotypic traits, including adaptations to local conditions, disease resistance, and unique performance features. Here, we report for the first time SNP-based genetic characterization of the Russian Korolyok, translated as “kinglet,” relative to five other dwarf/small breeds: Cochin Bantam, Hamburg Bantam Silver Spangled, Polish White-crested Black, Red White-tailed Dwarf and Silkie White. We estimated phenotypes, heterozygosity, inbreeding, effective population size, and runs of homozygosity (ROHs). Some breeds had higher genetic diversity and others showed elevated inbreeding rates in their genomes. With lower effective population sizes (both presently and in the past), rare breeds came from a limited number of ancestors or were under strong selection pressure over many generations. Within 22 ROHs, we identified 26 prioritized candidate genes (GRB10, RPRD1A, APOOL, EAF2, SEMA5, HACD2, GALANT1, DACH2, CHM, POF1B, HDX, SLC15A2, PDIA5, SEC22, NR2F2, ARRDC4, IGF1R, SYNM, TMEM263, etc.). Our data offer whole-genome insights into genetic variability, history, phylogeny, selective sweeps, and candidate genes of a distinct indigenous Russian chicken breed and other bantam/dwarf breeds. Full article

Ultrafast laser processing is constrained by an inherent throughput–resolution trade-off, typically addressed either by high-speed single-beam scanning or by parallel processing approaches. Here, a scanning-based dynamic mask projection concept is presented, combining both strategies by integrating a digital micromirror device (DMD) for dynamic binary amplitude mask generation with galvanometric scanning for high-speed lateral repositioning of the projected pattern. A high-numerical-aperture microscope objective is used to project the mask for thin film laser ablation with sub-micrometer feature sizes, while scanning extends the processing area beyond a single projected pattern, ultimately limited by the objective’s field of view. The concept is demonstrated by selective single-pulse pattern ablation of 10 nm thick tantalum nitride (TaN) thin films on glass substrates using 230 fs pulses at a center wavelength of 515 nm. The optical system enables a 770 nm minimum feature size across a scan field with an area-equivalent circular diameter of 550 µm. Dynamic mask projection combined with fast scanning offers a scalable route to high-throughput laser nanoprocessing and is relevant to fabrication and processing of nanomaterials, digital mask lithography, and micro- and nanomachining. Full article

To address the small ATC-63 record set for collapse-oriented motion selection and the limited interpretability of data-driven approaches, this study proposes a framework for identifying structural-collapse-critical ground motions. Using 5074 records from the PEER NGA-West2 database, we applied STA/LTA event detection and extracted multi-source features. A Gaussian mixture model (GMM) was then used to perform unsupervised clustering and identify four physically interpretable groups. LightGBM, XGBoost, and Random Forest were employed to test the separability of the cluster labels, with all three models achieving F1 scores above 0.89 and LightGBM reaching an accuracy of about 93%. SHAP-based feature-importance analysis was used at the model level to clarify feature contributions and improve interpretability. Cluster 2 exhibits markedly higher relative seismic energy, stronger time-domain variability, and more dominant frequencies, forming a typical strong-motion hazard signature. For external engineering verification, 22 ATC-63 far-field records were mapped onto the full dataset to examine cluster-level enrichment and coverage. Cluster 2 shows significant enrichment in engineering markers and high coverage and is therefore identified as the collapse-sensitive phenotype cluster (COP). Overall, the framework provides a technical basis for ground-motion selection in collapse assessment, fragility analysis, and design evaluation. Full article

Background: Pain remains one of the most debilitating and prevalent symptoms in cancer patients. However, assessment based solely on subjective self-report tools is limited by cognitive impairment and the heterogeneous nature of cancer pain. Since evidence on the ability of physiological biosignals to discriminate cancer pain intensity and pain phenotypes in real clinical settings remains limited, this study explored the potential of biosignals to discriminate between pain intensity and pain type. Methods: Electrodermal activity (EDA) and electrocardiogram (ECG) signals were recorded in cancer patients using the BITalino (r)evolution board (sampling frequency 1000 Hz). EDA was processed to extract skin conductance responses (SCRs) using continuous decomposition analysis (CDA) and trough-to-peak (TTP) methods. Heart rate variability (HRV) features were extracted in both time and frequency domains, including low frequency (LF), high frequency (HF), and the LF/HF ratio. Non-parametric Kruskal–Wallis tests were performed to compare biosignal parameters across pain intensity (Numeric Rating Scale, NRS: low 1–3; medium 4–6; and high 7–10) and pain types (nociceptive, neuropathic, mixed, and breakthrough cancer pain—BTCP). Results: Data from 61 patients were analyzed. For EDA, the maximum skin conductance response amplitude (MaxCDA) significantly differed across intensity groups (p = 0.037). Post hoc analysis showed a significant difference between the low- and high-intensity groups (p = 0.015), with the low-intensity group exhibiting a higher mean MaxCDA (0.063 µS) than the high-intensity group (0.024 µS). Several EDA parameters were significantly associated with pain type. The number of SCRs (TTP) (p = 0.015) and maximum SCR amplitude (TTP) (p = 0.040) were significantly lower in the mixed pain group compared with the nociceptive and neuropathic groups. No HRV parameters showed significant associations with pain intensity or pain type. BTCP did not significantly affect any biosignal parameters. Subgroup analyses showed that EDA features discriminating mixed pain were preserved in patients without bone metastases, BTCP, or high opioid burden, whereas no clinical variable modified the association between biosignals and pain intensity and type. Conclusions: In this investigation, selected EDA parameters were associated with cancer pain intensity and pain type, whereas heart rate variability measures did not show significant discrimination under the present methodological conditions. These findings suggest that EDA may provide complementary information on pain-related autonomic alterations in oncology patients. However, biosignals should not be considered standalone indicators of pain, and their interpretation requires integration with clinical variables and pharmacological context. Further studies adopting multimodal and longitudinal approaches are needed to clarify their role in automatic pain assessment in cancer care. Full article

Almost half of patients with triple-negative breast cancer (TNBC) develop brain metastasis (TNBCBM), a marker of poor prognosis. TNBC is a more aggressive breast cancer subtype which lacks ER, PR, and HER2 expression, and thus, exploring predictive biomarkers is crucial to improving TNBCBM outcomes through targeted therapy. To curate these biomarkers, peer-reviewed publications from 2010 to 2025 were extracted from PubMed, Scopus, Embase, Cochrane, and Web of Science if they evaluated clinicopathological biomarkers of TNBCBM. A total of 130 studies (60 clinical and 70 pre-clinical) were included. Publications most often featured transcriptomic studies, growth factor receptors, and immune microenvironment markers with 37, 19, and 17 studies identified, respectively. While TNBC aggressiveness has been linked to metastasis, advancing stage, and poor prognosis, several studies focused on utilizing circulating protein and transcriptomic biomarkers for early detection. While few pathways appeared specifically for TNBCBM, investigating these biomarkers further may allow for improved risk stratification, clinical trial design, patient selection, and therapeutic development. Identification of the most promising biomarkers will pave the way for improved prognosis of the most lethal complications of TNBC. Full article

Path integral Monte Carlo simulations and closure computations of quantum fluid triplet structures in the diffraction regime are presented. The principal aim is to shed some more light on the long-standing problem of quantum fluid triplet structures. This topic can be tackled via path integrals in an exact, though computationally demanding, way. The traditional approximate frameworks provided by triplet closures are complementary sources of information that (unexpectedly) may produce, at a much lower cost, useful results. To explore this topic further, the systems selected in this work are helium-3 under supercritical conditions and the quantum hard-sphere fluid on its crystallization line. The fourth-order propagator in the Jang-Jang-Voth’s form (for helium-3) and Cao–Berne’s pair action (for hard spheres) are employed in the corresponding path integral simulations; helium-3 interactions are described with Janzen–Aziz’s pair potential. The closures used are Kirkwood superposition, Jackson–Feenberg convolution, the intermediate AV3, and the symmetrized form of Denton–Ashcroft approximation. The centroid and instantaneous triplet structures, in the real and the Fourier spaces, are investigated by focusing on salient equilateral and isosceles features. To accomplish this goal, additional simulations and closure calculations at the structural pair level are also carried out. The basic theoretical and technical points are described in some detail, the obtained results complete the structural properties reported by this author elsewhere for the abovementioned systems, and a meaningful comparison between the path integral and the closure results is made. In particular, the results illustrate the very slow convergence of the path integral triplet calculations and the behaviors of certain salient Fourier components, such as the double-zero momentum transfers or the equilateral maxima, which may be associated with distinct fluid conditions (e.g., far and near quantum freezing). Closures are shown to yield valuable triplet information over a wide range of conditions, as ascertained from the analyzed centroid structures, which mimic those of fluids at densities higher than the actual ones; thus, closures should remain a part of quantum fluid triplet studies. Full article

The chemical composition of vegetable cooking oils is a key parameter in determining the quality of their products. Antioxidants are widely used in these products to extend their shelf life. In this study, the concentration of butylated hydroxytoluene (BHT) in edible oil was quantitatively determined by Raman spectroscopy combined with chemometrics. Initially, Raman spectra of edible oil samples with varying concentrations of BHT were obtained. Subsequently, three variable selection methods were applied to the pre-processed spectra. Optimised characteristic wavelengths were then used to establish a Radial Basis Function (RBF) neural network and partial least squares (PLS) models. The impact of variable selection on feature wavelengths was evaluated for both models in both independent and combined cases. The results demonstrate that the features identified through multiple variable selection methods correlate highly with the BHT content and can be utilised to develop high-precision detection models. The findings indicate that the PLS model, optimised using competitive adaptive reweighting (CARS), achieved the best prediction performance, with an average $R_P^2$ of 0.9687, and RMSEP of 3.1211. These results demonstrate the feasibility of using Raman spectroscopy combined with chemometrics for the rapid screening of BHT in edible oils. While the current study focuses on a broad concentration range to validate the method’s linearity, further optimisation is required for trace-level detection to meet strict regulatory limits. Full article

With the widespread deployment of automotive millimeter-wave radars, mutual interference and broadband noise severely degrade the signal-to-noise ratio (SNR) of range–Doppler (RD) maps, leading to the loss of weak targets. Existing deep learning methods rely on difficult-to-obtain paired training samples and often cause excessive target smoothing due to a lack of physical constraints. To address these challenges, this paper proposes RIME-Net, a physics-guided unpaired learning framework designed to jointly achieve radar interference mitigation and weak target enhancement. First, based on a cycle-consistent adversarial architecture, we designed the Interference Mitigation Network (IM-Net). IM-Net integrates spectral consistency loss and identity mapping constraints, learning a robust mapping from the interference domain to the clean domain without paired supervision, effectively suppressing low-rank interference and preserving signal integrity. Second, to recover target details attenuated during denoising, we propose the saliency-aware Target Enhancement Network (TE-Net). TE-Net combines multi-scale residual blocks and channel-spatial attention mechanisms, selectively enhancing weak target features based on saliency priors. Extensive experiments on diverse datasets show that RIME-Net significantly outperforms existing supervised and model-driven methods in terms of SINR, recall, and structural similarity, providing a robust solution for reliable radar perception in complex electromagnetic environments. Full article

Beef freshness is a critical indicator of meat quality and safety, and its rapid, non-destructive detection is of significant importance for ensuring consumer health and enhancing quality control throughout the meat industry chain. This study developed a novel methodology for non-destructive beef freshness assessment using visible and near-infrared (Vis-NIR) spectroscopy combined with machine learning, explainable artificial intelligence (xAI) techniques, and the SHapley Additive exPlanations (SHAP) framework. An improved hybrid heuristic method, particle swarm optimization–genetic algorithm (PSOGA), was used for feature selection, optimizing the wavelength subset for predicting beef quality indicators, including total volatile basic nitrogen (TVB-N) and color parameters (L*, a*, and b*). The eXtreme Gradient Boosting (XGBoost) was employed for regression modeling, and the results showed that PSOGA significantly outperforms traditional methods, with the PSOGA-XGBoost model achieving a satisfactory prediction accuracy (R²p values of 0.9504 for TVB-N, 0.9540 for L*, 0.8939 for a*, and 0.9416 for b*). The SHAP framework identified the key wavelengths as 1236 nm and 1316 nm for TVB-N, 728 nm for L*, 576 nm for a*, and 604 nm for b*, providing valuable insights into the determination of key wavelengths and enhancing the interpretability of the model. The results demonstrated the effectiveness of PSOGA and SHAP, providing a promising analytical method for monitoring beef freshness. Full article

Ion channels are fundamental membrane proteins that mediate selective ion flow across biological membranes and thereby govern excitability, signaling, and homeostasis in virtually all cell types. Although channel function is determined by intrinsic structural features, the surrounding lipid milieu is now recognized as a decisive regulatory layer. Lipids tune ion channel activity through complementary mechanisms: they can bind directly to channel proteins, reshape bilayer physical properties, or act as signaling messengers that couple extracellular cues to channel gating. In addition, the organization of membranes into lipid microdomains such as rafts and caveolae can cluster channels with receptors and scaffolds, enhancing signaling specificity and efficiency. Recent advances in cryo-electron microscopy and molecular simulations have expanded our understanding of these lipid–channel interactions, revealing lipids as active modulators rather than passive structural components. This review provides a comprehensive overview of the principles by which lipids regulate ion channel function and highlights the biological and potential clinical significance of this fundamental interplay. Full article

Background: Thrombotic thrombocytopenic purpura (TTP) is a rare but life-threatening thrombotic microangiopathy (TMA) caused by severe deficiency of the von Willebrand factor–cleaving protease ADAMTS13. Pregnancy is a recognized trigger for both immune-mediated and congenital TTP and is associated with increased maternal and fetal morbidity. Clinical overlap with other pregnancy-associated TMAs, including preeclampsia and Hemolysis, Elevated Liver enzymes, and Low Platelet count (HELLP) syndrome, often delays diagnosis. This review synthesizes current evidence on pathophysiology, diagnostic uncertainty, and gestation-specific management of pregnancy-associated TTP, highlighting differences between immune-mediated and congenital disease. Methods: This is a narrative review. We performed a targeted literature search of PubMed/MEDLINE (from inception to December 2025) to identify English-language publications. The study types included were case reports/series, observational studies, large database studies, randomized trials, reviews, and relevant guidelines addressing TMA in pregnancy, with emphasis on immune-mediated and congenital TTP. Search terms included “pregnancy”, “thrombotic thrombocytopenic purpura”, “hereditary TTP”, “acquired TTP”, “ADAMTS13,” “thrombotic microangiopathy,” “HELLP,” “postpartum”, and “complement-mediated TMA” alone or in combination. The search was supplemented by manual screening of reference lists and key guidelines. Articles were selected based on relevance to diagnosis and management of pregnancy-associated TTP. Conference abstracts and non-peer-reviewed sources were not routinely included and were considered only when peer-reviewed evidence was limited. Results: Pregnancy-associated TTP remains a major diagnostic challenge due to overlapping clinical and laboratory features with other obstetric thrombotic microangiopathies. Distinguishing immune-mediated from congenital TTP is essential, as management and prognosis differ substantially. Prompt recognition and early initiation of therapeutic plasma exchange, immunosuppression, or prophylactic plasma therapy markedly improve maternal outcomes. Rapid ADAMTS13 testing, structured risk stratification, and multidisciplinary care are central to optimal management. Fetal outcomes are closely linked to gestational age at onset and timeliness of therapy. Conclusions: Early differentiation of TTP from other pregnancy-associated TMAs is critical for maternal and fetal survival. Advances in rapid ADAMTS13 diagnostics and emerging targeted therapies, including caplacizumab and recombinant ADAMTS13, offer opportunities to improve precision management and outcomes in future pregnancies. Full article

Monitoring the freshness of Salmo salar within cold chain logistics is paramount for ensuring food safety. However, conventional physicochemical and microbiological assays are impeded by inherent limitations, including destructiveness and significant time latency, rendering them inadequate for the real-time, non-invasive inspection demands of modern industry. Here, we present a novel detection framework synergizing hyperspectral imaging (400–1000 nm) with the Transformer deep learning architecture. Through a rigorous comparative analysis of twelve preprocessing protocols and four feature wavelength selection algorithms (Lasso, Genetic Algorithm, Successive Projections Algorithm, and Random Frog), prediction models for Total Volatile Basic Nitrogen (TVB-N) and Total Viable Count (TVC) were established. Furthermore, the capacity of the Transformer to capture long-range spectral dependencies was systematically investigated. Experimental results demonstrate that the model integrating Savitzky-Golay (SG) smoothing with the Transformer yielded optimal performance across the full spectrum, achieving determination coefficients (R²) of 0.9716 and 0.9721 for the Prediction Sets of TVB-N and TVC, respectively. Following the extraction of 30 characteristic wavelengths via the Successive Projections Algorithm (SPA), the streamlined model retained exceptional predictive precision (R² ≥ 0.95) while enhancing computational efficiency by a factor of approximately six. This study validates the superiority of attention-mechanism-based deep learning algorithms in hyperspectral data analysis. These findings provide a theoretical foundation and technical underpinning for the development of cost-effective, high-efficiency portable multispectral sensors, thereby facilitating the intelligent transformation of the aquatic product supply chain. Full article

Background: Correct preoperative evaluation of glioma grade and molecular profile is a prerequisite for tailored treatment strategies. Specifically, the 1p/19q codeletion status represents a major prognostic and therapeutic marker in low-grade gliomas (LGGs). Nevertheless, its assessment is presently performed through invasive histopathological and genetic studies, thus underlining the need for non-invasive alternative approaches. Methods: We introduce a non-invasive radiomics framework that combines quantitative MRI features with sophisticated ML and DL approaches for glioma grading and 1p/19q codeletion status prediction. High-dimensional radiomic features characterizing tumor geometry, intensity, and texture were derived from preoperative MRI-based tumor delineations. Features were normalized and optimized using correlation-based feature selection. Several traditional ML classifiers were compared and contrasted with DL models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and a CNN-Long Short-Term Memory (LSTM) hybrid model tailored to exploit both spatial feature hierarchies and feature correlations. Model validation was conducted using five-fold cross-validation and an independent test dataset, with accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) metrics. Results: Among all the models tested, the hybrid CNN-LSTM model performed the best, with an accuracy of 88.1% and an AUC of 0.93, outperforming conventional ML approaches and single-model DL architectures. Explainability analysis showed that the radiomic features of tumor heterogeneity and morphology had the most prominent impact on model performance. Conclusions: These findings indicate that the combination of radiomic features with hybrid DL models is capable of making non-invasive predictions of glioma grade and 1p/19q codeletion status. The new computational model has the potential to be used as a supplementary approach in precision neuro-oncology. Full article