Search Results (314)

Background: Breast and ovarian cancers (BC and OC) are prevalent malignancies in women globally, with germline variants in the BRCA2 gene significantly increasing the risk of developing these cancers. Despite extensive studies, the frequency and impact of BRCA2 variants in women from Jalisco, Mexico, remain underexplored. Objective: The aim of this study was to identify and characterize BRCA2 gene variants in Mexican women diagnosed with BC and OC and to assess their functional and structural consequences using computational analyses. Methodology: Genomic DNA from 140 Mexican women with BC and/or OC, selected based on clinical criteria suggestive of BRCA2 variants, was sequenced using NGS targeting BRCA2 coding regions. Functional effects were predicted with Ensembl VEP, SIFT, and PolyPhen-2. Structural impacts of missense variants were assessed using HOPE and AlphaFold models. Results: BRCA2 variants were identified in 12.86% of patients, with higher frequency in OC (21.05%) than BC (12%). Several mapped to key functional domains, including BRC repeats and the DNA-binding domain. Many were predicted as deleterious or probably damaging, though clinical classifications were often conflicting. Structural analysis indicated potential disruptions in protein stability or interactions for most missense variants. Clinically, BRCA2-positive BC patients were younger at diagnosis and showed a trend toward lower complete response. Conclusion: BRCA2 variants were found in 12.86% of patients, including six VUSs not reported in other populations. Several affected key functional domains with predicted deleterious effects. Findings support the need for genetic panels tailored to the Mexican population. Full article

Background/Objectives: Breast cancer (BC) has emerged as a significant threat among female malignancies, resulting in approximately 670,000 fatalities. The capacity to identify BC has advanced over the past two decades because of deep learning (DL), machine learning (ML), and artificial intelligence. The early detection of BC is crucial; yet, conventional diagnostic techniques, including MRI, mammography, and biopsy, are costly, time-intensive, less sensitive, incorrect, and necessitate skilled physicians. This narrative review will examine six novel imaging approaches for BC diagnosis. Methods: Optical coherence tomography (OCT) surpasses existing approaches by providing non-invasive, high-resolution imaging. Raman Spectroscopy (RS) offers detailed chemical and structural insights into cancer tissue that traditional approaches cannot provide. Photoacoustic Imaging (PAI) provides superior optical contrast, exceptional ultrasonic resolution, and profound penetration and visualization capabilities. Hyperspectral Imaging (HSI) acquires spatial and spectral data, facilitating non-invasive tissue classification with superior accuracy compared to grayscale imaging. Contrast-Enhanced Spectral Mammography (CESM) utilizes contrast agents and dual energy to improve the visualization of blood vessels, enhance patient comfort, and surpass standard mammography in sensitivity. Multispectral Imaging (MSI) enhances tissue classification by employing many wavelength bands, resulting in high-dimensional images that surpass the ultrasound approach. The imaging techniques studied in this study are very useful for diagnosing tumors, staging them, and guiding surgery. They are not detrimental to morphological or immunohistochemical analysis, which is the gold standard for diagnosing breast cancer and determining molecular characteristics. Results: These imaging modalities provide enhanced sensitivity, specificity, and diagnostic accuracy. Notwithstanding their considerable potential, the majority of these procedures are not employed in standard clinical practices. Conclusions: Validations, standardization, and large-scale clinical trials are essential for the real-time application of these approaches. The analyzed studies demonstrated that the novel modalities displayed enhanced diagnostic efficacy, with reported sensitivities and specificities often exceeding those of traditional imaging methods. The results indicate that they may assist in early detection and surgical decision-making; however, for widespread adoption, they must be standardized, cost-reduced, and subjected to extensive clinical trials. This study offers a concise summary of each methodology, encompassing the methods and findings, while also addressing the many limits encountered in the imaging techniques and proposing solutions to mitigate these issues for future applications. Full article

Background/objectives: Pharmaceutical cocrystals have emerged as a promising strategy to enhance the solubility and bioavailability of poorly water-soluble drugs. Indomethacin (IND), classified as a Biopharmaceutics Classification System (BCS) Class II drug, exhibits low solubility but high permeability. This study aims to develop a synthesis method, evaluate cocrystal solubility/stability and the physicochemical properties of the pure components, and describe cocrystal solubility using a mathematical model. Methods: Cocrystals were synthesized via solvent evaporation, using ethanol, methanol, and ethyl acetate. The pure components, IND and benzoic acid (AcBz) were dissolved in each solvent and maintained in a thermostabilizer for 24 h. Cocrystal formation was confirmed by UV-V spectroscopy, differential scanning calorimetry (DSC), and infrared (IR) spectroscopy. Results: The results showed that the solubility of the cocrystals decreased with increasing benzoic acid concentration. Mathematical modelling revealed that solubility can be expressed as the product of the solubilities of the individual components and the stability constant of the solution complex. Among the solvents tested, ethanol exhibited the highest solubility and equilibrium constant (K_eq) for IND–AcBz cocrystals, suggesting a greater molecular affinity and enhanced cocrystal formation. Conclusions: These findings demonstrate that the formation of the novel INDAcBz cocrystal significantly enhances Indomethacin solubility and thermodynamic stability. These results validate benzoic acid as an effective coformer and establish phase solubility diagrams (PSD) as predictive tools for rational cocrystal design, supporting the future development of optimized pharmaceutical formulations. Full article

Background and Objectives: Breast cancer (BC) is a global health concern for women; the disease contributes to significant morbidity and mortality. A key element in the diagnosis of BC involves the histopathological diagnosis, which determines patient management and therapy. However, BC is a multifaceted disease, limiting access to early diagnosis and, therefore, treatment. Artificial intelligence (AI) is transforming diagnostics in the medical field, especially in the detection of BC. Due to the increased availability of digital slides, it has facilitated the effective integration of AI in breast cancer diagnosis. Diagnosis poses a great challenge, even for experienced pathologists, due to the heterogeneity of this malignancy. Analysing microscopic slides by pathologists requires a considerable amount of time. Implementation of AI into routine workflows holds potential to improve diagnostic sensitivity and inter-observer concordance, and to increase efficiency by reducing the review time, thereby helping to alleviate the burden of diagnosing BC. Previous studies mainly address imaging modalities or oncology broadly, while a few specifically concentrates on the histopathological aspect of breast cancer. This review aims to explore the novel synthesis of AI advancements in digital pathology, including tumour classification, grading, lymph node staging, and biomarker evaluation, and discuss their potential incorporation into clinical workflows. We will also discuss the current barriers and prospects for future advancements. Materials and Methods: A literature search was conducted in PubMed and Google Scholar using the mentioned keywords. Articles published in English until July 2025 were reviewed and synthesised narratively. Results: Recent studies demonstrate that AI models such as convolutional neural networks (CNNs), YOLO, and RetinaNet achieve high accuracy in tumour detection, histological grading, lymph node metastasis localisation, and biomarker analysis. The reported performance values range from 75% to over 95% accuracy across various tasks, with gains in diagnostic sensitivity and inter-observer concordance, and reduced review time in assisted workflows. However, certain limitations, such as data variability, external validation in clinical practice, and ethical concerns, restrict the growth and optimal performance of AI and its clinical applicability. Conclusions: The future for AI looks promising, as it is rapidly evolving. By analysing evidence across multiple domains, this review evaluates both opportunities and persisting barriers, offering practical overviews for future clinical transition. AI cannot replace pathologists; however, it has the capabilities to enhance diagnostic precision, efficiency, and ultimately patient outcomes. It is only a matter of time before AI is adopted into healthcare. Full article

The advancement of smart grid technologies necessitates the integration of cutting-edge computational methods to enhance predictive energy optimization. This study proposes a multi-faceted approach by incorporating (1) Deep Reinforcement Learning (DRL) agents trained using data from digital twins (DTs) to optimize energy consumption in real time, (2) Physics-Informed Neural Networks (PINNs) to seamlessly embed physical laws within the optimization process, ensuring model accuracy and interpretability, and (3) blockchain (BC) technology to facilitate secure and transparent communication across the smart grid infrastructure. The model was trained and validated using comprehensive datasets, including smart meter energy consumption data, renewable energy outputs, dynamic pricing, and user preferences collected from IoT devices. The proposed framework achieved superior predictive performance with a Mean Absolute Error (MAE) of 0.237 kWh, Root Mean Square Error (RMSE) of 0.298 kWh, and an R-squared (R²) value of 0.978, indicating a 97.8% explanation of data variance. Classification metrics further demonstrated the model’s robustness, achieving 97.7% accuracy, 97.8% precision, 97.6% recall, and an F1 Score of 97.7%. Comparative analysis with traditional models like Linear Regression, Random Forest, SVM, LSTM, and XGBoost revealed the superior accuracy and real-time adaptability of the proposed method. In addition to enhancing energy efficiency, the model reduced energy costs by 35%, maintained a 96% user comfort index, and increased renewable energy utilization to 40%. This study demonstrates the transformative potential of integrating PINNs, DT, and blockchain technologies to optimize energy consumption in smart grids, paving the way for sustainable, secure, and efficient energy management systems. Full article

Background/Objectives: Breast cancer (BC) is the most commonly diagnosed cancer in women and the leading cause of cancer-related deaths globally. Molecular subtyping is crucial for prognosis and treatment planning, with immunohistochemistry (IHC) being the most commonly used method. However, IHC has limitations, including observer variability, a lack of standardization, and a lack of reproducibility. Gene expression profiling is considered the ground truth for molecular subtyping; unfortunately, this is expensive and inaccessible to many institutions. This study investigates the potential of an artificial intelligence (AI) model to predict BC molecular subtypes directly from hematoxylin and eosin (H&E)-stained whole-slide images (WSIs). Methods: A pretrained deep learning framework based on multiple-instance learning (MIL) was validated on the Stavanger Breast Cancer (SBC) dataset, consisting of 538 BC cases. Three classification tasks were assessed, including two-class [triple negative BC (TNBC) vs. non-TNBC], three-class (luminal vs. HER2-positive vs. TNBC), and four-class (luminal A vs. luminal B vs. HER2-positive vs. TNBC) groups. Performance metrics were used for the evaluation of the AI model. Results: The AI model demonstrated strong performance in distinguishing TNBC from non-TNBC (AUC = 0.823, accuracy = 0.833, F1-score = 0.824). However, performance declined with an increasing number of classes. Conclusions: The study highlights the potential of AI in BC molecular subtyping from H&E WSIs, offering an easily applicable and standardized method to IHC. Future improvements should focus on optimizing multi-class classification and validating AI-based methods against gene expression analyses for enhanced clinical applicability. Full article

As with other non-coding RNAs (ncRNAs), the aberrant expression of long non-coding RNAs (lncRNAs) can be associated with different forms of cancers, including breast cancer (BC). Various lncRNAs may either promote or suppress cell proliferation, metastasis, and other related cancer signaling pathways by interacting with other cellular machinery, thus affecting the expression of BC-related genes. However, lncRNAs are characterized by features that are unlike protein-coding genes, which pose unique challenges when it comes to their study and utility. They are highly diverse and may display contradictory functions depending on factors like the BC subtype, isoform diversity, epigenetic regulation, subcellular localization, interactions with various molecular partners, and the tumor microenvironment (TME), which contributes to the intratumoral heterogeneity and phenotypic plasticity. While lncRNAs have potential clinical utility, their functional heterogeneity coupled with a current paucity of knowledge of their functions present challenges for clinical translation. Strategies to address this heterogeneity include improving classification systems, employing CRISPR/Cas tools for functional studies, utilizing single-cell and spatial sequencing technologies, and prioritizing robust targets for therapeutic development. A comprehensive understanding of the lncRNA functional heterogeneity and context-dependent behavior is crucial for advancing BC research and precision medicine. This review discusses the sources of lncRNA heterogeneity, their implications in BC biology, and approaches to resolve knowledge gaps in order to harness lncRNAs for clinical applications. Full article

Convolutional neural network (CNN) models are widely used for environmental sound classification (ESC). However, 2-D convolutions assume translation invariance along both time and frequency axes, while in practice the frequency dimension is not shift-invariant. Additionally, single-scale convolutions limit the receptive field, leading to incomplete feature representation. To address these issues, we introduce a parallel time-frequency multi-scale attention (PTFMSA) module that integrates local and global attention across multiple scales to improve dynamic convolution in order to overcome these problems. We also introduce the parallel branch structure to avoid mutual interference of information in case of extracting time and frequency domain features. Additionally, we utilize learnable parameters that can dynamically adjust the weights of different branches during network training. Building on this module, we develop PTFMSAN, a compact network that processes raw waveforms directly for ESC. To further strengthen learning, between-class (BC) training is applied. Experiments on the ESC-50 dataset show that PTFMSAN outperforms the baseline model, achieving a classification accuracy of 90%, competitive among CNN-based networks. We also performed ablation experiments to verify the effectiveness of each module. Full article

Breast cancer is a widespread disease involving abnormal (uncontrolled) growth of breast tissue cells along with the formation of a tumor and metastasis. Breast cancer cases occur mostly among women. Early detection and regular screening have significantly improved survival rates. This research classifies breast cancer and non-breast cancer cases using machine learning algorithms based on the Breast Cancer Coimbra dataset by optimizing the classifier performance and feature selection methodology. In addition, this research identifies the influential features responsible for BC classification by using diverse counterfactual explanations. The Rotation Forest classifier algorithm is used to classify breast cancer and non-breast cancer cases. The hyperparameters of this algorithm are optimized using the Optuna optimizer. Three wrapper-based feature selection techniques (Sequential Forward Selection, Sequential Backward Selection, and Exhaustive Feature Selection) are used to select the most relevant features. An ensemble environment is also created using the best feature subsets of these methods, incorporating both soft and hard voting strategies. Experimental results show that the hard voting strategy achieves an accuracy of 85.71%, F1-score of 83.87%, precision of 92.85%, and recall of 76.47%. In contrast, the soft voting strategy obtains an accuracy of 80.00%, F1-score of 77.42%, precision of 85.71%, and recall of 70.59%. These findings demonstrate that hard voting achieves noticeably better performance. The misclassification outcomes of both strategies are explored using Diverse Counterfactual Explanations, revealing that BMI and Glucose values are most influential in predicting correct classes, whereas the HOMA, Adiponectin, and Resistin values have little influence. Full article

Background/Objectives: This study aimed to develop a novel amorphous solid dispersion (ASD) platform using poly(2-ethyl-2-oxazoline) (PEtOx) for the solubility enhancement of poorly water-soluble drugs. Fenofibrate (FB), a Biopharmaceutics Classification System (BCS) Class II drug, was selected as the model drug. The novelty of this work lies in the formulation of dual-matrix systems by blending PEtOx of varying molecular weights (50 kDa, 200 kDa, 500 kDa) with solubility-enhancing polymers, Soluplus^® and Kollidon^® VA64, to investigate component compatibility, synergistic solubility enhancement, and the influence of PEtOx molecular weight on drug release. Methods: ASDs were prepared via hot-melt extrusion (HME) and characterized using differential scanning calorimetry (DSC), scanning electron microscopy (SEM), powder X-ray diffraction (PXRD), and Fourier transform–infrared spectroscopy (FTIR) to confirm FB amorphization and evaluate drug–polymer interactions. In vitro dissolution testing was performed to assess drug release performance, and stability studies were conducted at ambient conditions for one month to evaluate physical stability. Results: DSC, PXRD, and FTIR confirmed the successful amorphization of FB and good miscibility between PEtOx and the selected excipients. In vitro dissolution studies showed an 8–12-fold increase in FB release from ASDs compared to crystalline drug. Lower-molecular-weight PEtOx grades yielded faster release profiles, while binary blends with Soluplus^® or Kollidon^® VA64 enabled tailored drug release. Stability testing indicated that all formulations maintained their amorphous state over one month. Conclusions: PEtOx-based ASDs represent a versatile platform for enhancing the solubility and dissolution of poorly water-soluble drugs. By adjusting polymer molecular weight and combining with complementary excipients, release profiles can be optimized to achieve improved performance and stability. Full article

Blood cell detection and enumeration play a crucial role in medical diagnostics. However, traditional methods often face limitations in accurately detecting smaller or overlapping cells, which can result in misclassifications and reduced reliability. To overcome these challenges related to detection failures and classification inaccuracies, this study presents an enhanced YOLO-based algorithm, specifically designed for blood cell detection, referred to as YOLO-BC. This novel approach aims to improve both detection precision and classification accuracy, particularly in complex scenarios where cells are difficult to distinguish due to size variability and overlapping. The Effective Multi-Scale Attention (EMSA) is integrated into the C2f module, enhancing feature maps by applying attention across multiple scales to refine the representation of blood cell features. Omni-Dimensional Dynamic Convolution (ODConv) is employed to replace the standard convolution module, adaptively combining kernels from multiple dimensions to improve feature representation for diverse blood cell types. For the experiments, the BCCD (Blood Cell Count and Detection) dataset is utilized, alongside data augmentation techniques. In terms of experimental evaluation, YOLO-BC outperforms YOLOv8 with a 3.1% improvement in mAP@50, a 3.7% increase in mAP@50:95, and a 2% increase in F1-score, all based on the same dataset and IoU parameters. Notably, small objects such as platelets are also detected with high accuracy. These findings highlight the effectiveness and potential clinical applicability of YOLO-BC for automated blood cell detection. Full article

Background/Objectives: This study evaluated the feasibility of predicting bone conduction (BC) thresholds and classifying air–bone gap (ABG) status using only air conduction (AC) data obtained from pure tone audiometry (PTA). Methods: A total of 60,718 PTA records from five tertiary hospitals in the Republic of Korea were utilized. Input features included AC thresholds (0.25–8 kHz), age, and sex, while outputs were BC thresholds (0.25–4 kHz) and ABG classification based on 10 dB and 15 dB criteria. Five machine learning models—deep neural network (DNN), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), random forest (RF), and extreme gradient boosting (XGB)—were trained using 5-fold cross-validation with Synthetic Minority Over-sampling Technique (SMOTE). Model performance was evaluated based on accuracy, sensitivity, precision, and F1 score under ±5 dB and ±10 dB thresholds for BC prediction. Results: LSTM and BiLSTM outperformed DNN in predicting BC thresholds, achieving ~60% accuracy within ±5 dB and ~80% within ±10 dB. For ABG classification, all models performed better with the 10 dB criterion than the 15 dB. Tree-based models (RF, XGB) achieved the highest classification accuracy (up to 0.512) and precision (up to 0.827). Confidence intervals for all metrics were within ±0.01, indicating stable results. Conclusions: AI models can accurately predict BC thresholds and ABG status using AC data alone. These findings support the integration of AI-driven tools into clinical audiology and telemedicine, particularly for remote screening and diagnosis. Future work should focus on clinical validation and implementation to expand accessibility in hearing care. Full article

Background/Objectives: Reflecting the interaction between dissolution and absorption, the biphasic dissolution system is an appealing approach for estimating the intestinal absorption of drugs in humans. The study aims to characterize the suitability of the biphasic in vitro dissolution testing to set up an in vitro–in vivo correlation (IVIVC) for the original and generic immediate-release (IR) tablets of a Biopharmaceutics Classification System (BCS) Class II drug, bicalutamide (BIC). Methods: USP apparatus II paddle was used to conduct dissolution testing. A level A IVIVC was obtained between in vitro partitioning and in vivo absorption data of the original drug. The single-compartmental modeling was used for pharmacokinetic (PK) analysis. The generic product’s plasma concentrations were estimated. Results: There was a good correlation between in vitro and in vivo data (r² = 0.98). The area under the concentration–time curve (AUC) and maximum plasma concentration (C_max) ratios for generic/original were 1.04 ± 0.01 and 0.951 ± 0.026 (mean ± SD), respectively. Conclusions: The biphasic dissolution testing may present an in vivo predictive tool for developing generic products of poorly soluble and highly permeable drugs such as BIC, which are characterized by pH-independent poor solubility. Full article

Background/Objectives: Breast cancer (BC) is the most prevalent cancer worldwide, with approximately 40% of early-stage BC patients developing recurrence despite initial treatments. Current diagnostic methods, such as mammography and solid tissue biopsies, face limitations in sensitivity, accessibility, and the ability to characterize tumor heterogeneity or monitor systemic disease progression. Methods: To address these gaps, this study investigates a fully automated analysis workflow using data derived from fluorescent Whole-Slide Imaging (fWSI) for detecting and classifying rare cells (circulating tumor and tumor microenvironment cells) in peripheral blood samples. Our methodology integrates supervised machine learning algorithms for rare event detection, immunofluorescence-based classification, and statistical quantification of cellular features. Results: Using a fWSI dataset of 534 cancer and non-cancer peripheral blood samples, the automated model demonstrated high concordance with manual annotation, achieving up to 98.9% accuracy and a precision-sensitivity AUC of 83.2%. Morphometric analysis of rare cells identified significant differences between normal donors, early-stage BC, and late-stage BC cohorts, with distinct clusters emerging in late-stage BC. Conclusions: These findings highlight the potential of liquid biopsy and algorithmic approaches for improving BC diagnostics and staging, offering a scalable, minimally invasive solution to enhance clinical decision-making. Future work aims to refine the automated framework to minimize errors and improve the robustness across diverse cohorts. Full article

Background/Objectives: The static in vitro permeability assay based on cell monolayers has been widely used in the pharmaceutical industry and recognized by regulatory agencies as a surrogate method for BCS classification. However, the application of such an experiment to study the effects of formulations is limited by the oversensitivity to the excipient effect on drug permeability. In this article, we studied the effects of common excipients on the permeability of moderately and poorly absorbed model compounds across cell monolayers, using two approaches to control said oversensitivity. Methods: Drug permeability across MDCK-wt was assessed in the absence (control) or presence (treatment) of excipients, using minoxidil as a high-permeability marker. The effects of excipients were parameterized as a permeability ratio (PR = treatment/control) without or with normalization (nPR) by minoxidil permeability. Metrics were compared by either ANOVA (p < 0.01) or confidence intervals (CI90, as per bioequivalence metrics) to identify excipient effects. Results: Acyclovir and hydrochlorothiazide showed the highest and lowest number of interactions, respectively. The most impactful excipients were sodium lauryl sulfate, microcrystalline cellulose, and sodium starch glycolate. Unexpectedly, nPR increased the number of excipient effects across model drugs (19 vs. 21). Alternatively, the CI90 approach was more sensitive than ANOVA in identifying excipient effects (41 vs. 32). Conclusions: Minoxidil was not able to control the anticipated oversensitivity of cell-based permeability experiments. Meanwhile, ANOVA was overall able to reduce oversensitivity to excipient effects on drug permeability compared to CI90. Nonetheless, there might be a niche for CI90 analysis when comparing the performance of two formulations on the permeability of moderately and poorly absorbed drugs. Full article