Cancer Res. 2025 Apr 30. doi: 10.1158/0008-5472.CAN-24-4190. Online ahead of print.

ABSTRACT

Accurate preoperative assessment of lymph node metastasis (LNM) and overall survival (OS) status is essential for patients with locally advanced gastric cancer (LAGC) receiving neoadjuvant chemotherapy (NAC), providing timely guidance for clinical decision-making. However, current approaches to evaluate LNM and OS have limited accuracy. In this study, we used longitudinal CT images from 1,021 LAGC patients to develop and validate a multi-task deep learning model named co-attention tri-oriented spatial Mamba (CTSMamba) to simultaneously predict LNM and OS. CTSMamba was trained and validated on 398 patients, and the performance was further validated on 623 patients at two additional centers. Notably, CTSMamba exhibited significantly more robust performance than a clinical model in predicting LNM across all of the cohorts. Additionally, integrating CTSMamba survival scores with clinical predictors further improved personalized OS prediction. These results support the potential of CTSMamba to accurately predict LNM and OS from longitudinal images, potentially providing clinicians with a tool to inform individualized treatment approaches and optimized prognostic strategies.

PMID:40305075 | DOI:10.1158/0008-5472.CAN-24-4190

Med Phys. 2025 Apr 30. doi: 10.1002/mp.17859. Online ahead of print.

ABSTRACT

BACKGROUND: The presence of metal objects in computed tomography (CT) imaging introduces severe artifacts that degrade image quality and hinder accurate diagnosis. While several deep learning-based metal artifact reduction (MAR) methods have been proposed, they often exhibit poor performance on unseen data and require large datasets to train neural networks.

PURPOSE: In this work, we propose a sinogram inpainting method for metal artifact reduction that leverages a neural attenuation field (NAF) as a prior. This new method, dubbed NAFMAR, operates in a self-supervised manner by optimizing a model-based neural field, thus eliminating the need for large training datasets.

METHODS: NAF is optimized to generate prior images, which are then used to inpaint metal traces in the original sinogram. To address the corruption of x-ray projections caused by metal objects, a 3D forward projection of the original corrupted image is performed to identify metal traces. Consequently, NAF is optimized using a metal trace-masked ray sampling strategy that selectively utilizes uncorrupted rays to supervise the network. Moreover, a metal-aware loss function is proposed to prioritize metal-associated regions during optimization, thereby enhancing the network to learn more informed representations of anatomical features. After optimization, the NAF images are rendered to generate NAF prior images, which serve as priors to correct original projections through interpolation. Experiments are conducted to compare NAFMAR with other prior-based inpainting MAR methods.

RESULTS: The proposed method provides an accurate prior without requiring extensive datasets. Images corrected using NAFMAR showed sharp features and preserved anatomical structures. Our comprehensive evaluation, involving simulated dental CT and clinical pelvic CT images, demonstrated the effectiveness of NAF prior compared to other prior information, including the linear interpolation and data-driven convolutional neural networks (CNNs). NAFMAR outperformed all compared baselines in terms of structural similarity index measure (SSIM) values, and its peak signal-to-noise ratio (PSNR) value was comparable to that of the dual-domain CNN method.

CONCLUSIONS: NAFMAR presents an effective, high-fidelity solution for metal artifact reduction in 3D tomographic imaging without the need for large datasets.

PMID:40305006 | DOI:10.1002/mp.17859

Discov Oncol. 2025 Apr 30;16(1):645. doi: 10.1007/s12672-025-02279-8.

ABSTRACT

Skin Cancer is an extensive and possibly dangerous disorder that requires early detection for effective treatment. Add specific global statistics on skin cancer prevalence and mortality to emphasize the importance of early detection. Example: "Skin cancer accounts for 1 in 5 diagnosed cancers globally, with melanoma causing over 60,000 deaths annually. Manual skin cancer screening is both time-intensive and expensive. Deep learning (DL) techniques have shown exceptional performance in various applications and have been applied to systematize skin cancer diagnosis. However, training DL models for skin cancer diagnosis is challenging due to limited available data and the risk of overfitting. Traditionally approaches have High computational costs, a lack of interpretability, deal with numerous hyperparameters and spatial variation have always been problems with machine learning (ML) and DL. An innovative method called adaptive learning has been developed to overcome these problems. In this research, we advise an intelligent computer-aided system for automatic skin cancer diagnosis using a two-stage transfer learning approach and Pre-trained Convolutional Neural Networks (CNNs). CNNs are well-suited for learning hierarchical features from images. Annotated skin cancer photographs are utilized to detect ROIs and reset the initial layer of the pre-trained CNN. The lower-level layers learn about the characteristics and patterns of lesions and unaffected areas by fine-tuning the model. To capture high-level, global features specific to skin cancer, we replace the fully connected (FC) layers, responsible for encoding such features, with a new FC layer based on principal component analysis (PCA). This unsupervised technique enables the mining of discriminative features from the skin cancer images, effectively mitigating overfitting concerns and letting the model adjust structural features of skin cancer images, facilitating effective detection of skin cancer features. The system shows great potential in facilitating the initial screening of skin cancer patients, empowering healthcare professionals to make timely decisions regarding patient referrals to dermatologists or specialists for further diagnosis and appropriate treatment. Our advanced adaptive fine-tuned CNN approach for automatic skin cancer diagnosis offers a valuable tool for efficient and accurate early detection. By leveraging DL and transfer learning techniques, the system has the possible to transform skin cancer diagnosis and improve patient outcomes.

PMID:40304929 | DOI:10.1007/s12672-025-02279-8

Jpn J Ophthalmol. 2025 Apr 30. doi: 10.1007/s10384-025-01205-3. Online ahead of print.

ABSTRACT

PURPOSE: To investigate the retinal age gap, defined as the difference between deep learning-predicted retinal age and chronological age, as a potential biomarker of systemic health in the Japanese population.

STUDY DESIGN: Prospective cohort study.

METHODS: Data from the Nagahama Study, a large-scale Japanese cohort study, were used. Participants were divided into fine-tuning (n=2,261) and analysis (n=6,070) cohorts based on their visit status across the two periods. The fine-tuning cohort only included individuals without a history of systemic or cardiovascular diseases. A deep learning model, originally released in the Japan Ocular Imaging Registry, was fine-tuned using a fine-tuning cohort to predict retinal age from images. This refined model was then applied to the analysis cohort to calculate retinal age gaps. We conducted cross-sectional and longitudinal analyses to examine the association of these gaps with systemic and cardiovascular diseases.

RESULTS: The retinal age-prediction model achieved a mean absolute error of 3.00-3.42 years. Cross-sectional analysis revealed significant associations between the retinal age gap and a history of diabetes (β = 1.08, p < 0.001) and hyperlipidemia (β = -0.67, p < 0.001). Longitudinal analysis showed no significant association between the baseline retinal age gap and disease onset. However, onset of hypertension (β = 0.35, p = 0.049) and hyperlipidemia (β = 0.34, p = 0.035) showed marginal associations with an increase in retinal age gap over time.

CONCLUSION: The retinal age gap is a promising biomarker for systemic health, particularly in relation to diabetes, hypertension, and hyperlipidemia.

PMID:40304887 | DOI:10.1007/s10384-025-01205-3

Brain Inform. 2025 Apr 30;12(1):11. doi: 10.1186/s40708-025-00256-z.

ABSTRACT

Recent advancements in cloud-based machine learning (ML) now allow for the rapid and remote identification of neural-biomarkers associated with common neuro-developmental disorders from neuroimaging datasets. Due to the sensitive nature of these datasets, secure deep learning (DL) algorithms are essential. Although, fully homomorphic encryption (FHE)-based methods have been proposed to maintain data confidentiality and privacy, however, existing FHE deep convolutional neural network (CNN) models still face some issues such as low accuracy, high encryption/decryption latency, energy inefficiency, long feature extraction times, and significant cipher-image expansion. To address these issues, this study introduces the HoRNS-CNN model, which integrates the energy-efficient features of the residue number system FHE scheme (RNS-FHE scheme) with the high accuracy of pre-trained deep CNN models in the cloud for efficient, privacy-preserving predictions and provide some proofs of its energy efficiency and homomorphism. The RNS-FHE scheme's FPGA implementation includes embedded RNS pixel-bitstream homomorphic encoder/decoder circuits for encrypting 8-bit grayscale pixels, with cloud CNN models performing remote classification on the encrypted images. In the HoRNS-CNN architecture, the ReLU activation functions of deep CNNs were initially trained for stability and later adapted for homomorphic computations using a Taylor polynomial approximation of degree 3 and batch normalization to achieve high accuracy. The findings show that the HoRNS-CNN model effectively manages cipher-image expansion with an asymptotic complexity of O n 3 , offering better performance and faster feature extraction compared to its peers. The model can predict 400,000 neural-biomarker features in one hour, providing an effective tool for analyzing neuroimages while ensuring privacy and security.

PMID:40304880 | DOI:10.1186/s40708-025-00256-z

Brain Inform. 2025 Apr 30;12(1):10. doi: 10.1186/s40708-025-00257-y.

ABSTRACT

Despite significant advancements in brain tumor classification, many existing models suffer from complex structures that make them difficult to interpret. This complexity can hinder the transparency of the decision-making process, causing models to rely on irrelevant features or normal soft tissues. Besides, these models often include additional layers and parameters, which further complicate the classification process. Our work addresses these limitations by introducing a novel methodology that combines Explainable AI (XAI) techniques with a Convolutional Neural Network (CNN) architecture. The major contribution of this paper is ensuring that the model focuses on the most relevant features for tumor detection and classification, while simultaneously reducing complexity, by minimizing the number of layers. This approach enhances the model's transparency and robustness, giving clear insights into its decision-making process through XAI techniques such as Gradient-weighted Class Activation Mapping (Grad-Cam), Shapley Additive explanations (Shap), and Local Interpretable Model-agnostic Explanations (LIME). Additionally, the approach demonstrates better performance, achieving 99% accuracy on seen data and 95% on unseen data, highlighting its generalizability and reliability. This balance of simplicity, interpretability, and high accuracy represents a significant advancement in the classification of brain tumor.

PMID:40304860 | DOI:10.1186/s40708-025-00257-y

Abdom Radiol (NY). 2025 Apr 30. doi: 10.1007/s00261-025-04966-0. Online ahead of print.

ABSTRACT

BACKGROUND: The prognosis and treatment outcomes for patients with rectal cancer are critically dependent on an accurate and comprehensive preoperative evaluation.Three-dimensional endorectal ultrasound (3D-ERUS) has demonstrated high accuracy in the T staging of rectal cancer. Thus, we aimed to develop a computer-aided diagnosis (CAD) tool using a deep learning model for the preoperative T-staging of rectal cancer with 3D-ERUS.

METHODS: We retrospectively analyzed the data of 216 rectal cancer patients who underwent 3D-ERUS. The patients were randomly assigned to a training cohort (n = 156) or a testing cohort (n = 60). Radiologists interpreted the 3D-ERUS images of the testing cohort with and without the CAD tool. The diagnostic performance of the CAD tool and its impact on the radiologists' interpretations were evaluated.

RESULTS: The CAD tool demonstrated high diagnostic efficacy for rectal cancer tumors of all T stages, with the best diagnostic performance achieved for T1-stage tumors (AUC, 0.85; 95% CI, 0.73-0.93). With assistance from the CAD tool, the AUC for T1 tumors improved from 0.76 (95% CI, 0.63-0.86) to 0.80 (95% CI, 0.68-0.94) (P = 0.020) for junior radiologist 2. For junior radiologist 1, the AUC improved from 0.61 (95% CI, 0.48-0.73) to 0.79 (95% CI, 0.66-0.88) (P = 0.013) for T2 tumors and from 0.73 (95% CI, 0.60-0.84) to 0.84 (95% CI, 0.72-0.92) (P = 0.038) for T3 tumors. The diagnostic consistency (κ value) also improved from 0.31 to 0.64 (P = 0.005) for the junior radiologists and from 0.52 to 0.66 (P = 0.005) for the senior radiologists.

CONCLUSION: A CAD tool utilizing a deep learning model based on 3D-ERUS images showed strong performance in T staging rectal cancer. This tool could improve the performance of and consistency between radiologists in preoperatively assessing rectal cancer patients.

PMID:40304753 | DOI:10.1007/s00261-025-04966-0

Orbit. 2025 Apr 30:1-7. doi: 10.1080/01676830.2025.2497460. Online ahead of print.

ABSTRACT

PURPOSE: To develop and validate a deep learning model for the detection of functional blepharoptosis from external ocular photographs, and to quantify the impact of augmenting the training data with synthetic images on model performance.

METHODS: External ocular photographs of 771 eyes from patients aged ≥ 21 years seen at a tertiary oculoplastic clinic. including 639 with clinically diagnosed functional blepharoptosis and 132 without, were obtained and cropped. These were then randomly assigned into training (n = 539), validation (n = 76) and test (n = 156) subsets, to train and evaluate a baseline deep learning model. Additional synthetic data from a pretrained StyleGAN model was then used to augment the training set (n = 2000), to train and evaluate an augmented deep learning model. Analysis of the performance of both models was then performed.

RESULTS: Accuracy of the deep learning models was assessed in terms of sensitivity and specificity in identifying eye images with functionally significant blepharoptosis. A sensitivity of 0.68 (0.60-0.76), specificity of 0.89 (0.77-1.00) and AUC of 0.87 (0.81-0.93) was obtained by the baseline model, and a sensitivity of 0.95 (0.92-0.99), specificity of 0.67 (0.49-0.84) and AUC of 0.91 (0.86-0.96) by the GAN augmented model.

CONCLUSIONS: Functional blepharoptosis can be detected from external ocular photographs with high confidence, and the use of synthetic data from generative models has the potential to further improve the model performance.

PMID:40304715 | DOI:10.1080/01676830.2025.2497460

Radiol Artif Intell. 2025 May;7(3):e250224. doi: 10.1148/ryai.250224.

NO ABSTRACT

PMID:40304577 | DOI:10.1148/ryai.250224

Neurosurgery. 2025 Apr 30. doi: 10.1227/neu.0000000000003466. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVES: Machine learning (ML) in surgical video analysis offers promising prospects for training and decision support in surgery. The past decade has seen key advances in ML-based operative workflow analysis, though existing applications mostly feature shorter surgeries (<2 hours) with limited scene changes. The aim of this study was to develop and evaluate a ML model capable of automated operative workflow recognition for retrosigmoid vestibular schwannoma (VS) resection. In doing so, this project furthers previous research by applying workflow prediction platforms to lengthy (median >5 hours duration), data-heavy surgeries, using VS resection as an exemplar.

METHODS: A video dataset of 21 microscopic retrosigmoid VS resections was collected at a single institution over 3 years and underwent workflow annotation according to a previously agreed expert consensus (Approach, Excision, and Closure phases; and Debulking or Dissection steps within the Excision phase). Annotations were used to train a ML model consisting of a convolutional neural network and a recurrent neural network. 5-fold cross-validation was used, and performance metrics (accuracy, precision, recall, F1 score) were assessed for phase and step prediction.

RESULTS: Median operative video time was 5 hours 18 minutes (IQR 3 hours 21 minutes-6 hours 1 minute). The "Tumor Excision" phase accounted for the majority of each case (median 4 hours 23 minutes), whereas "Approach and Exposure" (28 minutes) and "Closure" (17 minutes) comprised shorter phases. The ML model accurately predicted operative phases (accuracy 81%, weighted F1 0.83) and dichotomized steps (accuracy 86%, weighted F1 0.86).

CONCLUSION: This study demonstrates that our ML model can accurately predict the surgical phases and intraphase steps in retrosigmoid VS resection. This demonstrates the successful application of ML in operative workflow recognition on low-volume, lengthy, data-heavy surgical videos. Despite this, there remains room for improvement in individual step classification. Future applications of ML in low-volume high-complexity operations should prioritize collaborative video sharing to overcome barriers to clinical translation.

PMID:40304484 | DOI:10.1227/neu.0000000000003466

Chem Commun (Camb). 2025 Apr 30. doi: 10.1039/d5cc00961h. Online ahead of print.

ABSTRACT

Mitochondrial autophagy is closely related to abnormal NAD(P)H and pH. Here, we synthesized a dual-response fluorescent probe with high selectivity for NAD(P)H and sensitivity in the physiological pH range, for simultaneous imaging analysis of mitochondrial NAD(P)H and pH, holding potential as a novel tool for understanding mitochondria-associated diseases.

PMID:40305089 | DOI:10.1039/d5cc00961h

Elife. 2025 Apr 30;12:RP90668. doi: 10.7554/eLife.90668.

ABSTRACT

The Staphylococcus aureus clonal complex 8 (CC8) is made up of several subtypes with varying levels of clinical burden; from community-associated methicillin-resistant S. aureus USA300 strains to hospital-associated (HA-MRSA) USA500 strains and ancestral methicillin-susceptible (MSSA) strains. This phenotypic distribution within a single clonal complex makes CC8 an ideal clade to study the emergence of mutations important for antibiotic resistance and community spread. Gene-level analysis comparing USA300 against MSSA and HA-MRSA strains have revealed key horizontally acquired genes important for its rapid spread in the community. However, efforts to define the contributions of point mutations and indels have been confounded by strong linkage disequilibrium resulting from clonal propagation. To break down this confounding effect, we combined genetic association testing with a model of the transcriptional regulatory network (TRN) to find candidate mutations that may have led to changes in gene regulation. First, we used a De Bruijn graph genome-wide association study to enrich mutations unique to the USA300 lineages within CC8. Next, we reconstructed the TRN by using independent component analysis on 670 RNA-sequencing samples from USA300 and non-USA300 CC8 strains which predicted several genes with strain-specific altered expression patterns. Examination of the regulatory region of one of the genes enriched by both approaches, isdH, revealed a 38-bp deletion containing a Fur-binding site and a conserved single-nucleotide polymorphism which likely led to the altered expression levels in USA300 strains. Taken together, our results demonstrate the utility of reconstructed TRNs to address the limits of genetic approaches when studying emerging pathogenic strains.

PMID:40305082 | DOI:10.7554/eLife.90668

Proc Natl Acad Sci U S A. 2025 May 6;122(18):e2427151122. doi: 10.1073/pnas.2427151122. Epub 2025 Apr 30.

ABSTRACT

In the common cellular space, hundreds of binding reactions occur reliably and simultaneously without disruptive mutual interference. The design principles that enable this remarkable compatibility have not yet been adequately elucidated. In order to delineate these principles, we consider the intracellular sensing of transition metals in bacteria-an integral part of cellular metal homeostasis. Protein cytosolic sensors typically interact with metals through three types of lateral chain residues, containing oxygen, nitrogen, or sulfur. The very existence of complete sets of mutually compatible sensors is a nontrivial problem solved by evolution, since each metal sensor has to bind to its cognate metal without being "mismetallated" by noncognate competitors. Here, based solely on theoretical considerations and limited information about binding constants for metal-amino acid interactions, we are able to predict possible "sensor compositions," i.e., the residues forming the binding sites. We find that complete transition-metal sensor sets are severely limited in their number by compatibility requirements, leaving only a handful of possible sensor compositions for each transition metal. Our theoretical results turn out to be broadly consistent with experimental data on known bacterial sensors. If applicable to other cytosolic binding interactions, the results generated by our approach imply that compatibility requirements may play a crucial role in the organization and functioning of intracellular processes.

PMID:40305046 | DOI:10.1073/pnas.2427151122

Microbiol Spectr. 2025 Apr 30:e0284824. doi: 10.1128/spectrum.02848-24. Online ahead of print.

ABSTRACT

As primary colonizers of the tooth surface, oral streptococci play a crucial role in dental caries development. Numerous natural compounds, including flavonoids, are emerging as promising agents for inhibiting dental biofilm formation without compromising bacterial viability, underscoring their potential in non-bactericidal antibiofilm strategies. This study investigated the effects and mechanism of action of the unmodified plant flavonoid cyanidin on the growth and sucrose-dependent biofilm formation of oral streptococci, with a particular focus on the cariogenic pathogen Streptococcus mutans. At concentrations above 100 µg/mL, cyanidin significantly inhibited biofilm formation by S. mutans without impacting bacterial viability. The flavonoid reduced the biomass of surface-associated bacteria and exopolysaccharides (EPS), particularly by inhibiting water-insoluble glucan (WIG) production mediated by the glucosyltransferases GtfB and GtfC. While cyanidin did not exhibit a bactericidal effect on early colonizer streptococci, such as Streptococcus sanguinis, Streptococcus gordonii, Streptococcus oralis, and Streptococcus mitis, it showed a significant inhibitory effect on bacterial acidogenicity and mixed-species streptococcal biofilms in the presence of S. mutans. Remarkably, cyanidin gradually reduced the proportion of S. mutans in the mixed biofilm, suggesting a selective impact that may promote a more commensal-dominant community by disrupting S. mutans glucan production and biofilm competitiveness.

IMPORTANCE: The identification of compounds with potent antibiofilm effects that do not compromise bacterial viability presents a promising strategy for oral health management. By preventing biofilm formation and keeping bacteria in a planktonic state, such agents could enhance bacterial susceptibility to targeted therapies, including probiotics or phage-based treatments. Cyanidin, which exhibits strong antibiofilm activity against oral streptococcal biofilms, reduces bacterial acidogenicity and may promote a more commensal-dominant biofilm in vitro, potentially hindering the maturation of cariogenic biofilms.

PMID:40304465 | DOI:10.1128/spectrum.02848-24

Phys Chem Chem Phys. 2025 Apr 30. doi: 10.1039/d5cp00604j. Online ahead of print.

ABSTRACT

Gas-phase activation and dissociation studies of biomolecules, proteins and their non-covalent complexes using X-rays hold great promise for revealing new insights into the structure and function of biological samples. This is due to the unique properties of X-ray molecular interactions, such as site-specific and rapid ionization. In this perspective, we report and discuss the promise of first proof-of-principle studies of X-ray-induced dissociation of native (structurally preserved) biological samples ranging from small 17 kDa monomeric proteins up to large 808 kDa non-covalent protein assemblies conducted at a synchrotron (PETRA III) and a free-electron laser (FLASH2). A commercially available quadrupole time-of-flight mass spectrometer (Q-Tof Ultima US, Micromass/Waters), modified for high-mass analysis by MS Vision, was further adapted for integration with the open ports at the corresponding beamlines. The protein complexes were transferred natively into the gas phase via nano-electrospray ionization and subsequently probed by extreme ultraviolet (FLASH2) or soft X-ray (PETRA III) radiation, in either their folded state or following collision-induced activation in the gas phase. Depending on the size of the biomolecule and the activation method, protein fragmentation, dissociation, or enhanced ionization were observed. Additionally, an extension of the setup by ion mobility is described, which can serve as a powerful tool for structural separation of biomolecules prior to X-ray probing. The first experimental results are discussed in the broader context of current and upcoming X-ray sources, highlighting their potential for advancing structural biology in the future.

PMID:40304431 | DOI:10.1039/d5cp00604j

Nucleic Acids Res. 2025 Apr 22;53(8):gkaf355. doi: 10.1093/nar/gkaf355.

ABSTRACT

The structure-specific endonuclease, XPF-ERCC1, plays a central role in DNA damage repair. This nuclease is known to be important for nucleotide excision repair, interstrand crosslink repair, and DNA double-strand repair. We found that the arginine methyltransferase, CARM1/PRMT4, is essential for XPF stabilization and maintenance of intracellular protein levels. Loss of CARM1 results in a decrease in XPF protein levels and a concomitant decrease in ERCC1 protein. A similar destabilization of XPF protein was observed in cells expressing a mutant in which XPF arginine 568 was replaced by lysine. Loss of CARM1 impaired XPF-ERCC1 accumulation at the site of damage and delayed removal of cyclobutane pyrimidine dimers by UV. As a result, CARM1-deficient cells showed increased UV sensitivity. Our results provide insight into the importance of CARM1 not only in the mechanism of XPF-ERCC1 complex stabilization but also in the maintenance of genome stability.

PMID:40304182 | DOI:10.1093/nar/gkaf355

Transbound Emerg Dis. 2025 Jan 21;2025:5018632. doi: 10.1155/tbed/5018632. eCollection 2025.

ABSTRACT

Cutaneous leishmaniasis (CL) remains a significant global public health disease, with the critical distinction and exact detection between responsive and unresponsive cases dictating treatment strategies and patient outcomes. However, image-based methods for differentiating these groups are unexplored. This study addresses this gap by developing a deep learning (DL) model utilizing transfer learning to automatically identify responses in CL lesions. A dataset of 102 lesion images (51 per class; equally distributed across train, test, and validation sets) is employed. The DenseNet161, VGG16, and ResNet18 networks, pretrained on a massive image dataset, are fine-tuned for our specific task. The models achieved an accuracy of 76.47%, 73.53%, and 55.88% on the test data, respectively, with a sensitivity of 80%, 75%, and 100% and specificity of 73.68%, 72.22%, and 53.12%, individually. Transfer learning successfully addressed the limited sample size challenge, demonstrating the models' potential for real-world application. This work underscores the significance of automated response detection in CL, paving the way for treatment and improved patient outcomes. While acknowledging limitations like the sample size, the need for collaborative efforts is emphasized to expand datasets and further refine the model. This approach stands as a beacon of hope in the contest against CL, illuminating the path toward a future where data-driven diagnostics guide effective treatment and alleviate the suffering of countless patients. Moreover, the study could be a turning point in eliminating this important global public health and widespread disease.

PMID:40302757 | PMC:PMC12016710 | DOI:10.1155/tbed/5018632

Technol Health Care. 2025 Apr 30:9287329251333750. doi: 10.1177/09287329251333750. Online ahead of print.

ABSTRACT

BackgroundHeart disease is the leading cause of death worldwide and predicting it is a complex task requiring extensive expertise. Recent advancements in IoT-based illness prediction have enabled accurate classification using sensor data.ObjectiveThis research introduces a methodology for heart disease classification, integrating advanced data preprocessing, feature selection, and deep learning (DL) techniques tailored for IoT sensor data.MethodsThe work employs Clustering-based Data Imputation and Normalization (CDIN) and Robust Mahalanobis Distance-based Outlier Detection (RMDBOD) for preprocessing, ensuring data quality. Feature selection is achieved using the Improved Binary Quantum-based Avian Navigation Optimization (IBQANO) algorithm, and classification is performed with the Deep Long-Term Recurrent Convolutional Network (DLRCN), fine-tuned using the Adaptive Botox Optimization Algorithm (ABOA).ResultsThe proposed models tested on the Hungarian, UCI, and Cleveland heart disease datasets demonstrate significant improvements over existing methods. Specifically, the Cleveland dataset model achieves an accuracy of 99.72%, while the UCI dataset model achieves an accuracy of 99.41%.ConclusionThis methodology represents a significant advancement in remote healthcare monitoring, crucial for managing conditions like high blood pressure, especially in older adults, offering a reliable and accurate solution for heart disease prediction.

PMID:40302494 | DOI:10.1177/09287329251333750

Front Pharmacol. 2025 Apr 15;16:1546836. doi: 10.3389/fphar.2025.1546836. eCollection 2025.

ABSTRACT

Vitiligo is a chronic autoimmune condition characterized by the destruction of melanocytes, leading to patchy loss of skin depigmentation. Although its precise cause remains unclear, recent evidence suggests that metabolic disturbances, particularly oxidative stress and mitochondrial dysfunction, may play a significant role in the pathogenesis of the disease. Oxidative stress is thought to damage melanocytes and trigger inflammatory responses, culminating in melanocyte immune-mediate destruction. Additionally, patients with vitiligo often exhibit extra-cutaneous metabolic abnormalities such as abnormal glucose metabolism, dyslipidemia, high fasting plasma glucose levels, high blood pressure, out of range C-peptide and low biological antioxidant capacity, suggesting a potential link between metabolic impairment and vitiligo development. This implies that the loss of functional melanocytes mirrors a more general systemic targetable dysfunction. Notably, therapies targeting metabolic pathways, particularly those involving mitochondrial metabolism, such as the peroxisome proliferator-activated nuclear receptor γ (PPARγ) agonists, are currently being investigated as potential treatments for vitiligo. PPARγ activation restores mitochondrial membrane potential, mitochondrial DNA copy number and, consequently, ATP production. Moreover, PPARγ agonists counteract oxidative stress, reduce inflammation, inhibit apoptosis, and maintain fatty acid metabolism, in addition to the well-known capability to enhance insulin sensitivity. Additionally, increasing evidence of a strong relationship between metabolic alterations and vitiligo pathogenesis suggests a role for other approved anti-diabetic treatments, like metformin and fibrates, in vitiligo treatment. Taken together, these data support the use of approaches alternative to traditional immune-suppressive treatments for the treatment of vitiligo.

PMID:40303919 | PMC:PMC12037623 | DOI:10.3389/fphar.2025.1546836

Pediatr Diabetes. 2023 Apr 17;2023:4395556. doi: 10.1155/2023/4395556. eCollection 2023.

ABSTRACT

BACKGROUND: In cystic fibrosis-related diabetes (CFRD) screening, oral glucose tolerance test (OGTT) thresholds for detecting prediabetes and diabetes are defined by the 2-hour glucose (2 hG). Intermediate OGTT glucoses, between 0 and 2 hours, that are ≥200 mg/dL are deemed "indeterminate," although lower 1-hour glucose (1 hG) thresholds identify those at increased risk of type 2 diabetes in other populations, and may also better predict clinical decline in CF. Studies of 1 hG thresholds <200 mg/dL in people with CF are limited.

METHODS: A single center, retrospective chart review was performed of patients with 1 hG available on OGTTs collected between 2010 and 2019. In patients with ≥2 OGTTs, Kaplan-Meier analysis estimated likelihood of progression to CFRD based on a high vs. low 1 hG. In patients with ≥1 OGTT, mixed-effects models tested whether baseline 1 hG and 2 hG predicted growth and lung function trajectories.

RESULTS: A total of 243 individuals with CF were identified with at least 1 OGTT including a 1 hG, and n = 177 had ≥2 OGTTs. Baseline age (mean ± SD) was 12.4 ± 2.6 years with 3.2 ± 1.4 years of follow-up. Twenty-eight developed CFRD. All who developed CFRD had a 1 hG ≥ 155 mg/dL prior to 2 hG > 140 mg/dL. The average 1 hG was 267 mg/dL when 2 hG ≥ 200 mg/dL. In a subset with baseline 2 hG < 140 mg/dL, 1 hG ≥ 140 mg/dL conferred an increased 5 years risk of CFRD (p=0.036). Baseline 2 hG predicted decline in FEV1%predicted, but 1 hG did not.

CONCLUSIONS: In youth with CF, 1 hG ≥ 140 mg/dl is an early indicator of CFRD risk. However, 2 hG, rather than 1 hG, predicted lung function decline.

PMID:40303238 | PMC:PMC12016879 | DOI:10.1155/2023/4395556