J Opt Soc Am A Opt Image Sci Vis. 2024 Sep 1;41(9):1786-1793. doi: 10.1364/JOSAA.526142.

ABSTRACT

Disorders affecting the retina pose a considerable risk to human vision, with an array of factors including aging, diabetes, hypertension, obesity, ocular trauma, and tobacco use exacerbating this issue in contemporary times. Optical coherence tomography (OCT) is a rapidly developing imaging modality that is capable of identifying early signs of vascular, ocular, and central nervous system abnormalities. OCT can diagnose retinal diseases through image classification, but quantifying the laceration area requires image segmentation. To overcome this obstacle, we have developed an innovative deep learning framework that can perform both tasks simultaneously. The suggested framework employs a parallel mask-guided convolutional neural network (PM-CNN) for the classification of OCT B-scans and a grade activation map (GAM) output from the PM-CNN to help a V-Net network (GAM V-Net) to segment retinal lacerations. The guiding mask for the PM-CNN is obtained from the auxiliary segmentation job. The effectiveness of the dual framework was evaluated using a combined dataset that encompassed four publicly accessible datasets along with an additional real-time dataset. This compilation included 11 categories of retinal diseases. The four publicly available datasets provided a robust foundation for the validation of the dual framework, while the real-time dataset enabled the framework's performance to be assessed on a broader range of retinal disease categories. The segmentation Dice coefficient was 78.33±0.15%, while the classification accuracy was 99.10±0.10%. The model's ability to effectively segment retinal fluids and identify retinal lacerations on a different dataset was an excellent demonstration of its generalizability.

PMID:39889044 | DOI:10.1364/JOSAA.526142

J Opt Soc Am A Opt Image Sci Vis. 2024 Sep 1;41(9):1702-1712. doi: 10.1364/JOSAA.529243.

ABSTRACT

Phase retrieval is about reconstructing original vectors/images from their Fourier intensity measurements. Deep learning methods have been introduced to solve the phase retrieval problem; however, most of the proposed approaches cannot improve the reconstruction quality of phase and amplitude of original images simultaneously. In this paper, we present a distributed amplitude and phase conditional generative adversarial network (D-APUCGAN) to achieve the high quality of phase and amplitude images at the same time. D-APUCGAN includes UCGAN, AUCGAN/PUCGAN, and APUCGAN. In this paper, we introduce the content loss function to constrain the similarity between the reconstructed image and the source image through the Frobenius norm and the total variation modulus. The proposed method promotes the quality of phase images better than just using amplitude images to train. The numerical experimental results show that the proposed cascade strategies are significantly effective and remarkable for natural and unnatural images, DIV2K testing datasets, MNIST dataset, and realistic data. Comparing with the conventional neural network methods, the evaluation metrics of PSNR and SSIM values in the proposed method are refined by about 2.25 dB and 0.18 at least, respectively.

PMID:39889034 | DOI:10.1364/JOSAA.529243

J Opt Soc Am A Opt Image Sci Vis. 2024 Oct 1;41(10):1987-1993. doi: 10.1364/JOSAA.531198.

ABSTRACT

The $\pm 1$st order diffraction of gratings is widely used in spectral analysis. However, when the incident light is non-monochromatic, the higher-order diffractions generated by traditional diffraction gratings are always superimposed on the useful first-order diffraction, complicating subsequent spectral decoding. In this paper, single-order diffraction gratings with a sinusoidal transmittance, called hexagonal diffraction gratings (HDGs), are designed using a convolutional neural network based on deep learning algorithm. The trained convolutional neural network can accurately retrieve the structural parameters of the HDGs. Simulation and experimental results confirm that the HDGs can effectively suppress higher-order diffractions above the third order. The intensity of third-order diffraction is reduced from 20% of the first-order diffraction to less than that of the background. This higher-order diffraction suppression property of the HDGs is promising for applications in fields such as synchrotron radiation, astrophysics, and soft x-ray lasers.

PMID:39889023 | DOI:10.1364/JOSAA.531198

J Opt Soc Am A Opt Image Sci Vis. 2024 Oct 1;41(10):1979-1986. doi: 10.1364/JOSAA.525577.

ABSTRACT

The invention of microscopy- and nanoscopy-based imaging technology opened up different research directions in life science. However, these technologies create the need for larger storage space, which has negative impacts on the environment. This scenario creates the need for storing such images in a memory-efficient way. Compact image representation (CIR) can solve the issue as it targets storing images in a memory-efficient way. Thus, in this work, we have designed a deep-learning-based CIR technique that selects key pixels using the guided U-Net (GU-Net) architecture [Asian Conference on Pattern Recognition, p. 317 (2023)], and then near-original images are constructed using a conditional generative adversarial network (GAN)-based architecture. The technique was evaluated on two microscopy- and two scanner-captured-image datasets and obtained good performance in terms of storage requirements and quality of the reconstructed images.

PMID:39889022 | DOI:10.1364/JOSAA.525577

Sci Adv. 2025 Jan 31;11(5):eads4224. doi: 10.1126/sciadv.ads4224. Epub 2025 Jan 31.

ABSTRACT

Optical neural networks (ONNs) are a promising computational alternative for deep learning due to their inherent massive parallelism for linear operations. However, the development of energy-efficient and highly parallel optical nonlinearities, a critical component in ONNs, remains an outstanding challenge. Here, we introduce a nonlinear optical microdevice array (NOMA) compatible with incoherent illumination by integrating the liquid crystal cell with silicon photodiodes at the single-pixel level. We fabricate NOMA with more than half a million pixels, each functioning as an optical analog of the rectified linear unit at ultralow switching energy down to 100 femtojoules per pixel. With NOMA, we demonstrate an optical multilayer neural network. Our work holds promise for large-scale and low-power deep ONNs, computer vision, and real-time optical image processing.

PMID:39888986 | DOI:10.1126/sciadv.ads4224

PLoS One. 2025 Jan 31;20(1):e0317990. doi: 10.1371/journal.pone.0317990. eCollection 2025.

ABSTRACT

The continuous development of the field of artificial intelligence, not only makes people's lives more convenient but also plays a role in the supervision and protection of people's lives and property safety. News of the fire is not uncommon, and fire has become the biggest hidden danger threatening the safety of public life and property. In this paper, a deep learning-based flame recognition method for complex scenes, GGSYOLOv5, is proposed. Firstly, a global attention mechanism (GAM) was added to the CSP1 module in the backbone part of the YOLOv5 network, and then a parameterless attention mechanism was added to the feature fusion part. Finally, packet random convolution (GSConv) was used to replace the original convolution at the output end. A large number of experiments show that the detection accuracy rate is 4.46% higher than the original algorithm, and the FPS is as high as 64.3, which can meet the real-time requirements. Moreover, the algorithm is deployed in the Jetson Nano embedded development board to build the flame detection system.

PMID:39888970 | DOI:10.1371/journal.pone.0317990

PLoS One. 2025 Jan 31;20(1):e0317996. doi: 10.1371/journal.pone.0317996. eCollection 2025.

ABSTRACT

Compared with histological examination of lung cancer, cytology is less invasive and provides better preservation of complete morphology and detail. However, traditional cytological diagnosis requires an experienced pathologist to evaluate all sections individually under a microscope, which is a time-consuming process with low interobserver consistency. With the development of deep neural networks, the You Only Look Once (YOLO) object-detection model has been recognized for its impressive speed and accuracy. Thus, in this study, we developed a model for intraoperative cytological segmentation of pulmonary lesions based on the YOLOv8 algorithm, which labels each instance by segmenting the image at the pixel level. The model achieved a mean pixel accuracy and mean intersection over union of 0.80 and 0.70, respectively, on the test set. At the image level, the accuracy and area under the receiver operating characteristic curve values for malignant and benign (or normal) lesions were 91.0% and 0.90, respectively. In addition, the model was deemed suitable for diagnosing pleural fluid cytology and bronchoalveolar lavage fluid cytology images. The model predictions were strongly correlated with pathologist diagnoses and the gold standard, indicating the model's ability to make clinical-level decisions during initial diagnosis. Thus, the proposed method is useful for rapidly localizing lung cancer cells based on microscopic images and outputting image interpretation results.

PMID:39888907 | DOI:10.1371/journal.pone.0317996

Medicine (Baltimore). 2025 Jan 31;104(5):e41460. doi: 10.1097/MD.0000000000041460.

ABSTRACT

The present study aims to evaluate the adverse events associated with Capmatinib using real-world data, providing a reference basis for its rational use in clinical practice. Relevant data from the Food and Drug Administration adverse event reporting system database was mined. Next, reporting odds ratio and Bayesian confidence propagation neural network method were used to analyze real-world adverse events associated with Capmatinib. The study revealed significant adverse event signals of Capmatinib, primarily involving general disorders and administration site conditions, cardiac disorders, gastrointestinal disorders, respiratory, thoracic and mediastinal disorders, neoplasms benign, malignant and unspecified (including cysts and polyps) and investigations, among others. A total of 79 signals were identified, with 13 of them not mentioned in the drug's specifications. Taken together, our comprehensive analysis of the Food and Drug Administration adverse event reporting system database enhances the understanding of Capmatinib's safety profile, thereby contributing to informed decision-making in its clinical application and facilitating the timely management of associated adverse reactions.

PMID:39889151 | DOI:10.1097/MD.0000000000041460

PLoS One. 2025 Jan 31;20(1):e0303130. doi: 10.1371/journal.pone.0303130. eCollection 2025.

ABSTRACT

OBJECTIVE: To evaluate the effect and safety of sclerotherapy in patients with difficult-to-resect venous malformations treated with ethanolamine oleate.

DESIGN AND SETTING: This investigator-initiated clinical trial employed a multicenter, single-arm design and was conducted in Japan.

PATIENTS: Overall, 44 patients with difficult-to-resect venous malformations were categorized into two cohorts: 22 patients with cystic-type malformations and 22 patients with diffuse-type malformations, including children (<15 years old).

INTERVENTIONS: Adult patients received injections of 5% ethanolamine oleate solution, double diluted with contrast or normal saline, with a maximum dose of 0.4 mL/kg. The same method of administration was used for children (<15 years old). The maximum volume of the prepared solution in one treatment was 30 mL.

EVALUATION METHODS: Treatment effect was assessed by evaluating the difference in lesion volume using magnetic resonance imaging as a primary endpoint and differences in pain using a visual analog scale as a key secondary endpoint.

RESULTS: Among the 45 patients who consented, one was excluded owing to potential intracranial involvement of venous malformations during screening. Regarding the primary outcome, 26 of 44 patients (59.1%, 95% confidence interval: 44.41-72.31%) achieved ≥ 20% reduction in malformation volume, with 16 patients having cystic lesions (72.7%, 51.85-86.85%) and 10 patients having diffuse lesions (45.5%, 26.92-65.34%). Both cohorts showed significant improvement in self-reported pain scores associated with lesions 3 months post-sclerotherapy. No death or serious adverse events occurred. Hemoglobinuria was observed in 23 patients (52%), a known drug-related adverse event. Prompt initiation of haptoglobin therapy led to full recovery within a month for these patients.

CONCLUSIONS: Ethanolamine oleate shows potential as a therapeutic sclerosing agent for patients with difficult-to-resect venous malformations.

PMID:39888898 | DOI:10.1371/journal.pone.0303130

Proteomics. 2025 Jan 31:e202400109. doi: 10.1002/pmic.202400109. Online ahead of print.

NO ABSTRACT

PMID:39888210 | DOI:10.1002/pmic.202400109

Clin Pharmacol Ther. 2025 Jan 31. doi: 10.1002/cpt.3567. Online ahead of print.

ABSTRACT

The safety of systemic fluoropyrimidines (e.g., 5-fluorouracil, capecitabine) is impacted by germline genetic variants in DPYD, which encodes the dihydropyrimidine dehydrogenase (DPD) enzyme that functions as the rate-limiting step in the catabolism of this drug class. Genetic testing to identify those with DPD deficiency can help mitigate the risk of severe and life-threatening fluoropyrimidine-induced toxicities. Globally, the integration of DPYD genetic testing into patient care has varied greatly, ranging from being required as the standard of care in some countries to limited clinical use in others. Thus, implementation strategies have evolved differently across health systems and countries. The primary objective of this tutorial is to provide practical considerations and best practice recommendations for the implementation of DPYD-guided systemic fluoropyrimidine dosing. We adapted the Exploration, Preparation, Implementation, and Sustainment (EPIS) framework to cover topics including the clinical evidence supporting DPYD genotyping to guide fluoropyrimidine therapy, regulatory guidance for DPYD genotyping, key stakeholder engagement, logistics for DPYD genotyping, development of point-of-care clinical decision support tools, and considerations for the creation of sustainable and scalable DPYD genotype-integrated workflows. This guide also describes approaches to counseling patients about DPYD testing and result disclosure, along with examples of patient and provider educational resources. Together, DPYD testing and clinical practice integration aim to promote safe prescribing of fluoropyrimidine therapy and decrease the risk of severe and life-threatening fluoropyrimidine toxicities.

PMID:39887719 | DOI:10.1002/cpt.3567

Acta Diabetol. 2025 Jan 31. doi: 10.1007/s00592-025-02457-8. Online ahead of print.

NO ABSTRACT

PMID:39888447 | DOI:10.1007/s00592-025-02457-8

J Cachexia Sarcopenia Muscle. 2025 Feb;16(1):e13708. doi: 10.1002/jcsm.13708.

ABSTRACT

BACKGROUND: Regenerative capacity of skeletal muscles decreases with age. Deficiency in cystic fibrosis transmembrane conductance regulator (CFTR) is associated with skeletal muscle weakness as well as epithelial cell senescence. However, whether and how CFTR plays a role in skeletal muscle regeneration and aging were unclear.

METHODS: Vastus lateralis biopsy samples from male and female human subjects (n = 23) of 7- to 86-year-old and gastrocnemii tissues from mice of 4- to 29-month-old were examined for CFTR expression. Skeletal muscle tissues or cultured myoblasts from mice carrying CFTR mutation (DF508) at 4- to 18-month-old were used for assessment of muscle mass, contractile force and regenerative capacity as well as myogenic and autophagy signalling. Overexpression of LC3-β, an autophagy mediator, was conducted to reverse myogenic defects in DF508 myoblasts. Adenoviruses containing CFTR gene or pharmaceuticals that enhance CFTR (VX809) were locally injected into the gastrocnemius or femoris quadricep to rescue age-related skeletal muscle defects in mice.

RESULTS: mRNA levels of CFTR in human vastus lateralis exhibited significantly negative correlations with age (r = -0.87 in males and -0.62 in females, p < 0.05). Gastrocnemius mRNA level of CFTR decreased by 77.7 ± 4.6% in 29-month-old wild-type mice compared to the 4-month-old. At 18-month-old, DF508 mice showed significantly reduced lean mass (by 35.6%), lower specific twitch force of the gastrocnemius (by 46.2%), decrease in fast/slow-twitch muscle isoform ratio as well as downregulation of myogenic (e.g., MYOD and MYOG) or autophagy/mitophagy (e.g., LC3-β) genes, compared to age-matched wild-types. Post-injury gastrocnemius regeneration was found impaired in DF508 mice. Myoblast cultures from DF508 mice showed defective myogenic differentiation, which was reversed by overexpressing LC3-β. In aged (> 15-month-old) mice, overexpressing CFTR or VX809 restored the expression of autophagy or myogenic genes, increased mitochondrial LC3-β level and improved skeletal muscle mass and function.

CONCLUSION: Age-related reduction in skeletal muscle expression of CFTR impairs autophagy and myogenesis, exacerbating skeletal muscle aging. Enhancing CFTR might be a potential treatment strategy for age-related skeletal muscle disorders.

PMID:39887939 | DOI:10.1002/jcsm.13708

Ann Am Thorac Soc. 2025 Feb;22(2):311. doi: 10.1513/AnnalsATS.22i2Erratum.

NO ABSTRACT

PMID:39887697 | DOI:10.1513/AnnalsATS.22i2Erratum

Ann Am Thorac Soc. 2025 Feb;22(2):183-184. doi: 10.1513/AnnalsATS.202411-1229ED.

NO ABSTRACT

PMID:39887694 | DOI:10.1513/AnnalsATS.202411-1229ED

Med Biol Eng Comput. 2025 Jan 31. doi: 10.1007/s11517-025-03284-3. Online ahead of print.

ABSTRACT

The lumen centerline of the coronary artery allows vessel reconstruction used to detect stenoses and plaques. Discrete-action-based centerline extraction methods suffer from artifacts and plaques. This study aimed to develop a continuous-action-based method which performs more effectively in cases involving artifacts or plaques. A continuous-action deep reinforcement learning-based model was trained to predict the artery's direction and radius value. The model is based on an Actor-Critic architecture. The Actor learns a deterministic policy to output the actions made by an agent. These actions indicate the centerline's direction and radius value consecutively. The Critic learns a value function to evaluate the quality of the agent's actions. A novel DDR reward was introduced to measure the agent's action (both centerline extraction and radius estimate) at each step. The method achieved an average OV of 95.7%, OF of 93.6%, OT of 97.3%, and AI of 0.22 mm in 80 test data. In 53 cases with artifacts or plaques, it achieved an average OV of 95.0%, OF of 91.5%, OT of 96.7%, and AI of 0.23 mm. The 95% limits of agreement between the reference and estimated radius values were - 0.46 mm and 0.43 mm in the 80 test data. Experiments demonstrate that the Actor-Critic architecture can achieve efficient centerline extraction and radius estimate. Compared with discrete-action-based methods, our method performs more effectively in cases involving artifacts or plaques. The extracted centerlines and radius values allow accurate coronary artery reconstruction that facilitates the detection of stenoses and plaques.

PMID:39888471 | DOI:10.1007/s11517-025-03284-3

Med Biol Eng Comput. 2025 Jan 31. doi: 10.1007/s11517-025-03308-y. Online ahead of print.

ABSTRACT

Smart homes have the potential to enable remote monitoring of the health and well-being of older adults, leading to improved health outcomes and increased independence. However, current approaches only consider a limited set of daily activities and do not combine data from individuals. In this work, we propose the use of deep learning techniques to model behavior at the population level and detect significant deviations (i.e., anomalies) while taking into account the whole set of daily activities (41). We detect and visualize daily routine patterns, train a set of recurrent neural networks for behavior modelling with next-day prediction, and model errors with a normal distribution to identify significant deviations while considering the temporal component. Clustering of daily routines achieves a silhouette score of 0.18 and the best model obtains a mean squared error in next day routine prediction of 4.38%. The mean number of deviated activities for the anomalies in the train and test set are 3.6 and 3.0, respectively, with more than 60% of anomalies involving three or more deviated activities in the test set. The methodology is scalable and can incorporate additional activities into the analysis.

PMID:39888470 | DOI:10.1007/s11517-025-03308-y

Clin Oral Investig. 2025 Jan 31;29(2):101. doi: 10.1007/s00784-025-06156-0.

ABSTRACT

OBJECTIVE: This study aimed to apply the DeepLabv3 + model and compare it with the U-Net model in terms of detecting and segmenting apical lesions on panoramic radiography.

METHODS: 260 panoramic images that contain apical lesions in different regions were collected and randomly divided into training and test datasets. All images were manually annotated for apical lesions using Computer Vision Annotation Tool software by two independent dental radiologists and a master reviewer. The DeepLabv3 + model, one of the state-of-the-art deep semantic segmentation models, was utilized using Python programming language and the TensorFlow library and applied to the prepared datasets. The model was compared with the U-Net model applied to apical lesions and other medical image segmentation problems in the literature.

RESULTS: The DeepLabv3 + and U-Net models were applied to the same datasets with the same hyper-parameters. The AUC and recall results of the DeepLabv3 + were 29.96% and 61.06% better than the U-Net model. However, the U-Net model gets 69.17% and 25.55% better precision and F1-score results than the DeepLabv3 + model. The difference in the IoU results of the models was not statistically significant.

CONCLUSIONS: This paper comprehensively evaluated the DeepLabv3 + model and compared it with the U-Net model. Our experimental findings indicated that DeepLabv3 + outperforms the U-Net model by a substantial margin for both AUC and recall metrics. According to those results, for detecting apical lesions, we encourage researchers to use and improve the DeepLabv3 + model.

CLINICAL RELEVANCE: The DeepLabv3 + model has the poten tial to improve clinical diagnosis and treatment planning and save time in the clinic.

PMID:39888441 | DOI:10.1007/s00784-025-06156-0

Microsc Res Tech. 2025 Jan 31. doi: 10.1002/jemt.24792. Online ahead of print.

ABSTRACT

Skin cancer is recognized as one of the most harmful cancers worldwide. Early detection of this cancer is an effective measure for treating the disease efficiently. Traditional skin cancer detection methods face scalability challenges and overfitting issues. To address these complexities, this study proposes a random cat swarm optimization (CSO)with an ensemble convolutional neural network (RCS-ECNN) method to categorize the different stages of skin cancer. In this study, two deep learning classifiers, deep neural network (DNN) and Keras DNN (KDNN), are utilized to identify the stages of skin cancer. In this method, an effective preprocessing phase is presented to simplify the classification process. The optimal features are selected using the feature extraction phase. Then, the GrabCut algorithm is employed to carry out the segmentation process. Also, the CSO is employed to enhance the effectiveness of the method. The HAM10000 and ISIC datasets are utilized to evaluate the RCS-ECNN method. The RCS-ECNN method achieved an accuracy of 99.56%, a recall of 99.66%, a specificity value of 99.254%, a precision value of 99.18%, and an F1-score value of 98.545%, respectively. The experimental results demonstrated that the RCS-ECNN method outperforms the existing techniques.

PMID:39888306 | DOI:10.1002/jemt.24792

Proteomics. 2025 Jan 31:e202400321. doi: 10.1002/pmic.202400321. Online ahead of print.

ABSTRACT

Metaproteomics enables the large-scale characterization of microbial community proteins, offering crucial insights into their taxonomic composition, functional activities, and interactions within their environments. By directly analyzing proteins, metaproteomics offers insights into community phenotypes and the roles individual members play in diverse ecosystems. Although database-dependent search engines are commonly used for peptide identification, they rely on pre-existing protein databases, which can be limiting for complex, poorly characterized microbiomes. De novo sequencing presents a promising alternative, which derives peptide sequences directly from mass spectra without requiring a database. Over time, this approach has evolved from manual annotation to advanced graph-based, tag-based, and deep learning-based methods, significantly improving the accuracy of peptide identification. This Viewpoint explores the evolution, advantages, limitations, and future opportunities of de novo sequencing in metaproteomics. We highlight recent technological advancements that have improved its potential for detecting unsequenced species and for providing deeper functional insights into microbial communities.

PMID:39888246 | DOI:10.1002/pmic.202400321