Drug Dev Ind Pharm. 2025 Feb 18:1-18. doi: 10.1080/03639045.2025.2468811. Online ahead of print.

ABSTRACT

OBJECTIVE: This review aims to explore innovative therapeutic strategies, with a particular focus on recent advancements in drug delivery systems using bioinspired nanomaterials such as solid lipid nanoparticles (SLNs) and nanostructured lipid carriers (NLCs) for the Idiopathic pulmonary fibrosis (IPF).

SIGNIFICANCE OF THE REVIEW: Current treatments for IPF, including the FDA-approved anti-fibrotic agents pirfenidone and nintedanib, primarily aim to slow disease progression rather than reverse fibrosis. Bioinspired nanomaterials like SLNs and NLCs have shown promise in enhancing the efficacy of anti-fibrotic agents by improving drug solubility, stability, and targeted delivery. These systems not only minimize systemic side effects but also maximize therapeutic impact in lung tissues, offering a new hope for improved patient management and outcomes in this debilitating disease.

KEY FINDINGS: SLNs facilitate sustained drug release and have demonstrated potential in delivering phosphodiesterase type 5 inhibitors effectively to lung cells. NLCs, on the other hand, exhibit superior biocompatibility and controlled release properties, making them suitable for pulmonary applications. Studies indicate that both SLNs and NLCs can enhance the bioavailability of drugs like ciprofloxacin and montelukast, thereby improving treatment outcomes in pulmonary conditions.

CONCLUSIONS: The integration of nanotechnology into anti-fibrotic therapy represents a significant advancement in addressing the challenges posed by IPF. By leveraging the unique properties of SLNs and NLCs, there is potential to overcome the limitations of current treatments and provide new therapeutic options that offer better management and improved outcomes for patients suffering from this debilitating disease.

PMID:39963904 | DOI:10.1080/03639045.2025.2468811

Proc Natl Acad Sci U S A. 2025 Feb 25;122(8):e2420961122. doi: 10.1073/pnas.2420961122. Epub 2025 Feb 18.

ABSTRACT

Protein Serine Kinase H1 (PSKH1) was recently identified as a crucial factor in kidney development and is overexpressed in prostate, lung, and kidney cancers. However, little is known about PSKH1 regulatory mechanisms, leading to its classification as a "dark" kinase. Here, we used biochemistry and mass spectrometry to define PSKH1's consensus substrate motif, protein interactors, and how interactors, including Ca2+ sensor proteins, promote or suppress activity. Intriguingly, despite the absence of a canonical Calmodulin binding motif, Ca2+-Calmodulin activated PSKH1 while, in contrast, the ER-resident Ca2+ sensor of the Cab45, Reticulocalbin, Erc55, Calumenin (CREC) family, Reticulocalbin-3, suppressed PSKH1 catalytic activity. In addition to antagonistic regulation of the PSKH1 kinase domain by Ca2+ sensing proteins, we identified UNC119B as a protein interactor that activates PSKH1 via direct engagement of the kinase domain. Our findings identify complementary allosteric mechanisms by which regulatory proteins tune PSKH1's catalytic activity and raise the possibility that different Ca2+ sensors may act more broadly to tune kinase activities by detecting and decoding extremes of intracellular Ca2+ concentrations.

PMID:39964718 | DOI:10.1073/pnas.2420961122

Eur J Nutr. 2025 Feb 18;64(2):97. doi: 10.1007/s00394-025-03600-5.

ABSTRACT

PURPOSE: Recent studies indicate that shift work may affect workers' eating habits and overall well-being. This study aimed to assess differences in eating patterns, sleep quality, and mental health between Italian shift and non-shift workers, with a focus on individual chronotype and the type of shift work (day vs. night shift).

METHODS: The cross-sectional study involved 322 subjects (166 shift and 156 non-shift workers). Eating habits were evaluated using a 7-day diary and the Medi-Lite questionnaire. Sleep quality was assessed with the Pittsburgh Sleep Quality Index (PSQI), and mental health with the Depression Anxiety Stress Scales (DASS). Individual chronotype was defined using the Morningness-Eveningness Questionnaire.

RESULTS: No significant differences in daily energy, macronutrient, and micronutrient intake between the two groups, nor in the temporal pattern of eating. However, shift workers had significantly (p < 0.05) lower adherence to the Mediterranean diet (MD) (7.6 ± 2.3 vs 8.1 ± 2.2) compared to non-shift workers. Shift workers also reported significantly poorer sleep quality (mean PSQI score 7.6 ± 3.7 vs. 5.8 ± 3.0) and higher levels of anxiety and stress symptoms. Among shift workers, those with an evening chronotype had significantly lower MD adherence than those with a morning chronotypes. Additionally, night shift workers experienced more sleep disturbances compared to day ones.

CONCLUSION: Shift workers reported lower MD adherence, poorer sleep quality, and a higher prevalence of anxiety and stress symptoms compared to a similar group of non-shift workers. Evening chronotypes and night shift work were associated with worse eating habits and sleep quality.

PMID:39964501 | DOI:10.1007/s00394-025-03600-5

Genomics Proteomics Bioinformatics. 2025 Feb 18:qzaf009. doi: 10.1093/gpbjnl/qzaf009. Online ahead of print.

ABSTRACT

In recent years, computational methods for quantifying cell type proportions from transcription data have gained significant attention, particularly those reference-based methods which have demonstrated high accuracy. However, there is currently a lack of comprehensive evaluation and guidance for available reference-based deconvolution methods in cell proportion deconvolution analysis. In this study, we introduce Deconvolution Evaluator (Deconer), a comprehensive toolkit for the evaluation of reference-based deconvolution methods. Deconer provides various simulated and real gene expression datasets, including both bulk and single-cell sequencing data, and offers multiple visualization interfaces. By utilizing Deconer, we conducted systematic comparisons of 16 reference-based deconvolution methods from different perspectives, including method robustness, accuracy in deconvolving rare components, signature gene selection, and building external reference. We also performed an in-depth analysis of the application scenarios and challenges in cell proportion deconvolution methods. Finally, we provided constructive suggestions for users in selecting and developing cell proportion deconvolution algorithms. This work presents novel insights to researchers, assisting them in choosing appropriate toolkits, applying solutions in clinical contexts, and advancing the development of deconvolution tools tailored to gene expression data. The tutorials, manual, source code, and demo data of Deconer are publicly available at https://honchkrow.github.io/Deconer/.

PMID:39963994 | DOI:10.1093/gpbjnl/qzaf009

Mol Biol Evol. 2025 Feb 18:msaf045. doi: 10.1093/molbev/msaf045. Online ahead of print.

ABSTRACT

In the wake of pandemics like COVID-19, which have zoonotic origins, the role of wildlife as reservoirs for emerging infectious diseases has garnered heightened attention. Migratory birds, traversing continents, represent a potent but under-researched vector for the spread of infectious diseases, including novel coronaviruses. This study delves into the genetic diversity and transmission dynamics of coronaviruses in migratory birds, presenting pivotal findings. From April 2019 to April 2023, we screened 5,263 migratory bird samples collected from Shanghai, China, identifying 372 coronavirus-positive samples belonging to five avian-related coronavirus subgenera and subsequently obtaining 120 complete genome sequences. To facilitate further research with a global perspective, the study curated all available 19,000 avian-associated CoVs and expanded the original 12 species to 16, including three novel coronavirus species identified in our study and one re-classified species from the public domain. The study illuminates the intricate genetic evolution and transmission dynamics of birds-related coronaviruses on a global scale. A notable aspect of our research is the identification of complex recombination patterns within the spike protein across different virus species and subgenera, highlighting migratory birds as a reservoir of coronavirus. Notably, the coronaviruses found in migratory birds, predominantly from the orders Anseriformes, Charadriiformes, and Pelecaniformes, with domestic ducks from Anseriformes playing a key role in bridging the transmission of coronaviruses between migratory and non-migratory birds. These findings reveal the genetic and recombination characteristics of coronaviruses in migratory birds, emphasizing the critical role of ecologically pivotal bird species in coronavirus transmission and genetic diversity shaping.

PMID:39963938 | DOI:10.1093/molbev/msaf045

Pathologie (Heidelb). 2025 Feb 18. doi: 10.1007/s00292-025-01418-w. Online ahead of print.

ABSTRACT

BACKGROUND: Besides reactions of the IgE-mediated immediate type, medicamentous therapies can cause a variety of different mucocutaneous adverse events. Exanthematous manifestations require a fast and certain diagnosis due to their extent, sometimes rapid progression, and mucous membrane or organ involvement.

OBJECTIVES: The spectrum of non-IgE-mediated exanthematic drug reactions is covered.

MATERIAL AND METHODS: The most relevant reactions are portrayed clinically and histopathologically.

RESULTS: Displayed are classical maculo-papular drug eruption, lichenoid drug reaction, acute generalized exanthematic pustulosis (AGEP), severe potentially life-threatening drug reactions such as Stevens-Johnson syndrome (SJS), and toxic epidermal necrolysis (TEN) as well as generalized bullous fixed drug eruption (GBFDE), drug reaction with eosinophilia and systemic symptoms (DRESS), and some others.

CONCLUSIONS: Cutaneous drug-related side effects cover a broad spectrum. Important for the correct treatment is a reliable diagnosis. In the case of severe, life-threatening drug reactions, however, permanent discontinuation of the drug is essential.

PMID:39964515 | DOI:10.1007/s00292-025-01418-w

Front Public Health. 2025 Feb 3;13:1520467. doi: 10.3389/fpubh.2025.1520467. eCollection 2025.

ABSTRACT

Rare diseases, affecting millions globally, pose a significant healthcare burden despite impacting a small population. While approximately 70% of all rare diseases are genetic and often begin in childhood, diagnosis remains slow and only 5% have approved treatments. The UN emphasizes improved access to primary care (diagnostic and potentially therapeutic) for these patients and their families. Next-generation sequencing (NGS) offers hope for earlier and more accurate diagnoses, potentially leading to preventative measures and targeted therapies. In here, we explore the therapeutic landscape for rare diseases, analyzing drugs in development and those already approved by the European Medicines Agency (EMA). We differentiate between orphan drugs with market exclusivity and repurposed existing drugs, both crucial for patients. By analyzing market size, segmentation, and publicly available data, this comprehensive study aims to pave the way for improved understanding of the treatment landscape and a wider knowledge accessibility for rare disease patients.

PMID:39963479 | PMC:PMC11830808 | DOI:10.3389/fpubh.2025.1520467

Front Public Health. 2025 Feb 3;13:1520467. doi: 10.3389/fpubh.2025.1520467. eCollection 2025.

ABSTRACT

Rare diseases, affecting millions globally, pose a significant healthcare burden despite impacting a small population. While approximately 70% of all rare diseases are genetic and often begin in childhood, diagnosis remains slow and only 5% have approved treatments. The UN emphasizes improved access to primary care (diagnostic and potentially therapeutic) for these patients and their families. Next-generation sequencing (NGS) offers hope for earlier and more accurate diagnoses, potentially leading to preventative measures and targeted therapies. In here, we explore the therapeutic landscape for rare diseases, analyzing drugs in development and those already approved by the European Medicines Agency (EMA). We differentiate between orphan drugs with market exclusivity and repurposed existing drugs, both crucial for patients. By analyzing market size, segmentation, and publicly available data, this comprehensive study aims to pave the way for improved understanding of the treatment landscape and a wider knowledge accessibility for rare disease patients.

PMID:39963479 | PMC:PMC11830808 | DOI:10.3389/fpubh.2025.1520467

BMC Pulm Med. 2025 Feb 17;25(1):81. doi: 10.1186/s12890-024-03455-2.

ABSTRACT

BACKGROUND: This study aimed to assess how Elexacaftor/Tezacaftor/Ivacaftor (ETI) influences lung function, Body Mass Index (BMI), Sweat Test (ST) and mental health of Cystic Fibrosis (CF) patients, emphasizing on depression and anxiety.

METHODS: We conducted an observational, prospective, multicentre study including 108 patients over 18 years old who initiated ETI therapy between December 2019 and December 2023. Patients underwent regular evaluations, including clinical, functional, and microbiological assessments, alongside completion of quality of life, anxiety, and depression questionnaires. We evaluated whether there was a difference in anxiety and depression levels over time.

RESULTS: After 12 months of treatment, significant improvements were noted in BMI, lung function (FEV1%), ST and various aspects of quality of life (CFQ-R). However, anxiety and depression levels did not differ significantly during the follow-up. When we stratified our sample by key groups, we observed that younger patients (under 28 years) and those with homozygous Phe508del mutations experienced significant higher anxiety with no differences on depression. Furthermore, anxiety and depression demonstrated a moderate correlation, strengthening over time.

CONCLUSIONS: Treatment with ETI establishes significant improvements in lung function, BMI, ST and quality of life in patients with CF. However, despite these positive outcomes, there were no significant changes observed in levels of anxiety and depression, except for individuals with homozygous mutation type and those younger than 28 years old, who exhibited significant higher levels of anxiety.

PMID:39962495 | DOI:10.1186/s12890-024-03455-2

Front Plant Sci. 2025 Feb 3;16:1473153. doi: 10.3389/fpls.2025.1473153. eCollection 2025.

ABSTRACT

Sowing uniformity is an important evaluation indicator of mechanical sowing quality. In order to achieve accurate evaluation of sowing uniformity in hybrid rice mechanical sowing, this study takes the seeds in a seedling tray of hybrid rice blanket-seedling nursing as the research object and proposes a method for evaluating sowing uniformity by combining image processing methods and the ODConv_C2f-ECA-WIoU-YOLOv8n (OEW-YOLOv8n) network. Firstly, image processing methods are used to segment seed image and obtain seed grids. Next, an improved model named OEW-YOLOv8n based on YOLOv8n is proposed to identify the number of seeds in a unit seed grid. The improved strategies include the following: (1) Replacing the Conv module in the Bottleneck of C2f modules with the Omni-Dimensional Dynamic Convolution (ODConv) module, where C2f modules are located at the connection between the Backbone and Neck. This improvement can enhance the feature extraction ability of the Backbone network, as the new modules can fully utilize the information of all dimensions of the convolutional kernel. (2) An Efficient Channel Attention (ECA) module is added to the Neck for improving the network's capability to extract deep semantic feature information of the detection target. (3) In the Bbox module of the prediction head, the Complete Intersection over Union (CIoU) loss function is replaced by the Weighted Intersection over Union version 3 (WIoUv3) loss function to improve the convergence speed of the bounding box loss function and reduce the convergence value of the loss function. The results show that the mean average precision (mAP) of the OEW-YOLOv8n network reaches 98.6%. Compared to the original model, the mAP improved by 2.5%. Compared to the advanced object detection algorithms such as Faster-RCNN, SSD, YOLOv4, YOLOv5s YOLOv7-tiny, and YOLOv10s, the mAP of the new network increased by 5.2%, 7.8%, 4.9%, 2.8% 2.9%, and 3.3%, respectively. Finally, the actual evaluation experiment showed that the test error is from -2.43% to 2.92%, indicating that the improved network demonstrates excellent estimation accuracy. The research results can provide support for the mechanized sowing quality detection of hybrid rice and the intelligent research of rice seeder.

PMID:39963535 | PMC:PMC11830705 | DOI:10.3389/fpls.2025.1473153

Front Plant Sci. 2025 Feb 3;16:1462694. doi: 10.3389/fpls.2025.1462694. eCollection 2025.

ABSTRACT

The increasing availability of genetic and genomic resources has underscored the need for automated microscopic phenotyping in plant-pathogen interactions to identify genes involved in disease resistance. Building on accumulated experience and leveraging automated microscopy and software, we developed BluVision Micro, a modular, machine learning-aided system designed for high-throughput microscopic phenotyping. This system is adaptable to various image data types and extendable with modules for additional phenotypes and pathogens. BluVision Micro was applied to screen 196 genetically diverse barley genotypes for interactions with powdery mildew fungi, delivering accurate, sensitive, and reproducible results. This enabled the identification of novel genetic loci and marker-trait associations in the barley genome. The system also facilitated high-throughput studies of labor-intensive phenotypes, such as precise colony area measurement. Additionally, BluVision's open-source software supports the development of specific modules for various microscopic phenotypes, including high-throughput transfection assays for disease resistance-related genes.

PMID:39963527 | PMC:PMC11832026 | DOI:10.3389/fpls.2025.1462694

Front Artif Intell. 2025 Feb 3;7:1449329. doi: 10.3389/frai.2024.1449329. eCollection 2024.

ABSTRACT

The accurate classification of potato leaf diseases plays a pivotal role in ensuring the health and productivity of crops. This study presents a unified approach for addressing this challenge by leveraging the power of Explainable AI (XAI) and transfer learning within a deep Learning framework. In this research, we propose a transfer learning-based deep learning model that is tailored for potato leaf disease classification. Transfer learning enables the model to benefit from pre-trained neural network architectures and weights, enhancing its ability to learn meaningful representations from limited labeled data. Additionally, Explainable AI techniques are integrated into the model to provide interpretable insights into its decision-making process, contributing to its transparency and usability. We used a publicly available potato leaf disease dataset to train the model. The results obtained are 97% for validation accuracy and 98% for testing accuracy. This study applies gradient-weighted class activation mapping (Grad-CAM) to enhance model interpretability. This interpretability is vital for improving predictive performance, fostering trust, and ensuring seamless integration into agricultural practices.

PMID:39963448 | PMC:PMC11830750 | DOI:10.3389/frai.2024.1449329

Front Ophthalmol (Lausanne). 2025 Feb 3;4:1497848. doi: 10.3389/fopht.2024.1497848. eCollection 2024.

ABSTRACT

INTRODUCTION: Glaucoma, optic neuritis (ON), and non-arteritic anterior ischemic optic neuropathy (NAION) produce distinct patterns of retinal ganglion cell (RGC) damage. We propose a booster Variational Autoencoder (bVAE) to capture spatial variations in RGC loss and generate latent space (LS) montage maps that visualize different degrees and spatial patterns of optic nerve bundle injury. Furthermore, the bVAE model is capable of tracking the spatial pattern of RGC thinning over time and classifying the underlying cause.

METHODS: The bVAE model consists of an encoder, a display decoder, and a booster decoder. The encoder decomposes input ganglion cell layer (GCL) thickness maps into two display latent variables (dLVs) and eight booster latent variables (bLVs). The dLVs capture primary spatial patterns of RGC thinning, while the display decoder reconstructs the GCL map and creates the LS montage map. The bLVs add finer spatial details, improving reconstruction accuracy. XGBoost was used to analyze the dLVs and bLVs, estimating normal/abnormal GCL thinning and classifying diseases (glaucoma, ON, and NAION). A total of 10,701 OCT macular scans from 822 subjects were included in this study.

RESULTS: Incorporating bLVs improved reconstruction accuracy, with the image-based root-mean-square error (RMSE) between input and reconstructed GCL thickness maps decreasing from 5.55 ± 2.29 µm (two dLVs only) to 4.02 ± 1.61 µm (two dLVs and eight bLVs). However, the image-based structural similarity index (SSIM) remained similar (0.91 ± 0.04), indicating that just two dLVs effectively capture the main GCL spatial patterns. For classification, the XGBoost model achieved an AUC of 0.98 for identifying abnormal spatial patterns of GCL thinning over time using the dLVs. Disease classification yielded AUCs of 0.95 for glaucoma, 0.84 for ON, and 0.93 for NAION, with bLVs further increasing the AUCs to 0.96 for glaucoma, 0.93 for ON, and 0.99 for NAION.

CONCLUSION: This study presents a novel approach to visualizing and quantifying GCL thinning patterns in optic neuropathies using the bVAE model. The combination of dLVs and bLVs enhances the model's ability to capture key spatial features and predict disease progression. Future work will focus on integrating additional image modalities to further refine the model's diagnostic capabilities.

PMID:39963427 | PMC:PMC11830743 | DOI:10.3389/fopht.2024.1497848

J Multidiscip Healthc. 2025 Feb 12;18:787-799. doi: 10.2147/JMDH.S492163. eCollection 2025.

ABSTRACT

OBJECTIVE: To evaluate the effectiveness of deep learning technology based on generative adversarial networks (GANs) in reducing motion artifacts in cardiac magnetic resonance (CMR) cine sequences.

METHODS: The training and testing datasets consisted of 2000 and 200 pairs of clear and blurry images, respectively, acquired through simulated motion artifacts in CMR cine sequences. These datasets were used to establish and train a deep learning GAN model. To assess the efficacy of the deep learning network in mitigating motion artifacts, 100 images with simulated motion artifacts and 37 images with real-world motion artifacts encountered in clinical practice were selected. Image quality pre- and post-optimization was assessed using metrics including Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Leningrad Focus Measure, and a 5-point Likert scale.

RESULTS: After GAN optimization, notable improvements were observed in the PSNR, SSIM, and focus measure metrics for the 100 images with simulated artifacts. These metrics increased from initial values of 23.85±2.85, 0.71±0.08, and 4.56±0.67, respectively, to 27.91±1.74, 0.83±0.05, and 7.74±0.39 post-optimization. Additionally, the subjective assessment scores significantly improved from 2.44±1.08 to 4.44±0.66 (P<0.001). For the 37 images with real-world artifacts, the Tenengrad Focus Measure showed a significant enhancement, rising from 6.06±0.91 to 10.13±0.48 after artifact removal. Subjective ratings also increased from 3.03±0.73 to 3.73±0.87 (P<0.001).

CONCLUSION: GAN-based deep learning technology effectively reduces motion artifacts present in CMR cine images, demonstrating significant potential for clinical application in optimizing CMR motion artifact management.

PMID:39963324 | PMC:PMC11830935 | DOI:10.2147/JMDH.S492163

Front Bioinform. 2025 Feb 3;5:1520382. doi: 10.3389/fbinf.2025.1520382. eCollection 2025.

ABSTRACT

Proteins, composed of amino acids, are crucial for a wide range of biological functions. Proteins have various interaction sites, one of which is the protein-ligand binding site, essential for molecular interactions and biochemical reactions. These sites enable proteins to bind with other molecules, facilitating key biological functions. Accurate prediction of these binding sites is pivotal in computational drug discovery, helping to identify therapeutic targets and facilitate treatment development. Machine learning has made significant contributions to this field by improving the prediction of protein-ligand interactions. This paper reviews studies that use machine learning to predict protein-ligand binding sites from sequence data, focusing on recent advancements. The review examines various embedding methods and machine learning architectures, addressing current challenges and the ongoing debates in the field. Additionally, research gaps in the existing literature are highlighted, and potential future directions for advancing the field are discussed. This study provides a thorough overview of sequence-based approaches for predicting protein-ligand binding sites, offering insights into the current state of research and future possibilities.

PMID:39963299 | PMC:PMC11830693 | DOI:10.3389/fbinf.2025.1520382

Front Neurosci. 2025 Feb 3;19:1461654. doi: 10.3389/fnins.2025.1461654. eCollection 2025.

ABSTRACT

INTRODUCTION: Music has a profound impact on human emotions, capable of eliciting a wide range of emotional responses, a phenomenon that has been effectively harnessed in the field of music therapy. Given the close relationship between music and language, researchers have begun to explore how music influences brain activity and cognitive processes by integrating artificial intelligence with advancements in neuroscience.

METHODS: In this study, a total of 120 subjects were recruited, all of whom were students aged between 19 and 26 years. Each subject is required to listen to six 1-minute music segments expressing different emotions and speak at the 40-second mark. In terms of constructing the classification model, this study compares the classification performance of deep neural networks with other machine learning algorithms.

RESULTS: The differences in EEG signals between different emotions during speech are more pronounced compared to those in a quiet state. In the classification of EEG signals for speaking and quiet states, using deep neural network algorithms can achieve accuracies of 95.84% and 96.55%, respectively.

DISCUSSION: Under the stimulation of music with different emotions, there are certain differences in EEG between speaking and resting states. In the construction of EEG classification models, the classification performance of deep neural network algorithms is superior to other machine learning algorithms.

PMID:39963261 | PMC:PMC11830716 | DOI:10.3389/fnins.2025.1461654

J Biomed Opt. 2025 Feb;30(2):026001. doi: 10.1117/1.JBO.30.2.026001. Epub 2025 Feb 17.

ABSTRACT

SIGNIFICANCE: Current methods for complex conjugate removal (CCR) in frequency-domain optical coherence tomography (FD-OCT) often require additional hardware components, which increase system complexity and cost. A software-based solution would provide a more efficient and cost-effective alternative.

AIM: We aim to develop a deep learning approach to effectively remove complex conjugate artifacts (CCAs) from OCT scans without the need for extra hardware components.

APPROACH: We introduce a deep learning method that employs generative adversarial networks to eliminate CCAs from OCT scans. Our model leverages both conventional intensity images and phase images from the OCT scans to enhance the artifact removal process.

RESULTS: Our CCR-generative adversarial network models successfully converted conventional OCT scans with CCAs into artifact-free scans across various samples, including phantoms, human skin, and mouse eyes imaged in vivo with a phase-stable swept source-OCT prototype. The inclusion of phase images significantly improved the performance of the deep learning models in removing CCAs.

CONCLUSIONS: Our method provides a low-cost, data-driven, and software-based solution to enhance FD-OCT imaging capabilities by the removal of CCAs.

PMID:39963188 | PMC:PMC11831228 | DOI:10.1117/1.JBO.30.2.026001

Nanoscale. 2025 Feb 18. doi: 10.1039/d4nr05458j. Online ahead of print.

ABSTRACT

In addition to its excellent photoelectronic properties, the CsPbBr3 perovskite has been reported as a low thermal conductivity (k) material. However, few studies investigated the microscopic mechanisms underlying its low k. Studying its thermal transport behavior is crucial for understanding its thermal properties and thus improving its thermal stability. Here, we train a DFT-level deep-learning potential (DP) of CsPbBr3 and explore its ultra-low k using nonequilibrium molecular dynamics (NEMD). The k calculated using NEMD is 0.43 ± 0.01 W m-1 K-1, which is consistent with experimental results. Furthermore, the Cs vacancy contributes to the decrease in k due to the distortion of the Pb-Br cage, which enhances phonon scattering and reduces the phonon lifetime. Our research reveals the significant potential of machine learning force fields in thermal and phonon behavior research and the valuable insights gained from defect-regulated thermal conductivity.

PMID:39963065 | DOI:10.1039/d4nr05458j

J Pathol Transl Med. 2025 Feb 18. doi: 10.4132/jptm.2024.10.23. Online ahead of print.

ABSTRACT

BACKGROUND: High-grade serous ovarian carcinoma (HGSC) exhibits significant heterogeneity, posing challenges for effective clinical categorization. Understanding the histomorphological diversity within HGSC could lead to improved prognostic stratification and personalized treatment approaches.

METHODS: We applied the Histomic Atlases of Variation Of Cancers model to whole slide images from The Cancer Genome Atlas dataset for ovarian cancer. Histologically distinct tumor clones were grouped into common histomic clusters. Principal component analysis and K-means clustering classified HGSC samples into three groups: highly differentiated (HD), intermediately differentiated (ID), and lowly differentiated (LD).

RESULTS: HD tumors showed diverse patterns, lower densities, and stronger eosin staining. ID tumors had intermediate densities and balanced staining, while LD tumors were dense, patternless, and strongly hematoxylin-stained. RNA sequencing revealed distinct patterns in mitochondrial oxidative phosphorylation and energy metabolism, with upregulation in the HD, downregulation in the LD, and the ID positioned in between. Survival analysis showed significantly lower overall survival for the LD compared to the HD and ID, underscoring the critical role of mitochondrial dynamics and energy metabolism in HGSC progression.

CONCLUSIONS: Deep learning-based histologic analysis effectively stratifies HGSC into clinically relevant prognostic groups, highlighting the role of mitochondrial dynamics and energy metabolism in disease progression. This method offers a novel approach to HGSC categorization.

PMID:39962925 | DOI:10.4132/jptm.2024.10.23

Front Plant Sci. 2025 Feb 3;16:1559814. doi: 10.3389/fpls.2025.1559814. eCollection 2025.

NO ABSTRACT

PMID:39963532 | PMC:PMC11830810 | DOI:10.3389/fpls.2025.1559814