mBio. 2025 Apr 3:e0077825. doi: 10.1128/mbio.00778-25. Online ahead of print.

NO ABSTRACT

PMID:40176258 | DOI:10.1128/mbio.00778-25

BMC Complement Med Ther. 2025 Apr 2;25(1):124. doi: 10.1186/s12906-025-04837-7.

ABSTRACT

INTRODUCTION: Cancer is currently the second most common cause of death worldwide and is often treated with chemotherapy. Music therapy is a widely used adjunct therapy offered in oncology settings to attenuate negative impacts of treatment on patient's physical and mental health; however, music therapy research during chemotherapy is relatively scarce. The aim of this study is to evaluate the impact of group music therapy sessions with patients and caregivers on their perceived anxiety, stress, and wellbeing levels and the perception of chemotherapy-induced side effects for patients.

MATERIALS AND METHODS: This is a retrospective cohort study following the STROBE guidelines. From April to October 2022, 41 group music therapy sessions including 141 patients and 51 caregivers were conducted. Participants filled out pre- and post-intervention Visual Analogue Scales (VAS) assessing their anxiety, stress, and wellbeing levels, and for patients the intensity of chemotherapy-induced side effects.

RESULTS: The results show a statistically significant decrease of anxiety and stress levels (p < .001), an increase in well-being of patients and caregivers (p < .001, p = .009), and a decrease in patients' perceived intensity of chemotherapy-induced side effects (p = .003). Calculated effect sizes were moderate for anxiety, stress, and well-being levels, and small for chemotherapy-induced side effects.

DISCUSSION: This is the first study regarding group music therapy sessions for cancer patients and their caregivers during chemotherapy in Colombia. Music therapy has been found to be a valuable strategy to reduce psychological distress in this population and to provide opportunities for fostering self-care and social interaction.

CONCLUSIONS: Music therapy should be considered as a valuable complementary therapy during chemotherapy. However, it is crucial to conduct prospective studies with parallel group designs to confirm these preliminary findings.

PMID:40176020 | DOI:10.1186/s12906-025-04837-7

J Health Popul Nutr. 2025 Apr 2;44(1):103. doi: 10.1186/s41043-025-00845-y.

ABSTRACT

Sturge-Weber Syndrome (SWS), is a rare neuro-oculo-cutaneous disorder that presents unique diagnostic and management challenges, particularly in resource-limited settings. This editorial reflects on a recent case of an undiagnosed SWS in an Ethiopian refugee patient in Sudan, highlighting systemic barriers to healthcare access during a time of war and the importance of clinical vigilance. We advocate for local and global initiatives to further enhance diagnostic capabilities, develop integrated care systems in recognition and management of such a rare and complex condition.

PMID:40176099 | DOI:10.1186/s41043-025-00845-y

Adv Pharmacol. 2025;103:65-80. doi: 10.1016/bs.apha.2025.01.004. Epub 2025 Feb 12.

ABSTRACT

The high rate of medication failures poses a significant challenge for the pharmaceutical sector. Selecting appropriate data from experiments for ADMET (absorption, distribution, metabolism, excretion, and toxicity) prediction and applying it effectively in the context of physiological characteristics is difficult. Currently, ADMET prediction is conducted early in the drug design process to filter out molecules with weak pharmacokinetic properties. Numerous ADMET models for prediction have been designed using computational methods. Verified ADMET datasets have been determined through experiments, utilizing key classifying factors and descriptors to develop in silico approaches. This chapter discusses the relevance of ADMET evaluation in drug design, methodologies for model creation, available ADMET predictive tools, and the limitations of these predicted models.

PMID:40175055 | DOI:10.1016/bs.apha.2025.01.004

Sci Rep. 2025 Apr 2;15(1):11236. doi: 10.1038/s41598-025-89911-3.

ABSTRACT

The early detection of brain tumors is very important for treating them and improving the quality of life for patients. Through advanced imaging techniques, doctors can now make more informed decisions. This paper introduces a framework for a tumor detection system capable of grading gliomas. The system's implementation begins with the acquisition and analysis of brain magnetic resonance images. Key features indicative of tumors and gliomas are extracted and classified as independent components. A deep learning model is then employed to categorize these gliomas. The proposed model classifies gliomas into three primary categories: meningioma, pituitary, and glioma. Performance evaluation demonstrates a high level of accuracy (99.21%), specificity (98.3%), and sensitivity (97.83%). Further research and validation are essential to refine the system and ensure its clinical applicability. The development of accurate and efficient tumor detection systems holds significant promise for enhancing patient care and improving survival rates.

PMID:40175410 | DOI:10.1038/s41598-025-89911-3

Rev Neurol (Paris). 2025 Apr 1:S0035-3787(25)00487-4. doi: 10.1016/j.neurol.2025.03.006. Online ahead of print.

ABSTRACT

In recent years, artificial intelligence (AI) has become an increasingly prominent focus of medical research, significantly impacting epileptology as well. Studies on deep learning (DL) and machine learning (ML) - the core of AI - have explored their applications in epilepsy imaging, primarily focusing on lesion detection, lateralization and localization of epileptogenic areas, postsurgical outcome prediction and automatic differentiation between people with epilepsy and healthy individuals. Various AI-driven approaches are being investigated across different neuroimaging modalities, with the ultimate goal of integrating these tools into clinical practice to enhance the diagnosis and treatment of epilepsy. As computing power continues to advance, the development, research integration, and clinical implementation of AI applications are expected to accelerate, making them even more effective and accessible. However, ensuring the safety of patient data will require strict regulatory measures. Despite these challenges, AI represents a transformative opportunity for medicine, particularly in epilepsy neuroimaging. Since ML and DL models thrive on large datasets, fostering collaborations and expanding open-access databases will become increasingly pivotal in the future.

PMID:40175210 | DOI:10.1016/j.neurol.2025.03.006

Acad Radiol. 2025 Apr 1:S1076-6332(25)00210-7. doi: 10.1016/j.acra.2025.03.015. Online ahead of print.

ABSTRACT

RATIONALE AND OBJECTIVES: Routine knee MRI protocols for 1.5 T and 3 T scanners, do not include T2*-w gradient echo (T2*W) images, which are useful in several clinical scenarios such as the assessment of cartilage, synovial blooming (deposition of hemosiderin), chondrocalcinosis and the evaluation of the physis in pediatric patients. Herein, we aimed to develop an open-source deep learning model that creates synthetic T2*W images of the knee using fat-suppressed intermediate-weighted images.

MATERIALS AND METHODS: A cycleGAN model was trained with 12,118 sagittal knee MR images and tested on an independent set of 2996 images. Diagnostic interchangeability of synthetic T2*W images was assessed against a series of findings. Voxel intensity of four tissues was evaluated with Bland-Altman plots. Image quality was assessed with the use of root mean squared error (NRMSE), structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR). Code, model and a standalone executable file are provided on github.

RESULTS: The model achieved a median NRMSE, PSNR and SSIM of 0.5, 17.4, and 0.5, respectively. Images were found interchangeable with an intraclass correlation coefficient >0.95 for all findings. Mean voxel intensity was equal between synthetic and conventional images. Four types of artifacts were identified: geometrical distortion (86/163 cases), object insertion/omission (11/163 cases), a wrap-around-like (26/163 cases) and an incomplete fat-suppression artifact (120/163 cases), which had a median 0 impact (no impact) on the diagnosis.

CONCLUSION: In conclusion, the developed open-source GAN model creates synthetic T2*W images of the knee of high diagnostic value and quality. The identified artifacts had no or minor effect on the diagnostic value of the images.

PMID:40175204 | DOI:10.1016/j.acra.2025.03.015

Adv Pharmacol. 2025;103:325-348. doi: 10.1016/bs.apha.2025.01.016. Epub 2025 Feb 16.

ABSTRACT

Artificial Intelligence (AI) has revolutionized drug discovery by enhancing data collection, integration, and predictive modeling across various critical stages. It aggregates vast biological and chemical data, including genomic information, protein structures, and chemical interactions with biological targets. Machine learning techniques and QSAR models are applied by AI to predict compound behaviors and predict potential drug candidates. Docking simulations predict drug-protein interactions, while virtual screening eliminates large chemical databases through efficient sifting. Similarly, AI supports de novo drug design by generating novel molecules, optimized against a particular biological target, using generative models such as generative adversarial network (GAN), always finding lead compounds with the most desirable pharmacological properties. AI used in clinical trials improves efficiency by pinpointing responsive patient cohorts leveraging genetic profiles and biomarkers and maintaining propriety such as dataset diversity and compliance with regulations. This chapter aimed to summarize and analyze the mechanism of AI to accelerate drug discovery by streamlining different processes that enable informed decisions and bring potential life-saving therapies to market faster, amounting to a breakthrough in pharmaceutical research and development.

PMID:40175048 | DOI:10.1016/bs.apha.2025.01.016

Adv Pharmacol. 2025;103:101-120. doi: 10.1016/bs.apha.2025.01.008. Epub 2025 Feb 6.

ABSTRACT

The lengthy and costly drug discovery process is transformed by deep learning, a subfield of artificial intelligence. Deep learning technologies expedite the procedure, increasing treatment success rates and speeding life-saving procedures. Deep learning stands out in target identification and lead selection. Deep learning greatly accelerates initial stage by analyzing large datasets of biological data to identify possible therapeutic targets and rank targeted drug molecules with desired features. Predicting possible adverse effects is another significant challenge. Deep learning offers prompt and efficient assistance with toxicology prediction in a very short time, deep learning algorithms can forecast a new drug's possible harm. This enables to concentrate on safer alternatives and steer clear of late-stage failures brought on by unanticipated toxicity. Deep learning unlocks the possibility of drug repurposing; by examining currently available medications, it is possible to find whole new therapeutic uses. This method speeds up development of diseases that were previously incurable. De novo drug discovery is made possible by deep learning when combined with sophisticated computational modeling, it can create completely new medications from the ground. Deep learning can recommend and direct towards new drug candidates with high binding affinities and intended therapeutic effects by examining molecular structures of disease targets. This provides focused and personalized medication. Lastly, drug characteristics can be optimized with aid of deep learning. Researchers can create medications with higher bioavailability and fewer toxicity by forecasting drug pharmacokinetics. In conclusion, deep learning promises to accelerate drug development, reduce costs, and ultimately save lives.

PMID:40175037 | DOI:10.1016/bs.apha.2025.01.008

Adv Pharmacol. 2025;103:163-184. doi: 10.1016/bs.apha.2025.01.011. Epub 2025 Feb 16.

ABSTRACT

With the advancing technology, the way to drug discovery has evolved. The use of AI and computational methods have revolutionized the methods to develop novel therapeutics. In previous years, the methods to discover new drugs included high-throughput screening and bioassays which were labor-dependent, extremely expensive and had high probability to inaccurate results. The introduction of Computational studies has changed the process by introducing various methods to determine hit compounds and their methods of analysis. Methods such as molecular docking, virtual screening, and dynamics have changed the path to optimize and produce lead molecules. Similarly, network pharmacology also works on the identification of target proteins complex disease pathways with the help of protein-protein interactions and obtaining hub proteins. Various tools such as STRING database, cytoscape and metascape are employed in the study to construct a network between the proteins responsible for the disease progression and helps to obtain the vital target proteins, simplifying the process of drug-target identification. These approaches when employed together, results in obtaining results with better precision and accuracy which can be further validated experimentally, saving the resources and time. This chapter highlights the foundation of computational approaches in drug discovery and provides a detailed understanding of how these approaches are helping the researchers to produce novel solutions using artificial intelligence and machine learning.

PMID:40175040 | DOI:10.1016/bs.apha.2025.01.011

Adv Pharmacol. 2025;103:101-120. doi: 10.1016/bs.apha.2025.01.008. Epub 2025 Feb 6.

ABSTRACT

The lengthy and costly drug discovery process is transformed by deep learning, a subfield of artificial intelligence. Deep learning technologies expedite the procedure, increasing treatment success rates and speeding life-saving procedures. Deep learning stands out in target identification and lead selection. Deep learning greatly accelerates initial stage by analyzing large datasets of biological data to identify possible therapeutic targets and rank targeted drug molecules with desired features. Predicting possible adverse effects is another significant challenge. Deep learning offers prompt and efficient assistance with toxicology prediction in a very short time, deep learning algorithms can forecast a new drug's possible harm. This enables to concentrate on safer alternatives and steer clear of late-stage failures brought on by unanticipated toxicity. Deep learning unlocks the possibility of drug repurposing; by examining currently available medications, it is possible to find whole new therapeutic uses. This method speeds up development of diseases that were previously incurable. De novo drug discovery is made possible by deep learning when combined with sophisticated computational modeling, it can create completely new medications from the ground. Deep learning can recommend and direct towards new drug candidates with high binding affinities and intended therapeutic effects by examining molecular structures of disease targets. This provides focused and personalized medication. Lastly, drug characteristics can be optimized with aid of deep learning. Researchers can create medications with higher bioavailability and fewer toxicity by forecasting drug pharmacokinetics. In conclusion, deep learning promises to accelerate drug development, reduce costs, and ultimately save lives.

PMID:40175037 | DOI:10.1016/bs.apha.2025.01.008

Eur J Pharm Sci. 2025 Mar 31:107079. doi: 10.1016/j.ejps.2025.107079. Online ahead of print.

ABSTRACT

Hospital care is a highly complex process, requiring comprehensive documentation of all aspects of the patient journey in electronic health records. A critical component of this care is the accurate tracking of patient medications. International standards are not consistently incorporated into the electronic medication systems currently in use worldwide, and their interoperability remains an unresolved issue. We recognised the need to develop a set of standardised data elements that ensure consistent and accurate documentation. Although the medication systems studied exhibit various strengths and weaknesses and can satisfactorily document certain aspects of the medication process, none achieve the necessary level of optimal documentation. Our paper presents a new perspective on medication recording by identifying the electronic data requirements for all events in an itemized, complete, historical, and standardized manner. To address this gap, we collected, defined, and introduced the essential data elements required for the comprehensive documentation of medication sub-processes for the first time in our study. The Fast Health Interoperability Resources (FHIR) data exchange standard was employed for designing these data requirements. Our research identified and categorised 138 data elements essential for describing the complete medication process, including medication description, requests, dispensation, and administration. These data elements were divided into fundamental and supplementary categories. We developed a survey form to assess medication systems. In a pilot study, we tested the quality of 5 medication systems, currently in operation in Hungary. Our analysis assessed the accuracy of the electronic recording of medication and the correspondence of the recorded data elements with international standards. None of the systems demonstrated the ability to document medication accurately or capture all fundamental data elements. The best-performing system managed to record 63% of all fundamental data elements, while the worst-performing system managed only to document 30%. The names and the values of data elements in these systems did not comply with international standards either. The primary clinical pharmaceutical usefulness of this study was to enhance the digital documentation of medication in hospitals to meet comprehensive data recording requirements, ensure greater compliance, and improve their suitability for enriching clinical health data files, enabling real-world studies, pharmacovigilance analyses, and the identification of drug repositioning opportunities.

PMID:40174662 | DOI:10.1016/j.ejps.2025.107079

Am J Ophthalmol. 2025 Mar 31:S0002-9394(25)00162-X. doi: 10.1016/j.ajo.2025.03.043. Online ahead of print.

ABSTRACT

OBJECTIVE: Neurotrophic keratopathy (NK) is a rare disease characterized by the loss of corneal innervation and increased vulnerability to injury. The diagnosis and treatment of NK can be challenging for pediatric patients and their caregivers. This study explores the experiences of caregivers navigating the diagnostic and treatment journey of pediatric patients with neurotrophic keratopathy.

DESIGN: This study is a qualitative study using semi-structured interviews.

SUBJECTS: Ten caregivers of pediatric patients with NK who had undergone corneal neurotization (CN) surgery.

METHODS: Caregivers were interviewed about their experiences related to the diagnostic process, treatment challenges, lifestyle changes, and the impact of CN surgery. Interviews were recorded, transcribed, and analyzed using an inductive-deductive approach to identify recurring themes.

MAIN OUTCOMES: Caregiver experiences and perceptions of diagnostic delays, information-seeking behaviors, lifestyle changes, and the effects of CN surgery on corneal health and quality of life.

RESULTS: Five key themes emerged from the analysis: (1) Delays in diagnosis due to insufficient specialist knowledge; (2) Caregivers' proactive efforts in seeking information; (3) Substantial lifestyle changes required by NK; (4) The impact of CN surgery on corneal health and quality of life; and (5) Variability in healthcare experiences, highlighting the need for effective communication. Caregivers expressed frustration with diagnostic delays and highlighted their reliance on external support networks.

CONCLUSIONS: This study illustrates the need for enhanced awareness among clinicians about NK and the benefits of narrative medicine in fostering caregiver-provider relationships. The challenges reported by families navigating NK inform strategies that may improve diagnosis and treatment of NK.

PMID:40174715 | DOI:10.1016/j.ajo.2025.03.043

Mol Divers. 2025 Apr 2. doi: 10.1007/s11030-025-11135-4. Online ahead of print.

ABSTRACT

The process of various virus entry into host cells, including SARS-CoV-2, is mediated by clathrin-mediated endocytosis (CME). AP-2 plays a crucial role in this process by recognizing membrane receptors and binding with clathrin, facilitating the formation of clathrin-coated vesicles and promoting CME. AAK1 catalyzes the phosphorylation of AP2M1 subunit at Thr156. Therefore, suppressing AAK1 activity can hinder virus invasion by blocking CME. indicating that AAK1 could be a potential target for developing novel antiviral drugs against SARS-CoV-2. In this study, we present a series of novel AAK1 inhibitors based on previously reported AAK1 inhibitors. Drug design was carried out by fusing the 1H-indazole scaffold of SGC-AAK1-1 with pharmacophore groups of compound 6, and further optimized with the assistance of molecular docking. Among the 42 compounds novelly synthesized, compounds 9i, 9s, 11f and 11l exhibited comparable antiviral activity against SARS-CoV-2 infection compared to reference compound 6 at the concentration of 3 μM. Particularly, 11f showed almost no cytotoxicity at all tested concentrations. Additionally, 11f exhibited favorable predictive pharmacokinetic properties. These findings support the potential of 11f as a lead compound for developing antiviral drugs targeting SARS-CoV-2 infection, as well as potentially other viruses which are dependent on the CME process to enter host cells. In summary, we have expanded the structural types of AAK1 inhibitors and successfully obtained effective AAK1 inhibitors with antiviral capabilities.

PMID:40175846 | DOI:10.1007/s11030-025-11135-4

Am J Hum Genet. 2025 Mar 24:S0002-9297(25)00102-8. doi: 10.1016/j.ajhg.2025.03.003. Online ahead of print.

ABSTRACT

Pharmacogenomic (PGx) information is essential for precision medicine, enabling drug prescriptions to be personalized according to an individual's genetic background. Almost all individuals will carry a genetic marker that affects their drug response, so the ideal drug prescription for these individuals will differ from the population-level guidelines. Currently, PGx information is often not available at first prescription, reducing its effectiveness. In the Netherlands, pharmacogenetic information is most often obtained using dedicated single-gene assays, making it expensive and time consuming to generate complete multi-gene PGx profiles. We therefore hypothesized that we could also use genome-wide oligonucleotide genotyping arrays to generate comprehensive PGx information (PGx passports), thereby decreasing the cost and time required for PGx testing and lowering the barrier to generating PGx information prior to first prescription. Taking advantage of existing genetic data generated in two biobanks, we developed and validated Asterix, a low-cost, clinical-grade PGx passport pipeline for 12 PGx genes. In these biobanks, we performed and clinically validated genetic variant calling and statistical phasing and imputation. In addition, we developed and validated a CYP2D6 copy-number-variant-calling tool, forgoing the need to use separate PCR-based copy-number detection. Ultimately, we returned 1,227 PGx passports to biobank participants via a layperson-friendly app, improving knowledge of PGx among citizens. Our study demonstrates the feasibility of a low-cost, clinical-grade PGx passport pipeline that could be readily implemented in clinical settings to enhance personalized healthcare, ensuring that patients receive the most effective and safe drug therapy based on their unique genetic makeup.

PMID:40174590 | DOI:10.1016/j.ajhg.2025.03.003

Int J Biol Macromol. 2025 Mar 31:142627. doi: 10.1016/j.ijbiomac.2025.142627. Online ahead of print.

ABSTRACT

Pseudomonas aeruginosa (P. aeruginosa) is a multidrug-resistant opportunistic pathogen responsible for chronic obstructive pulmonary disease (COPD), cystic fibrosis, and ventilator-associated pneumonia (VAP), leading to cancer. Developing an efficacious vaccine remains the most promising strategy for combating P. aeruginosa infections. In this study, we employed an advanced in silico strategy to design a highly efficient and stable mRNA vaccine using immunoinformatics tools. Whole proteome data were utilized to identify highly immunogenic vaccine candidates using subtractive proteomics. Three extracellular proteins were prioritized for T- and linear B-cell epitope prediction. Beta-definsin protein sequence was incorporated as an adjuvant at the N-terminus of the construct. A total of 3 CTL, 3 HTL, and 3 linear B cell highly immunogenic epitopes were combined using specific linkers to design this multi-peptide construct. The 5' and 3' UTR sequences, Kozak sequence with a stop codon, and signal peptides followed by a poly-A tail were incorporated into the above vaccine construct to create our final mRNA vaccine. The vaccines exhibited antigenicity scores >0.88, ensuring high antigenicity with no allergenic or toxic. Physiochemical properties analysis revealed high solubility and thermostability. Three-dimensional structural analysis determined high-quality structures. Vaccine-receptor docking and molecular dynamic simulations demonstrated strong molecular interactions, stable binding affinities, dynamic nature, and structural stability of this vaccine, with significant immunogenic responses of the immune system against the vaccine. The immunological simulation indicates successful cellular and humoral immune responses to defend against P. aeruginosa infection. Validation of the study outcomes necessitates both experimental and clinical testing.

PMID:40174835 | DOI:10.1016/j.ijbiomac.2025.142627

EBioMedicine. 2025 Apr 1;114:105670. doi: 10.1016/j.ebiom.2025.105670. Online ahead of print.

ABSTRACT

BACKGROUND: Small airways (<2 mm diameter) are major sites of airflow obstruction in chronic obstructive pulmonary disease (COPD). This study aimed to quantify the impact of small airway disease, characterized by narrowing, occlusion, and obliteration, on airflow parameters in smokers and end-stage patients with COPDs.

METHODS: We performed computational fluid dynamics (CFD) simulations of inspiratory airflow in three lung groups: control non-used donor lungs (no smoking/emphysema history), non-used donor lungs with a smoking history and emphysema, and explanted end-stage COPD lungs. Each group included four lungs, with two tissue cylinders. Micro-CT-scanned small airways were segmented into 3D models for CFD simulations to quantify pressure, resistance, and shear stress. CFD results were benchmarked against simplified linear and Weibel models.

FINDINGS: CFD simulations showed higher pressures in COPD vs. controls (p = 0.0091) and smokers (p = 0.015), along with increased resistance (p = 0.0057 vs. controls; p = 0.0083 vs. smokers) and up to a tenfold rise in shear stress (p = 0.010 vs. controls). Narrowing and occlusion were shown to independently increase pressure, resistance, and shear stress, which were validated through segmentation corrections. Pressures and resistance assessed with simplified models were up to seven-fold higher for smokers and even 72 higher for COPD compared with CFD values.

INTERPRETATION: These findings show that increased airflow parameters can explain the association between small airway disease and airflow limitation in COPD, underscoring small airway vulnerability. Additionally, they highlight the limitations of theoretical models in accurately capturing small airway disease.

FUNDING: Supported by the KU Leuven (C16/19/005).

PMID:40174553 | DOI:10.1016/j.ebiom.2025.105670

Alzheimers Res Ther. 2025 Apr 2;17(1):70. doi: 10.1186/s13195-025-01705-x.

ABSTRACT

BACKGROUND: Although interactions between amyloid-beta and tau proteins have been implicated in Alzheimer's disease (AD), the precise mechanisms by which these interactions contribute to disease progression are not yet fully understood. Moreover, despite the growing application of deep learning in various biomedical fields, its application in integrating networks to analyze disease mechanisms in AD research remains limited. In this study, we employed BIONIC, a deep learning-based network integration method, to integrate proteomics and protein-protein interaction data, with an aim to uncover factors that moderate the effects of the Aβ-tau interaction on mild cognitive impairment (MCI) and early-stage AD.

METHODS: Proteomic data from the ROSMAP cohort were integrated with protein-protein interaction (PPI) data using a Deep Learning-based model. Linear regression analysis was applied to histopathological and gene expression data, and mutual information was used to detect moderating factors. Statistical significance was determined using the Benjamini-Hochberg correction (p < 0.05).

RESULTS: Our results suggested that astrocytes and GPNMB + microglia moderate the Aβ-tau interaction. Based on linear regression with histopathological and gene expression data, GFAP and IBA1 levels and GPNMB gene expression positively contributed to the interaction of tau with Aβ in non-dementia cases, replicating the results of the network analysis.

CONCLUSIONS: These findings suggest that GPNMB + microglia moderate the Aβ-tau interaction in early AD and therefore are a novel therapeutic target. To facilitate further research, we have made the integrated network available as a visualization tool for the scientific community (URL: https://igcore.cloud/GerOmics/AlzPPMap ).

PMID:40176187 | DOI:10.1186/s13195-025-01705-x

Cancer Imaging. 2025 Apr 2;25(1):47. doi: 10.1186/s40644-025-00869-x.

ABSTRACT

OBJECTIVE: This study evaluates the impact of deep learning-enhanced T1-weighted VIBE sequences (DL-VIBE) on image quality and procedural parameters during MR-guided thermoablation of liver malignancies, compared to standard VIBE (SD-VIBE).

METHODS: Between September 2021 and February 2023, 34 patients (mean age: 65.4 years; 13 women) underwent MR-guided microwave ablation on a 1.5 T scanner. Intraprocedural SD-VIBE sequences were retrospectively processed with a deep learning algorithm (DL-VIBE) to reduce noise and enhance sharpness. Two interventional radiologists independently assessed image quality, noise, artifacts, sharpness, diagnostic confidence, and procedural parameters using a 5-point Likert scale. Interrater agreement was analyzed, and noise maps were created to assess signal-to-noise ratio improvements.

RESULTS: DL-VIBE significantly improved image quality, reduced artifacts and noise, and enhanced sharpness of liver contours and portal vein branches compared to SD-VIBE (p < 0.01). Procedural metrics, including needle tip detectability, confidence in needle positioning, and ablation zone assessment, were significantly better with DL-VIBE (p < 0.01). Interrater agreement was high (Cohen κ = 0.86). Reconstruction times for DL-VIBE were 3 s for k-space reconstruction and 1 s for superresolution processing. Simulated acquisition modifications reduced breath-hold duration by approximately 2 s.

CONCLUSION: DL-VIBE enhances image quality during MR-guided thermal ablation while improving efficiency through reduced processing and acquisition times.

PMID:40176185 | DOI:10.1186/s40644-025-00869-x

Plant Methods. 2025 Apr 2;21(1):48. doi: 10.1186/s13007-025-01325-4.

ABSTRACT

Plant leaf diseases significantly threaten agricultural productivity and global food security, emphasizing the importance of early and accurate detection and effective crop health management. Current deep learning models, often used for plant disease classification, have limitations in capturing intricate features such as texture, shape, and color of plant leaves. Furthermore, many of these models are computationally expensive and less suitable for deployment in resource-constrained environments such as farms and rural areas. We propose a novel Lightweight Deep Learning model, Depthwise Separable Convolution with Spatial Attention (LWDSC-SA), designed to address limitations and enhance feature extraction while maintaining computational efficiency. By integrating spatial attention and depthwise separable convolution, the LWDSC-SA model improves the ability to detect and classify plant diseases. In our comprehensive evaluation using the PlantVillage dataset, which consists of 38 classes and 55,000 images from 14 plant species, the LWDSC-SA model achieved 98.7% accuracy. It presents a substantial improvement over MobileNet by 5.25%, MobileNetV2 by 4.50%, AlexNet by 7.40%, and VGGNet16 by 5.95%. Furthermore, to validate its robustness and generalizability, we employed K-fold cross-validation K=5, which demonstrated consistently high performance, with an average accuracy of 98.58%, precision of 98.30%, recall of 98.90%, and F1 score of 98.58%. These results highlight the superior performance of the proposed model, demonstrating its ability to outperform state-of-the-art models in terms of accuracy while remaining lightweight and efficient. This research offers a promising solution for real-world agricultural applications, enabling effective plant disease detection in resource-limited settings and contributing to more sustainable agricultural practices.

PMID:40176127 | DOI:10.1186/s13007-025-01325-4