Front Artif Intell. 2025 Mar 18;8:1565287. doi: 10.3389/frai.2025.1565287. eCollection 2025.

ABSTRACT

Vehicle-to-Everything (V2X) communication promises to revolutionize road safety and efficiency. However, challenges in data sharing and network reliability impede its full realization. This paper addresses these challenges by proposing a novel Deep Learning (DL) approach for traffic prediction in V2X environments. We employ Bidirectional Long Short-Term Memory (BiLSTM) networks and compare their performance against other prominent DL architectures, including unidirectional LSTM and Gated Recurrent Unit (GRU). Our findings demonstrate that the BiLSTM model exhibits superior accuracy in predicting traffic patterns. This enhanced prediction capability enables more efficient resource allocation, improved network performance, and enhanced safety for all road users, reducing fuel consumption, decreased emissions, and a more sustainable transportation system.

PMID:40176965 | PMC:PMC11962783 | DOI:10.3389/frai.2025.1565287

J Sleep Res. 2025 Apr 3:e70061. doi: 10.1111/jsr.70061. Online ahead of print.

ABSTRACT

Automated sleep staging on wearable data could improve our understanding and management of epilepsy. This study evaluated sleep scoring by a deep learning model on 223 night-sleep recordings from 50 patients measured in the hospital with an electroencephalogram (EEG) and a wearable device. The model scored the sleep stage of every 30-s epoch on the EEG and wearable data, and we compared the output with a clinical expert on 20 nights, each for a different patient. The Bland-Altman analysis examined differences in the automated staging in both modalities, and using mixed-effect models, we explored sleep differences between patients with and without seizures. Overall, we found moderate accuracy and Cohen's kappa on the model scoring of standard EEG (0.73 and 0.59) and the wearable (0.61 and 0.43) versus the clinical expert. F1 scores also varied between patients and the modalities. The sensitivity varied by sleep stage and was very low for stage N1. Moreover, sleep staging on the wearable data underestimated the duration of most sleep macrostructure parameters except N2. On the other hand, patients with seizures during the hospital admission slept more each night (6.37, 95% confidence interval [CI] 5.86-7.87) compared with patients without seizures (5.68, 95% CI 5.24-6.13), p = 0.001, but also spent more time in stage N2. In conclusion, wearable EEG and accelerometry could monitor sleep in patients with epilepsy, and our approach can help automate the analysis. However, further steps are essential to improve the model performance before clinical implementation. Trial Registration: The SeizeIT2 trial was registered in clinicaltrials.gov, NCT04284072.

PMID:40176726 | DOI:10.1111/jsr.70061

Bioinform Adv. 2025 Mar 20;5(1):vbaf044. doi: 10.1093/bioadv/vbaf044. eCollection 2025.

ABSTRACT

SUMMARY: Modern biological research critically depends on public databases. The introduction and propagation of errors within and across databases can lead to wasted resources as scientists are led astray by bad data or have to conduct expensive validation experiments. The emergence of generative artificial intelligence systems threatens to compound this problem owing to the ease with which massive volumes of synthetic data can be generated. We provide an overview of several key issues that occur within the biological data ecosystem and make several recommendations aimed at reducing data errors and their propagation. We specifically highlight the critical importance of improved educational programs aimed at biologists and life scientists that emphasize best practices in data engineering. We also argue for increased theoretical and empirical research on data provenance, error propagation, and on understanding the impact of errors on analytic pipelines. Furthermore, we recommend enhanced funding for the stewardship and maintenance of public biological databases.

AVAILABILITY AND IMPLEMENTATION: Not applicable.

PMID:40177265 | PMC:PMC11964588 | DOI:10.1093/bioadv/vbaf044

Bioact Mater. 2025 Mar 19;49:437-455. doi: 10.1016/j.bioactmat.2025.02.008. eCollection 2025 Jul.

ABSTRACT

Peptide metabolites are emerging biomolecules with numerous possibilities in biomaterial-based regenerative medicine due to their inherent bioactivities. These small, naturally occurring compounds are intermediates or byproducts of larger proteins and peptides, and they can have profound effects, such as antiviral therapeutics, proangiogenic agents, and regenerative medicinal applications. This study is among the first to focus on using thymosin β4 protein-derived metabolites to pioneer novel applications for peptide metabolites in biomaterials. This study found that the novel peptide metabolite acetyl-thymosin β4 (amino acid 1-17) (Ac-Tβ1-17) exhibited significant protease inhibition activity against SARS-CoV-2, surpassing its precursor protein. Additionally, Ac-Tβ1-17 demonstrated beneficial effects, such as cell proliferation, wound healing, and scavenging of reactive oxygen species (ROS) in human umbilical vein endothelial cells (HUVEC). Integrating Ac-Tβ1-17 into a peptide-based scaffold facilitated cell growth and angiogenesis inside the scaffold and through gradual release into the surrounding environment. The Ac-Tβ1-17 peptide treatment induced significant biochemical responses in HUVEC, increasing Akt, ERK, PI3K, MEK, and Bcl-2 gene expression and proangiogenic proteins. Ac-Tβ1-17 peptide treatment showed similar results in ex vivo by enhancing mouse fetal metatarsal growth and angiogenesis. These findings highlight the potential of natural protein metabolites to generate biologically active peptides, offering a novel strategy for enhancing biomaterial compatibility. This approach holds promise for developing therapeutic biomaterials using peptide metabolites, presenting exciting prospects for future research and applications.

PMID:40177110 | PMC:PMC11964602 | DOI:10.1016/j.bioactmat.2025.02.008

Front Pharmacol. 2025 Mar 19;16:1580686. doi: 10.3389/fphar.2025.1580686. eCollection 2025.

NO ABSTRACT

PMID:40176899 | PMC:PMC11961961 | DOI:10.3389/fphar.2025.1580686

Front Genet. 2025 Mar 19;16:1579848. doi: 10.3389/fgene.2025.1579848. eCollection 2025.

ABSTRACT

Periodontitis, a prevalent global oral health issue, is primarily characterized by chronic inflammation resulting from bacterial infection. Periodontitis primarily affects the tissues surrounding and supporting the teeth, encompassing the gingival tissue, periodontal attachment apparatus, and the bony socket. The disease mechanism results from intricate interactions between hereditary factors, the body's defense mechanisms, and shifts in the composition of oral microbiota, with each element playing a crucial role in the initiation and advancement of the pathological process. The early symptoms of periodontitis are often not obvious, resulting in patients often not seeking medical attention until they are seriously ill, so finding biomarkers for periodontitis is essential for timely diagnosis and treatment. In this study, we selected two datasets (GSE10334 and GSE16134) by in-depth analysis of publicly available sequencing data of affected and unaffected gum tissue in periodontitis patients in the GEO database. To identify key genes associated with periodontitis pathogenesis and explore potential therapeutic biomarkers, we employed two complementary computational approaches: Random Forest, a robust machine learning algorithm for feature selection, and Weighted Gene Co-expression Network Analysis (WGCNA), a systems biology method for identifying co-expressed gene modules. Through comprehensive analysis of these combined datasets, our objective is to elucidate the underlying molecular pathways governing periodontal disease progression, thereby identifying novel therapeutic targets that may facilitate the design of improved clinical interventions for this condition. This study establishes a substantial scientific foundation that contributes to both clinical applications and fundamental research in periodontitis. The findings not only offer valuable insights for developing early diagnostic strategies and therapeutic interventions but also provide a robust theoretical framework to guide future investigations into the molecular mechanisms underlying this complex disease.

PMID:40176796 | PMC:PMC11961894 | DOI:10.3389/fgene.2025.1579848

Philos Trans R Soc Lond B Biol Sci. 2025 Apr 3;380(1923):20240021. doi: 10.1098/rstb.2024.0021. Epub 2025 Apr 3.

ABSTRACT

Animal vocalizations contain a varying degree of nonlinear phenomena (NLP) caused by irregular or chaotic vocal organ dynamics. Several hypotheses have been proposed to explain NLP presence, from unintentional by-products of poor vocal technique to having a functional communicative role. We aimed to disentangle the role of sex, age and physiological constraints in the occurrence of NLP in the songs of the lemur Indri indri, which are complex harmonic vocal displays organized in phrases. Age and sex affected the presence and type of NLP in songs. In particular, the proportion of the phenomena considered decreased with age, except for subharmonics. Subharmonics potentially mediate the perception of lower pitch, making signallers appear larger. Subharmonics and frequency jumps occurred in lower-pitched notes than regular units, while chaos and sidebands occurred in higher-pitched units. This suggests that different types of NLP can be associated with different vocal constraints. Finally, indris might present short-term vocal fatigue, with units occurring in the last position of a phrase having the highest probability of containing NLP. The presence of NLP in indris might result from proximate causes, such as physiological constraints, and ultimate causes, such as evolutionary pressures, which shaped the communicative role of NLP.This article is part of the theme issue 'Nonlinear phenomena in vertebrate vocalizations: mechanisms and communicative functions'.

PMID:40176518 | DOI:10.1098/rstb.2024.0021

Philos Trans R Soc Lond B Biol Sci. 2025 Apr 3;380(1923):20240019. doi: 10.1098/rstb.2024.0019. Epub 2025 Apr 3.

ABSTRACT

African penguins (Spheniscus demersus) extensively use high-frequency food solicitation signals (begging calls) to request food from parents. We studied the occurrence of nonlinear vocal phenomena (NLP) in begging calls in 91 hand-reared penguin chicks at the Southern African Foundation for the Conservation of Coastal Birds. For each chick, we recorded the begging calls daily, from the hatching of wild abandoned eggs to the release of the chicks into the wild approximately three months later. We found that most (70%) of begging calls contain NLP. The most frequently observed are sidebands (54.1%) and deterministic chaos (71.4%), and these phenomena often coexist (26.5%). We suggest that the aperiodic chaotic features of begging calls assist in increasing adults' attention and avoiding habituation. The occurrence of NLP also depends on the penguins' age, with older chicks producing more NLP in their calls. Moreover, we found that NLP significantly increased in chicks after contracting a respiratory disease (for example, bacterial infections or aspergillosis). Such findings might be useful for the timely diagnosis of penguins needing veterinary treatment, contributing to conservation efforts for this endangered species.This article is part of the theme issue 'Nonlinear phenomena in vertebrate vocalizations: mechanisms and communicative functions'.

PMID:40176507 | DOI:10.1098/rstb.2024.0019

Philos Trans R Soc Lond B Biol Sci. 2025 Apr 3;380(1923):20240009. doi: 10.1098/rstb.2024.0009. Epub 2025 Apr 3.

ABSTRACT

Nonlinear acoustic phenomena (NLP) likely facilitate the expression of distress in animal vocalizations, making calls perceptually rough and hard to ignore. Yet, their function in adult human vocal communication remains poorly understood. Here, to examine the production and perception of acoustic correlates of pain in spontaneous human nonverbal vocalizations, we take advantage of childbirth-a natural context in which labouring women typically produce a range of highly evocative loud vocalizations, including moans and screams-as they experience excruciating pain. We combine acoustic analyses of these real-life pain vocalizations with psychoacoustic experiments involving the playback of natural and synthetic calls to both naïve and expert listeners. We show that vocalizations become acoustically rougher, higher in fundamental frequency (pitch), less stable, louder and longer as child labour progresses, paralleling a rise in women's self-assessed pain. In perception experiments, we show that both naïve listeners and obstetric professionals assign the highest pain ratings to vocalizations produced in the final expulsion phase of labour. Experiments with synthetic vocal stimuli confirm that listeners rely largely on nonlinear phenomena to assess pain. Our study confirms that nonlinear phenomena communicate intense, pain-induced distress in humans, consistent with their widespread function to signal distress and arousal in vertebrate vocal signals.This article is part of the theme issue 'Nonlinear phenomena in vertebrate vocalizations: mechanisms and communicative functions'.

PMID:40176506 | DOI:10.1098/rstb.2024.0009

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

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