Acad Radiol. 2025 Jan 15:S1076-6332(24)01040-7. doi: 10.1016/j.acra.2024.12.052. Online ahead of print.

ABSTRACT

RATIONALE AND OBJECTIVES: Photon Counting CT (PCCT) offers advanced imaging capabilities with potential for substantial radiation dose reduction; however, achieving this without compromising image quality remains a challenge due to increased noise at lower doses. This study aims to evaluate the effectiveness of a deep learning (DL)-based denoising algorithm in maintaining diagnostic image quality in whole-body PCCT imaging at reduced radiation levels, using real intraindividual cadaveric scans.

MATERIALS AND METHODS: Twenty-four cadaveric human bodies underwent whole-body CT scans on a PCCT scanner (NAEOTOM Alpha, Siemens Healthineers) at four different dose levels (100%, 50%, 25%, and 10% mAs). Each scan was reconstructed using both ADMIRE level 2 and a DL algorithm (ClariCT.AI, ClariPi Inc.), resulting in 192 datasets. Objective image quality was assessed by measuring CT value stability, image noise, and contrast-to-noise ratio (CNR) across consistent regions of interest (ROIs) in the liver parenchyma. Two radiologists independently evaluated subjective image quality based on overall image clarity, sharpness, and contrast. Inter-rater agreement was determined using Spearman's correlation coefficient, and statistical analysis included mixed-effects modeling to assess objective and subjective image quality.

RESULTS: Objective analysis showed that the DL denoising algorithm did not significantly alter CT values (p ≥ 0.9975). Noise levels were consistently lower in denoised datasets compared to the Original (p < 0.0001). No significant differences were observed between the 25% mAs denoised and the 100% mAs original datasets in terms of noise and CNR (p ≥ 0.7870). Subjective analysis revealed strong inter-rater agreement (r ≥ 0.78), with the 50% mAs denoised datasets rated superior to the 100% mAs original datasets (p < 0.0001) and no significant differences detected between the 25% mAs denoised and 100% mAs original datasets (p ≥ 0.9436).

CONCLUSION: The DL denoising algorithm maintains image quality in PCCT imaging while enabling up to a 75% reduction in radiation dose. This approach offers a promising method for reducing radiation exposure in clinical PCCT without compromising diagnostic quality.

PMID:39818525 | DOI:10.1016/j.acra.2024.12.052

Curr Biol. 2025 Jan 10:S0960-9822(24)01701-9. doi: 10.1016/j.cub.2024.12.033. Online ahead of print.

ABSTRACT

An emerging frontier in ecology explores how organisms integrate social information into movement behavior and the extent to which information exchange occurs across species boundaries.1,2,3 Most migratory landbirds are thought to undertake nocturnal migratory flights independently, guided by endogenous programs and individual experience.4,5 Little research has addressed the potential for social information exchange aloft during nocturnal migration, but social influences that aid navigation, orientation, or survival could be valuable during high-risk migration periods.1,2,6,7,8 We captured audio of >18,000 h of nocturnal bird migration and used deep learning to extract >175,000 in-flight vocalizations of 27 species of North American landbirds.9,10,11,12 We used vocalizations to test whether migrating birds distribute non-randomly relative to other species in flight, accounting for migration phenology, geography, and other non-social factors. We found that migrants engaged in distinct associations with an average of 2.7 ± 1.9 SD other species. Social associations were stronger among species with similar wing morphologies and vocalizations. These results suggest that vocal signals maintain in-flight associations that are structured by flight speed and behavior.11,13,14 For small-bodied and short-lived bird species, transient social associations could play an important role in migratory decision-making by supplementing endogenous or experiential information sources.15,16,17 This research provides the first quantitative evidence of interspecific social associations during nocturnal bird migration, supporting recent calls to rethink songbird migration with a social lens.2 Substantial recent declines in bird populations18,19 may diminish the frequency and strength of social associations during migration, with currently unknown consequences for populations.

PMID:39818216 | DOI:10.1016/j.cub.2024.12.033

Dev Cell. 2025 Jan 13:S1534-5807(24)00777-9. doi: 10.1016/j.devcel.2024.12.038. Online ahead of print.

ABSTRACT

The 5' UTRs of mRNAs are critical for translation regulation during development, but their in vivo regulatory features are poorly characterized. Here, we report the regulatory landscape of 5' UTRs during early zebrafish embryogenesis using a massively parallel reporter assay of 18,154 sequences coupled to polysome profiling. We found that the 5' UTR suffices to confer temporal dynamics to translation initiation and identified 86 motifs enriched in 5' UTRs with distinct ribosome recruitment capabilities. A quantitative deep learning model, Danio Optimus 5-Prime (DaniO5P), identified a combined role for 5' UTR length, translation initiation site context, upstream AUGs, and sequence motifs on ribosome recruitment. DaniO5P predicts the activities of maternal and zygotic 5' UTR isoforms and indicates that modulating 5' UTR length and motif grammar contributes to translation initiation dynamics. This study provides a first quantitative model of 5' UTR-based translation regulation in development and lays the foundation for identifying the underlying molecular effectors.

PMID:39818206 | DOI:10.1016/j.devcel.2024.12.038

J Hazard Mater. 2024 Dec 31;487:136989. doi: 10.1016/j.jhazmat.2024.136989. Online ahead of print.

ABSTRACT

Microplastics are ubiquitous and appear to be harmful, however, the full extent to which these inflict harm has not been fully elucidated. Analysing environmental sample data is challenging, as the complexity in real data makes both automated and manual analysis either unreliable or time-consuming. To address challenges, we explored a dense feed-forward neural network (DNN) for classifying Fourier transform infrared (FTIR) spectroscopic data. The DNN provides conditional class distributions over 16 microplastic categories given an FTIR spectrum, exceeding number of categories in other works. Our results indicate that this DNN, which is significantly smaller than contemporary models, outperforms other models and even human classification performance. Specifically, while the model broadly reproduces the decisions of human annotators, in cases of disagreement either both were incorrect or the human annotation was incorrect. The errors not being reproduced indicate that the DNN is making informed generalisable decisions. Additionally, this work indicates that there exists an upper limit on metrics measuring performance, where metrics measure agreement between human and model predictions. This work indicates that a small and efficient DNN can making high throughput analysis of difficult FTIR data possible, where predictions match or exceed the reliability typical to low-throughput methods.

PMID:39818049 | DOI:10.1016/j.jhazmat.2024.136989

Med Image Anal. 2025 Jan 4;101:103453. doi: 10.1016/j.media.2025.103453. Online ahead of print.

ABSTRACT

This study introduces HCC-Net, a novel wavelet-based approach for the accurate diagnosis of hepatocellular carcinoma (HCC) from abdominal ultrasound (US) images using artificial neural networks. The HCC-Net integrates the discrete wavelet transform (DWT) to decompose US images into four sub-band images, a lesion detector for hierarchical lesion localization, and a pattern-augmented classifier for generating pattern-enhanced lesion images and subsequent classification. The lesion detection uses a hierarchical coarse-to-fine approach to minimize missed lesions. CoarseNet performs initial lesion localization, while FineNet identifies any lesions that were missed. In the classification phase, the wavelet components of detected lesions are synthesized to create pattern-augmented images that enhance feature distinction, resulting in highly accurate classifications. These augmented images are classified into 'Normal,' 'Benign,' or 'Malignant' categories according to their morphologic features on sonography. The experimental results demonstrate the significant effectiveness of the proposed coarse-to-fine detection framework and pattern-augmented classifier in lesion detection and classification. We achieved an accuracy of 96.2 %, a sensitivity of 97.6 %, and a specificity of 98.1 % on the Samsung Medical Center dataset, indicating HCC-Net's potential as a reliable tool for liver cancer screening.

PMID:39818008 | DOI:10.1016/j.media.2025.103453

Neural Netw. 2025 Jan 10;185:107128. doi: 10.1016/j.neunet.2025.107128. Online ahead of print.

ABSTRACT

Artificial neural networks (ANNs) can help camera-based remote photoplethysmography (rPPG) in measuring cardiac activity and physiological signals from facial videos, such as pulse wave, heart rate and respiration rate with better accuracy. However, most existing ANN-based methods require substantial computing resources, which poses challenges for effective deployment on mobile devices. Spiking neural networks (SNNs), on the other hand, hold immense potential for energy-efficient deep learning owing to their binary and event-driven architecture. To the best of our knowledge, we are the first to introduce SNNs into the realm of rPPG, proposing a hybrid neural network (HNN) model, the Spiking-PhysFormer, aimed at reducing power consumption. Specifically, the proposed Spiking-PhyFormer consists of an ANN-based patch embedding block, SNN-based transformer blocks, and an ANN-based predictor head. First, to simplify the transformer block while preserving its capacity to aggregate local and global spatio-temporal features, we design a parallel spike transformer block to replace sequential sub-blocks. Additionally, we propose a simplified spiking self-attention mechanism that omits the value parameter without compromising the model's performance. Experiments conducted on four datasets-PURE, UBFC-rPPG, UBFC-Phys, and MMPD demonstrate that the proposed model achieves a 10.1% reduction in power consumption compared to PhysFormer. Additionally, the power consumption of the transformer block is reduced by a factor of 12.2, while maintaining decent performance as PhysFormer and other ANN-based models.

PMID:39817982 | DOI:10.1016/j.neunet.2025.107128

Int J Med Inform. 2025 Jan 14;195:105792. doi: 10.1016/j.ijmedinf.2025.105792. Online ahead of print.

ABSTRACT

BACKGROUND: The wireless capsule endoscope (CE) is a valuable diagnostic tool in gastroenterology, offering a safe and minimally invasive visualization of the gastrointestinal tract. One of the few drawbacks identified by the gastroenterology community is the time-consuming task of analyzing CE videos.

OBJECTIVES: This article investigates the feasibility of a computer-aided diagnostic method to speed up CE video analysis. We aim to generate a significantly smaller CE video with all the anomalies (i.e., diseases) identified by the medical doctors in the original video.

METHODS: The summarized video consists of the original video frames classified as anomalous by a pre-trained convolutional neural network (CNN). We evaluate our approach on a testing dataset with eight CE videos captured with five CE types and displaying multiple anomalies.

RESULTS: On average, the summarized videos contain 93.33% of the anomalies identified in the original videos. The average playback time of the summarized videos is just 10 min, compared to 58 min for the original videos.

CONCLUSION: Our findings demonstrate the potential of deep learning-aided diagnostic methods to accelerate CE video analysis.

PMID:39817978 | DOI:10.1016/j.ijmedinf.2025.105792

Int J Biol Macromol. 2025 Jan 14:139913. doi: 10.1016/j.ijbiomac.2025.139913. Online ahead of print.

ABSTRACT

The macromolecular components of the seed coat, particularly lignin, play a critical role in regulating seed viability. In the maize-soybean intercropping (MSI) system, shading stress was reported to enhance the viability of soybean seeds. However, the specific role of seed coat lignin in this process remains poorly understood. In this study, we demonstrated that soybean seed coats derived from the MSI system exhibit significantly higher lignin content and mechanical resistance compared to those from the sole cropping systems. Further investigations with artificial shading treatments revealed a substantial impact on the accumulation of phenylpropanoids in soybean seeds. Notably, shading applied during the reproductive stage resulted in decreased levels of anthocyanins, proanthocyanidins, and isoflavones, while simultaneously increasing lignin content. Moreover, both the mechanical resistance of the seed coats and the seeds' longevity under deteriorative conditions improved significantly compared to the normal light control. Gene expression and metabolomics analyses indicated that shading stress promotes the expression of key genes involved in lignin biosynthesis within the soybean seed coats, increasing the amount of several intermediate metabolites. Taken together, these findings reveal that shading stress in the MSI system promotes the biosynthesis and accumulation of lignin in soybean seed coats and thereby regulating seed longevity.

PMID:39818396 | DOI:10.1016/j.ijbiomac.2025.139913

Int J Biol Macromol. 2025 Jan 14:139900. doi: 10.1016/j.ijbiomac.2025.139900. Online ahead of print.

ABSTRACT

A new gene coding for an iron-containing enzyme was identified in the genome of Acinetobacter radioresistens. Bioinformatics analysis allowed the assignment of the protein to DyP peroxidases, due to the presence of conserved residues involved in heme binding and catalysis. Moreover, Ar-DyP is located in an operon coding also for other enzymes involved in iron uptake and regulation. The crystal structure of Ar-DyP determined at 1.85 Å resolution shows that the heme pocket Ar-DyP is "wet" forming a continuous hydrogen-bond network that enables the communication between heme and distal residues. Moreover, as shown by the crystal structure and covalent crosslinking experiments, Ar-DyP uses a long-range electron transfer pathway involving His-181 and Tyr-241, in the active site and on the surface of the enzyme, respectively. This pathway allows oxidation of substrates of different sizes, including Kraft lignin. Indeed, the biochemical characterization showed that Ar-Dyp oxidizes ABTS and Reactive Blue 19 (turnover numbers of 500 and 464 min-1, respectively), but also phenolic compounds such as guaiacol and pyrogallol (turnover numbers of 7.4 and 1.8 min-1 respectively). Overall, the data shows that Ar-DyP is a promising candidate for applications in lignin valorization, bioremediation and industrial processes involving the breakdown of phenolic compounds.

PMID:39818373 | DOI:10.1016/j.ijbiomac.2025.139900

Animal. 2024 Dec 19;19(2):101404. doi: 10.1016/j.animal.2024.101404. Online ahead of print.

ABSTRACT

Domestic pigs (Sus scrofa) raised under natural conditions can show their complete behavioural repertoire. However, rooting behaviour can have a great impact on the environment. In the context of the promotion of farm animal welfare and environmental concerns, this study investigated the potential of nose-clips as a less invasive alternative to nose-rings for the management of rooting behaviour of free-ranging pigs. We collected behavioural data and salivary cortisol levels on two groups: an experimental group (n = 17) with nose-clips and a control group (n = 17) without nose-clips. After the nose-clipping, we observed a temporary increase in anxiety-related behaviour and cortisol levels during the 1st week, followed by a return to pre-application levels in the following weeks. We found a temporary decrease in affiliative interactions involving the nose during the 1st week after the application of nose-clips, whereas no differences in affiliative interactions without nose contact and aggression levels were observed. Moreover, nose-clips effectively reduced destructive excavation behaviours, without leading to a simultaneous increase in other exploratory behaviours. In conclusion, our findings show that nose-clips could be a solution that mitigates destructive rooting while preserving social interactions and animal welfare. Further research is essential to consolidate these findings and assess the long-term implications of this approach.

PMID:39818027 | DOI:10.1016/j.animal.2024.101404

J Affect Disord. 2025 Jan 14:S0165-0327(25)00067-9. doi: 10.1016/j.jad.2025.01.054. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: To determine whether there is disproportionate reporting of hepatobiliary disorders in the United States (US) FDA Adverse Event Reporting System (FAERS) for individuals prescribed ketamine or esketamine.

DESIGN: We identified Medical Dictionary for Regulatory Activities (MedDRA) terms in the FAERS related to hepatobiliary disorders.

MAIN MEASURES: Formulations of ketamine and esketamine were evaluated for the proportionality of reporting for each hepatobiliary disorder parameter using the reporting odds ratio (ROR). We also estimated the lower limits of 95 % confidence intervals of information components (IC025) to determine whether the association was significant. Acetaminophen was used as the positive reference agent and lithium as the neutral reference agent.

KEY RESULTS: We observed disproportionately lower reporting of hepatitis, liver disorder, liver injury, drug-induced liver injury, hepatic failure, and acute hepatic failure for ketamine compared to acetaminophen. Additionally, we observed disproportionately higher reporting of hepatic function abnormalities and hepatic cytolysis for ketamine compared to acetaminophen. For esketamine, we did not find disproportionate reporting of any hepatobiliary toxicity relative to acetaminophen. However, for ketamine, there was disproportionate lower reporting of hepatic function abnormalities, liver disorder and hepatic cirrhosis. In contrast, for esketamine, there was disproportionately higher reporting of hepatic failure.

CONCLUSIONS: Although causality has not been established, the data support recommendations for periodic monitoring of liver function tests, as well as clinical surveillance for stigmata of hepatobiliary disease in individuals receiving chronic exposure to ketamine and esketamine.

PMID:39818335 | DOI:10.1016/j.jad.2025.01.054

Biometrics. 2025 Jan 7;81(1):ujae169. doi: 10.1093/biomtc/ujae169.

ABSTRACT

Pharmacogenomics stands as a pivotal driver toward personalized medicine, aiming to optimize drug efficacy while minimizing adverse effects by uncovering the impact of genetic variations on inter-individual outcome variability. Despite its promise, the intricate landscape of drug metabolism introduces complexity, where the correlation between drug response and genes can be shaped by numerous nongenetic factors, often exhibiting heterogeneity across diverse subpopulations. This challenge is particularly pronounced in datasets such as the International Warfarin Pharmacogenetic Consortium (IWPC), which encompasses diverse patient information from multiple nations. To capture the between-patient heterogeneity in dosing requirement, we formulate a novel change surface model as a model-based approach for multiple subgroup identification in complex datasets. A key feature of our approach is its ability to accommodate nonlinear subgroup divisions, providing a clearer understanding of dynamic drug-gene associations. Furthermore, our model effectively handles high-dimensional data through a doubly penalized approach, ensuring both interpretability and adaptability. We propose an iterative 2-stage method that combines a change point detection technique in the first stage with a smoothed local adaptive majorize-minimization algorithm for surface regression in the second stage. Performance of the proposed methods is evaluated through extensive numerical studies. Application of our method to the IWPC dataset leads to significant new findings, where 3 subgroups subject to different pharmacogenomic relationships are identified, contributing valuable insights into the complex dynamics of drug-gene associations in patients.

PMID:39817854 | DOI:10.1093/biomtc/ujae169

J Pediatr Endocrinol Metab. 2025 Jan 17. doi: 10.1515/jpem-2024-0566. Online ahead of print.

ABSTRACT

OBJECTIVES: There is limited research on thyroid function in pediatric patients with cystic fibrosis (pwCF). This study aimed to determine the frequency of thyroid dysfunction in children and adolescents with CF and to evaluate iodine deficiency and selenium status in pwCF.

METHODS: Sixty-two CF patients and 62 control subjects were evaluated. The anthropometric measurements, nutritional status, FEV1(Forced-expiratory-volume in 1 s) percentage, thyroid function tests (TSH, FT4, FT3), urinary iodine and selenium levels, hospitalization status in the last six months, antibiotic usage, and colonization status with staphylococcus or pseudomonas were assessed for the cases.

RESULTS: The mean age of the patient group was 10.84 ± 4.04 years. All CF patients were receiving multivitamin supplementation. Malnutrition was present in 50 % of patients, bacterial colonization in 29 %, FEV1 decrease in 38.5 %, subclinical hypothyroidism (SH) in 12.9 %, iodine deficiency in 87 % and exocrine pancreatic insufficiency in 100 %. T3 levels were found to be higher in pwCF. No significant difference was found between malnutrition and FEV1 and urinary iodine and selenium levels. Compared to the control group, pwCF had lower urinary iodine levels.

CONCLUSIONS: To the best of our knowledge, our study is one of the few in the literature to investigate urinary selenium levels alongside iodine in PwCF. Further research is needed to clarify and interpret elevated urinary selenium levels in this context. It was shown that iodine deficiency and the rate of SH were relatively high in pwCF. However, it was still thought that correcting iodine deficiency in these patients could improve thyroid dysfunction associated with CF.

PMID:39817663 | DOI:10.1515/jpem-2024-0566

Scand J Med Sci Sports. 2025 Jan;35(1):e70018. doi: 10.1111/sms.70018.

ABSTRACT

Previous studies in sports science suggested that regular exercise has a positive impact on human health. However, the effects of endurance sports and their underlying mechanisms are still not completely understood. One of the main debates regards the modulation of immune dynamics in high-intensity exercise. As part of the "Run 4 Science" project in Verona, Italy, we conducted a single-cell RNA sequencing analysis on half-marathon amateur runners to investigate the transcriptional dynamics of peripheral blood mononuclear cells following endurance exercise. Blood samples were collected from four participants before and after running a half-marathon to carry out a comprehensive transcriptomic analysis of immune cells at the single-cell level. Our analysis revealed significant alterations in the transcriptional profiles following endurance physical exercise. Modulations in myeloid cells suggested the activation of stress response (6 related pathways, p < 0.04) and pathways related to viral processes (4 related pathways, p < 0.03), while in lymphoid cells they hinted to a shift towards immune activation (24 related pathways, p < 0.01). Additionally, transcriptional changes in platelets point to an activation of the coagulation process (5 related pathways, p < 0.005). Single-cell data was also analyzed following a pseudo-bulk approach (i.e., simulating a bulk RNAseq experiment) to gain further biological insights. Our findings suggest that a pseudo-bulk analysis could offer complementary findings to classical single-cell analysis methods and demonstrate that endurance physical exercise, such as running a half-marathon, induces substantial changes in the transcriptional dynamics of immune cells. These insights contribute to a better understanding of the immune modulation mediated by endurance exercise and may inform future training routines or nutritional guidelines based on individual gene expression levels.

PMID:39817606 | DOI:10.1111/sms.70018

Anal Methods. 2025 Jan 16. doi: 10.1039/d4ay01759e. Online ahead of print.

ABSTRACT

Plastic waste management is one of the key issues in global environmental protection. Integrating spectroscopy acquisition devices with deep learning algorithms has emerged as an effective method for rapid plastic classification. However, the challenges in collecting plastic samples and spectroscopy data have resulted in a limited number of data samples and an incomplete comparison of relevant classification algorithms. To address this issue, we propose a plastic spectroscopy generation model and conduct a systematic analysis and comparison of different algorithms' performance from multiple perspectives, based on data augmentation. This paper first performs cubic interpolation, normalization, S-G filtering, linear detrending, and standard normal variate (SNV) transformations as preprocessing methods on plastic spectral data collected from public datasets using techniques such as Fourier Transform Infrared Spectroscopy (FTIR), Raman Spectroscopy (RAMAN), and Laser Induced Breakdown Spectroscopy (LIBS). The results, based on Principal Component Analysis (PCA) visualization, demonstrate that the preprocessing steps help improve classification accuracy. Additionally, PCA loading is used to explain the chemical classification features of each spectral device. Secondly, to tackle the issue of insufficient sample size, we propose a plastic spectroscopy generation model based on C-GAN, which effectively handles multi-class spectroscopy generation. The generated spectra are subjectively validated through difference spectroscopy and t-SNE to confirm their consistency with real spectra, and this conclusion is objectively validated using Maximum Mean Discrepancy (MMD). Finally, we compared the classification accuracy of machine learning algorithms, including Support Vector Machine (SVM), Back Propagation Neural Network (BP), K-Nearest Neighbors (KNN), Random Forest (RF), and Decision Tree (DT), with deep learning algorithms such as GoogleNet and ResNet under various conditions. The results indicate that after data augmentation using the plastic spectrum generation model, the accuracy of each classification model improved by at least 3% compared to pre-augmentation levels. Notably, for data collected via FTIR, the classification accuracy reached a peak of 0.991 under the 1D-ResNet model when the data were augmented twofold. For small sample datasets, traditional machine learning algorithms, such as SVM and RF, demonstrated high stability and accuracy, with only minimal differences compared to deep learning algorithms. However, on large sample datasets, deep learning algorithms showed a stronger advantage. Regarding data input formats, 1D input models generally outperformed 2D input models. Grad-CAM visualizations further illustrated that the 1D-ResNet model achieved the highest classification accuracy, primarily due to its ability to more accurately identify peak features in the data.

PMID:39817628 | DOI:10.1039/d4ay01759e

Elife. 2025 Jan 16;13:RP100545. doi: 10.7554/eLife.100545.

ABSTRACT

Viral adhesion to host cells is a critical step in infection for many viruses, including monkeypox virus (MPXV). In MPXV, the H3 protein mediates viral adhesion through its interaction with heparan sulfate (HS), yet the structural details of this interaction have remained elusive. Using AI-based structural prediction tools and molecular dynamics (MD) simulations, we identified a novel, positively charged α-helical domain in H3 that is essential for HS binding. This conserved domain, found across orthopoxviruses, was experimentally validated and shown to be critical for viral adhesion, making it an ideal target for antiviral drug development. Targeting this domain, we designed a protein inhibitor, which disrupted the H3-HS interaction, inhibited viral infection in vitro and viral replication in vivo, offering a promising antiviral candidate. Our findings reveal a novel therapeutic target of MPXV, demonstrating the potential of combination of AI-driven methods and MD simulations to accelerate antiviral drug discovery.

PMID:39817728 | DOI:10.7554/eLife.100545

Front Parasitol. 2024 Dec 10;3:1508167. doi: 10.3389/fpara.2024.1508167. eCollection 2024.

ABSTRACT

Nematode parasitic infections continue to be a major health problem for humans and animals. Drug resistance to currently available treatments only worsen the problem. Drug discovery is expensive and time-consuming, making drug repurposing an enticing option. Emodepside, a broad-spectrum anthelmintic, has shown efficacy in the treatment of nematode parasitic infections in cats and dogs. It is now being considered and trialed for the treatment of onchocerciasis, trichuriasis (whipworm), and hookworm infections in humans. Its unique mechanism of action distinguishes it from traditional anthelmintics, positioning it as a promising candidate for combating resistance to other current drugs. Here, we provide a brief review of the available information on emodepside's pharmacokinetics, safety, and tolerability. We highlight the potential benefits and risks associated with its use, examining key toxicity effects. By exploring the literature, we aim to provide insights into the risks associated with emodepside that may impact its application in veterinary and human medicine. Although emodepside demonstrates a favorable safety profile, continued monitoring of its toxicity is crucial, particularly in vulnerable populations. This mini-review serves as a concise resource for researchers and clinicians interested in anthelmintic therapy.

PMID:39817180 | PMC:PMC11732007 | DOI:10.3389/fpara.2024.1508167

Narra J. 2024 Dec;4(3):e968. doi: 10.52225/narra.v4i3.968. Epub 2024 Oct 7.

ABSTRACT

The most common type of liver cancer is hepatocellular carcinoma (HCC), accounting for 75-85% of cases. Despite its associated side effects, sorafenib remains the standard treatment for HCC. Given the critical need to improve therapeutic efficacy while minimizing adverse effects, alternative drugs must be thoroughly investigated. Numerous studies indicate that combining sorafenib with metformin results in a more favorable treatment profile. The aim of this study was to employ bioinformatics methodologies to elucidate the molecular pathways and genetic underpinnings of metformin's efficacy in HCC treatment. Genes associated with metformin and its action against HCC (Huh-7 and HepG2 cells) were acquired from the NCBI-GEO data collection by utilizing pre-determined keywords. Subsequently, pathways implicated in metformin-mediated HCC treatment were analyzed through the Kyoto Encyclopedia of Genes and Genomes (KEGG). Our analysis revealed the involvement of multiple pathways, with metabolic pathways implicated in 80% of the total cases. Neurodegenerative pathways were involved in only around 60% of the total cases. These findings align with the multifaceted mechanisms of metformin's action, encompassing adenosine monophosphate-activated protein kinase activation, apoptosis induction, insulin regulation, anti-inflammatory responses, and modulation of cell proliferation. This comprehensive investigation sheds light on the intricate molecular landscape underpinning metformin's therapeutic efficacy in HCC, thereby informing potential avenues for optimizing treatment strategies.

PMID:39816125 | PMC:PMC11731935 | DOI:10.52225/narra.v4i3.968

Narra J. 2024 Dec;4(3):e938. doi: 10.52225/narra.v4i3.938. Epub 2024 Dec 12.

ABSTRACT

Cervical cancer is the fourth most common cancer among women globally, and studies have shown that genetic variants play a significant role in its development. A variety of germline and somatic mutations are associated with cervical cancer. However, genomic data derived from these mutations have not been extensively utilized for the development of repurposed drugs for cervical cancer. The objective of this study was to identify novel potential drugs that could be repurposed for cervical cancer treatment through a bioinformatics approach. A comprehensive genomic and bioinformatics database integration strategy was employed to identify potential drug target genes for cervical cancer. Using the GWAS and PheWAS databases, a total of 232 genes associated with cervical cancer were identified. These pharmacological target genes were further refined by applying a biological threshold of six functional annotations. The drug target genes were then cross-referenced with cancer treatment candidates using the DrugBank database. Among the identified genes, LTA, TNFRSF1A, PRKCZ, PDE4B, and PARP were highlighted as promising targets for repurposed drugs. Notably, these five target genes overlapped with 12 drugs that could potentially be repurposed for cervical cancer treatment. Among these, talazoparib, a potent PARP inhibitor, emerged as a particularly promising candidate. Interestingly, talazoparib is currently being investigated for safety and tolerability in other cancers but has not yet been studied in the context of cervical cancer. Further clinical trials are necessary to validate this finding and explore its potential as a repurposed drug for cervical cancer.

PMID:39816079 | PMC:PMC11731801 | DOI:10.52225/narra.v4i3.938