Open Heart. 2025 Feb 11;12(1):e003088. doi: 10.1136/openhrt-2024-003088.

ABSTRACT

AIMS: Clopidogrel is the most commonly prescribed thienopyridine as part of dual anti-platelet therapy for the treatment of cardiovascular diseases. However, clopidogrel responsiveness shows variability based on CYP2C19 polymorphism. Therefore, we planned a study with an objective of evaluating safety, tolerability, pharmacodynamics and pharmacokinetics of a novel thienopyridine antiplatelet agent AT-10 in healthy Indian subjects compared with standard dosage regimen of clopidogrel based on their CYP2C19 genotyping.

METHODS: Two CYP2C19 genotype-based groups were identified, that is, poor metabolisers and extensive metabolisers, with 20 subjects in each group (n=40) for participating in a randomised, two-period, crossover study. Each study period lasted 6 days including administration of loading and maintenance doses of AT-10 (40 mg/10 mg) or clopidogrel (300 mg/75 mg). The pharmacokinetics and pharmacodynamics were assessed on day 1 and day 6 at several time intervals.

RESULTS: Overall result of pharmacodynamic parameters showed that mean %inhibition of platelet aggregation between AT-10 and clopidogrel in all subjects at 6 hours postdose (loading dose) (AT-10: clopidogrel; 73.30% vs 18.53%) and 6 hours postdose on day 6 (maintenance dose) (AT-10: clopidogrel; 83.41% vs 51.19 %) obtained from the AT-10 group was significantly higher than the clopidogrel group. Further, %inhibition of platelet aggregation from AT-10 treatment in poor metaboliser group was significantly higher than the clopidogrel treatments in extensive metaboliser group.Overall pharmacokinetic comparison in all subjects indicates that AT-10 gives greater exposure to active Metabolite H4 than clopidogrel.

CONCLUSION: AT-10 showed better inhibition of platelet aggregation in poor metabolizers as compared to Clopidogrel. AT-10 may emerge as a potential alternative to Clopidogrel as an anti-platelet drug. It can be further developed in clinical studies for the unmet medical needs in management of CVDs and overcome the pharmacogenomic limitations of Clopidogrel.

TRIAL REGISTRATION NUMBER: Clinical Trial Registry-India URL: http://ctri.nic.in.

REGISTRATION NUMBER: CTRI/2021/03/032206.

PMID:39933830 | DOI:10.1136/openhrt-2024-003088

Value Health Reg Issues. 2025 Feb 10;47:101085. doi: 10.1016/j.vhri.2025.101085. Online ahead of print.

ABSTRACT

OBJECTIVES: This study aimed to provide the first evidence of the socioeconomic burden of cystic fibrosis (CF) in Czechia.

METHODS: In a cross-sectional questionnaire-based primary data collection conducted from 2020 to 2021 among Czech patients with CF, we collected demographic, clinical, and healthcare resource use data, out-of-pocket and social transfer costs, and questionnaires: Cystic Fibrosis Questionnaire-Revised, Work Productivity and Activity Impairment, EQ-5D, and Zarit Burden Interview. Productivity loss/costs were assessed using the human capital approach with patient patient-assumed life expectancy of 45 years and caregiver retirement age of 64 years and discounted by 3%.

RESULTS: A total of 257 patients completed the questionnaires (37% of the Czech CF population). The average age was 17 years; most were females (59%), and the average forced expiratory volume in 1 second was 81.4% (SD 25.4%). A total of 107 patients had caregivers with an average age of 39 years and a significant caregiver time burden (extra 4.6 hours/day). The average Zarit Burden Interview score (25.4) was comparable with advanced cancer, dementia, or Duchenne muscular dystrophy. The proportion of unemployed caregivers was 10× higher than the general population (31% vs 3.2%). Total out-of-pocket family costs related to CF were €278/month, mainly for medicines (€105), foods (€73), and transport (€59); 25% received a disability pension and 18% other social security benefits. The work impairment of employed patients and caregivers was 25% and 15%, respectively, mostly due to presenteeism. Total lifetime productivity costs extrapolated to all Czech patients with CF (n = 687) and their caregivers were €155 181 286 (€225 883/person).

CONCLUSIONS: The societal burden imposed on Czech patients with CF and their caregivers is significant. Caregivers seem to be affected by higher disease activity more than patients.

PMID:39933437 | DOI:10.1016/j.vhri.2025.101085

Plant Methods. 2025 Feb 11;21(1):16. doi: 10.1186/s13007-025-01334-3.

ABSTRACT

In the era of machine learning-driven plant imaging, the production of annotated datasets is a very important contribution. In this data paper, a unique annotated dataset of seedling emergence kinetics is proposed. It is composed of almost 70,000 RGB-depth frames and more than 700,000 plant annotations. The dataset is shown valuable for training deep learning models and performing high-throughput phenotyping by imaging. The ability of such models to generalize to several species and outperform the state-of-the-art owing to the delivered dataset is demonstrated. We also discuss how this dataset raises new questions in plant phenotyping.

PMID:39934882 | DOI:10.1186/s13007-025-01334-3

BMC Med Inform Decis Mak. 2025 Feb 11;25(1):73. doi: 10.1186/s12911-025-02895-y.

ABSTRACT

BACKGROUND: Despite recent progress in misinformation detection methods, further investigation is required to develop more robust fact-checking models with particular consideration for the unique challenges of health information sharing. This study aimed to identify the most effective approach for detecting and classifying reliable information versus misinformation health content shared on Twitter/X related to COVID-19.

METHODS: We have used 7 different machine learning/deep learning models. Tweets were collected, processed, labeled, and analyzed using relevant keywords and hashtags, then classified into two distinct datasets: "Trustworthy information" versus "Misinformation", through a labeling process. The cosine similarity metric was employed to address oversampling the minority of the Trustworthy information class, ensuring a more balanced representation of both classes for training and testing purposes. Finally, the performance of the various fact-checking models was analyzed and compared using accuracy, precision, recall, and F1-score ROC curve, and AUC.

RESULTS: For measures of accuracy, precision, F1 score, and recall, the average values of TextConvoNet were found to be 90.28, 90.28, 90.29, and 0.9030, respectively. ROC AUC was 0.901."Trustworthy information" class achieved an accuracy of 85%, precision of 93%, recall of 86%, and F1 score of 89%. These values were higher than other models. Moreover, its performance in the misinformation category was even more impressive, with an accuracy of 94%, precision of 88%, recall of 94%, and F1 score of 91%.

CONCLUSION: This study showed that TextConvoNet was the most effective in detecting and classifying trustworthy information V.S misinformation related to health issues that have been shared on Twitter/X.

PMID:39934858 | DOI:10.1186/s12911-025-02895-y

BMC Med Inform Decis Mak. 2025 Feb 11;25(1):71. doi: 10.1186/s12911-024-02828-1.

ABSTRACT

This study proposes a deep learning-based motion assessment method that integrates the pose estimation algorithm (Keypoint RCNN) with signal processing techniques, demonstrating its reliability and effectiveness.The reliability and validity of this method were also verified.Twenty college students were recruited to pedal a stationary bike. Inertial sensors and a smartphone simultaneously recorded the participants' cycling movement. Keypoint RCNN(KR) algorithm was used to acquire 2D coordinates of the participants' skeletal keypoints from the recorded movement video. Spearman's rank correlation analysis, intraclass correlation coefficient (ICC), error analysis, and t-test were conducted to compare the consistency of data obtained from the two movement capture systems, including the peak frequency of acceleration, transition time point between movement statuses, and the complexity index average (CIA) of the movement status based on multiscale entropy analysis.The KR algorithm showed excellent consistency (ICC1,3=0.988) between the two methods when estimating the peak acceleration frequency. Both peak acceleration frequencies and CIA metrics estimated by the two methods displayed a strong correlation (r > 0.70) and good agreement (ICC2,1>0.750). Additionally, error values were relatively low (MAE = 0.001 and 0.040, MRE = 0.00% and 7.67%). Results of t-tests showed significant differences (p = 0.003 and 0.030) for various acceleration CIAs, indicating our method could distinguish different movement statuses.The KR algorithm also demonstrated excellent intra-session reliability (ICC = 0.988). Acceleration frequency analysis metrics derived from the KR method can accurately identify transitions among movement statuses. Leveraging the KR algorithm and signal processing techniques, the proposed method is designed for individualized motor function evaluation in home or community-based settings.

PMID:39934805 | DOI:10.1186/s12911-024-02828-1

BMC Bioinformatics. 2025 Feb 11;26(1):50. doi: 10.1186/s12859-025-06068-6.

ABSTRACT

BACKGROUND: Piwi-interacting RNAs (piRNAs) are well established for monitoring and protecting the genome from transposons in germline cells. Recently, numerous studies provided evidence that piRNAs also play important roles in regulating mRNA transcript levels. Despite their significant role in regulating cellular RNA levels, the piRNA targeting rules are not well defined, especially in mammals, which poses obstacles to the elucidation of piRNA function.

RESULTS: Given the complexity and current limitation in understanding the mammalian piRNA targeting rules, we designed a deep learning model by selecting appropriate deep learning sub-networks based on the targeting patterns of piRNA inferred from previous experiments. Additionally, to alleviate the problem of insufficient data, a transfer learning approach was employed. Our model achieves a good discriminatory power (Accuracy: 98.5%) in predicting an independent test dataset. Finally, this model was utilized to predict the targets of all mouse and human piRNAs available in the piRNA database.

CONCLUSIONS: In this research, we developed a deep learning framework that significantly advances the prediction of piRNA targets, overcoming the limitations posed by insufficient data and current incomplete targeting rules. The piRNA target prediction network and results can be downloaded from https://github.com/SofiaTianjiaoZhang/piRNATarget .

PMID:39934678 | DOI:10.1186/s12859-025-06068-6

BMC Bioinformatics. 2025 Feb 11;26(1):48. doi: 10.1186/s12859-025-06051-1.

ABSTRACT

BACKGROUND: Alterations of metabolism, including changes in mitochondrial metabolism as well as glutathione (GSH) metabolism are a well appreciated hallmark of many cancers. Mitochondrial GSH (mGSH) transport is a poorly characterized aspect of GSH metabolism, which we investigate in the context of cancer. Existing functional annotation approaches from machine (ML) or deep learning (DL) models based only on protein sequences, were unable to annotate functions in biological contexts.

RESULTS: We develop a flexible ML framework for functional annotation from diverse feature data. This hybrid ML framework leverages cancer cell line multi-omics data and other biological knowledge data as features, to uncover potential genes involved in mGSH metabolism and membrane transport in cancers. This framework achieves strong performance across functional annotation tasks and several cell line and primary tumor cancer samples. For our application, classification models predict the known mGSH transporter SLC25A39 but not SLC25A40 as being highly probably related to mGSH metabolism in cancers. SLC25A10, SLC25A50, and orphan SLC25A24, SLC25A43 are predicted to be associated with mGSH metabolism in multiple biological contexts and structural analysis of these proteins reveal similarities in potential substrate binding regions to the binding residues of SLC25A39.

CONCLUSION: These findings have implications for a better understanding of cancer cell metabolism and novel therapeutic targets with respect to GSH metabolism through potential novel functional annotations of genes. The hybrid ML framework proposed here can be applied to other biological function classifications or multi-omics datasets to generate hypotheses in various biological contexts. Code and a tutorial for generating models and predictions in this framework are available at: https://github.com/lkenn012/mGSH_cancerClassifiers .

PMID:39934670 | DOI:10.1186/s12859-025-06051-1

Sci Rep. 2025 Feb 11;15(1):5080. doi: 10.1038/s41598-025-89752-0.

ABSTRACT

The detection and classification of arrhythmia play a vital role in the diagnosis and management of cardiac disorders. Many deep learning techniques are utilized for arrhythmia classification in current research but only based on ECG data, lacking the mathematical foundations of cardiac electrophysiology. A finite element model (FEM) of the human heart based on the FitzHugh-Nagumo (FHN) model was established for cardiac electrophysiology simulation and the ECG signals were acquired from the FEM results of representative points. Two different kinds of arrhythmia characterized by major anomalies of parameters a and ɛ in the FHN model were simulated, and the synthetic ECG signals were obtained respectively. A multi-objective optimization method based on non-dominated sorting was incorporated into the crayfish optimization algorithm to optimize the key parameters in VMD, then a variational mode decomposition technique for ECG signal processing based on a multi-objective crayfish optimization algorithm (MOCOA-VMD) was proposed, wherein the spectral kurtosis and KL divergence were determined as the indicators for decomposition. The Pareto optimal front was generated by MOCOA and the intrinsic mode functions of VMD with the best combination of K and α were obtained. A deep attention model based on MOCOA-VMD was constructed for ECG signal classification. The ablation study was implemented to verify the effectiveness of the proposed signal decomposition method and deep attention modules. The performance of the model based on MOCOA-VMD achieves the best accuracy of 94.35%, much higher than the model constructed by modules of EEMD, VMD and CNN. Moreover, Bayesian optimization was carried out to fine-tune the hyperparameters batch size, learning rate, epochs, and momentum. After TPE optimization, the deep model's performance achieved a maximum accuracy of 95.91%. The MIT-BIH arrhythmia database was further utilized for model validation, ascertaining its robustness and generalizability. The proposed deep attention modeling and classification strategy can help in arrhythmia signal processing and may offer inspiration for other signal processing fields as well.

PMID:39934416 | DOI:10.1038/s41598-025-89752-0

Sci Rep. 2025 Feb 11;15(1):5117. doi: 10.1038/s41598-025-89768-6.

ABSTRACT

CorneAI, a deep learning model designed for diagnosing cataracts and corneal diseases, was assessed for its impact on ophthalmologists' diagnostic accuracy. In the study, 40 ophthalmologists (20 specialists and 20 residents) classified 100 images, including iPhone 13 Pro photos (50 images) and diffuser slit-lamp photos (50 images), into nine categories (normal condition, infectious keratitis, immunological keratitis, corneal scar, corneal deposit, bullous keratopathy, ocular surface tumor, cataract/intraocular lens opacity, and primary angle-closure glaucoma). The iPhone and slit-lamp images represented the same cases. After initially answering without CorneAI, the same ophthalmologists responded to the same cases with CorneAI 2-4 weeks later. With CorneAI's support, the overall accuracy of ophthalmologists increased significantly from 79.2 to 88.8% (P < 0.001). Specialists' accuracy rose from 82.8 to 90.0%, and residents' from 75.6 to 86.2% (P < 0.001). Smartphone image accuracy improved from 78.7 to 85.5% and slit-lamp image accuracy from 81.2 to 90.6% (both, P < 0.001). In this study, CorneAI's own accuracy was 86%, but its support enhanced ophthalmologists' accuracy beyond the CorneAI's baseline. This study demonstrated that CorneAI, despite being trained on diffuser slit-lamp images, effectively improved diagnostic accuracy, even with smartphone images.

PMID:39934383 | DOI:10.1038/s41598-025-89768-6

Sci Rep. 2025 Feb 11;15(1):5121. doi: 10.1038/s41598-025-88734-6.

ABSTRACT

Stock market stability relies on the shares, investors, and stakeholders' participation and global commodity exchanges. In general, multiple factors influence the stock market stability to ensure profitable returns and commodity transactions. This article presents a contradictory-factor-based stability prediction model using the sigmoid deep learning paradigm. Sigmoid learning identifies the possible stabilizations of different influencing factors toward a profitable stock exchange. In this model, each influencing factor is mapped with the profit outcomes considering the live shares and their exchange value. The stability is predicted using sigmoid and non-sigmoid layers repeatedly until the maximum is reached. This stability is matched with the previous outcomes to predict the consecutive hours of stock market changes. Based on the actual changes and predicted ones, the sigmoid function is altered to accommodate the new range. The non-sigmoid layer remains unchanged in the new changes to improve the prediction precision. Based on the outcomes the deep learning's sigmoid layer is trained to identify abrupt changes in the stock market.

PMID:39934296 | DOI:10.1038/s41598-025-88734-6

Sci Rep. 2025 Feb 11;15(1):5048. doi: 10.1038/s41598-025-89646-1.

ABSTRACT

Deep learning (DL) and explainable artificial intelligence (XAI) have emerged as powerful machine-learning tools to identify complex predictive data patterns in a spatial or temporal domain. Here, we consider the application of DL and XAI to large omic datasets, in order to study biological aging at the molecular level. We develop an advanced multi-view graph-level representation learning (MGRL) framework that integrates prior biological network information, to build molecular aging clocks at cell-type resolution, which we subsequently interpret using XAI. We apply this framework to one of the largest single-cell transcriptomic datasets encompassing over a million immune cells from 981 donors, revealing a ribosomal gene subnetwork, whose expression correlates with age independently of cell-type. Application of the same DL-XAI framework to DNA methylation data of sorted monocytes reveals an epigenetically deregulated inflammatory response pathway whose activity increases with age. We show that the ribosomal module and inflammatory pathways would not have been discovered had we used more standard machine-learning methods. In summary, the computational deep learning framework presented here illustrates how deep learning when combined with explainable AI tools, can reveal novel biological insights into the complex process of aging.

PMID:39934290 | DOI:10.1038/s41598-025-89646-1

Sci Rep. 2025 Feb 11;15(1):5084. doi: 10.1038/s41598-025-89693-8.

ABSTRACT

Skin cancer can be prevalent in people of any age group who are exposed to ultraviolet (UV) radiation. Among all other types, melanoma is a notable severe kind of skin cancer, which can be fatal. Melanoma is a malignant skin cancer arising from melanocytes, requiring early detection. Typically, skin lesions are classified either as benign or malignant. However, some lesions do exist that don't show clear cancer signs, making them suspicious. If unnoticed, these suspicious lesions develop into severe melanoma, requiring invasive treatments later on. These intermediate or suspicious skin lesions are completely curable if it is diagnosed at their early stages. To tackle this, few researchers intended to improve the image quality of the infected lesions obtained from the dermoscopy through image reconstruction techniques. Analyzing reconstructed super-resolution (SR) images allows early detection, fine feature extraction, and treatment plans. Despite advancements in machine learning, deep learning, and complex neural networks enhancing skin lesion image quality, a key challenge remains unresolved: how the intricate textures are obtained while performing significant up scaling in medical image reconstruction? Thus, an artificial intelligence (AI) based reconstruction algorithm is proposed to obtain the fine features from the intermediate skin lesion from dermoscopic images for early diagnosis. This serves as a non-invasive approach. In this research, a novel melanoma information improvised generative adversarial network (MELIIGAN) framework is proposed for the expedited diagnosis of intermediate skin lesions. Also, designed a stacked residual block that handles larger scaling factors and the reconstruction of fine-grained details. Finally, a hybrid loss function with a total variation (TV) regularization term switches to the Charbonnier loss function, a robust substitute for the mean square error loss function. The benchmark dataset results in a structural index similarity (SSIM) of 0.946 and a peak signal-to-noise ratio (PSNR) of 40.12 dB as the highest texture information, evidently compared to other state-of-the-art methods.

PMID:39934265 | DOI:10.1038/s41598-025-89693-8

Microbiome. 2025 Feb 11;13(1):50. doi: 10.1186/s40168-024-02015-4.

ABSTRACT

BACKGROUND: Beta-diversity is a fundamental ecological metric for exploring dissimilarities between microbial communities. On the functional dimension, metaproteomics data can be used to quantify beta-diversity to understand how microbial community functional profiles vary under different environmental conditions. Conventional approaches to metaproteomic functional beta-diversity often treat protein functions as independent features, ignoring the evolutionary relationships among microbial taxa from which different proteins originate. A more informative functional distance metric that incorporates evolutionary relatedness is needed to better understand microbiome functional dissimilarities.

RESULTS: Here, we introduce PhyloFunc, a novel functional beta-diversity metric that incorporates microbiome phylogeny to inform on metaproteomic functional distance. Leveraging the phylogenetic framework of weighted UniFrac distance, PhyloFunc innovatively utilizes branch lengths to weigh between-sample functional distances for each taxon, rather than differences in taxonomic abundance as in weighted UniFrac. Proof of concept using a simulated toy dataset and a real dataset from mouse inoculated with a synthetic gut microbiome and fed different diets show that PhyloFunc successfully captured functional compensatory effects between phylogenetically related taxa. We further tested a third dataset of complex human gut microbiomes treated with five different drugs to compare PhyloFunc's performance with other traditional distance methods. PCoA and machine learning-based classification algorithms revealed higher sensitivity of PhyloFunc in microbiome responses to paracetamol. We provide PhyloFunc as an open-source Python package (available at https://pypi.org/project/phylofunc/ ), enabling efficient calculation of functional beta-diversity distances between a pair of samples or the generation of a distance matrix for all samples within a dataset.

CONCLUSIONS: Unlike traditional approaches that consider metaproteomics features as independent and unrelated, PhyloFunc acknowledges the role of phylogenetic context in shaping the functional landscape in metaproteomes. In particular, we report that PhyloFunc accounts for the functional compensatory effect of taxonomically related species. Its effectiveness, ecological relevance, and enhanced sensitivity in distinguishing group variations are demonstrated through the specific applications presented in this study. Video Abstract.

PMID:39934908 | DOI:10.1186/s40168-024-02015-4

BMC Bioinformatics. 2025 Feb 11;26(1):48. doi: 10.1186/s12859-025-06051-1.

ABSTRACT

BACKGROUND: Alterations of metabolism, including changes in mitochondrial metabolism as well as glutathione (GSH) metabolism are a well appreciated hallmark of many cancers. Mitochondrial GSH (mGSH) transport is a poorly characterized aspect of GSH metabolism, which we investigate in the context of cancer. Existing functional annotation approaches from machine (ML) or deep learning (DL) models based only on protein sequences, were unable to annotate functions in biological contexts.

RESULTS: We develop a flexible ML framework for functional annotation from diverse feature data. This hybrid ML framework leverages cancer cell line multi-omics data and other biological knowledge data as features, to uncover potential genes involved in mGSH metabolism and membrane transport in cancers. This framework achieves strong performance across functional annotation tasks and several cell line and primary tumor cancer samples. For our application, classification models predict the known mGSH transporter SLC25A39 but not SLC25A40 as being highly probably related to mGSH metabolism in cancers. SLC25A10, SLC25A50, and orphan SLC25A24, SLC25A43 are predicted to be associated with mGSH metabolism in multiple biological contexts and structural analysis of these proteins reveal similarities in potential substrate binding regions to the binding residues of SLC25A39.

CONCLUSION: These findings have implications for a better understanding of cancer cell metabolism and novel therapeutic targets with respect to GSH metabolism through potential novel functional annotations of genes. The hybrid ML framework proposed here can be applied to other biological function classifications or multi-omics datasets to generate hypotheses in various biological contexts. Code and a tutorial for generating models and predictions in this framework are available at: https://github.com/lkenn012/mGSH_cancerClassifiers .

PMID:39934670 | DOI:10.1186/s12859-025-06051-1

Transl Stroke Res. 2025 Feb 11. doi: 10.1007/s12975-025-01332-6. Online ahead of print.

ABSTRACT

Cerebral small vessel disease (CSVD) is a global brain disorder that is characterized by a series of clinical, neuroimaging, and neuropathological manifestations. However, the molecular pathophysiological mechanisms of CSVD have not been thoroughly investigated. Liquid chromatography-tandem mass spectrometry-based proteomics has broad application prospects in biomedicine. It is used to elucidate disease-related molecular processes and pathophysiological pathways, thus providing an important opportunity to explore the pathophysiological mechanisms of CSVD. Serum samples were obtained from 96 participants (58 with CSVD and 38 controls) consecutively recruited from The First Affiliated Hospital of Zhengzhou University. After removing high-abundance proteins, the serum samples were analyzed using high-resolution mass spectrometry. Bioinformatics methods were used for in-depth analysis of the obtained proteomic data, and the results were verified experimentally. Compared with the control group, 52 proteins were differentially expressed in the sera of the CSVD group. Furthermore, analyses indicated the involvement of these differentially expressed proteins in CSVD through participation in the overactivation of complement and coagulation cascades and dysregulation of insulin-like growth factor-binding proteins. The proteomic biomarker panel identified by the machine learning model combined with clinical features is expected to facilitate the diagnosis of CSVD (AUC = 0.947, 95% CI = 0.895-0.978). The study is the most in-depth study on CSVD proteomics to date and suggests that the overactivation of the complement cascade and the dysregulation of IGFBP on- IGF may be closely correlated with the occurrence and progression of CSVD, offering the potential to develop peripheral blood biomarkers and providing new insights into the biological basis of CSVD.

PMID:39934548 | DOI:10.1007/s12975-025-01332-6

Nat Microbiol. 2025 Feb 11. doi: 10.1038/s41564-025-01951-7. Online ahead of print.

NO ABSTRACT

PMID:39934405 | DOI:10.1038/s41564-025-01951-7

Nat Chem Biol. 2025 Feb 11. doi: 10.1038/s41589-025-01846-y. Online ahead of print.

ABSTRACT

Measuring pharmacodynamics (PD)-the biochemical effects of drug dosing-and correlating them with therapeutic efficacy in animal models is crucial for the development of effective drugs but traditional PD studies are labor and resource intensive. Here we developed a kinase-modulated bioluminescent indicator (KiMBI) for rapid, noninvasive PD assessment of Akt-targeted drugs, minimizing drug and animal use. Using KiMBI, we performed a structure-PD relationship analysis on the brain-active Akt inhibitor ipatasertib by generating and characterizing two novel analogs. One analog, ML-B01, successfully inhibited Akt in both the brain and the body. Interestingly, capivasertib, ipatasertib and ML-B01 all exhibited PD durations beyond their pharmacokinetic profiles. Furthermore, KiMBI revealed that the PD effects of an Akt-targeted proteolysis-targeting chimera degrader endured for over 3 days. Thus, bioluminescence imaging with Akt KiMBI provides a noninvasive and efficient method for in vivo visualization of the PD of Akt inhibitors and degraders.

PMID:39934397 | DOI:10.1038/s41589-025-01846-y

Nat Commun. 2025 Feb 11;16(1):1549. doi: 10.1038/s41467-025-56797-8.

ABSTRACT

The TRAFAC (translation factors) GTPase OLA1 plays a critical role in various stress responses and is implicated in the regulation of tumor progression. It is conserved from bacteria to eukaryotes and regulates the translation through binding to the ribosome. Here, we report the cryo-electron microscopy structure of its Escherichia coli homolog, YchF, with the 50S subunit. In this structure, YchF is positioned at the side of the 50S subunit by engaging with uL14, bL19, and rRNA helix H62 through its helical and ATPase domains. We further demonstrate that the helical domain is essential for OLA1/YchF to function. A comprehensive analysis of the structure and Ribo-seq data points out that OLA1/YchF promotes the splitting of ribosomes into subunits on D/E-rich mRNA. Our findings provide crucial structural insights into the molecular mechanism of OLA1/YchF-associated translation-stalling regulation, which maintains the translation of genes involved in stress response and tumor progression.

PMID:39934121 | DOI:10.1038/s41467-025-56797-8

Curr Opin Biotechnol. 2025 Feb 10;92:103260. doi: 10.1016/j.copbio.2025.103260. Online ahead of print.

ABSTRACT

Development of microorganisms into mature bioproduction host strains has typically been a slow and circuitous process, wherein multiple groups apply disparate approaches with minimal coordination over decades. To help organize and streamline host development efforts, we introduce the Tier System for Host Development, a conceptual model and guide for developing microbial hosts that can ultimately lead to a systematic, standardized, less expensive, and more rapid workflow. The Tier System is made up of three Tiers, each consisting of a unique set of strain development Targets, including experimental tools, strain properties, experimental information, and process models. By introducing the Tier System, we hope to improve host development activities through standardization and systematization pertaining to nontraditional chassis organisms.

PMID:39933241 | DOI:10.1016/j.copbio.2025.103260

BMC Nephrol. 2025 Feb 11;26(1):67. doi: 10.1186/s12882-025-04011-8.

ABSTRACT

BACKGROUND: Kidney dysfunction among adults living with Human Immuno-Deficiency Virus (HIV) increases the risk of drug-related side effects, acute kidney injury, hospitalization, and progression to end-stage kidney disease. In developing regions like Africa, where access to kidney transplants and dialysis is limited, early detection of kidney disease among adults living with HIV has significant clinical and financial implications. Therefore, the objective of this review was to determine the pooled prevalence and identify associated factors of kidney dysfunction among adults living with HIV in Africa.

METHODS: The report was presented according to the Preferred Reporting Items for Systematic Review and Meta-Analyses checklists. The articles were searched using PubMed/MEDLINE, EMBASE, Scopus, Wiley Online Library, CINAHL/EBSCO, OVID/Wolters Kluwer, Cochrane Library, Google Scholar, Science Direct, and African Journal Online. Data were extracted using Microsoft Excel and exported to STATA MP Version 11 Software for analysis. Heterogeneity of studies was assessed by Cochran's Q test and I2 statistics. Publication bias was detected by the visual inspection of the funnel plot and statistical Egger's test.

RESULTS: In this study, the pooled prevalence of kidney dysfunction among adults living with HIV in Africa is estimated to be 16.85% (95% CI: 13.08 - 20.62, I²=96.2%, p-value = 0.000). Female sex (POR = 1.82; 95% CI; 1.31, 2.53), age ≥ 50 years (POR = 8.94; 95% CI: 1.82, 43.93), body mass index ≥ 30 kg/m² (POR = 4.70; 95% CI: 3.07, 7.22), diabetes mellitus (POR = 2.84; 95% CI: 1.59, 5.07), CD4 count < 200 cells/mm³ (POR = 3.64; 95% CI: 1.63, 8.13) and anemia (POR = 3.73, 95% CI = 2.00-6.94) were factors associated with kidney dysfunction among adults living with HIV.

CONCLUSIONS: This study revealed that the pooled prevalence of kidney dysfunction among adults living with HIV in Africa remains significant. Female sex, age ≥ 50 years, body mass index ≥ 30 kg/m², diabetes mellitus, CD4 count < 200 cells/mm³ and anemia were factors associated with kidney dysfunction. To reduce the morbidity and mortality associated with kidney dysfunction, it is advisable to create awareness and initiating early interventions through health education during their follow-up time, and initiating suitable medication at an early stage.

PMID:39934651 | DOI:10.1186/s12882-025-04011-8