Sci Rep. 2025 May 27;15(1):18582. doi: 10.1038/s41598-025-03428-3.

ABSTRACT

A serious adverse reaction (SADR) may follow a vaccination against SARS-CoV-2 infection. We aimed to explore symptoms and reporting trends of SADRs to anti-SARS-CoV-2 vaccines based on the EudraVigilance database. This retrospective observational study analysed 250,966 suspected SADRs (with 62.8% reported in females), following the administration of 733,837,251 vaccine doses against SARS-CoV-2. Pfizer BioNTech (Comirnaty-Tozinameran), Moderna (Spikevax-Elastomeran), Janssen (Jcovden) and AstraZeneca (Vaxzevria) vaccines were analysed. The assessment included 897 types of SADRs across 12 categories. The most common clinical manifestations of SADRs to anti-SARS-CoV-2 vaccines vaccines encompassed neuropsychiatric (n = 121,877), cardiovascular (n = 78,167), as well as musculoskeletal and connective tissue disorders (n = 63,994). After summarising all SADRs, vaccination with Comirnaty was associated with the lowest risk of experiencing SADRs (754/million administered doses), followed by Spikevax (785/million doses), Jcovden (1,248/million doses) and Vaxzevria (2,301/million doses; p < 0.001). Regarding the vaccine administration timelines, the reporting of SADRs tends to be delayed and occurs over a longer time (p < 0.001). SADRs associated with anti-SARS-CoV-2 vaccines seem to be relatively rare. Compared to adenovirus-based vector vaccines (Jcovden, Vaxzevria), mRNA vaccines appear to offer improved safety profiles (Comirnaty, Spikevax). The risk of SADR to any SARS-CoV-2 vaccine seems to be outweighed by the benefits of active immunization against the virus.

PMID:40425703 | DOI:10.1038/s41598-025-03428-3

PLoS One. 2025 May 27;20(5):e0324293. doi: 10.1371/journal.pone.0324293. eCollection 2025.

ABSTRACT

Cotton is a major cash crop, and increasing its production is extremely important worldwide, especially in agriculture-led economies. The crop is susceptible to various diseases, leading to decreased yields. In recent years, advancements in deep learning methods have enabled researchers to develop automated methods for detecting diseases in cotton crops. Such automation not only assists farmers in mitigating the effects of the disease but also conserves resources in terms of labor and fertilizer costs. However, accurate classification of multiple diseases simultaneously in cotton remains challenging due to multiple factors, including class imbalance, variation in disease symptoms, and the need for real-time detection, as most existing datasets are acquired under controlled conditions. This research proposes a novel method for addressing these challenges and accurately classifying seven classes, including six diseases and a healthy class. We address the class imbalance issue through synthetic data generation using conventional methods like scaling, rotating, transforming, shearing, and zooming and propose a customized StyleGAN for synthetic data generation. After preprocessing, we combine features extracted from MobileNet and VGG16 to create a comprehensive feature vector, passed to three classifiers: Long Short Term Memory Units, Support Vector Machines, and Random Forest. We propose a StackNet-based ensemble classifier that takes the output probabilities of these three classifiers and predicts the class label among six diseases-Bacterial blight, Curl virus, Fusarium wilt, Alternaria, Cercospora, Greymildew-and a healthy class. We trained and tested our method on publicly available datasets, achieving an average accuracy of 97%. Our robust method outperforms state-of-the-art techniques to identify the six diseases and the healthy class.

PMID:40424461 | DOI:10.1371/journal.pone.0324293

Bioinformatics. 2025 May 27:btaf319. doi: 10.1093/bioinformatics/btaf319. Online ahead of print.

ABSTRACT

MOTIVATION: Drug side effects refer to harmful or adverse reactions that occur during drug use, unrelated to the therapeutic purpose. A core issue in drug side effect prediction is determining the frequency of these drug side effects in the population, which can guide patient medication use and drug development. Many computational methods have been developed to predict the frequency of drug side effects as an alternative to clinical trials. However, existing methods typically build regression models on five frequency classes of drug side effects and tend to overfit the training set, leading to boundary handling issues and the risk of overfitting.

RESULTS: To address this problem, we develop a multi-source similarity fusion-based model, named MSSF, for predicting five frequency classes of drug side effects. Compared to existing methods, our model utilizes the multi-source feature fusion module and the self-attention mechanism to explore the relationships between drugs and side effects deeply and employs Bayesian variational inference to more accurately predict the frequency classes of drug side effects. The experimental results indicate that MSSF consistently achieves superior performance compared to existing models across multiple evaluation settings, including cross-validation, cold-start experiments, and independent testing. The visual analysis and case studies further demonstrate MSSF's reliable feature extraction capability and promise in predicting the frequency classes of drug side effects.

AVAILABILITY: The source code of MSSF is available on GitHub (https://github.com/dingxlcse/MSSF.git) and archived on Zenodo (DOI: 10.5281/zenodo.15462041).

SUPPLEMENTARY INFORMATION: Additional files are available at Bioinformatics online.

PMID:40424358 | DOI:10.1093/bioinformatics/btaf319

PLoS One. 2025 May 27;20(5):e0322225. doi: 10.1371/journal.pone.0322225. eCollection 2025.

ABSTRACT

With the digital transformation of the financial industry, credit score prediction, as a key component of risk management, faces increasingly complex challenges. Traditional credit scoring methods often have difficulty in fully capturing the characteristics of large-scale, high-dimensional financial data, resulting in limited prediction performance. To address these issues, this paper proposes a credit score prediction model that combines CNNs and BiGRUs, and uses the GWO algorithm for hyperparameter tuning. CNN performs well in feature extraction and can effectively capture patterns in customer historical behaviors, while BiGRU is good at handling time dependencies, which further improves the prediction accuracy of the model. The GWO algorithm is introduced to further improve the overall performance of the model by optimizing key parameters. Experimental results show that the CNN-BiGRU-GWO model proposed in this paper performs well on multiple public credit score datasets, significantly improving the accuracy and efficiency of prediction. On the LendingClub loan dataset, the MAE of this model is 15.63, MAPE is 4.65%, RMSE is 3.34, and MSE is 12.01, which are 64.5%, 68.0%, 21.4%, and 52.5% lower than the traditional method plawiak of 44.07, 14.51%, 4.25, and 25.29, respectively. In addition, compared with traditional methods, this model also shows stronger advantages in adaptability and generalization ability. By integrating advanced technologies, this model not only provides an innovative technical solution for credit score prediction, but also provides valuable insights into the application of deep learning in the financial field, making up for the shortcomings of existing methods and demonstrating its potential for wide application in financial risk management.

PMID:40424348 | DOI:10.1371/journal.pone.0322225

PLoS One. 2025 May 27;20(5):e0322555. doi: 10.1371/journal.pone.0322555. eCollection 2025.

ABSTRACT

Human Action Recognition (HAR) has grown significantly because of its many uses, including real-time surveillance and human-computer interaction. Various variations in routine human actions make the recognition process of action more difficult. In this paper, we proposed a novel deep learning architecture known as Inverted Bottleneck Residual with Self-Attention (InBRwSA). The proposed architecture is based on two different modules. In the first module, 6-parallel inverted bottleneck residual blocks are designed, and each block is connected with a skip connection. These blocks aim to learn complex human actions in many convolutional layers. After that, the second module is designed based on the self-attention mechanism. The learned weights of the first module are passed to self-attention, extract the most essential features, and can easily discriminate complex human actions. The proposed architecture is trained on the selected datasets, whereas the hyperparameters are chosen using the particle swarm optimization (PSO) algorithm. The trained model is employed in the testing phase for the feature extraction from the self-attention layer and passed to the shallow wide neural network classifier for the final classification. The HMDB51 and UCF 101 are frequently used as action recognition standard datasets. These datasets are chosen to allow for meaningful comparison with earlier research. UCF101 dataset has a wide range of activity classes, and HMDB51 has varied real-world behaviors. These features test the generalizability and flexibility of the presented model. Moreover, these datasets define the evaluation scope within a particular domain and guarantee relevance to real-world circumstances. The proposed technique is tested on both datasets, and accuracies of 78.80% and 91.80% were achieved, respectively. The ablation study demonstrated that a margin of error value of 70.1338 ± 3.053 (±4.35%) and 82.7813 ± 2.852 (±3.45%) for the confidence level 95%,1.960σx̄ is obtained for HMDB51 and UCF datasets respectively. The training time for the highest accuracy for HDMB51 and UCF101 is 134.09 and 252.10 seconds, respectively. The proposed architecture is compared with several pre-trained deep models and state-of-the-art (SOTA) existing techniques. Based on the results, the proposed architecture outperformed existing techniques.

PMID:40424287 | DOI:10.1371/journal.pone.0322555

PLoS One. 2025 May 27;20(5):e0324916. doi: 10.1371/journal.pone.0324916. eCollection 2025.

ABSTRACT

Traditional communication signal detection heavily relies on manually designed features, making it difficult to fully characterize the essential characteristics of the signal, resulting in limited detection accuracy. Based on this, the study innovatively combines Multiple Input Multiple Output (MIMO) with orthogonal frequency division multiplexing technology to construct a data-driven detection system. The system adopts a Multi-DNN method with a dual-DNN cascade structure and mixed activation function design to optimize the channel estimation and signal detection coordination process of the MIMO part. At the same time, a DCNet decoder based on a convolutional neural network batch normalization mechanism is designed to suppress inter-subcarrier interference in OFDM systems effectively. The results showed that on the simulation training set, the accuracy of the research model was 93.8%, the symbol error rate was 17.6%, the throughput was 81.3%, and the modulation error rate was 0.004%. On the simulation test set, its accuracy, symbol error rate, throughput, and modulation error rate were 90.7%, 18.1%, 81.2%, and 0.006%. In both 2.4 GHz and 5 GHz WiFi signals, the signal detection accuracy of the research model reached 91.5% and 91.6%, with false detection rates of 1.9% and 1.5%, and missed detection rates of 1.6% and 4.2%. In resource consumption assessment, the detection speed of this model reached 120 signals/s, with an average latency of 50 ms. The model loading time was only 2.4 s, and the CPU usage was as low as 25%, with moderate memory usage. Overall, the research model has achieved significant results in improving detection accuracy, optimizing real-time performance, and reducing resource consumption. It has broad application prospects in the field of communication signal detection.

PMID:40424260 | DOI:10.1371/journal.pone.0324916

PLoS One. 2025 May 27;20(5):e0321270. doi: 10.1371/journal.pone.0321270. eCollection 2025.

ABSTRACT

Deep learning techniques are widely used in the field of medicine and image classification. In past studies, SwimTransformer and RepVGG are very efficient and classical deep learning models. Multi-scale feature fusion and attention mechanisms are effective means to enhance the performance of deep learning models. In this paper, we introduce a novel Swim-Rep fusion network, along with a new multi-scale feature fusion module called multi-scale strip pooling fusion module(MPF) and a new attention module called Faster Recurrent Criss Cross Polarized Attention (FRCPA), both of which excel at extracting multi-dimensional cross-attention and fine-grained features. Our fully supervised model achieved an impressive accuracy of 99.82% on the MIT-BIH database, outperforming the ViT model classifier by 0.12%. Additionally, our semi-supervised model demonstrated strong performance, achieving 98.4% accuracy on the validation set. Experimental results on the remote sensing image classification dataset RSSCN7 demonstrate that our new base model achieves a classification accuracy of 92.5%, which is 8.57% better than the classification performance of swim-transformer-base and 12.9% better than that of RepVGG-base, and increasing the depth of the module yields superior performance.

PMID:40424251 | DOI:10.1371/journal.pone.0321270

PLoS One. 2025 May 27;20(5):e0324595. doi: 10.1371/journal.pone.0324595. eCollection 2025.

ABSTRACT

A zero-day vulnerability is a critical security weakness of software or hardware that has not yet been found and, for that reason, neither the vendor nor the users are informed about it. These vulnerabilities may be taken advantage of by malicious people to execute cyber-attacks leading to severe effects on organizations and individuals. Given that nobody knows and is aware of these weaknesses, it becomes challenging to detect and prevent them. For the real-time zero-day vulnerabilities detection, we bring out a novel reinforcement learning (RL) methodology with the help of Deep Q-Networks (DQN). It works by learning the vulnerabilities without any prior knowledge of vulnerabilities, and it is evaluated using rigorous statistical metrics. Traditional methods are surpassed by this one that is able to adjust to changing threats and cope with intricate state spaces while providing scalability to cybersecurity personnel. In this paper, we introduce a new methodology that uses reinforcement learning for zero-day vulnerability detection. Zero-day vulnerabilities are security weaknesses that have never been exposed or published and are considered highly dangerous for systems and networks. Our method exploits reinforcement learning, a sub-type of machine learning which trains agents to make decisions and take actions to maximize an approximation of some underlying cumulative reward signal and discover patterns and features within data related to zero-day discovery. Training of the agent could allow for real-time detection and classification of zero-day vulnerabilities. Our approach will have the potential as a powerful tool of detection and defense against zero-day vulnerabilities and probably brings significant benefits to security experts and researchers in the field of cyber-security. The new method of discovering vulnerabilities that this approach provides has many comparative advantages over the previous approaches. It is applicable to systems with complex behaviour, such as the ones presented throughout this thesis, and can respond to new security threats in real time. Moreover, it does not require any knowledge about vulnerability itself. Because of that, it will discover hidden weak points. In the present paper, we analyzed the statistical evaluation of forecasted values for several parameters in a reinforcement learning environment. We have taken 1000 episodes for training the model and a further 1000 episodes for forecasting using the trained model. We used statistical measures in the evaluation, which showed that the Alpha value was at 0.10, thereby indicating good accuracy in the forecast. Beta was at 0.00, meaning no bias within the forecast. Gamma was also at 0.00, resulting in a very high level of precision within the forecast. MASE was 3.91 and SMAPE was 1.59, meaning that a very minimal percentage error existed within the forecast. The MAE value was at 6.34, while the RMSE was 10.22, meaning a relatively low average difference within actuals and the forecasted values. Results The results demonstrate the effectiveness of reinforcement learning models in solving complex problems and suggest that the model improves in accuracy with more training data added.

PMID:40424227 | DOI:10.1371/journal.pone.0324595

PLoS One. 2025 May 27;20(5):e0323951. doi: 10.1371/journal.pone.0323951. eCollection 2025.

ABSTRACT

[This corrects the article DOI: 10.1371/journal.pone.0305859.].

PMID:40424208 | DOI:10.1371/journal.pone.0323951

J Sci Food Agric. 2025 May 27. doi: 10.1002/jsfa.14386. Online ahead of print.

ABSTRACT

BACKGROUND: In the process of tobacco sorting, removing non-tobacco related materials (NTRMs) is crucial for the quality of tobacco products. Because of the small size of NTRMs and the abundance and stacking of tobacco leaves, detection of NTRMs is still difficult.

RESULTS: Based on YOLOv8s (You Only Look Once, version 8, small), the present study proposed an efficient YSFER-Tobacco (YOLOv8s-SPDConv-FasterNet-EMA-RTDETRDecoder-Tobacco) model for detection of NTRMs. We replaced some down sampling convolutions in the backbone with SPDConv modules and reconstructed the C2f module using FasterNet and EMA to reduce redundant convolution operations and improve feature extraction capabilities. Finally, the RTDETRDecoder from RT-DETR was employed to improve the head component, resulting in more efficient end-to-end target identification. Experimental results demonstrate that YSFER-Tobacco achieved good model performance, with F1, mAP50, recall and precision reaching 96.1%, 97.2%, 95.7% and 96.5%, respectively, compared to YOLOv8s, which increased by 2.5%, 0.9%, 3.2% and 1.7%. YSFER-Tobacco also outperformed other classical object detection models for detection of NTRMs in tobacco sorting process.

CONCLUSION: Our study demonstrates the effectiveness and superiority of YSFER-Tobacco, providing theoretical support for assessing the quality of tobacco sorting, and has promising application prospects. In addition, we replicated the tobacco sorting environment and created the first dataset, Tobacco-2619, containing 2619 clear images with NTRMs. The dataset and code are available online (https://github.com/Ikaros-sc/Tobacco). © 2025 Society of Chemical Industry.

PMID:40424192 | DOI:10.1002/jsfa.14386

Elife. 2025 May 27;13:RP94029. doi: 10.7554/eLife.94029.

ABSTRACT

Despite the recent breakthrough of AlphaFold (AF) in the field of protein sequence-to-structure prediction, modeling protein interfaces and predicting protein complex structures remains challenging, especially when there is a significant conformational change in one or both binding partners. Prior studies have demonstrated that AF-multimer (AFm) can predict accurate protein complexes in only up to 43% of cases (Yin et al., 2022). In this work, we combine AF as a structural template generator with a physics-based replica exchange docking algorithm to better sample conformational changes. Using a curated collection of 254 available protein targets with both unbound and bound structures, we first demonstrate that AF confidence measures (pLDDT) can be repurposed for estimating protein flexibility and docking accuracy for multimers. We incorporate these metrics within our ReplicaDock 2.0 protocol to complete a robust in silico pipeline for accurate protein complex structure prediction. AlphaRED (AlphaFold-initiated Replica Exchange Docking) successfully docks failed AF predictions, including 97 failure cases in Docking Benchmark Set 5.5. AlphaRED generates CAPRI acceptable-quality or better predictions for 63% of benchmark targets. Further, on a subset of antigen-antibody targets, which is challenging for AFm (20% success rate), AlphaRED demonstrates a success rate of 43%. This new strategy demonstrates the success possible by integrating deep learning-based architectures trained on evolutionary information with physics-based enhanced sampling. The pipeline is available at https://github.com/Graylab/AlphaRED.

PMID:40424178 | DOI:10.7554/eLife.94029

PLoS Biol. 2025 May 27;23(5):e3003208. doi: 10.1371/journal.pbio.3003208. Online ahead of print.

ABSTRACT

Bacteria deploy a diverse arsenal of toxic effectors to antagonize competitors, profoundly influencing the composition of microbial communities. Previous studies have identified an interbacterial toxin predicted to exhibit proteolytic activity that is broadly distributed among gram-negative bacteria. However, the precise mechanism of intoxication remains unresolved. Here, we demonstrate that one such protease toxin from Escherichia coli, Cpe1, disrupts DNA replication and chromosome segregation by cleaving conserved sequences within the ATPase domain of type II DNA topoisomerases GyrB and ParE. This cleavage effectively inhibits topoisomerase-mediated relaxation of supercoiled DNA, resulting in impaired bacterial growth. Cpe1 belongs to the papain-like cysteine protease family and is associated with toxin delivery pathways, including the type VI secretion system and contact-dependent growth inhibition. The structure of Cpe1 in complex with its immunity protein reveals a neutralization mechanism involving competitive substrate binding rather than active site occlusion, distinguishing it from previously characterized effector-immunity pairs. Our findings unveil a unique mode of interbacterial intoxication and provide insights into how bacteria protect themselves from self-poisoning by protease toxins.

PMID:40424468 | DOI:10.1371/journal.pbio.3003208

Sci Signal. 2025 May 27;18(888):eadu3794. doi: 10.1126/scisignal.adu3794. Epub 2025 May 27.

ABSTRACT

Delineating the mechanisms that control the movement of cells is central to understanding diverse physiological and pathophysiological processes. The transcriptional coactivator YAP is important during development and associated with cancer metastasis. Here, we found that YAP promoted cell migration by modulating a Rho family guanosine triphosphatase (GTPase) switch involving Rac1 and RhoA, which are key regulators of cytoskeletal dynamics. YAP transcriptionally transactivated the gene encoding the Rac1 guanine nucleotide exchange factor TRIO by directly binding to its intronic enhancer. This led to the activation of Rac1 and inhibition of RhoA, which increased cell migration and invasion in vitro and in vivo. This YAP-dependent program was observed across many cell types, including human breast epithelial cells and astrocytes, but it was particularly enhanced in a patient-specific manner in glioblastoma (GBM), the most common malignant brain tumor. Additionally, YAP-TRIO signaling activated STAT3, a transcription factor implicated in invasive growth in cancer, suggesting potential for cross-talk with this pathway to exacerbate invasive behavior. Clinically, hyperactivation of YAP, TRIO, and STAT3 gene signatures in GBM were associated with poor survival outcomes in patients. Our findings suggest that the YAP-TRIO-Rho-GTPase signaling network regulates invasive cell spread in both physiological and pathological contexts.

PMID:40424361 | DOI:10.1126/scisignal.adu3794

Elife. 2025 May 27;13:RP94029. doi: 10.7554/eLife.94029.

ABSTRACT

Despite the recent breakthrough of AlphaFold (AF) in the field of protein sequence-to-structure prediction, modeling protein interfaces and predicting protein complex structures remains challenging, especially when there is a significant conformational change in one or both binding partners. Prior studies have demonstrated that AF-multimer (AFm) can predict accurate protein complexes in only up to 43% of cases (Yin et al., 2022). In this work, we combine AF as a structural template generator with a physics-based replica exchange docking algorithm to better sample conformational changes. Using a curated collection of 254 available protein targets with both unbound and bound structures, we first demonstrate that AF confidence measures (pLDDT) can be repurposed for estimating protein flexibility and docking accuracy for multimers. We incorporate these metrics within our ReplicaDock 2.0 protocol to complete a robust in silico pipeline for accurate protein complex structure prediction. AlphaRED (AlphaFold-initiated Replica Exchange Docking) successfully docks failed AF predictions, including 97 failure cases in Docking Benchmark Set 5.5. AlphaRED generates CAPRI acceptable-quality or better predictions for 63% of benchmark targets. Further, on a subset of antigen-antibody targets, which is challenging for AFm (20% success rate), AlphaRED demonstrates a success rate of 43%. This new strategy demonstrates the success possible by integrating deep learning-based architectures trained on evolutionary information with physics-based enhanced sampling. The pipeline is available at https://github.com/Graylab/AlphaRED.

PMID:40424178 | DOI:10.7554/eLife.94029

Curr Opin Oncol. 2025 Apr 22. doi: 10.1097/CCO.0000000000001148. Online ahead of print.

ABSTRACT

PURPOSE OF REVIEW: Maintenance therapy (MT), particularly antiangiogenic approaches such metronomic chemotherapy (MC), correspond to the continuous administration of low-dose anticancer agents in a context of minimal residual disease. While widely used in pediatric acute lymphoblastic leukemia for decades, MT has recently shown promise in solid tumors. Additionally, antivascular endothelial growth factor (VEGF) tyrosine kinase inhibitor (TKI) are increasingly explored in pediatric sarcomas.

RECENT FINDINGS: This review summarize current evidence on MT efficacy in pediatric sarcomas, focusing on MC and TKIs. It examines their impact on the tumor microenvironment and cancer progression, as well as potential future applications, including standalone use or combination with targeted therapies, immunotherapies and/or drug repurposing.

SUMMARY: MT has been demonstrated to improve outcomes in specific sarcomas, especially high-risk localized rhabdomyosarcoma, and has therefore become a standard of care. Its role in other sarcomas, such as Ewing sarcoma and osteosarcoma, is under investigation. However, critical challenges remain, including optimizing drug selection, treatment duration, and patient stratification to maximize benefits.

PMID:40421977 | DOI:10.1097/CCO.0000000000001148

Entropy (Basel). 2025 Apr 27;27(5):473. doi: 10.3390/e27050473.

ABSTRACT

In this study, we propose a computational approach that applies text mining and deep learning to conduct controversy detection on social media platforms. Unlike previous research, our method integrates multidimensional and heterogeneous information from social media into a heterogeneous signed attributed network, encompassing various users' attributes, semantic information, and structural heterogeneity. We introduce a deep dual-layer self-supervised algorithm for community detection and analyze controversy within this network. A novel controversy metric is devised by considering three dimensions of controversy: community distinctions, betweenness centrality, and user representations. A comparison between our method and other classical controversy measures such as Random Walk, Biased Random Walk (BRW), BCC, EC, GMCK, MBLB, and community-based methods reveals that our model consistently produces more stable and accurate controversy scores. Additionally, we calculated the level of controversy and computed p-values for the detected communities on our crawled dataset Weibo, including #Microblog (3792), #Comment (45,741), #Retweet (36,126), and #User (61,327). Overall, our model had a comprehensive and nuanced understanding of controversy on social media platforms. To facilitate its use, we have developed a user-friendly web server.

PMID:40422428 | DOI:10.3390/e27050473

J Neurol. 2025 May 27;272(6):423. doi: 10.1007/s00415-025-13108-x.

NO ABSTRACT

PMID:40423827 | DOI:10.1007/s00415-025-13108-x

Neurol Int. 2025 Apr 24;17(5):65. doi: 10.3390/neurolint17050065.

ABSTRACT

Background/Objectives: Several genetic factors are related to the risk of Alzheimer's disease (AD) and the response to cholinesterase inhibitors (ChEIs) (donepezil, galantamine, and rivastigmine) or memantine. However, findings have been controversial, and, to the best of our knowledge, admixed populations have not been previously evaluated. We aimed to determine the impact of genetic and non-genetic factors on the risk of AD and the short-term response to ChEIs and memantine in patients with AD from Mexico. Methods: This study included 117 patients from two specialty hospitals in Mexico City, Mexico. We evaluated cognitive performance via clinical evaluations and neuropsychological tests. Nineteen variants in ABCB1, ACHE, APOE, BCHE, CHAT, CYP2D6, CYP3A5, CHRNA7, NR1I2, and POR were assessed through TaqMan assays or PCR. Results: Minor alleles of the ABCB1 rs1045642, ACHE rs17884589, and CHAT rs2177370 and rs3793790 variants were associated with the risk of AD; meanwhile, CHRNA7 rs6494223 and CYP3A5 rs776746 were identified as low-risk variants in AD. BCHE rs1803274 was associated with worse cognitive functioning. None of the genetic and non-genetic factors studied were associated with the response to pharmacological treatment. Conclusions: We identified potential genetic variants related to the risk of AD; meanwhile, no factor was observed to impact the response to pharmacological therapy in patients with AD from Mexico.

PMID:40423221 | DOI:10.3390/neurolint17050065

J Pers Med. 2025 May 2;15(5):185. doi: 10.3390/jpm15050185.

ABSTRACT

Background/Objectives: Solid organ transplant (SOT) recipients are exposed to multiple medications, many of which have pharmacogenetic (PGx) prescribing recommendations. This study leveraged data from a population-scale biobank and an enterprise data warehouse to determine the prevalence of actionable exposures to PGx medications among kidney, heart, and lung transplant recipients during the first six months post-transplant. Methods: We conducted a retrospective analysis of adult SOT patients with genetic data available from the Colorado Center for Personalized Medicine (CCPM) biobank and clinical data from Health Data Compass (HDC). We evaluated 29 variants in 13 pharmacogenes and 42 Clinical Pharmacogenetics Implementation Consortium (CPIC) level A or B medications (i.e., sufficient evidence to recommend at least one prescribing action based on genetics). The primary outcome was actionable exposure to a PGx medication (i.e., actionable phenotype and a prescription for an affected PGx medication). Results: The study included 358 patients. All patients were prescribed at least one PGx medication, and 49.4% had at least one actionable exposure to a PGx medication during the first six months post-transplant. The frequency of actionable exposure was highest for tacrolimus (15.4%), followed by proton pump inhibitors (PPIs) (15.1%) and statins (12.8%). Statin actionable exposures significantly differed by transplant type, likely due to variations in prescribing patterns and actionable phenotypes for individual statins. Conclusions: Our findings highlight the potential clinical utility of PGx testing among SOT patients. Further studies are needed to address the impact on clinical outcomes and the optimal timing of PGx testing in the SOT population.

PMID:40423057 | DOI:10.3390/jpm15050185

Metabolites. 2025 May 13;15(5):323. doi: 10.3390/metabo15050323.

ABSTRACT

Background/Objectives: Metabolomics, in combination with genetic data, is a powerful approach to study the biochemical consequences of genetic variation. We assessed the impact of human gene knockouts (KOs) on the metabolite levels of Estonia Biobank (EstBB) participants and integrated the results with electronic health record data. Methods: In 150,000 EstBB genotyped participants, we identified 723 KOs with 152 different predicted loss of function (pLoF) variants in 115 genes. For those KOs and 258 controls, 1387 metabolites were profiled using ultra-high-performance liquid chromatography-tandem mass spectrometry. Results: We identified 48 associations linking rare pLoF variants in 22 genes to 43 metabolites. Out of 48 associations, 27 (56%) were found in genes that cause inborn errors of metabolism. The top associations identified in our analysis included genes and metabolites involved in the degradation pathway of the pyrimidine bases uracil and thymine (DPYD and UPB1). We found DPYD gene KOs to be associated with elevated levels of Uracil, confirming that DPD-deficiency is a leading cause of severe 5-Fluorouracil toxicity. Overall, 54% of reported associations are gene targets of approved drugs or bioactive drug-like compounds. Conclusions: Our findings contribute to assessing the impact of human KOs on metabolite levels and offer insights into gene functions, disease mechanism, and drug target validation.

PMID:40422899 | DOI:10.3390/metabo15050323