Sci Rep. 2025 Jan 30;15(1):3756. doi: 10.1038/s41598-025-87226-x.

ABSTRACT

The expansion of LEAN and small batch manufacturing demands flexible automated workstations capable of switching between sorting various wastes over time. To address this challenge, our study is focused on assessing the ability of the Segment Anything Model (SAM) family of deep learning architectures to separate highly variable objects during robotic waste sorting. The proposed two-step procedure for generic versatile visual waste sorting is based on the SAM architectures (original SAM, FastSAM, MobileSAMv2, and EfficientSAM) for waste object extraction from raw images, and the use of classification architecture (MobileNetV2, VGG19, Dense-Net, Squeeze-Net, ResNet, and Inception-v3) for accurate waste sorting. Such a pipeline brings two key advantages that make it more applicable in industry practice by: 1) eliminating the necessity for developing dedicated waste detection and segmentation algorithms for waste object localization, and 2) significantly reducing the time and costs required for adapting the solution to different use cases. With the proposed procedure, switching to a new waste type sorting is reduced to only two steps: The use of SAM for the automatic object extraction, followed by their separation into corresponding classes used to fine-tune the classifier. Validation on four use cases (floating waste, municipal waste, e-waste, and smart bins) shows robust results, with accuracy ranging from 86 to 97% when using the MobileNetV2 with SAM and FastSAM architectures. The proposed approach has a high potential to facilitate deployment, increase productivity, lower expenses, and minimize errors in robotic waste sorting while enhancing overall recycling and material utilization in the manufacturing industry.

PMID:39885307 | DOI:10.1038/s41598-025-87226-x

Sci Rep. 2025 Jan 30;15(1):3790. doi: 10.1038/s41598-025-88103-3.

ABSTRACT

Numerous studies have proven the potential of deep learning models for classifying wildlife. Such models can reduce the workload of experts by automating species classification to monitor wild populations and global trade. Although deep learning models typically perform better with more input data, the available wildlife data are ordinarily limited, specifically for rare or endangered species. Recently, citizen science programs have helped accumulate valuable wildlife data, but such data is still not enough to achieve the best performance of deep learning models compared to benchmark datasets. Recent studies have applied the hierarchical classification of a given wildlife dataset to improve model performance and classification accuracy. This study applied hierarchical classification by transfer learning for classifying Amazon parrot species. Specifically, a hierarchy was built based on diagnostic morphological features. Upon evaluating model performance, the hierarchical model outperformed the non-hierarchical model in detecting and classifying Amazon parrots. Notably, the hierarchical model achieved the mean Average Precision (mAP) of 0.944, surpassing the mAP of 0.908 achieved by the non-hierarchical model. Moreover, the hierarchical model improved classification accuracy between morphologically similar species. The outcomes of this study may facilitate the monitoring of wild populations and the global trade of Amazon parrots for conservation purposes.

PMID:39885290 | DOI:10.1038/s41598-025-88103-3

Sci Rep. 2025 Jan 30;15(1):3770. doi: 10.1038/s41598-025-85199-5.

ABSTRACT

The diverse types and sizes, proximity to non-nodule structures, identical shape characteristics, and varying sizes of nodules make them challenging for segmentation methods. Although many efforts have been made in automatic lung nodule segmentation, most of them have not sufficiently addressed the challenges related to the type and size of nodules, such as juxta-pleural and juxta-vascular nodules. The current research introduces a Squeeze-Excitation Dilated Attention-based Residual U-Net (SEDARU-Net) with a robust intensity normalization technique to address the challenges related to different types and sizes of lung nodules and to achieve an improved lung nodule segmentation. After preprocessing the images with the intensity normalization method and extracting the Regions of Interest by YOLOv3, they are fed into the SEDARU-Net with dilated convolutions in the encoder part. Then, the extracted features are given to the decoder part, which involves transposed convolutions, Squeeze-Excitation Dilated Residual blocks, and skip connections equipped with an Attention Gate, to decode the feature maps and construct the segmentation mask. The proposed model was evaluated using the publicly available Lung Nodule Analysis 2016 (LUNA16) dataset, achieving a Dice Similarity Coefficient of 97.86%, IoU of 96.40%, sensitivity of 96.54%, and precision of 98.84%. Finally, it was shown that each added component to the U-Net's structure and the intensity normalization technique increased the Dice Similarity Coefficient by more than 2%. The proposed method suggests a potential clinical tool to address challenges related to the segmentation of lung nodules with different types located in the proximity of non-nodule structures.

PMID:39885263 | DOI:10.1038/s41598-025-85199-5

Sci Rep. 2025 Jan 30;15(1):3803. doi: 10.1038/s41598-025-88251-6.

ABSTRACT

In the task of pavement distress recognition and classification, the complexity of the pavement environment, the small proportion of distresses in images, significant variation in distress scales, and the influence of features such as vehicles and traffic signs in the data make distress feature extraction challenging. This paper proposes a spectrum focus transformer (SFT) layer, which processes the signal spectrum and focuses on important frequency components. Initially, by thoroughly analyzing the frequency domain characteristics of image data, frequency value distribution information is obtained to achieve fine-tuning of different frequency components. Subsequently, frequency information and images are learned and weighted in the frequency domain, thereby enhancing the capability to capture pavement distress regions. Experiments conducted on the road pavement distress dataset revealed through heatmap analysis that distress regions received increased attention, achieving an accuracy of 97.73%. This performance demonstrates a higher accuracy compared to other models.

PMID:39885250 | DOI:10.1038/s41598-025-88251-6

Sci Rep. 2025 Jan 30;15(1):3746. doi: 10.1038/s41598-025-87979-5.

ABSTRACT

Accurate malaria diagnosis with precise identification of Plasmodium species is crucial for an effective treatment. While microscopy is still the gold standard in malaria diagnosis, it relies heavily on trained personnel. Artificial intelligence (AI) advances, particularly convolutional neural networks (CNNs), have significantly improved diagnostic capabilities and accuracy by enabling the automated analysis of medical images. Previous models efficiently detected malaria parasites in red blood cells but had difficulty differentiating between species. We propose a CNN-based model for classifying cells infected by P. falciparum, P. vivax, and uninfected white blood cells from thick blood smears. Our best-performing model utilizes a seven-channel input and correctly predicted 12,876 out of 12,954 cases. We also generated a cross-validation confusion matrix that showed the results of five iterations, achieving 63,654 out of 64,126 true predictions. The model's accuracy reached 99.51%, a precision of 99.26%, a recall of 99.26%, a specificity of 99.63%, an F1 score of 99.26%, and a loss of 2.3%. We are now developing a system based on real-world quality images to create a comprehensive detection tool for remote regions where trained microscopists are unavailable.

PMID:39885248 | DOI:10.1038/s41598-025-87979-5

Sci Rep. 2025 Jan 30;15(1):3763. doi: 10.1038/s41598-025-87171-9.

ABSTRACT

Diabetic retinopathy stands as a leading cause of blindness among people. Manual examination of DR images is labor-intensive and prone to error. Existing methods to detect this disease often rely on handcrafted features which limit the adaptability and classification accuracy. Thus, the aim of this research is to develop an automated and efficient system for early detection and accurate grading of diabetic retinopathy severity with less time consumption. In our research, we have developed a deep neural network named RSG-Net (Retinopathy Severity Grading) to classify DR into 4 stages (multi-class classification) and 2 stages (binary classification). The dataset utilized in this study is Messidor-1. In preprocessing, we have used Histogram Equalization to improve image contrast and denoising techniques to remove noise and artifacts which enhanced the clarity of the fundus images. We applied data augmentation techniques to our preprocessed images in order to tackle class imbalance issues. Augmentation techniques involve flipping, rotation, zooming and adjustment of color, contrast and brightness. The proposed RSG-Net model contains convolutional layers to perform automatic feature extraction from the input images and batch normalization layers to improve training speed and performance. The model also contains max pooling, drop out and fully connected layers. Our proposed RSG-Net model achieved a testing accuracy of 99.36%, specificity of 99.79% and a sensitivity of 99.41% in classifying diabetic retinopathy into 4 grades and it achieved 99.37% accuracy, 100% sensitivity and 98.62% specificity in classifying DR into 2 grades. The performance of RSG-Net is also compared with other state-of-the-art methodologies where it outperformed these methods.

PMID:39885230 | DOI:10.1038/s41598-025-87171-9

Sci Rep. 2025 Jan 30;15(1):3839. doi: 10.1038/s41598-025-88439-w.

ABSTRACT

With the rapid development of Internet of Things (IoT) technology, embedded devices in various computer vision scenarios can realize real-time target detection and recognition tasks, such as intelligent manufacturing, automatic driving, smart home, and so on. YOLOv8, as an advanced deep learning model in the field of target detection, has attracted much attention for its excellent detection speed, high precision, and multi-task processing capability. However, since IoT embedded devices typically own limited computing resources, direct deployment of YOLOv8 is a big challenge, especially for real-time detection tasks. To address this vital issue, this work proposes and deploys an optimized lightweight real-time detection network model that well-suits for IoT embedded devices, denoted as FRYOLO. To evaluate its performance, a case study based on real-time fresh and defective fruit detection in the production line is performed. Characterized by low training cost and high detection performance, this model accurately detects various types of fruits and their states, as the experimental results show that FRYOLO achieves 84.7% in recall and 89.0% in mean Average Precision (mAP), along with a precision of 92.5%. In addition, it provides a detection frame rate of up to 33 Frames Per Second (FPS), satisfying the real-time requirement. Finally, an intelligent production line system based on FRYOLO is implemented, which not only provides robust technical support for the efficient operation of fruit production processes but also demonstrates the availability of the proposed network model in practical IoT applications.

PMID:39885208 | DOI:10.1038/s41598-025-88439-w

Sci Data. 2025 Jan 30;12(1):185. doi: 10.1038/s41597-025-04482-2.

ABSTRACT

We present an air-to-air multi-sensor and multi-view fixed-wing UAV dataset, MMFW-UAV, in this work. MMFW-UAV contains a total of 147,417 fixed-wing UAVs images captured by multiple types of sensors (zoom, wide-angle, and thermal imaging sensors), displaying the flight status of fixed-wing UAVs of different sizes, appearances, structures, and stabilized flight velocities from multiple aerial perspectives (top-down, horizontal, and bottom-up views), aiming to cover the full-range of perspectives with multi-modal image data. Quality control processes of semi-automatic annotation, manual check, and secondary refinement are performed on each image. To the best of our knowledge, MMFW-UAV is the first one-to-one multi-modal image dataset for fixed-wing UAVs with high-quality annotations. Several mainstream deep learning-based object detection architectures are evaluated on MMFW-UAV and the experimental results demonstrate that MMFW-UAV can be utilized for fixed-wing UAV identification, detection, and monitoring. We believe that MMFW-UAV will contribute to various fixed-wing UAVs-based research and applications.

PMID:39885165 | DOI:10.1038/s41597-025-04482-2

Drug Metab Dispos. 2025 Jan;53(1):100010. doi: 10.1124/dmd.124.001917. Epub 2024 Nov 22.

ABSTRACT

Pirfenidone (PIR) is used in the treatment of idiopathic pulmonary fibrosis. After oral administration, it is metabolized by cytochrome P450 1A2 to 5-hydroxylpirfenidone (5-OH PIR) and further oxidized to 5-carboxylpirfenidone (5-COOH PIR), a major metabolite excreted in the urine (90% of the dose). This study aimed to identify enzymes that catalyze the formation of 5-COOH PIR from 5-OH PIR in the human liver. 5-COOH PIR was formed from 5-OH PIR in the presence of NAD+ by human liver microsomes (HLMs) more than by human liver cytosol (HLC), with the concomitant formation of the aldehyde form (5-CHO PIR) as an intermediate metabolite. By purifying enzymes from HLMs, alcohol dehydrogenases (ADHs) were identified as candidate enzymes catalyzing 5-CHO PIR formation, although ADHs are localized in the cytoplasm. Among constructed recombinant ADH1-5 expressed in HEK293T cells, only ADH4 efficiently catalyzed 5-CHO PIR formation from 5-OH PIR with a Km value (29.0 ± 4.9 μM), which was close to that by HLMs (59.1 ± 4.6 μM). In contrast to commercially available HLC, HLC prepared in-house clearly showed substantial 5-CHO PIR formation, and ADH4 protein levels were significantly (rs = 0.772, P < .0001) correlated with 5-CHO PIR formation in 25 HLC samples prepared in-house. Some components of the commercially available HLC may inhibit ADH4 activity. Disulfiram, an inhibitor of aldehyde dehydrogenases (ALDH), decreased 5-COOH PIR formation and increased 5-CHO PIR formation from 5-OH PIR in HLMs. ALDH2 knockdown in HepG2 cells by siRNA decreased 5-COOH PIR formation by 61%. SIGNIFICANCE STATEMENT: This study clarified that 5-carboxylpirfenidone formation from 5-hydroxylpirfenidone proceeds via a 2-step oxidation reaction catalyzed by ADH4 and disulfiram-sensitive enzymes, including ALDH2. Interindividual differences in the expression levels or functions of these enzymes could cause variations in the pharmacokinetics of pirfenidone.

PMID:39884816 | DOI:10.1124/dmd.124.001917

Nat Commun. 2025 Jan 30;16(1):1176. doi: 10.1038/s41467-025-56303-0.

ABSTRACT

A protein's molecular interactions and post-translational modifications (PTMs), such as phosphorylation, can be co-dependent and reciprocally co-regulate each other. Although this interplay is central for many biological processes, a systematic method to simultaneously study assembly states and PTMs from the same sample is critically missing. Here, we introduce SEC-MX (Size Exclusion Chromatography fractions MultipleXed), a global quantitative method combining Size Exclusion Chromatography and PTM-enrichment for simultaneous characterization of PTMs and assembly states. SEC-MX enhances throughput, allows phosphopeptide enrichment, and facilitates quantitative differential comparisons between biological conditions. Conducting SEC-MX on HEK293 and HCT116 cells, we generate a proof-of-concept dataset, mapping thousands of phosphopeptides and their assembly states. Our analysis reveals intricate relationships between phosphorylation events and assembly states and generates testable hypotheses for follow-up studies. Overall, we establish SEC-MX as a valuable tool for exploring protein functions and regulation beyond abundance changes.

PMID:39885126 | DOI:10.1038/s41467-025-56303-0

Nat Commun. 2025 Jan 30;16(1):1182. doi: 10.1038/s41467-025-56449-x.

ABSTRACT

Ruminococcus gnavus is a gut bacterium found in > 90% of healthy individuals, but its increased abundance is also associated with chronic inflammatory diseases, particularly Crohn's disease. Nevertheless, its global distribution and intraspecies genomic variation remain understudied. By surveying 12,791 gut metagenomes, we recapitulated known associations with metabolic diseases and inflammatory bowel disease. We uncovered a higher prevalence and abundance of R. gnavus in Westernized populations and observed bacterial relative abundances up to 83% in newborns. Next, we built a resource of R. gnavus isolates (N = 45) from healthy individuals and Crohn's disease patients and generated complete R. gnavus genomes using PacBio circular consensus sequencing. Analysis of these genomes and publicly available high-quality draft genomes (N = 333 genomes) revealed multiple clades which separated Crohn's-derived isolates from healthy-derived isolates. Presumed R. gnavus virulence factors could not explain this separation. Bacterial genome-wide association study revealed that Crohn's-derived isolates were enriched in genes related to mobile elements and mucin foraging. Together, we present a large R. gnavus resource that will be available to the scientific community and provide novel biological insights into the global distribution and genomic variation of R. gnavus.

PMID:39885121 | DOI:10.1038/s41467-025-56449-x

Plant Cell Rep. 2025 Jan 30;44(2):46. doi: 10.1007/s00299-025-03432-x.

ABSTRACT

Twenty-nine GRAS genes were identified in passion fruit, the N-terminal regions and 3D (three-dimensional) structures were closely related with their tissue-specific expression patterns. Candidate PeGRASs for enhancing stress resistance were identified. Passion fruit (Passiflora edulis Sims) is a tropical fruit crop with significant edible and ornamental value, but its growth and development are highly sensitive to environmental conditions. The plant-specific GRAS gene family plays critical roles in regulating growth, development, and stress responses. Here, we performed the first comprehensive analysis of the GRAS gene family in passion fruit. A total of 29 GRAS genes were identified and named PeGRAS1 to PeGRAS29 based on their chromosomal locations. Phylogenetic analysis using GRAS proteins from passion fruit, Arabidopsis, and rice revealed that PeGRAS proteins could be classified into 10 subfamilies. Compared to Arabidopsis, passion fruit lacked members from the LAS subfamily but gained one GRAS member (PeGRAS9) clustered with the rice-specific Os4 subfamily. Structural analysis performed in silico revealed that most PeGRAS members were intron less and exhibited conserved motif patterns near the C-terminus, while the N-terminal regions varied in sequence length and composition. Members within certain subfamilies including DLT, PAT1, and LISCL with similar unstructured N-terminal regions and 3D structures, exhibited similar tissue-specific expression patterns. While PeGRAS members with difference in these structural features, even within the same subfamily (e.g., DELLA), displayed distinct expression patterns. These findings highlighted that the N-terminal regions of GRAS proteins may be critical for their specific functions. Moreover, many PeGRAS members, particularly those from the PAT1 subfamily, were widely involved in stress responses, with PeGRAS19 and PeGRAS26 likely playing roles in cold tolerance, and PeGRAS25 and PeGRAS28 in drought resistance. This study provides a foundation for further functional research on PeGRASs and offers potential candidates for molecular breeding aimed at enhancing stress tolerance in passion fruit.

PMID:39885065 | DOI:10.1007/s00299-025-03432-x

Methods Mol Biol. 2025;2895:3-13. doi: 10.1007/978-1-0716-4350-1_1.

ABSTRACT

This introductory chapter traces the evolution of (bio)chemical assays, emphasizing the critical role of robust protocols in ensuring reproducibility-a fundamental aspect of scientific research. With the advent of systems biology, the need for standardized methods has intensified, particularly for integrating vast datasets in open-access formats. The historical progression from basic plant morphology to advanced chromatographic and spectroscopic techniques in phytochemistry highlights the necessity for precise, reproducible protocols.As metabolomics advances, there is a renewed focus on targeted approaches, shifting from broad, untargeted analyses to more specific, hypothesis-driven studies. This chapter also explores the future of analytical techniques, including cellomics and real-time metabolic flux measurements, which offer new insights into dynamic biochemical processes.Ultimately, this introduction underscores the importance of innovation in developing new methods that address current scientific challenges, particularly in pharmacognosy and analytical phytochemistry. The chapter sets the stage for the broader discussion on the necessity of well-designed protocols that facilitate effective data sharing and collaboration across research disciplines.

PMID:39885019 | DOI:10.1007/978-1-0716-4350-1_1

J Mol Biol. 2025 Jan 28:168961. doi: 10.1016/j.jmb.2025.168961. Online ahead of print.

ABSTRACT

N4-acetylcytidine (ac4C) is a crucial post-transcriptional modification in human mRNA, involving the acetylation of the nitrogen atom at the fourth position of cytidine. This modification, catalyzed by N-acetyltransferases such as NAT10, is primarily found in mRNA's coding regions and enhances translation efficiency and mRNA stability. ac4C is closely associated with various diseases, including cancer. Therefore, accurately identifying ac4C in human mRNA is essential for gaining deeper insights into disease pathogenesis and provides potential pathways for the development of novel medical interventions. In silico methods for identifying ac4C are gaining increasing attention due to their cost-effectiveness, requiring minimal human and material resources. In this study, we propose an efficient and accurate computational framework, Caps-ac4C, for the precise detection of ac4C in human mRNA. Caps-ac4C utilizes chaos game representation to encode RNA sequences into "images" and employs capsule networks to learn global and local features from these RNA "images". Experimental results demonstrate that Caps-ac4C achieves state-of-the-art performance, achieving 95.47% accuracy and 0.912 MCC on the test set, surpassing the current best methods by 10.69% accuracy and 0.216 MCC. In summary, Caps-ac4C represents the most accurate tool for predicting ac4C sites in human mRNA, highlighting its significant contribution to RNA modification research. For user convenience, we developed a user-friendly web server, which can be accessed for free at:https://awi.cuhk.edu.cn/Caps-ac4C/index.php.

PMID:39884569 | DOI:10.1016/j.jmb.2025.168961

Virol Sin. 2025 Jan 28:S1995-820X(25)00004-5. doi: 10.1016/j.virs.2025.01.004. Online ahead of print.

NO ABSTRACT

PMID:39884360 | DOI:10.1016/j.virs.2025.01.004

Structure. 2025 Jan 21:S0969-2126(25)00002-4. doi: 10.1016/j.str.2025.01.002. Online ahead of print.

ABSTRACT

Broadly neutralizing antibodies (nAbs) are vital therapeutic tools to counteract both pandemic and seasonal influenza threats. Traditional strategies for optimizing nAbs generally rely on labor-intensive, high-throughput mutagenesis screens. Here, we present an innovative structure-based design framework for the optimization of nAbs, which integrates epitope-paratope analysis, computational modeling, and rational design approaches, complemented by comprehensive experimental assessment. This approach was applied to optimize MEDI8852, a nAb targeting the stalk region of influenza A virus hemagglutinin (HA). The resulting variant, M18.1.2.2, shows a marked enhancement in both affinity and neutralizing efficacy, as demonstrated both in vitro and in vivo. Computational modeling reveals that this improvement can be attributed to the fine-tuning of interactions between the antibody's side-chains and the epitope residues that are highly conserved across the influenza A virus HA stalk. Our dry-wet iterative protocol for nAb optimization presented here yielded a promising candidate for influenza intervention.

PMID:39884272 | DOI:10.1016/j.str.2025.01.002

Talanta. 2025 Jan 23;287:127619. doi: 10.1016/j.talanta.2025.127619. Online ahead of print.

ABSTRACT

Cysteamine (CA) serves as a cystine-depleting agent employed in the management of cystinosis and a range of other medical conditions. Monitoring blood CA levels at the point of care is imperative due to the risk of toxicity associated with elevated CA dosages. An additional significant challenge is presented by the intricate composition of human plasma and the presence of various interfering biological thiols, which possess similar structures or properties. Here, this work proposes an AI-enhanced Lab-on-a-disc system, also termed AI-LOAD, for multiplexed point-of-care testing of cysteamine. The AI-LOAD system incorporates an online whole blood separation mechanism alongside a naked-eye colorimetric detection module, facilitating the rapid and precise visual identification of cysteamine. Remarkably, the system necessitates only 40 μL of whole blood to analyze eight samples within 3-min, achieving a limit of detection as low as 10 μM, which is lower than the physiological toxic concentration of 0.1 mM. By leveraging diverse colorimetric responses generated through interactions between gold nanoparticles of varying sizes and different biological thiols, combined with artificial intelligence methodologies, the system successfully accomplished specific recognition of various biological thiols with 100 % accuracy. The proposed AI-LOAD will drive advancements in centrifugal microfluidics for point-of-care testing, thereby holding potential for broader applications in future biomedical research and in vitro diagnosis.

PMID:39884122 | DOI:10.1016/j.talanta.2025.127619

J Pharm Health Care Sci. 2025 Jan 31;11(1):8. doi: 10.1186/s40780-025-00413-w.

ABSTRACT

BACKGROUND: The Anticholinergic Risk Scale and Total Anticholinergic Load were developed to assess the risks associated with anticholinergic drugs. Recently, the Japan Anticholinergic Risk Scale was introduced; however, the total anticholinergic load for adverse events has not been clarified, and the criteria for risk assessment in clinical practice have not been established. In this study, we used data from the Japanese Adverse Drug Event Report (JADER) database provided by the Pharmaceuticals and Medical Devices Agency to determine the total anticholinergic load associated with reported adverse events related to anticholinergic syndrome.

METHODS: Using JADER data from April 2004 to September 2023, we investigated the association between drugs included in the J-ARS and adverse events related to anticholinergic syndrome. In addition, we calculated the total anticholinergic load for each case involving a drug recorded in the JADER database and compared it with other adverse events associated with anticholinergic effects.

RESULTS: Based on the JADER data, we observed an association between anticholinergic syndrome-related adverse events and the drugs listed in the J-ARS, confirming the feasibility of calculating the total anticholinergic drug burden for each case. In the group reporting anticholinergic syndrome-related adverse events, the mean ± standard deviation of the total anticholinergic load was 4.20 ± 3.09.

CONCLUSIONS: The mean total anticholinergic load of anticholinergic syndrome-related adverse events obtained from the JADER database in this study supports the development of a comprehensive risk assessment of anticholinergic drugs in clinical practice.

PMID:39885610 | DOI:10.1186/s40780-025-00413-w

BMC Pharmacol Toxicol. 2025 Jan 30;26(1):24. doi: 10.1186/s40360-025-00858-7.

ABSTRACT

BACKGROUND: A number of pharmaceuticals, including antidepressants and antiepileptics, have a strong correlation with suicide risk. However, it is not entirely clear which of these medications are more strongly associated with suicide-related behaviors.

OBJECTIVE: This study aims to elucidate the drugs responsible for drug-associated suicidal ideation or self-injurious, recognizing the severe consequences associated with such outcomes. However, it is not entirely clear which specific medications are associated with higher levels of suicide-related behavior. Real-world data from the FDA adverse event reporting system database were analyzed to identify medications correlated with suicidal ideation or self-injurious.

METHODS: The reporting intensity of the High-Level Term "suicidal ideation or self-injurious behavior" and its Preferred Terms across distinct categories was assessed using the Reporting Odds Ratio (ROR) and Proportional Reporting Ratio (PRR).

RESULTS: We identified the top 20 drugs with the highest reporting frequencies, spanning sedative-hypnotics, antidepressants, antipsychotics, antiepileptics, antihypertensives, antipyretic/analgesic drugs, and antihyperglycemic drugs. Ranking these medications according to ROR, the top five medications with ROR values related to suicidal ideation or self-injurious were alprazolam, zolpidem, amphetamine, quetiapine, and fluoxetine. Further analysis showed that suicide-related adverse events were more frequently reported in females. Antiepileptics had the highest frequency of reported adverse events in the 51-55 year age group, compared to 16-20 years for antidepressants and 46-50 years for sedative-hypnotics.

CONCLUSION: Our study provides valuable information for clinical drug selection by presenting a potential list of medication classes commonly associated with drug-associated suicidal ideation or self-injurious behavior. We observed a large number of adverse event reports of suicidal ideation with duloxetine and relatively few reports of suicide attempts. Acetaminophen and amlodipine had substantial adverse event reports of completed suicides, but may not be associated with drug-induced suicidal behavior. On the other hand, some drugs mentioned in this study, such as quetiapine, aripiprazole, and lamotrigine, are recommended to be used after assessing the risk level of suicide in patients.

PMID:39885564 | DOI:10.1186/s40360-025-00858-7

Drug Ther Bull. 2025 Jan 30:dtb-2025-000006. doi: 10.1136/dtb.2025.000006. Online ahead of print.

NO ABSTRACT

PMID:39884824 | DOI:10.1136/dtb.2025.000006