J Intensive Care. 2025 Jun 5;13(1):31. doi: 10.1186/s40560-025-00803-0.

ABSTRACT

Diarrhea is common in critically ill patients and can lead to malnutrition, electrolyte imbalance, and dehydration. While its direct impact on outcomes, such as mortality or intensive care unit (ICU) stay, remains unclear due to inconsistent definitions, it often results from drug-induced causes, such as antibiotics and antacids. These agents can also contribute to dysbiosis and increase the risk of infections including Clostridioides difficile infections (CDI) and ventilator-associated pneumonia (VAP).Probiotics, defined as live beneficial microorganisms, can counteract dysbiosis by modulating immune responses, restoring microbial balance, and reducing intestinal inflammation. Evidence suggests that probiotics may help prevent diarrhea and secondary infections. Clinical trials and meta-analyses have shown that probiotics may reduce the incidence of VAP, length of ICU stay, duration of mechanical ventilation, and potential in-hospital mortality in critically ill patients.However, evaluating probiotic efficacy remains challenging due to the lack of standardized markers and the influence of confounding factors like antacid use. In a randomized controlled trial, synbiotic therapy was associated with improved fecal microbiota and reduced infections; however, the role of antacids was not addressed.Probiotics are generally safe, although rare adverse events, such as probiotic-associated bacteremia, have been reported, particularly in immunocompromised individuals.The 2024 Japanese Critical Care Nutrition Guidelines included a systematic review and meta-analysis supporting the potential benefits of probiotics in critically ill patients. However, due to significant heterogeneity in strains, dosing, duration, and concurrent antibiotic/antacid use, a weak recommendation (GRADE 2C; low certainty) was issued.Future research should focus on the standardized evaluation of diarrhea and microbiota changes, the use of objective markers-such as fecal pH and short-chain fatty acid levels-and clarification of the interactions of probiotics with other medications. Comprehensive bowel management, including the cautious use of antibiotics and antacids, may be essential to fully recognize the therapeutic potential of probiotics in critical care settings.

PMID:40474303 | DOI:10.1186/s40560-025-00803-0

Br J Gen Pract. 2025 Jun 5:BJGP.2024.0806. doi: 10.3399/BJGP.2024.0806. Online ahead of print.

ABSTRACT

Background Pharmacogenetics has the potential to optimise drug therapy and reduce adverse drug effects (ADEs) by tailoring treatment to a patient's genotype, particularly for chronic disorders managed in general practice (GP). However, the adoption of pharmacogenetics in GP remains slow. Aim This study aimed to evaluate the reproducibility of previously reported associations between genomic variants and medically important adverse drug effects (MIADEs) associated with high-risk medications in GP. Design and setting A retrospective study using data from the UK Biobank (UKBB), a population-based cohort of over 500,000 community-based participants. Method We identified high-risk medications prescribed in GP by linking serious ADEs from the Yellow Card database with English GP prescription data. These high-risk medications were then cross-examined with genomic variants associated with MIADEs from the Pharmacogenomics Knowledgebase (PharmGKB), to select variant-drug pairs for investigation within the UKBB. Results From 78 high-risk medications prescribed in GP and 56 PharmGKB annotations linked to MIADE risk, SLCO1B1 rs4149056 was the only variant with guideline-based prescribing recommendations. This variant, along with others of lower evidence levels, was analysed in the UKBB. No genotype-treatment interaction was observed for SLCO1B1 rs4149056 and statin-related muscle toxicity. Similarly, no interactions were detected for the remaining variants in either secondary or exploratory analyses. Conclusion No statistically significant genotype-treatment interactions were observed for MIADE risk associated with high-risk medications in GP. However, the limited predictive value of the assessed variants may reflect underlying phenotypic imprecision and methodological limitations. Hence, further research is needed to validate these results.

PMID:40473432 | DOI:10.3399/BJGP.2024.0806

Am J Gastroenterol. 2025 Jun 5. doi: 10.14309/ajg.0000000000003571. Online ahead of print.

NO ABSTRACT

PMID:40471859 | DOI:10.14309/ajg.0000000000003571

Comput Med Imaging Graph. 2025 May 29;124:102573. doi: 10.1016/j.compmedimag.2025.102573. Online ahead of print.

ABSTRACT

Video laryngoscopes have become increasingly vital in tracheal intubation, providing clear imaging that significantly improves success rates, especially for less experienced clinicians. However, accurate recognition of laryngeal structures remains challenging, which is critical for successful first-attempt intubation in emergency situations. This paper presents MPE-UNet, a deep learning model designed for precise segmentation of laryngeal structures from video laryngoscope images, aiming to assist clinicians in performing tracheal intubation more accurately and efficiently. MPE-UNet follows the classic U-Net architecture, which features an encoder-decoder structure and enhances it with advanced modules and innovative techniques at every stage. In the encoder, we designed an improved multi-scale feature extraction module, which better processes complex throat images. Additionally, a pyramid fusion attention module was incorporated into the skip connections, enhancing the model's ability to capture details by dynamically weighting and merging features from different levels. Moreover, a plug-and-play attention mechanism module was integrated into the decoder, further refining the segmentation process by focusing on important features. The experimental results show that the performance of the proposed method outperforms state-of-the-art methods.

PMID:40472423 | DOI:10.1016/j.compmedimag.2025.102573

J Agric Food Chem. 2025 Jun 5. doi: 10.1021/acs.jafc.5c03646. Online ahead of print.

ABSTRACT

The lack of suitable chemical research methodologies has hindered the discovery of rational daily diet combinations from large-scale dietary-derived compounds. Three deep learning models based on chemical properties for α-glucosidase inhibitors (AGIs), safety, and drug-drug interaction (DDI) were trained. The trained models screened potential AGIs from the FooDB database (approximately 70,000 food-derived compounds) and analyzed the interactions of the selected AGIs. 59 of the 75 selected AGIs from the FooDB database had not been reported before. Betulinic acid in combination with taraxasterol, betulin, and lupeol (all selected from the potential 75 AGIs) was predicted to have a synergistic effect in enhancing the inhibition of α-glucosidase, which was further confirmed by in vitro assays. These collective findings strongly suggest that the potential of deep learning methods based on chemical properties in solving the food chemistry research challenge of developing reasonable daily diet combinations.

PMID:40472393 | DOI:10.1021/acs.jafc.5c03646

Obstet Gynecol. 2025 Jun 5. doi: 10.1097/AOG.0000000000005944. Online ahead of print.

ABSTRACT

Publications on artificial intelligence (AI) applications have dramatically increased for most medical specialties, including obstetrics. Here, we review the most recent pertinent publications on AI programs in obstetrics, describe trends in AI applications for specific obstetric problems, and assess AI's possible effects on obstetric care. Searches were performed in PubMed (MeSH), MEDLINE, Ovid, ClinicalTrials.gov, Google Scholar, and Web of Science using a combination of keywords and text words related to "obstetrics," "pregnancy," "artificial intelligence," "machine learning," "deep learning," and "neural networks," for articles published between June 1, 2019, and May 31, 2024. A total of 1,768 articles met at least one search criterion. After eliminating reviews, duplicates, retractions, inactive research protocols, unspecified AI programs, and non-English-language articles, 207 publications remained for further review. Most studies were conducted outside of the United States, were published in nonobstetric journals, and focused on risk prediction. Study population sizes ranged widely from 10 to 953,909, and model performance abilities also varied widely. Evidence quality was assessed by the description of model construction, predictive accuracy, and whether validation had been performed. Most studies had patient groups differing considerably from U.S. populations, rendering their generalizability to U.S. patients uncertain. Artificial intelligence ultrasound applications focused on imaging issues are those most likely to influence current obstetric care. Other promising AI models include early risk screening for spontaneous preterm birth, preeclampsia, and gestational diabetes mellitus. The rate at which AI studies are being performed virtually guarantees that numerous applications will eventually be introduced into future U.S. obstetric practice. Very few of the models have been deployed in obstetric practice, and more high-quality studies are needed with high predictive accuracy and generalizability. Assuming these conditions are met, there will be an urgent need to educate medical students, postgraduate trainees and practicing physicians to understand how to effectively and safely implement this technology.

PMID:40472381 | DOI:10.1097/AOG.0000000000005944

PLoS One. 2025 Jun 5;20(6):e0324158. doi: 10.1371/journal.pone.0324158. eCollection 2025.

ABSTRACT

In industrial societies, most fishery research institutes collect the phenotypic data of fish manually, which is time-consuming, labor-intensive, error-prone, and results in incomplete data. Considering their stress reaction and the natural body extension to collect the phenotypic data of fish quickly and accurately, channel catfish was used as the research subject and a deep-learning-based method was developed to explore their phenotypic data, i.e., body length, full length, head length, body height, tail handle width, tail handle height, and body thickness. First, this study applied two cameras and another device built into an image acquisition system to obtain images of fish in the water. We then adopted an Hourglass module network to position nine and ten key points on the top and side view images, building two key point fish skeletons. Finally, 3D coordinate transformation and scale parameters were employed to obtain the phenotypic data. Compared with the ground truth of the phenotypic fish data, our study achieved a 3.7% average relative error in terms of the full length, and an average 9.6% relative error for all seven types of phenotypic data applied. Furthermore, the average time required for the image processing measurements was approximately 1s.

PMID:40472062 | DOI:10.1371/journal.pone.0324158

PLoS One. 2025 Jun 5;20(6):e0318795. doi: 10.1371/journal.pone.0318795. eCollection 2025.

ABSTRACT

This study presents an advanced adaptive network steganography paradigm that integrates deep learning methodologies with multimedia video analysis to enhance the universality and security of network steganography practices. The proposed approach utilizes a deep convolutional generative adversarial network-based architecture capable of fine-tuning steganographic parameters in response to the dynamic foreground, stable background, and spatio-temporal complexities of multimedia videos. Empirical evaluations using the MPII and UCF101 video repositories demonstrate that the proposed algorithm outperforms existing methods in terms of steganographic success and resilience. The framework achieves a 95% steganographic success rate and a peak signal-to-noise ratio (PSNR) of 48.3 dB, showing significant improvements in security and steganographic fidelity compared to contemporary techniques. These quantitative results underscore the potential of the approach for practical applications in secure multimedia communication, marking a step forward in the field of network steganography.

PMID:40472042 | DOI:10.1371/journal.pone.0318795

Brief Bioinform. 2025 May 1;26(3):bbaf260. doi: 10.1093/bib/bbaf260.

ABSTRACT

During DNA transcription, the central dogma states that DNA generates corresponding RNA sequences based on the principle of complementary base pairing. However, in the allopolyploid line by goldfish and common carp hybrids, there is a significant level of transcriptional infidelity. To explore deeper into the causes of transcriptional infidelity in this line, we developed a deep learning model to explore its underlying determinants. First, our model can accurately identify transcriptional infidelity sequences at the nucleotide resolution and effectively distinguish transcriptional infidelity regions at the subregional level. Subsequently, we utilized this model to quantitatively assess the importance of position-specific motifs. Furthermore, by integrating the relationship between transcription factors and their recognition motifs, we unveiled the distribution of position-specific transcription factor families and classes that influence transcriptional infidelity in this line. In summary, our study provides new insights into the deeper determinants of transcriptional infidelity in this line.

PMID:40471993 | DOI:10.1093/bib/bbaf260

PLoS One. 2025 Jun 5;20(6):e0310716. doi: 10.1371/journal.pone.0310716. eCollection 2025.

ABSTRACT

This study presents an initial model for bark beetle identification, serving as a foundational step toward developing a fully functional and practical identification tool. Bark beetles are known for extensive damage to forests globally, as well as for uniform and homoplastic morphology which poses identification challenges. Utilizing a MaxViT-based deep learning backbone which utilizes local and global attention to classify bark beetles down to the genus level from images containing multiple beetles. The methodology involves a process of image collection, preparation, and model training, leveraging pre-classified beetle species to ensure accuracy and reliability. The model's F1 score estimates of 0.99 and 1.0 indicates a strong ability to accurately classify genera in the collected data, including those previously unknown to the model. This makes it a valuable first step towards building a tool for applications in forest management and ecological research. While the current model distinguishes among 12 genera, further refinement and additional data will be necessary to achieve reliable species-level identification, which is particularly important for detecting new invasive species. Despite the controlled conditions of image collection and potential challenges in real-world application, this study provides the first model capable of identifying the bark beetle genera, and by far the largest training set of images for any comparable insect group. We also designed a function that reports if a species appears to be unknown. Further research is suggested to enhance the model's generalization capabilities and scalability, emphasizing the integration of advanced machine learning techniques for improved species classification and the detection of invasive or undescribed species.

PMID:40471899 | DOI:10.1371/journal.pone.0310716

JACC Clin Electrophysiol. 2025 May 6:S2405-500X(25)00253-1. doi: 10.1016/j.jacep.2025.04.003. Online ahead of print.

NO ABSTRACT

PMID:40471767 | DOI:10.1016/j.jacep.2025.04.003

IEEE J Biomed Health Inform. 2025 Jun;29(6):4362-4373. doi: 10.1109/JBHI.2025.3541849.

ABSTRACT

Fully-supervised deep neural networks have achieved remarkable progress in medical image segmentation, yet they heavily rely on extensive manually labeled data and exhibit inflexibility for unseen tasks. Few-shot segmentation (FSS) addresses these issues by predicting unseen classes from a few labeled support examples. However, most existing FSS models struggle to generalize to diverse target tasks distinct from training domains. Furthermore, designing promising network architectures for such tasks is expertise-intensive and laborious. In this paper, we introduce MOE-FewSeg, a novel automatic design method for FSS architectures. Specifically, we construct a U-shaped encoder-decoder search space that incorporates capabilities for information interaction and feature selection, thereby enabling architectures to leverage prior knowledge from publicly available datasets across diverse domains for improved prediction of various target tasks. Given the potential conflicts among disparate target tasks, we formulate the multi-task problem as a multi-objective optimization problem. We employ a multi-objective genetic algorithm to identify the Pareto-optimal architectures for these target tasks within this search space. Furthermore, to mitigate the impact of noisy labels due to dataset quality variations, we propose a noise-robust loss function named NRL, which encourages the model to de-emphasize larger loss values. Empirical results demonstrate that MOE-FewSeg outperforms manually designed architectures and other related approaches.

PMID:40471744 | DOI:10.1109/JBHI.2025.3541849

IEEE Trans Biomed Eng. 2025 Jun 5;PP. doi: 10.1109/TBME.2025.3577084. Online ahead of print.

ABSTRACT

OBJECTIVE: Accurate, non-invasive methods for estimating joint and muscle physiological states have the potential to greatly enhance control of wearable devices during real-world ambulation. Traditional modeling approaches and current estimation methods used to predict muscle dynamics often rely on complex equipment or computationally intensive simulations and have difficulty estimating across a broad spectrum of tasks or subjects.

METHODS: Our approach used deep learning (DL) models trained on kinematic inputs to estimate internal physiological states at the knee, including moment, power, velocity, and force. We assessed each model's performance against ground truth labels from both a commonly used, standard OpenSim musculoskeletal model without EMG (static optimization) and an EMG-informed method (CEINMS), across 28 different cyclic and noncyclic tasks.

RESULTS: EMG provided no benefit for joint moment/power estimation (e.g., biological moment), but was critical for estimating muscle states. Models trained with EMG-informed labels but without EMG as an input to the DL system significantly outperformed models trained without EMG (e.g., 33.7% improvement for muscle moment estimation) (p < 0.05). Models that included EMG-informed labels and EMG as a model input demonstrated even higher performance (49.7% improvement for muscle moment estimation) (p < 0.05), but require the availability of EMG during model deployment, which may be impractical.

CONCLUSION/SIGNIFICANCE: While EMG information is not necessary for estimating joint level states, there is a clear benefit during muscle level state estimation. Our results demonstrate excellent tracking of these states with EMG included only during training, highlighting the practicality of real-time deployment of this approach.

PMID:40471740 | DOI:10.1109/TBME.2025.3577084

Blood Adv. 2025 Jun 5:bloodadvances.2025016209. doi: 10.1182/bloodadvances.2025016209. Online ahead of print.

ABSTRACT

Platelets fulfill their essential physiological roles sensing the extracellular environment through their membrane proteins. The native membrane environment provides essential regulatory cues that impact the protein structure and mechanism of action. Single-particle cryogenic electron microscopy (cryo-EM) has transformed structural biology by allowing high-resolution structures of membrane proteins to be solved from homogeneous samples. Our recent breakthroughs in data processing now make it feasible to obtain atomic-level-resolution protein structures from crude preparations in their native environments by integrating cryo-EM with the "Build-and-Retrieve" (BaR) data processing methodology. We applied this iterative bottom-up methodology on resting human platelet membranes for an in-depth systems biology approach to uncover how lipids, metal binding, post-translational modifications, and co-factor associations in the native environment regulate platelet function at the molecular level. Here, we report using cryo-EM followed by the BaR method to solve the unmodified integrin αIIbβ3 structure directly from resting human platelet membranes in its inactivated and intermediate states at 2.75Å and 2.67Å, respectively. Further, we also solved a novel dimer conformation of αIIbβ3 at 2.85Å formed by two intermediate-states of αIIbβ3. This may indicate a previously unknown self-regulatory mechanism of αIIbβ3 in its native environment. In conclusion, our data show the power of using cryo-EM with the BaR method to determine three distinct structures including a novel dimer directly from natural sources. This approach allows us to identify unrecognized regulation mechanisms for proteins without artifacts due to purification processes. These data have the potential to enrich our understanding of platelet signaling circuitry.

PMID:40472320 | DOI:10.1182/bloodadvances.2025016209

Elife. 2025 Jun 5;13:RP94586. doi: 10.7554/eLife.94586.

ABSTRACT

A classic problem in metabolism is that fast-proliferating cells use seemingly wasteful fermentation for energy biogenesis in the presence of sufficient oxygen. This counterintuitive phenomenon, known as overflow metabolism or the Warburg effect, is universal across various organisms. Despite extensive research, its origin and function remain unclear. Here, we show that overflow metabolism can be understood through growth optimization combined with cell heterogeneity. A model of optimal protein allocation, coupled with heterogeneity in enzyme catalytic rates among cells, quantitatively explains why and how cells choose between respiration and fermentation under different nutrient conditions. Our model quantitatively illustrates the growth rate dependence of fermentation flux and enzyme allocation under various perturbations and is fully validated by experimental results in Escherichia coli. Our work provides a quantitative explanation for the Crabtree effect in yeast and the Warburg effect in cancer cells and can be broadly used to address heterogeneity-related challenges in metabolism.

PMID:40472190 | DOI:10.7554/eLife.94586

Hum Mol Genet. 2025 Jun 5:ddaf090. doi: 10.1093/hmg/ddaf090. Online ahead of print.

ABSTRACT

Shaker-type potassium channel genes (Kv1) have been linked to human epilepsies, including KCNA1 (Kv1.1), KCNA2 (Kv1.2), and more recently, KCNA3 (Kv1.3) and KCNA6 (Kv1.6). In this study, we report three early-onset epilepsy cases with de novo missense mutations in Shaker-type channel genes, including Kv1.3, KCNA4 (Kv1.4), and Kv1.6, identified through whole exome sequencing trio study. The newly identified Kv1.3-V478M, Kv1.6-T421I, and Kv1.4-V558L mutations are located within the channel selectivity filter or S6 hinge, both critical for channel gating. These variants are in paralogous locations of previously reported pathogenic variant in KCNA2. These mutations do not significantly affect trafficking and plasma membrane localization of the Kv channels. In contrast, our patch-clamp analysis in a cell-based system reveals that all three mutations cause severe loss-of-function channel properties. Additionally, our Drosophila model highlights the detrimental effects of Kv1.3-V478M on neural circuit activity. Current findings suggest that, similar to Kv1.1, Kv1.2, and Kv1.3, both loss-of-function and gain-of-function mutations in Kv1.6 may contribute to the phenotypic variability in epilepsy severity. Our study also extends the list of potassium channel genes implicated in human epilepsy, introducing Kv1.4 as a novel epilepsy-related gene.

PMID:40472070 | DOI:10.1093/hmg/ddaf090

Proc Natl Acad Sci U S A. 2025 Jun 10;122(23):e2501830122. doi: 10.1073/pnas.2501830122. Epub 2025 Jun 5.

ABSTRACT

Prior studies have indicated that the transcription factor Creb5 is expressed in the joint interzone, which contains the progenitors for all synovial joint tissues in both mouse and human embryos. In the absence of Creb5 function, most synovial joint interzones fail to form and the cartilage templates in the long bones remain fused. This earlier work did not clarify whether Creb5 initiates a cascade of signaling molecules, such as growth and differentiation factor 5 (Gdf5) and Wnt-family members, that in turn induce the formation of the joint interzone, or instead directly activates the expression of joint interzone markers. In the present study, an integrative analysis of the transcriptome, chromatin accessibility, and Creb5-occupancy in joint progenitors revealed that Creb5 directly binds to both its own two promoters and to the regulatory regions of Gdf5 and Sfrp2, each of whose expression in the joint interzone is Creb5-dependent. Functional enhancer analysis indicated that Creb5 binding sites in either the two Creb5 promoters, or in Gdf5 and Sfrp2 regulatory elements are necessary for these sequences to drive transgene expression in the developing synovial joints. While Creb5 directly drives Gdf5 and Sfrp2 expression in the inner joint interzone, Creb5 activates Barx1 expression specifically in the outer joint interzone. Our findings indicate that Creb5 initiates a regulatory network that both promotes the formation of synovial joints, and subsequently activates distinct transcriptional targets in the inner versus the outer regions of the joint interzone, thus regionalizing gene expression in the developing joint.

PMID:40472036 | DOI:10.1073/pnas.2501830122

Eur Heart J. 2025 Jun 5:ehaf370. doi: 10.1093/eurheartj/ehaf370. Online ahead of print.

ABSTRACT

BACKGROUND AND AIMS: Previous studies have highlighted the significance of RNA-binding proteins and alternative splicing (AS) in the progression of complex diseases, but the specific involvement of AS in heart failure (HF) remains unclear. This study aimed to elucidate the role of RNA-binding motif single-stranded interacting protein 1 (RBMS1), an RNA-binding protein, in the development of HF by regulating AS and its effect on cardiac fibrosis.

METHODS: The level of RBMS1 was investigated in the hearts of both HF patients and mice. Fibroblast-specific knockout RBMS1 mice were generated to investigate the role of RBMS1 in cardiac fibrosis and HF. Unbiased RNA sequencing and RNA immunoprecipitation combined with RNA pull-down were conducted to identify the downstream effector of RBMS1 in fibroblasts.

RESULTS: RBMS1 expression was increased in murine hearts following myocardial infarction, as well as in the hearts of patients with ischaemic cardiomyopathy and hypertrophic cardiomyopathy. Moreover, RBMS1 levels in the hearts of HF patients were positively associated with cardiac fibrosis. Furthermore, fibroblast-specific ablation of RBMS1 improved cardiac dysfunction by mitigating myocardial fibrosis. Mechanistically, RBMS1 regulated the alternative splicing of LIM domain 7 (LMO7) by binding to intron 19 and splicing out exon 20, resulting in the formation of the LMO7-Δe20 isoform, which thus activated the transforming growth factor (TGF)-β1 pathway by upregulating activator protein 1. More importantly, overexpression of LMO7-Δe20 in mice resulted in cardiac fibrosis and cardiac dysfunction, which was ablated after treatment with TGF-β1 pathway inhibitor SB431542. In addition, SB431542 attenuated the RBMS1-driven fibrogenesis in human cardiac fibroblasts. Strikingly, pharmacologically inhibiting RBMS1 by low-dose nortriptyline or antisense oligonucleotide-mediated RBMS1 deficiency alleviated myocardial fibrosis and improved cardiac function in HF mice.

CONCLUSIONS: These findings unveil a critical role of RBMS1 in regulating cardiac fibrosis through controlling the splicing of LMO7 to activate the TGF-β1 pathway. Genetic ablation or pharmacological inhibition of RBMS1 improves cardiac function in mice, suggesting its potential as a therapeutic target for HF.

PMID:40471706 | DOI:10.1093/eurheartj/ehaf370

Nurse Pract. 2024 Dec 1;49(12):33-34. doi: 10.1097/01.NPR.0000000000000263. Epub 2024 Nov 21.

NO ABSTRACT

PMID:40471158 | DOI:10.1097/01.NPR.0000000000000263

Nurse Pract. 2024 Dec 1;49(12):24-33. doi: 10.1097/01.NPR.0000000000000256. Epub 2024 Nov 21.

ABSTRACT

A pharmacogenomics-informed prescribing strategy examines genetic variations in individual patients for more personalized selection and dosing of psychiatric medications for which a clinical evidence base and/or clinical guidelines exist. Clinicians who prescribe psychiatric medications should be aware of the pharmacogenomic evidence base and existing guidelines relevant to medication selection, dosing, and interactions to ensure safe and effective treatment. Although pharmacogenomic testing does not replace current prescribing strategies, when used alongside them, it acts as a valuable clinical decision support tool that can improve the selection and dosing of specific psychiatric medications.

PMID:40471157 | DOI:10.1097/01.NPR.0000000000000256