J Genet Eng Biotechnol. 2025 Jun;23(2):100485. doi: 10.1016/j.jgeb.2025.100485. Epub 2025 Mar 29.

ABSTRACT

INTRODUCTION: Warts are dermal disorders resulting from HPV infection and can be transmitted by direct contact. Existing treatment approaches, such as topical treatment with salicylate, have low efficiency and demonstrate side effects. Thus, the discovery of potent drug treatments for skin warts is necessary. Here we propose the use of alternative medications for the possible treatment of skin warts with the help of comparative transcriptome analysis and drug repurposing approaches.

METHODS: Gene expression datasets related to HPV-induced warts and cervical cancer were extracted from the GEO database. Differentially expressed genes (DEGs) were identified using DESeq2 in the Galaxy database. Upregulated DEGs were assessed for druggability using the DGIdb tool. Gene ontology and enrichment analysis were performed to investigate the characteristics of druggable DEGs. A molecular docking virtual screening was conducted using PyRx software to identify potential therapeutic targets for skin warts. The interactions between selected drug candidates and the target protein were analyzed using the BIOVIA Discovery Studio. The physicochemical characteristics of potential pharmaceuticals were evaluated using the SwissADME database. Finally, the molecular dynamics (MD) simulation was performed to validate the stability and dynamic behavior of drug-protein interactions.

RESULTS: Based on the findings from gene expression profiling, Integrin Alpha-X (ITGAX, CD11c) has been identified as a candidate protein that is significantly upregulated in individuals afflicted with skin warts. Integrin Alpha-X plays a crucial role in mediating intercellular interactions during inflammatory processes and notably enhances the adhesion and chemotactic activity of monocytes. Through molecular docking, MD, and physicochemical analyses, it has been demonstrated that dihydroergotamine effectively inhibits the ITGAX protein, suggesting its potential as a therapeutic agent for the management of skin warts.

CONCLUSION: Dihydroergotamine can be repurposed as a potential drug in the treatment of skin warts by targeting Integrin Alpha-X protein.

PMID:40390498 | DOI:10.1016/j.jgeb.2025.100485

J Genet Eng Biotechnol. 2025 Jun;23(2):100502. doi: 10.1016/j.jgeb.2025.100502. Epub 2025 May 12.

ABSTRACT

Insomnia is a prevalent sleep disorder characterized by difficulty initiating or maintaining sleep, leading to severe health complications, increased mortality, and substantial socioeconomic burdens. Despite therapeutic advancements, effective pharmacological interventions remain limited, necessitating alternative approaches for drug discovery. This study aimed to identify potential therapeutic targets for insomnia by integrating gene network analysis, genomic data, and bioinformatics-driven drug repurposing strategies, aligning with the United Nations' Sustainable Development Goal (SDG) 3: Good Health and Well-being. Insomnia-associated Single Nucleotide Polymorphisms (SNPs) were retrieved from the GWAS catalog, yielding 3,952 loci. Insomnia risk genes were identified by linking these loci to proximal SNPs (r2 ≥ 0.8) in Asian populations using HaploReg v4.2, resulting in 1,765 candidate genes. A bioinformatics pipeline incorporating ten functional annotations and drug-gene interaction was employed to prioritize gene targets and identify novel repurposed drugs with potential biological relevance to insomnia. Drug-Gene Interaction Database (DGIdb) analysis identified seven druggable targets among 27 biologically significant insomnia risk genes, corresponding to 12 existing drugs. Notably, NRXN1 emerged as a highly promising target due to its strong functional annotation score and its known interaction with Duloxetine hydrochloride and nicotine polacrilex. This study underscores the potential of bioinformatics-driven gene network analysis in identifying drug repurposing candidates for insomnia. Further experimental validation is warranted to elucidate the therapeutic mechanisms of NRXN1 modulation in insomnia treatment.

PMID:40390493 | DOI:10.1016/j.jgeb.2025.100502

Clin Pharmacol Ther. 2025 May 20. doi: 10.1002/cpt.3712. Online ahead of print.

ABSTRACT

The Food and Drug Administration (FDA) Table of Inhibitors and the Flockhart Table™ are important references for identifying CYP2C19 inhibitors. Accurate inhibitor classification is essential for evaluating phenoconversion and managing drug-drug interactions. The objective of this study was to assess the concordance between inhibitor classifications according to the FDA and Flockhart tables and pharmacokinetic data from the primary literature. A scoping review of PubMed, product labels, and the Drug Interactions Database (DIDB®) up to April 2024 was conducted. Inhibitor-substrate pairs (e.g., fluoxetine-omeprazole) were assigned literature-reported classifications (i.e., weak, moderate, or strong) based on pharmacokinetic data. Concordance between literature-reported and listed classifications was evaluated. Of 90 inhibitor-substrate pairs identified, 66% involved sensitive substrates, which were the focus of our primary analysis. Among sensitive inhibitor-substrate pairs, 36% of FDA-listed and 45% of Flockhart-listed classifications were concordant with the literature. CYP2C19 phenotype appeared to impact concordance, with greater concordance among normal metabolizers for both FDA-listed (42%) and Flockhart-listed (50%) classifications. Steady state status and dosing also appeared to affect concordance. Discrepancies between listed and literature-reported classifications led to the following recommendations: (1) upgrade fluoxetine from moderate to strong in the Flockhart Table™, (2) upgrade fluconazole from moderate to strong in the Flockhart Table™, (3) downgrade omeprazole (and esomeprazole) from moderate to weak in the Flockhart Table™, and (4) include footnotes describing dose-dependent inhibition for fluvoxamine and fluconazole in both tables. These recommendations aim to improve the accuracy of CYP2C19 inhibitor classifications and the clinical utility of these tables.

PMID:40391533 | DOI:10.1002/cpt.3712

JAC Antimicrob Resist. 2025 May 19;7(3):dlaf077. doi: 10.1093/jacamr/dlaf077. eCollection 2025 Jun.

ABSTRACT

BACKGROUND: Cystic fibrosis (CF) is an autosomal recessive disorder caused by mutations in the CF transmembrane conductance (CFTR) gene, resulting in the secretion of hyperviscous mucus. Infective exacerbations are a major determinant of morbidity and mortality in CF patients. These infections are clinically challenging, and antimicrobial treatment should effectively target the organisms and be delivered early to improve survival. Ceftobiprole is a fifth-generation cephalosporin antibiotic that is not indicated for the treatment of CF. However, due to its activity against common causes of infective exacerbations in CF such as Staphylococcus aureus, including MRSA, and Pseudomonas aeruginosa where resistance has not developed, it has utility for managing infective exacerbations.

OBJECTIVES: To describe the use of ceftobiprole in the treatment of infective exacerbations in CF.

PATIENTS AND METHODS: Ten patients with CF (age 24-63; six male and four female) were treated with ceftobiprole for infective exacerbations following discussion within the multi-disciplinary team. In most patients, ceftobiprole was given concomitantly with other antibiotics.

RESULTS: All patients had positive sputum cultures for S. aureus (including nine MRSA), and seven patients had concomitant P. aeruginosa infection. Ceftobiprole treatment was associated with improved lung function, and markers of systemic inflammation decreased for most patients, with some variation. There was good tolerability in all but four patients.

CONCLUSIONS: Ceftobiprole presents a therapeutic option for susceptible infections in CF patients with limited treatment options. Its broad-spectrum coverage may help to reduce polypharmacy. However, further clinical studies are needed.

PMID:40391172 | PMC:PMC12086531 | DOI:10.1093/jacamr/dlaf077

Wiley Interdiscip Rev Nanomed Nanobiotechnol. 2025 May-Jun;17(3):e70015. doi: 10.1002/wnan.70015.

ABSTRACT

Surface-enhanced Raman scattering (SERS) is a transformative technique for molecular identification, offering exceptional sensitivity, signal specificity, and resistance to photobleaching, making it invaluable for disease diagnosis, monitoring, and spectroscopy-guided surgeries. Unlike traditional Raman spectroscopy, which relies on weak scattering signals, SERS amplifies Raman signals using plasmonic nanoparticles, enabling highly sensitive molecular detection. This technological advancement has led to the development of SERS nanotags with remarkable multiplexing capabilities for biosensing applications. Recent progress has expanded the use of SERS nanotags in bioimaging, theranostics, and more recently, liquid biopsy. The distinction between SERS and conventional Raman spectroscopy is highlighted, followed by an exploration of the molecular assembly of SERS nanotags. Significant progress in bioimaging is summarized, including in vitro studies on 2D/3D cell cultures, ex vivo tissue imaging, in vivo diagnostics, spectroscopic-guided surgery for tumor margin delineation, and liquid biopsy tools for detecting cancer and SARS-CoV-2. A particular focus is the integration of machine learning (ML) and deep learning algorithms to boost SERS nanotag efficacy in liquid biopsies. Finally, it addresses the challenges in the clinical translation of SERS nanotags and offers strategies to overcome these obstacles.

PMID:40391396 | DOI:10.1002/wnan.70015

Stat Comput. 2025;35(4):97. doi: 10.1007/s11222-025-10624-8. Epub 2025 May 17.

ABSTRACT

Individual treatment effect estimation has gained significant attention in recent data science literature. This work introduces the Double Neural Network (Double-NN) method to address this problem within the framework of extended fiducial inference (EFI). In the proposed method, deep neural networks are used to model the treatment and control effect functions, while an additional neural network is employed to estimate their parameters. The universal approximation capability of deep neural networks ensures the broad applicability of this method. Numerical results highlight the superior performance of the proposed Double-NN method compared to the conformal quantile regression (CQR) method in individual treatment effect estimation. From the perspective of statistical inference, this work advances the theory and methodology for statistical inference of large models. Specifically, it is theoretically proven that the proposed method permits the model size to increase with the sample size n at a rate of O ( n ζ ) for some 0 ≤ ζ < 1 , while still maintaining proper quantification of uncertainty in the model parameters. This result marks a significant improvement compared to the range 0 ≤ ζ < 1 2 required by the classical central limit theorem. Furthermore, this work provides a rigorous framework for quantifying the uncertainty of deep neural networks under the neural scaling law, representing a substantial contribution to the statistical understanding of large-scale neural network models.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11222-025-10624-8.

PMID:40391382 | PMC:PMC12085359 | DOI:10.1007/s11222-025-10624-8

NAR Genom Bioinform. 2025 May 19;7(2):lqaf058. doi: 10.1093/nargab/lqaf058. eCollection 2025 Jun.

ABSTRACT

Regulation of DNA or RNA at the transcriptional, post-transcriptional, and translational levels are key steps in the central dogma of molecular biology. DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs) play pivotal roles in the precise regulation of gene expression in these steps. Both of these two classes of proteins are nucleic acid-binding proteins (NABPs), so they exhibit significant similarity in both sequence and structure. However, traditional methods for identifying NABPs are typically time-consuming, costly, and challenging to scale up. Utilizing deep learning to classify proteins intelligently has emerged as a more efficient solution for these issues. In this study, we propose a phased classification method integrating ESM-2 with a dual-path neural network, called DRBP-EDP. Additionally, a refined approach to dataset construction is designed, resulting in the creation of high-quality protein classification datasets. The results demonstrated that the model achieved strong performance, with 90.03% accuracy in the first stage for classifying NABPs and non-nucleic acid-binding proteins, and 89.56% accuracy in the second stage for classifying DBPs and RBPs. To enhance accessibility and usability, DRBP-EDP has been developed in both executable and web-based versions, which are publicly available at https://doi.org/10.5281/zenodo.14092184 and https://github.com/MuQiang-MQ/DRBP-EDP.

PMID:40391089 | PMC:PMC12086546 | DOI:10.1093/nargab/lqaf058

Front Endocrinol (Lausanne). 2025 May 5;16:1506729. doi: 10.3389/fendo.2025.1506729. eCollection 2025.

ABSTRACT

INTRODUCTION: In recent years, artificial intelligence (AI) tools have become widely studied for thyroid ultrasonography (USG) classification. The real-world applicability of these developed tools as pre-operative diagnostic aids is limited due to model overfitting, clinician trust, and a lack of gold standard surgical histology as ground truth class label. The ongoing dilemma within clinical thyroidology is surgical decision making for indeterminate thyroid nodules (ITN). Genomic sequencing classifiers (GSC) have been utilised for this purpose; however, costs and availability preclude universal adoption creating an inequity gap. We conducted this review to analyse the current evidence of AI in ITN diagnosis without the use of GSC.

METHODS: English language articles evaluating the diagnostic accuracy of AI for ITNs were identified. A systematic search of PubMed, Google Scholar, and Scopus from inception to 18 February 2025 was performed using comprehensive search strategies incorporating MeSH headings and keywords relating to AI, indeterminate thyroid nodules, and pre-operative diagnosis. This systematic review and meta-analysis was conducted in accordance with methods recommended by the Cochrane Collaboration (PROSPERO ID CRD42023438011).

RESULTS: The search strategy yielded 134 records after the removal of duplicates. A total of 20 models were presented in the seven studies included, five of which were radiological driven, one utilised natural language processing, and one focused on cytology. The pooled meta-analysis incorporated 16 area under the curve (AUC) results derived from 15 models across three studies yielding a combined estimate of 0.82 (95% CI: 0.81-0.84) indicating moderate-to-good classification performance across machine learning (ML) and deep learning (DL) architectures. However, substantial heterogeneity was observed, particularly among DL models (I² = 99.7%, pooled AUC = 0.85, 95% CI: 0.85-0.86). Minimal heterogeneity was observed among ML models (I² = 0.7%), with a pooled AUC of 0.75 (95% CI: 0.70-0.81). Meta-regression analysis performed suggests potential publication bias or systematic differences in model architectures, dataset composition, and validation methodologies.

CONCLUSION: This review demonstrated the burgeoning potential of AI to be of clinical value in surgical decision making for ITNs; however, study-developed models were unsuitable for clinical implementation based on performance alone at their current states or lacked robust independent external validation. There is substantial capacity for further development in this field.

SYSTEMATIC REVIEW REGISTRATION: https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42023438011.

PMID:40391010 | PMC:PMC12086071 | DOI:10.3389/fendo.2025.1506729

Environ Sci Ecotechnol. 2025 Apr 24;25:100562. doi: 10.1016/j.ese.2025.100562. eCollection 2025 May.

ABSTRACT

Rainfall measurement at high quality and spatiotemporal resolution is essential for urban hydrological modeling and effective stormwater management. However, traditional rainfall measurement methods face limitations regarding spatial coverage, temporal resolution, and data accessibility, particularly in urban settings. Here, we show a novel rainfall estimation framework that leverages surveillance cameras to enhance estimation accuracy and spatiotemporal data coverage. Our hybrid approach consists of two complementary modules: the first employs an image-quality signature technique to detect rain streaks from video frames and selects optimal regions of interest (ROIs). The second module integrates depthwise separable convolution (DSC) layers with gated recurrent units (GRU) in a regression model to accurately estimate rainfall intensity using these ROIs. We evaluate the framework using video data from two locations with distinct rainfall patterns and environmental conditions. The DSC-GRU model achieves high predictive performance, with coefficient of determination (R2) values ranging from 0.89 to 0.93 when validated against rain gauge measurements. Remarkably, the model maintains strong performance during daytime and nighttime conditions, outperforming existing video-based rainfall estimation methods and demonstrating robust adaptability across variable environmental scenarios. The model's lightweight architecture facilitates efficient training and deployment, enabling practical real-time urban rainfall monitoring. This work represents a substantial advancement in rainfall estimation technology, significantly reducing estimation errors and expanding measurement coverage, and provides a practical, low-cost solution for urban hydrological monitoring.

PMID:40390707 | PMC:PMC12088784 | DOI:10.1016/j.ese.2025.100562

Zhen Ci Yan Jiu. 2025 May 25;50(5):531-537. doi: 10.13702/j.1000-0607.20250319.

ABSTRACT

The quantitative research on acupuncture manipulation techniques aims to transform traditional empirical operations into measurable parameters through interdisciplinary technologies. This paper comprehensively reviewed the biomechanical foundations and technological evolution of acupuncture manipulation quantification research. It delved into upgrades in biomechanical parameter collection, breakthroughs in analytical methods, and innovations and applications from computer vision, deep learning, and multimodal perception fusion. Looking ahead, the paper explored deepening multimodal fusion, construction of big data expert databases, and intelligent clinical applications. It proposed that through multidisciplinary technological integration, a digital acupuncture theory system with characteristics of traditional Chinese medicine can be established. These breakthroughs will promote the transformation of acupuncture from empirical practice to data-driven precision medicine, providing theoretical and technological foundations for modernization and international standardization of traditional Chinese medicine.

PMID:40390611 | DOI:10.13702/j.1000-0607.20250319

Adv Sci (Weinh). 2025 May 20:e2417635. doi: 10.1002/advs.202417635. Online ahead of print.

ABSTRACT

On-chip training in analog in-memory computing (AIMC) holds great promise for reducing data latency and enabling user-specific learning. However, analog synaptic devices face significant challenges, particularly during parallel weight updates in crossbar arrays, where non-uniform programming and disturbances often arise. Despite their importance, the disturbances that occur during training are difficult to quantify based on a clear mechanism, and as a result, their impact on training performance remains underexplored. This work precisely identifies and quantifies the disturbance effects in 6T1C synaptic devices based on oxide semiconductors and capacitors, whose endurance and variation have been validated but encounter worsening disturbance effects with device scaling. By clarifying the disturbance mechanism, three simple operational schemes are proposed to mitigate these effects, with their efficacy validated through device array measurements. Furthermore, to evaluate learning feasibility in large-scale arrays, real-time disturbance-aware training simulations are conducted by mapping synaptic arrays to convolutional neural networks for the CIFAR-10 dataset. A software-equivalent accuracy is achieved even under intensified disturbances, using a cell capacitor size of 50fF, comparable to dynamic random-access memory. Combined with the inherent advantages of endurance and variation, this approach offers a practical solution for hardware-based deep learning based on the 6T1C synaptic array.

PMID:40390534 | DOI:10.1002/advs.202417635

NMR Biomed. 2025 Jul;38(7):e70066. doi: 10.1002/nbm.70066.

ABSTRACT

Translating quantitative skeletal muscle MRI biomarkers into clinics requires efficient automatic segmentation methods. The purpose of this work is to investigate a simple yet effective iterative methodology for building a high-quality automatic segmentation model while minimizing the manual annotation effort. We used a retrospective database of quantitative MRI examinations (n = 70) of healthy and pathological thighs for training a nnU-Net segmentation model. Healthy volunteers and patients with various neuromuscular diseases, broadly categorized as dystrophic, inflammatory, neurogenic, and unlabeled NMDs. We designed an iterative procedure, progressively adding cases to the training set and using a simple visual five-level rating scale to judge the validity of generated segmentations for clinical use. On an independent test set (n = 20), we assessed the quality of the segmentation in 13 individual thigh muscles using standard segmentation metrics-dice coefficient (DICE) and 95% Hausdorff distance (HD95)-and quantitative biomarkers-cross-sectional area (CSA), fat fraction (FF), and water-T1/T2. We obtained high-quality segmentations (DICE = 0.88 ± 0.15/0.86 ± 0.14, HD95 = 6.35 ± 12.33/6.74 ± 11.57 mm), comparable to recent works, although with a smaller training set (n = 30). Inter-rater agreement on the five-level scale was fair to moderate but showed progressive improvement of the segmentation model along with the iterations. We observed limited differences from manually delineated segmentations on the quantitative outcomes (MAD: CSA = 65.2 mm2, FF = 1%, water-T1 = 8.4 ms, water-T2 = 0.35 ms), with variability comparable to manual delineations.

PMID:40390325 | DOI:10.1002/nbm.70066

Comput Struct Biotechnol J. 2025 Apr 24;27:1719-1731. doi: 10.1016/j.csbj.2025.04.030. eCollection 2025.

ABSTRACT

Breast cancer stands as one of the most prevalent malignancies affecting women. Alterations in molecular pathways in cancer cells represent key regulatory disruptions that drive malignancy, influencing cancer cell survival, proliferation, and potentially modulating therapeutic responsiveness. Therefore, decoding the intricate molecular mechanisms and identifying novel therapeutic targets through systematic computational approaches are essential steps toward advancing effective breast cancer treatments. In this study, we developed an integrative computational framework that combines single-cell RNA sequencing (scRNA-seq) and multi-omics analyses to delineate the functional characteristics of malignant cell subsets in breast cancer patients. Our analyses revealed a significant correlation between cholesterol biosynthesis and HER2 expression in malignant breast cancer cells, supported by proteomics data, gene expression profiles, drug treatment scores, and cell-surface HER2 intensity measurements. Given previous evidence linking cholesterol biosynthesis to HER2 membrane dynamics, we proposed a combinatorial strategy targeting both pathways. Experimental validation through clonogenic and viability assays demonstrated that simultaneous inhibition of cholesterol biosynthesis (via statins) and HER2 (via Neratinib) synergistically reduced malignant breast cancer cells, even in HER2-negative contexts. Through systematic analysis of scRNA-seq and multi-omics data, our study computationally identified and experimentally validated cholesterol biosynthesis and HER2 as novel combinatorial therapeutic targets in breast cancer. This data-driven approach highlights the potential of leveraging multiple molecular profiling techniques to uncover previously unexplored treatment strategies.

PMID:40391299 | PMC:PMC12088767 | DOI:10.1016/j.csbj.2025.04.030

Proteomics. 2025 May 20:e202500017. doi: 10.1002/pmic.202500017. Online ahead of print.

NO ABSTRACT

PMID:40390632 | DOI:10.1002/pmic.202500017

Zhen Ci Yan Jiu. 2025 May 25;50(5):519-525. doi: 10.13702/j.1000-0607.20250359.

ABSTRACT

In the past two decades, the domestic academic community of acupuncture and moxibustion has made some progress in anatomical and morphological characteristics of acupoints, as well as regulatory patterns and underlying mechanisms of needling at different acupoints. However, the developments of basic theory, clinical and scientific research are not balanced and lack of synergy, which has failed to form an internal driving force to promote the high-quality development of the discipline. On the basis of this "internal cause", with the increasing attention of international scholars to acupuncture and acupoints, "external causes" such as disciplinary background gaps, scientific cognitive modes and cultural differences further highlight the shortcomings and drawbacks in the construction of the modern theory of acupuncture and acupoints, and questions such as "detheorizing" and "discarding acupoints and storing needles" continue to emerge. To fundamentally answer such questions, we should take the key scientific issues such as "the key anatomical structure of acupoints" and "the specific and general regulation of acupoints" as the guide, promote the reconstruction and modern expression of traditional theories of acupuncture and moxibustion, formulate corresponding clinical control plans and evaluation standards for specific clinical problems and limited goals, carry out organized and purposeful basic and translational research on acupoints, establish a new format of "intra-disciplinary linkage and inter-disciplinary cooperation", so as to deeply reveal the anatomical structure basis of precise acupoint stimulation, elucidate the full view of characteristics of acupoint effects, explore the multi-target regulation and systems biology mechanisms of acupoints, build a contemporary acupuncture and moxibustion discipline system that keeps pace with the times, and lead the direction of acupuncture and moxibustion's international development.

PMID:40390609 | DOI:10.13702/j.1000-0607.20250359

Int Rev Cell Mol Biol. 2025;393:95-139. doi: 10.1016/bs.ircmb.2024.06.001. Epub 2024 Jun 25.

ABSTRACT

Over the course of a day, the circadian clock promotes a homeostatic balance between energy intake and energy expenditure by aligning metabolism with nutrient availability. In mammals, this process is driven by central clocks in the brain that control feeding behavior, the peripheral nervous system, and humoral outputs, as well as by peripheral clocks in non-brain tissues that regulate gene expression locally. Circadian organization of metabolism is critical, as circadian disruption is associated with increased risk of metabolic disease. Emerging evidence shows that circadian metabolism hinges upon inter-organ cross talk involving the liver, a metabolic hub that integrates many facets of systemic energy homeostasis. Here, we review spatiotemporal interactions, mainly metabolite exchange, signaling factors, and hormonal control, between the liver and skeletal muscle, pancreas, gut, microbiome, and adipose tissue. Modern society presents the challenge of circadian disturbances from rotating shift work to social jet lag and 24/7 food availability. Thus, it is important to better understand the mechanisms by which the clock system controls metabolic homeostasis and work toward targeted therapies.

PMID:40390465 | DOI:10.1016/bs.ircmb.2024.06.001

Int Rev Cell Mol Biol. 2025;393:1-14. doi: 10.1016/bs.ircmb.2024.06.002. Epub 2024 Jun 26.

ABSTRACT

The suprachiasmatic nucleus (SCN) functions as the master circadian pacemaker in mammals. Since 2015, facilitated by cutting-edge optogenetic and chemogenetic techniques, significant progress has been made in understanding the circuits involving the SCN that mediate diverse physiological functions. The time-specific and cell type-selective manipulation of neuronal activity within and outside the SCN drove the verification of both expected and previously unrecognized circuits operating for controlling various functions, including circadian locomotor activity, itch behavior, anticipatory thirst, aggression, corticosterone release, food-anticipatory activity, wakefulness, and photoperiod-related adaptive behavior. In addition, optogenetic/chemogenetic approaches verified the functional connection of the SCN to the control of body temperature, heart rate, and insulin sensitivity through as-yet-unknown circuit details. This review intends to provide an overview of SCN input/output pathways elucidated by optogenetics and chemogenetics. A fundamental question remains regarding the coherence of the identified numerous output pathways that are dictated by the SCN. Deciphering the potential coordination among the SCN's circuits via optogenetics and chemogenetics is needed to understand the mechanism underlying the harmonious regulation of multiple circadian physiologies.

PMID:40390460 | DOI:10.1016/bs.ircmb.2024.06.002

Elife. 2025 May 20;13:RP98800. doi: 10.7554/eLife.98800.

ABSTRACT

Homeostasis is a fundamental characteristic of living systems. Unlike rigidity, homeostasis necessitates that systems respond flexibly to diverse environments. Understanding the dynamics of biochemical systems when subjected to perturbations is essential for the development of a quantitative theory of homeostasis. In this study, we analyze the response of bacterial metabolism to externally imposed perturbations using kinetic models of Escherichia coli's central carbon metabolism in nonlinear regimes. We found that three distinct kinetic models consistently display strong responses to perturbations; in the strong responses, minor initial discrepancies in metabolite concentrations from steady-state values amplify over time, resulting in significant deviations. This pronounced responsiveness is a characteristic feature of metabolic dynamics, especially since such strong responses are seldom seen in toy models of the metabolic network. Subsequent numerical studies show that adenyl cofactors consistently influence the responsiveness of the metabolic systems across models. Additionally, we examine the impact of network structure on metabolic dynamics, demonstrating that as the metabolic network becomes denser, the perturbation response diminishes-a trend observed commonly in the models. To confirm the significance of cofactors and network structure, we constructed a simplified metabolic network model underscoring their importance. By identifying the structural determinants of responsiveness, our findings offer implications for bacterial physiology, the evolution of metabolic networks, and the design principles for robust artificial metabolism in synthetic biology and bioengineering.

PMID:40390360 | DOI:10.7554/eLife.98800

Health Sci Rep. 2025 May 19;8(5):e70835. doi: 10.1002/hsr2.70835. eCollection 2025 May.

ABSTRACT

BACKGROUND AND AIMS: Children with congenital heart disease (CHD) often require complex pharmacotherapy for symptom management and complication prevention. However, their unique physiological profiles increase vulnerability to drug-related side effects. This study aimed to identify specialists' perspectives on drug-related side effects and associated risk factors in pediatric CHD patients.

METHODS: A cross-sectional study was conducted in 2024 involving 20 pediatric cardiologists and pediatric cardiology fellows. Data were collected using two 5-point Likert scale questionnaires assessing commonly prescribed drugs, observed side effects, and associated risk factors in pediatric CHD patients. Data were analyzed using student's t-tests and descriptive statistics.

RESULTS: According to the findings, the most frequent side effects linked to common medications were hypokalemia (Furosemide; 4.5 ± 0.69), apnea (Prostaglandin E1; 4.5 ± 0.62), and bradycardia (Sotalol; 4.41 ± 0.51). Dosage and polypharmacy emerged as major risk factors, particularly for drugs like Digoxin and Heparin. Younger age, underlying health conditions, and specific drug combinations also increased the risk of side effects. The t-test revealed significant associations between participants' demographics (sex, age, and work experience) and their perceptions of drug-related side effects and risk factors.

CONCLUSIONS: The findings emphasize the need for a personalized approach to pharmacotherapy in pediatric CHD patients, requiring careful drug selection, dose optimization, and enhanced monitoring strategies. Drug-related side effects highlight the importance of implementing clinical decision support systems, routine therapeutic drug monitoring, and individualized dosing adjustments to mitigate risks. Future research should prioritize longitudinal studies to establish causality relationships, optimize treatment protocols, and improve medication safety in this vulnerable population.

PMID:40391269 | PMC:PMC12086803 | DOI:10.1002/hsr2.70835

Clin Transl Sci. 2025 May;18(5):e70253. doi: 10.1111/cts.70253.

ABSTRACT

Evaluating clinical relevance of potential drug-drug interactions is significant for managing safety risks. However, current approaches to the evaluation lack data on rare but serious adverse events. This study aims to develop an approach to assessing clinical relevance of potential drug-drug interactions that induce rare and serious adverse events, and test its performance. In the development, three key dimensions for evaluating clinical relevance were synthesized based on a literature review. A systematic five-step approach was proposed through designated dimensions and discussions within the research team. Subsequently, the approach was applied to patients with depression to validate its ability of demonstrating the dimensions, and exacting data on rare but serious adverse events. The test results showed varying signal intensities among different drug combinations in relation to adverse events including serotonin syndrome, long QT syndrome, and Torsade de Pointes. Advanced age was identified as a confounding factor for the QT prolongation signal. These findings operationalize Dimension one: Probabilities and risk factors for the occurrence of rare and serious adverse events. Besides, in the test, fatality occurred in 22.01% of the cases having drug-triggered QT prolongation. Advancing age was associated with the fatality (odds ratio = 1.03, 95% confidence interval = 1.01-1.07). The findings manifested Dimension two: Magnitude of adverse events and associated factors. Dimension three was achieved by findings of median time-to-onset of fatal serotonin syndrome and QT prolongation, which was one and 8 days, respectively. In summary, the proposed approach demonstrates effects in assessing the clinical relevance of potential drug-drug interactions.

PMID:40390272 | DOI:10.1111/cts.70253